Search

Soil texture affects soil water content and fertilizer and air permeability, and drives multiple physical and chemical processes occurring in the soil. Developing efficient and fast remote sensing technologies to predict the spatial distribution of soil texture has important theoretical and practical applications in soil quality evaluation and agricultural production planning. This paper discusses the application of remote sensing data, methods and models to predict soil texture, and the use of auxiliary data such as radar, terrain, and vegetation index data for soil texture prediction by remote sensing. The paper also proposes three methods for predicting the spatial distribution of soil texture based on remote sensing characteristics; namely, spectral response, characteristic wavelength selection, and remote sensing interpretation. It also focuses on the application effects of statistics, geostatistics, and machine learning models combined with remote sensing on spatial prediction of soil texture. The advantages, disadvantages, and application of several typical methods are compared, and the application conditions and accuracy verification methods of remote sensing prediction of soil texture are analyzed. Future research needs to focus on in-depth extraction of various remote sensing spectral features, acquisition of multiple types of environmental variables by remote sensing technologies, and development of a multi-algorithm hybrid model combining soil physical properties and data-driven machine learning features, so as to provide a basis and technical support for the spatial prediction of soil texture at different regional scales [ABSTRACT FROM AUTHOR]

In arid areas, the rapid expansion of agricultural activities has caused a series of ecological and environmental issues such as vegetation degradation, declining groundwater levels, and land desertification, posing significant challenges to the stability and development of inland river basins. The development of agricultural water suitability is the key to ensure the water and land resources efficiency and ecological health in arid areas. The core is how to implement the scientific control of irrigation scale and agricultural irrigation water consumption. Taking the Shiyang River Basin as the study area, this paper analyzed the variations of high and low flow for surface runoff in the basin based on the anomaly percentage and the runoff modulus ratio coefficient. After estimating the non-agricultural irrigation water demand, the water balance model was employed to calculate the available surface water and groundwater resources specifically for agricultural irrigation, while also considering water transformation process. Based on this, a multi-objective planting structure optimization model was established to determine the water-saving and efficient crop planting structure. To determine the appropriate irrigation scale in arid areas, a comprehensive model was constructed by improving the water-heat balance model, which incorporates various factors such as the water transformation process, crop coefficient in different growth stages, agricultural water saving level, and crop planting structure. Lastly, the suitable irrigation scales of the Shiyang River Basin under different scenarios were explored by using this model. The results showed that the runoff in Shiyang River Basin presented obvious high and low flow variations during the studied period. Specifically, the basin’s total surface water resources amounted to 17.47×108, 14.19 ×108, and 12.25×108 m³ during high, normal and low flow years, respectively. Regarding non-agricultural irrigation water demands, the critical and suitable ecology water demand scenarios were 3.43×108 and 5.57×108 m 3, respectively. Following the fulfillment of water resources demand for non-irrigation sectors, and under the joint regulation of surface water and groundwater resources, the available water consumption for agricultural irrigation were 18.97×108 -21.75×108, 14.75×108 -17.51 ×108, and 12.31×108 - 14.95×108 m³ in high, normal and low flow years, respectively. With the improvement of water saving level, the decrease of groundwater recharge led to the decrease of irrigation water consumption in the basin. After optimizing the crop planting structure, 14.13% of irrigation water saving can be achieved by reducing the economic benefit of 2.94%. The suitable irrigation scale determined by the suitable irrigation scale calculation model in arid areas considering the water transformation process was consistent with the actual situation, which is more reasonable than the conventional method in the inland river basin. Based on this method, the suitable irrigation scales of Shiyang River Basin under the current condition were 27.73×104 -31.66×104, 21.55×104 -25.76×104, and 18.01×104 -22.03×104 hm² in high, normal and low flow years, respectively. After improving the water-saving level and adjusting the planting structure, the suitable irrigation scale of the basin has increased. In 2020 (normal flow year), the actual irrigation area exceeded the critical suitable irrigation scale, necessitating reducing the irrigation area of 2.13×104 -6.34×104 hm² to balance the water and soil resources in the basin. The research results provide a theoretical foundation for decision makers to formulate water-appropriate agricultural development plans at the macro level. [ABSTRACT FROM AUTHOR]

Based on J. Boussinesq's analytical formula of stress component at any point in the elastic half space under load, the spatial stress state of soil at any point was obtained and it was introduced into the settlement calculation model. A method of introducing confining pressure into the settlement calculation of remolded loess was proposed. The influence of three parameters including deviatoric stress, moisture content and confining pressure to the strain development of remolded loess was analyzed by stress loading triaxial test. The strain law of remolded loess under different parameters was obtained, and the settlement calculation formula of remolded loess strain affected by multiple parameters was put forward. Finally, the reliability of the formula was verified by engineering examples. The results show that the deviatoric stress, moisture content and confining pressure have a significant effect on the strain of remolded loess. The deviatoric stress and moisture content are positively correlated with the compressive deformation of remolded loess, while the confining pressure is negatively correlated with the compressive deformation of remolded loess. The empirical formula of remolded loess strain fitted by three parameters is effective. Through the engineering case verification of Shanxi Lyuliang Airport, the model settlement calculation results are basically consistent with the actual settlement monitoring results, indicating that the model in this paper can be more reasonably and conveniently applied to the settlement calculation and analysis of the remolded loess engineering site. [ABSTRACT FROM AUTHOR]

The water hammer simulation for pump station water supply systems with free-surface flow commonly employs the method of characteristics (MOC), often neglecting the influence of unsteady friction and the interaction between pressure and free-surface flow. In complex pipeline network systems, MOC often requires interpolation calculations or adjustments to wave speeds for computation. However, this can lead to issues such as numerical instability and reduced accuracy. This study establishes a second-order finite volume method model that considers unsteady friction for real-time joint calculations of pressure and free-surface flow. The second-order Godunov scheme is utilized to discretize the flow control equations independently for the pressure and free-surface flow. Riemann solvers are employed to compute the discretized fluxes. Simultaneously, the unsteady friction effect is taken into account to accurately simulate energy dissipation during hydraulic transient processes. The model is validated using experimental data. And a variable time-step approach is adopted at the interface between the pressure and free-surface flow. Furthermore, sensitivity analyses are conducted on the dimensions and locations of the throttled surge chamber in practical engineering scenarios. The results indicate that when unsteady friction effects are not considered, the model only matches experimental values at the first peak. However, when unsteady friction effects are taken into account, the model's simulation results are in good agreement with experimental results in terms of pressure peak, decay, and periodicity, and the maximum value of the water hammer pressure error at the valve is only 4.58%. Compared to the characteristics method, the second-order Godunov scheme exhibits higher stability and produces simulation results that are essentially the same when the Courant number is less than 1 as when the Courant number is set to 1.0. The real)time joint calculation method established in this paper can adequately consider the impact of water level fluctuations of the pressure flow outlet pool and the backflow of the free-surface flow. The water hammer pressure and the water levels of the throttled surge chamber obtained from the joint calculation of pressure and free-surface flow exhibit a faster decay compared to the results obtained from pure pressure flow calculations. For power outage conditions of pumps, the engineering scenarios studied in this paper would result in significant water hammer pressures and negative pressures if surge chambers were not installed. Optimization studies on the throttled surge chamber show that as the diameter of the connection tube increases, the maximum pressure head, the highest surge level of the throttled surge chamber, and the maximum reverse rotation speed of the pump increase, and the lowest surge level of the throttled surge chamber decreases. As the diameter of the throttled surge chamber increases, the highest surge level decreases, the lowest surge level increases and the maximum pressure decreases all the time when the connection tube diameter is relatively small, and will decrease first and then increase when the connection tube diameter is relatively large; when the connection tube diameter 3.5 m, throttled surge chamber diameter 15 m can meet the requirements of the system control parameters and also reduce the amount of engineering work. The closer the throttled surge chamber is to the pump station, the larger the maximum pressure, reverse rotation speed, and highest surge level, and the smaller the lowest surge level. When the throttled surge chamber is arranged 1.0 km behind the pump, the maximum surge level will reach 172.35 m, which does not meet the requirements of the control standards. In summary, the established model exhibits high accuracy, stability, and applicability, providing valuable insights for water hammer simulations in pump station water supply systems with free-surface flow. [ABSTRACT FROM AUTHOR]

Food is the top priority for the people, and food safety has always been a key concern for the people's livelihood. Building a Knowledge graph for food safety and food events has attracted many scholars' attention in recent years. One of the key technologies to build a large structured knowledge graph is to extract triples from unstructured text. A triplet typically consists of a pair of entities and semantic relations between them. When extracting entities and relationships from Chinese food inspection announcements, many long sentences often appear in the text, which contain a large number of food names and food types. If we learn about their features by using the current deep learning models, the strong dependencies between words will be weakened by the long distance between words, this issue has received little attention in the extraction of entity and relationships in the food safety field. So to solve the problem of dependency relations between words in those sentences, this paper proposes a joint entity relation extraction model (TAG-JE) based on improved Chinese dependency syntax tree and multi feature fusion. TAG-JE improves the expression of Chinese dependency syntax tree that is adding a "CLS" tag before the natural sentence and establish dependency relations between the "CLS" tag and each word in the sentence. Then we use it to describe the syntactic structure of text, and converts the syntax tree into a character adjacency graph so as to match the syntax tree with the word processing mode of Chinese BERT encoding. Then we learn syntactic structure features from the encoded word adjacency graph through a graph neural network(GCN), and fuse them with the context features extracted by BERT. Context features and structural features can describe the linear and spatial representation of sentences. The weight of the fusion is automatically adjusted by a gate network. After the above steps, the fusion features of unstructured text will be obtained which can fully reflect the representation of the food inspection announcement. In the process of entity and relationship extraction, the joint extraction mode is adopted, because compared to separate extraction of entities and relationships, joint extraction reduces error accumulation and TAG-JE also adds a relation selector in relation analysis, relation selector is used to optimize the embedding features of relations, and the relation selector would be trained by reinforcement learning to improve the accuracy of relation extraction. To verify the effectiveness of TAG-JE, this paper compared its extraction performance with mainstream deep learning models such as BiLSTM+CRF, combined with IDCNN, BERT, and Attention to obtain three BiLSTM+CRF framework models. In addition to the classic models mentioned above, this paper also compares the TAG-JE model with mainstream models GraphRel and CasRel. The results showed that the accuracy, recall, and F1 values of TAG-JE reached 90.86%, 90.50%, and 90.68%, respectively, which showed a significant improvement compared to other models and demonstrated its knowledge mining ability in Chinese food inspection documents. TAG-JE provides technical support for the construction of food safety knowledge graph. In the experiments targeting Chinese public datasets, TAG-JE also achieved better results compared to GraphRel and CasRel, proving that TAG-JE also has good generalization performance. The research results provide reference for the construction of food safety knowledge graph. [ABSTRACT FROM AUTHOR]

Purple soil is widely distributed in southwestern China and is one of the important natural resources. Because of the loose parent rock and low degree of differentiation, purple soil is characterized by a loose structure that is easily eroded and high soil fertility. Soil erosion is very serious in the purple soil region, which is one of the main sources of sediment in the upper reaches of the Yangtze River, and has a certain impact on the agricultural production and engineering development in the purple soil region. After erosion, purple soil shows the characteristics of reduced dry density of soil and serious loss of fine particles, which affects its water-holding characteristics. In order to explore the changes of soil water-holding properties of purple soil after rainfall erosion, this study was carried out to analyze the effect of missing grain groups on the water-holding properties of sandy clayey purple soil under rainfall conditions by selecting three test methods, namely, axial translation technique, filter paper method, and saturated salt saturated saline solution vapor equilibrium technique for the three grain groups which are the most susceptible to changes in the rainfall conditions, and carrying out the Soil-Water Characteristic Curve (SWCC) test with the absence of grain groups within the full suction range to analyze the effect of the missing grain groups on the water-holding properties of sandy clayey purple soil. The results show that: 1) the missing grain group reduces the water-holding characteristics of sandy clayey purple soil, which is most obvious in the boundary effect zone and transition zone, and has less effect on the residual zone. The mass of the missing grain group is inversely proportional to the air intake value and the residual water content. 2) Using the grain size parameters (Constrained diameter、Average diameter、Median diameter、Effective diameter) to characterize the soil grading condition, the relationship between the soil grading curves and the characteristic values of SWCC was established. It was found that except for effective diameter, the other parameters were linearly inversely related to the air intake value and residual water content; 3) The data obtained from different methods were fitted using the Fredlund-Xing model, and it was found that the results of the data obtained from a single test method fitted better, but the SWCC significantly deviated from the test data points in the boundary effect zone and the transition zone. Root mean square error (RMSE)、 percent bias (PB) and mean absolute percent relative error (MAPRE) were introduced as evaluation indexes, and it was found that the suction range was the main factor affecting the fitting effect of SWCC, and within a certain suction range, the larger the range of data points involved, the better the model fitting effect was, and after a certain suction value was reached, the suction range had very little effect on the model fitting effect. It is proposed that to obtain the accurate SWCC of purple soil quickly, only the maximum suction value needs to be determined, and the number of test points with high suction is reduced, which can effectively shorten the SWCC test period. The results lay a foundation for the subsequent study of the role of soil erosion and water-holding characteristics of purple soil, and also provide reliable ideas for the prevention of soil erosion and the application of soil-water characteristic curves in the Three Gorges Reservoir area. [ABSTRACT FROM AUTHOR]

Assessing the sustainable development of agriculture is a hot topic in global agricultural research and an important task of rural revitalization and agricultural upgrading in the new era. Reform and opening up promote the rapid development of the agricultural economy, but the conflict between agricultural development and agricultural resources and ecological environment is serious. Since the 18th National Congress of the CPC, the sustainable development of agriculture has been given more prominence. It is of great significance to study the evaluation of agricultural sustainable development. Firstly, this paper studies the relevant literature on agricultural sustainable development evaluation at home and abroad. Based on wavelet transform, support vector machine(SVM), genetic algorithm (GA)and ant colony optimization(ACO) algorithm, a combination evaluation method of wavelet and intelligent optimization support vector machine is proposed. Secondly, by referring to the existing evaluation index system and combining with the actual situation of China's agricultural development, the alternative index set of agricultural sustainable development evaluation is established. Using SPSS software, the significance and correlation of 150 primary indexes are analyzed, and an evaluation index system including 62 indexes is established. Thirdly, using one-dimensional discrete stationary wavelet analysis, data denoising processing, genetic algorithm, ant colony algorithm, optimize the parameters of support vector machine, get better penalty parameters, kernel function, insensitivity coefficient, and then train the support vector machine. Fourth, taking 31 provinces (municipalities) in China as an example, this paper evaluates the agricultural sustainable development by applying the wavelet-GASVM model and the wavelet-ACOSVM model, and using the GA-SVM model and the ACO-SVM model to evaluate the agricultural sustainable development of 31 provinces (municipalities) in China. Fifthly, the reliability of the wavelet intelligent optimization SVM method is verified by changing the number of test sets and conducting multiple tests. The evaluation and simulation results show that: 1) The mean square error of agricultural sustainable development evaluation of Wavelte-GASVM is 9.641×10-5, and the correlation coefficient is 0.980; however, under the same training set and test set, the mean square error and correlation coefficient obtained by GASVM are 0.006 and 0.979, respectively. 2)The mean square error of Wavelet-ACOSVM agricultural sustainable development evaluation was 9.318×10-5, and the correlation coefficient was 0.972; the mean square error MSE and correlation coefficient of ACO-SVM are 0.016 and 0.953, respectively, under the same training set and test set. 3) Change the number of test sets and perform several tests and compare the results of GASVM model and ACO-SVM model before and after wavelet denoising respectively. It can be seen that under the GASVM model, the mean square error of the running results after wavelet denoising is smaller than that before wavelet denoising, and the correlation coefficient is larger than that before wavelet denoising, and the running time is shorter. Under the ACOSVM model, the mean square error of the running result after wavelet denoising is also smaller than that before wavelet denoising, and the correlation coefficient is larger than that before wavelet denoising, and the running time is shorter. Conclusion: The prediction accuracy and convergence speed of agricultural sustainable development evaluation by wavelet-GASVM and wavelet-ACOSVM are better than GA-SVM and ACO-SVM, respectively, and the effect of agricultural sustainable development evaluation by wavelet-GASVM is more ideal. This study provides a method reference for agricultural sustainable development assessment. [ABSTRACT FROM AUTHOR]

Ethnic costumes are colorful and reflect profound cultural heritage. Parsing the color composition and coloration mechanism of ethnic costumes will help to enrich and improve the color system, inherit and regenerate the ethnic costume culture. The formation of national costume color is frequently linked to the geographical surroundings, living habits, ethnic culture and religious belief. Image analysis technology has been utilized to quantify the color characteristics of national costumes and parse the color rules, which can broaden the scope of color regeneration services in contemporary art, design and cultural communication. She nationality is a Southeast Chinese ethnic group with distinctive clothes and accessories. With the nexus network model as the analysis tool, this paper utilizes image analysis technology and selects a total of 300 clothing images of the She nationality in Fujian, Zhejiang, and Jiangxi as samples to quantitatively analyze their colors and describe the differences in different regions. Firstly, the selected image samples are preprocessed. Secondly, in order to avoid over-balanced color of fixed clustering centers, ensure the accuracy of basic color extraction and obtain relatively accurate color center value and cluster size, an improved bipartite K-means adaptive clustering algorithm is used to cluster the sample images for the first time, and the integrated color map is formed according to the color proportion in the experiment from the perspective of speed and convenience. On this basis, in order to facilitate the comparison of the color quantity and coloration relationship in different regions, K-means clustering method is utilized to uniformly set the cluster size for secondary clustering of the comprehensive color map based on literature research and experience cognition. To analyze the differences in color composition of clothing of She nationality in Fujian, Zhejiang, and Jiangxi, the clothing color is decomposed into four color characteristic indexes, namely, color proportion, paired color co-occurrence rate, color space distance and color dispersion and a visual color nexus network model is built for various nationalities. Experimental results show that the color hues of costumes in the three regions are visually similar, but the color ratios and color order connection are extremely different. Moreover, the main colors of clothing in Zhejiang and accessories in Jiangxi make up a relatively high proportion. The color matching forms of clothing in Zhejiang area are the richest, while those of the other areas are relatively simple. In terms of color matching, the overall value of color dispersion of clothing accessories in Jiangxi and Fujian is the lowest, which reflects the relationship between the overall color application and color matching. The color extraction time taken for a single image is less than 0.2 seconds, and the color nexus network modeling takes 1.4 seconds on average. This method can efficiently assess the relationship between the color features and color matching of She ethnic costumes, as well as can visually exhibit the color composition form of the costumes. The analysis of the relationship between color applying form and color matching of She nationality costumes in this study can help to build She nationality clothing color system and serve as a reference solution for the color parsing of other ethnic costumes. Different from color measurement and color matching in the manufacture process and free from the constraints of multiple factors such as coverage and imaging environment, the color extracted in this paper only moderately reflects the color of She nationality clothing on the whole, and focuses more on the relationship between color application form and color matching. The proposed method can provide a reference for the color regeneration application of She nationality clothing in contemporary design. [ABSTRACT FROM AUTHOR]

Influenced by the ecological environment and other factors, the quality and yield of lotus root have been seriously affected by the occurrence of diseases and insect pests in recent years. With the improvement of living standards and the development of the lotus industry chain, people are looking for green food, high-yield and high-quality products. Nowadays, many farmers and planters are unable to accurately identify the diseases and pests of lotus due to lack of professional knowledge of diseases and insect pests control. There is a shortage of efficient, low-cost and automatic identification technology for the prevention and control of lotus diseases and insect pests. The diagnosis and identification of diseases and insect pests are of great significance for the prevention and control of diseases and insect pests in lotus fields. Over the past few years, deep learning technology has been widely used in the field of plant diseases and insect pests recognition to automatically extract the features of plant diseases and insect pests. In order to achieve an efficient and accurate diagnosis of lotus leaf diseases and insect pests, lotus leaf diseases and insect pests dataset was constructed and preliminary experiments were constructed on AlexNet, VGG-16, ResNet50, ResNeXt50, and DenseNet121 models. The experimental results indicated that DenseNet121 has the best performance on the dataset, lotus leaf diseases and insect pests identification model based on improved DenseNet was improved. Firstly, different methods for dynamic data enhancement were compared in this paper. The results show that resizing and randomly resizing the image is more accurate than directly resizing to the same size. The loss of detail information in part of the image is caused by over-compressing the image size, which affects the model’s recognition effect. The accuracy of the model was increased from 81.47% to 85.01% by using the data enhancement method of resize, random resized crop, random horizontal flip and random adjust sharpness. AdaMax optimizer was used to replace Stochastic Gradient Optimization optimizer and the accuracy of DenseNet model has been improved by 3.04 percentage points. The Stem block uses multi-layer small convolution for fast dimensionality reduction and a branch structure to combine convolution and maximum pooling. It improves the ability of the model to extract shallow features at a lower operating cost. The Squeeze and Excitation attention mechanism block and sharpen cosine similarity convolution were introduced in the Denselayer of the Dense Block and the Transition Layer. This method improved the recognition ability of the model to lotus leaf diseases, and verified the effectiveness of sharpen cosine convolution to improve the performance of the model. Transfer learning was performed on the Plantvillage dataset. The accuracy of the improved model is 91.34%, which 9.87 percentage points higher than before improvement and optimization. In order to solve the problem of monitoring diseases and insect pests in lotus fields, the improved model was applied to the identification of lotus field diseases and insect pests in UAV images. The calibration area of lotus leaf was cut and predicted by reasoning, then different masks were generated according to the model prediction results and added to the UAV image to generate a distribution map of lotus field diseases and insect pests. The recognition of lotus field diseases and insect pests in the UAV image was investigated, automatic classification and recognition of leaf spot, viral disease, Spodoptera litura, lotus Sclerotium leaf rot and lotus rhizome rot were realized. It provides a new method for efficient and accurate identification and dynamic monitoring of lotus diseases. It also supplies information supports for variable pesticide application and flight path planning in plant disease prevention and control based on UAV. [ABSTRACT FROM AUTHOR]

Accurate identification of rice pests is of great significance for timely protection and management of rice. However, the rice pests are similar with the background in the color and texture, and the morphology of the pests varies greatly during different growth stages. Therefore, it is difficult to accurately identify the rice pests in natural scenes. In this paper, the Light Weight Residual Network (LW-ResNet) composed of feature extraction, global optimization and local optimization modules was designed to improve the ability to identify rice pests in natural scene images. Firstly, in order to reduce the influence of complex background and enhance the feature extraction and expression capabilities of the residual network, the residual block is improved to constitute the feature extraction module. The improved residual block increases the number of convolutional layers and branches of the original residual block, which can effectively extract the deep global features of rice pest images. Secondly, the deep global features are further optimized through the convolutional layers in the global optimization module. Finally, in order to obtain the local discriminative characteristics of rice pest images to distinguish the morphological differences between similar pests, the lightweight attention sub-module constitutes the local optimization module. The light weight attention sub-module uses depth separable convolution to reduce the redundancy of channel features and realize the aggregation of different channel characteristics, so it can highlight the local key features of rice pests. Because the improvement of the residual block in the feature extraction module reduces the number of residual blocks, and the use of deep separable convolution in the attention sub-module and the channel-based global average pooling and global maximum pooling encoding operations reduce floating point operations, the LW-ResNet network has achieved lighter weight. In the HSV space, Gamma transform is used to preprocess the v component of rice pest images and then proceed to the data expansion. After the expansion, there are 4 380 images in the training set and 492 images in the test set. In order to verify the rationality and effectiveness of the method in this paper, in the training phase, the cosine learning rate decay strategy was used to train the network model. By analyzing the number of the improved residual blocks in the feature extraction module, the lightweight attention sub-module in the local optimization module, and the global optimization module, the rationality of the method in this paper was verified. In the testing phase, the LW-ResNet network model achieves a identification accuracy of 92.5% on the test data set of 13 types of rice pest images. The identification accuracy of the LW-ResNet network model is higher than that of classic convolutional neural network models such as VGG16, ResNet, and AlexNet. The parameter amount of the LW-ResNet model is 1.62×106, and the amount of floating-point operations is 0.34×109. The number of parameters and floating-point operations of the LW-ResNet model are both lower than those of MobileNetV3, which verified the effectiveness of the method in this paper. The LW-ResNet network model has achieved light weight and a good identification effect, so it can be used for rice pest identification on the mobile terminal. [ABSTRACT FROM AUTHOR]

Zinc is an important strategic mineral resource in national economic construction and plays an irreplaceable role. With the increasing types and yields of low-grade refractory mineral resources, and the increasingly prominent environmental pollution problems caused by resource development, bioleaching technology has been widely used in these types of mineral resources due to its advantages of simple process flow, less pollution, and ability to deal with low-grade ores. As one of the main zinc-containing minerals, sphalerite has problems such as long reaction period and slow dissolution kinetics in the leaching process, which needs to be broken through urgently. This paper focuses on the application of the "shrinking core model" in the kinetics of sphalerite. The effects of different bioleaching bacteria, Fe2+ concentration, temperature, and other factors on the dissolution process of minerals were discussed. The rate-control steps and key factors affecting the dissolution kinetics of minerals in the bioleaching system were revealed. The research direction of the dissolution kinetics of sphalerite bioleaching is prospected. [ABSTRACT FROM AUTHOR]

Marbling is an important index that affects the quality of beef. However, the evaluation of marbling in beef processing enterprises is operated by professional, who test the beef by comparing samples with standard images, which is subjective and susceptible to the environment. To solve these problems, this study proposed an intelligent classification method based on deep learning and designed a neural network with 4-layer convolution including input layer, convolutional layer, pooling layer, fully connected layer and output layer. The automatic extraction of marbling features was achieved, and a beef marbling detection application based on mobile phone was developed. In this study, 1 800 images were divided into calibration set, validation set and test set according to the ratio of 3:1:1. The training process of deep learning model depends on a large number of labeled samples, and the training effect of a small amount of data is poor. Therefore, methods like rotating, mirroring, adjusting brightness, contrast, and increasing noise were used to amplify the sample data in this study. Finally, 5 400 images of calibration set and 1 800 images of validation set are obtained. The calibration set was used to train and adjust the internal parameters of the network, and the validation set was used to test the model. In order to further explore the accuracy of the model, this method was compared with the traditional machine vision method. And the number of large fat particles, medium fat particles, small fat particles, the total area of fat and the evenness degree of fat distribution were calculated. According to the above characteristics, a multiple linear regression model was established to identify the grades. The results showed that the method used in this paper had good classification ability for marbling, and the detection accuracy of validation set was 97.67%, which was higher than the traditional machine vision method. Samples with error grade did not span two levels. Through the observation of the misjudged images ，the marbling richness of misjudged images was similar to that of misjudged images, and the marbling score was between the labeled grade and the misjudged grade. Finally, an application program for marbling detecting was written. In this application, you call use the model generated in this study to realize real-time detection for marbling. Then the application was used to recognition the samples of test set. The accuracy was 95.56%, and the recognition time was less than 0.5 seconds per image. In this paper, a beef marbling mobile phone evaluation system was developed based on the advantages of convolution neural network with high classification accuracy and fast speed of smart phones. This method could improve the detection efficiency of beef marbling. Compared with traditional detection equipment, the smart phone as a handheld collection terminal has the advantages of small size and high efficiency. The popularization of smart phones provides a broad development space for this method. Compared with ARM portable devices, this method uses mobile phones as the detection carrier, which saves the need to purchase additional auxiliary equipment and reduces the cost of hardware development. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the lack of relevant theories and specifications of capacity selection of greenhouse CO2supply equipment, based on the model analysis of the constant concentration control of CO2, the low range concentration control of CO2, and the constant gas flow control of CO2, a CO2balance model in greenhouse is established. There are very complex relationships between temperature, light intensity and CO2concentration and photosynthesis intensity. To maintain a constant CO2concentration in greenhouse, it is necessary to accurately regulate the supply of CO2according to the changes in light and temperature. In addition, to maintain CO2concentration above the saturation point, the constant concentration control of CO2is also an uneconomical way. So this mode of regulation is difficult to implement in large-scale greenhouse production. The constant concentration control of CO2is possible to be used only in experimental greenhouse which the light and temperature are accurately controlled. The low range concentration control of CO2also needs to adjust the supply of CO2frequently, therefore, the timeliness and accuracy requirements of CO2supply source are high. It is generally applicable to liquefied CO2supply system. The constant gas flow control of CO2is the easiest to implement in engineering, as long as the gas supply valve is opened or closed according to the set time, and other control equipment can be omitted. Therefore, this control model is very suitable for the CO2supply system which uses the recovered flue gas of the boiler as CO2source, and also suitable for liquid CO2supply system without considering economic factors. Combined with crop demand for CO2and the characteristics of low CO2emission of soil in the large-scale gutter-connected greenhouse for vegetable spatial soilless culture, a calculation method for the CO2supply load of equipment used for CO2 enrichment in greenhouse is proposed. Taking the recovered flue gas of a boiler and liquid CO2as the CO2sources, two CO2supply methods are proposed. The calculation method of the capacity of gas boiler and the volume of liquid CO2storage tank provide a theoretical basis for the design and selection of corresponding equipment. Taking a Venlo type greenhouse with the cultivation area of 49 200 m2 and the volume of 329 640 m3 as an example, the calculation method was checked. The calculation results showed that by using constant gas flow control of CO2, and under the condition that the concentration of CO2was 600×10-6 m³/m³ in greenhouse, only one gas boiler with a rated evaporation capacity of 10 t/h needed to be operated at the daytime to meet the demand of CO2enrichment, when the recovered flue gas of the boiler was used as CO2source. By using the low range concentration control of CO2, and under the condition that the concentration of CO2was 500×10-6 m³/m³ in greenhouse, a storage tank with a volume of 20 m³ was needed for contain liquefied CO2when using liquefied CO2as the CO2source. The measured CO2concentration in greenhouse showed that the calculation method of CO2supply load for equipment capacity selection established in this paper was feasible. The method proposed in this paper can be used for similar engineering design reference. [ABSTRACT FROM AUTHOR]

Modern mechanical equipment is very important to agricultural production. The introduction of modern machinery and equipment into the production process of traditional Chinese medicine is an effective way to solve the contradiction between the expansion of production scale of traditional Chinese medicine industry and the reduction of labor force. The mechanization development of traditional Chinese medicinal materials production is in the initial stage. The main goal at this stage is to realize the "from scratch" of the relevant technical equipments. However, due to the varieties of traditional Chinese medicine, complex production process, regional differences and other factors, it is difficult to judge the demand for mechanized production of traditional Chinese medicine and lack of methods. How to evaluate and analyze the the differences in the demand for mechanized development is of great significance to the research of technical equipment, the implementation of supply side reform and the promotion of the production and development of traditional Chinese medicine. According to the characteristics of traditional Chinese medicinal materials production, combined with the analysis of degree of mechanized operation, status of technology and equipment and levels of user demand, this paper constructs the evaluation index system for the development of technical and equipment of traditional Chinese medicinal materials production. The degree of mechanized operation indicates the ratio of mechanized operation area to the total planting area. The status of technology and equipment is alculated by weighting the five indexes of advance ment, economy, applicability, reliability and security. The levels of user demand is divided according to the proportion of users with demand, users with demand but no machinery available and the proportion of users with demand in different production process. Then an evaluation model combined with entropy weight method and hierarchical cluster analysis method is formed, and after classifying by the hierarchical cluster analysis method, the model is expressed functionally based on the Fisher discriminant method. In empirical research, the demonstration base of national comprehensive experimental station of traditional Chinese medicinal materials is selected as the research object, eight production processes of sowing, transplanting, fertilization, intertillage, plant protection, harvesting, cleaning and drying are selected for evaluation Through the investigation in 23 provinces of China, the production processes of traditional Chinese medicinal materials production are categorized into three types. The harvest, intertillage, and transplanting belonged to the type A, which have the highest demand level. The seeding is classified as type B, and the drying, cleaning, plants protection and fertilization are type C. The phenomenons of “no machinery available” and “unsuitable to use” are obvious for type A, and there is an urgent demand for the development of technology and equipment, the evaluation results are consistent with the investigation results. According to the further calculation results based on discriminant Function I, the central values of type A, type B and type C are -2.868, 1.904 and 1.675 respectively, which shows that the evaluation model of development demands classification can be accurately expressed by quantitative mathematical functions. The machinery of the type A production processes represented by harvest should be developed in priority in traditional Chinese medicinal materials production. The research and development of special machinery for the production of traditional Chinese medicinal materials should take the road of combining independent innovation research with the introduction of other main grain production machinery. The model proposed in this paper reflects the reasons and details at the same time of forming classification, which can provide reference for the development demand evaluation of other crops or regional agricultural machinery technology and equipment with backward mechanization development. [ABSTRACT FROM AUTHOR]

: In order to monitor illegal land use in real time, video surveillance technology was used to monitor the vulnerable areas of illegal land use. Excavator was one of the most important construction machinery in the engineering construction, an automatic real-time detection of excavator could provide important information for non-contact field monitoring of illegal land. But it was difficult to accurately detect the excavator due to the complex background, uneven illumination and partial occlusion in natural scene, This paper proposed a real-time excavator detection algorithm in natural scene based on the SSD-like (Single Shot Detector). Specifically, the lightweight network DDB (Depthwise Dense Block) was used as the basic network to extract shallow feature and fuse with high-level features in the excavator objection model to enhance the feature representation capability. Meanwhile, the BDM (Bottleneck Down-sampling Module) which was designed based on the lightweight network MobileNetV2 was used as the multi-scale feature extraction network to reduce the parameter quantity and computation. The data sets included 18 537 images of excavators with different shooting angles and natural scenes, 15 009 images were used as training set and 3 528 images were chosen as test set randomly. To enhance the diversity of training data, data set expansion methods such as rotation and image were adopted. Based on the Caffe deep learning framework, the proposed model in this paper was trained with end-to-end approximate joint methods and the model weight was fine-tuned by using SGD (Stochastic Gradient Descent) algorithm. The DDB module of the network was initialized with the weights pre-trained on the PASCAL VOC dataset, and the training time and resources were further reduced by transferring learning. Then the model pre-trained on the large data sets was transplanted to excavator object detection by transfer learning. The proposed method was transplanted and performed on the mainstream Jetson TX1 embedded hardware platform, and experiments on the actual data set of detecting excavator object at different angles of view and natural scenes. Experiment results showed that the parameter quantity and computational complexity of proposed model with BDM was reduced by 68.4% compared to SSD, the mAP (Mean Average Precision) of proposed method reached 90.6% on the testing set, which was 0.4% and 0.4% higher than that of SSD based on VGG16 basic net and MobileNetV2SSD based on MobileNetV2 basic net, respectively. The model size of propose method was 4.2 MB, which was about 1/25 and 1/4 of SSD and mobilenetv2ssd, respectively, and the time-consuming of each frame was 145.2 ms, which was 122.7% and 28.2% faster than SSD and MobileNetV2SSD, respectively. The proposed method not only had better generalization and robustness, but also can be better deployed on the embedded hardware platform which demonstrated the feasibility of real-time monitoring of the excavator at site of illegal land use. [ABSTRACT FROM AUTHOR]

In order to solve the problem that it is difficult to identify and segment the overlapping grape clusters in the natural conditions, this paper selected Xiahei grape as the experimental sample to carry out relevant research. Firstly, the RGB color channel was transformed into HSV color channel and YCbCr color channel. Through histogram analysis and comparison of each component of HSV color channel and YCbCr color channel, H component in HSV space was determined as the experimental object of image pre-processing. Then, K-means clustering algorithm was used to extract the H component in HSV color space. To obtain the region of overlapping grape clusters, Sobel operator was used to extract the contour of the overlapping grape clusters. Then the contour analysis method was used to analyze the contour of the overlapping grape clusters, and the contour extreme points and the central point of the overlapping grape clusters were obtained. By analyzing the aspect ratio of grape cluster region, we could judge the overlapping type of grape clusters. If it was a single grape cluster, it would be identified directly. If it was a veritcally overlapping grape cluster, rotated the image 90° anticlockwise, and then used the same processing method as the left and right overlapping grape clusters proposed in this paper. The general target grape cluster area was obtained by contour analysis. After that, we could solve the quasi-central point by the central point between the midpoint and the right extreme point, and the intersection point of the two lines between up and lower extreme points and left and right extreme points in the target grape cluster region. The improved Chan-Vese model limited the iteration times of the Chan-Vese model by using the distance between the center point and the quasi-central point. After that, based on the Chan-Vese model and the quasi-central point as the origin, the iterative identification of target grape cluster was carried out. The binary image processing was carried out for the grape cluster area obtained by Chan-Vese, and the contour information of the overlapping boundary was obtained. Finally, image fusion was used to fuse the grape cluster contour obtained by Chan-Vese model with the original contour. The test results show that the average recognition accuracy of the target grape cluster on the left of the overlapping grape cluster was 92.19%, and the average false positive rate was 2.82%. The average recognition accuracy of the target grape cluster on the right of the overlapping grape cluster was 87.22%, and the average false positive rate was 6.03%. The average recognition accuracy of all the target grape cluster is 89.71%, and the average false positive rate was 4.24%. Compared with the existing segmentation method which only based on contour analysis, the method proposed in this paper could recognize and segment the whole target grape cluster, and greatly improve the accuracy of recognition and segmentation. This method is more conducive to acquisition the information of the target grape cluster picking point，the study provide an efficient and stable recognition and segmentation algorithm for the grape picking robot to successfully pick overlapping grape clusters. [ABSTRACT FROM AUTHOR]

With the growth of population and the rapid development of social economy, the shortage of water resources and the pollution of water environment in Yanghe River Basin have become increasingly prominent, which leads to the water quality in the assessment section of Yanghe River Basin is difficult to meet the water quality standard. In order to achieve the efficient utilization of water resources and environmental protection, combining with the characteristics of economic development and water resources utilization, an optimal allocation model of agricultural water resources was established with the water quality constraints for Yanghe River Basin. Therefore, under the premise of the water quality of assessment section, the monthly water allocation of different crops and livestock, as well as the crop planting area and the livestock number were optimized to maximize agricultural economic benefit. The paper proposed a method to control water quality by supplying water and reducing pullution simultaneously. Moreover, based on the water water requirement of agriculture and animal husbandry, this paper introduced a method to avoid unbalanced water supply problems and reduce the dimension to improve the solution efficiency. The optimization results showed that the assessment section of the Bridge 8 could be adjusted to meet the water quality standard by increasing the planting area of the vegetables and the tubers with higher benefit, as well as the large livestock number, and decreasing the planting area of oilseeds with severer emission, as well as the small livestock number. Comparing with the actual values in 2014, the agricultural water allocation decreased by 3.38%, and the economic benefit increased by 11.96%. Therefore, the model was verified effectively. Furthermore, the paper analyzed the optimization allocation schemes under different conditions of the low, median and high flow years in 2020. The results showed that, the more water quantity available, the more agricultural water allocation and river recharge correspondingly. In this condition, water allocation of most water users and subzones were also increased, of which Huailai and Xuanhua had larger increments. Besides, the water allocation of crops was larger than that of livestock, and the grains and fruits had larger increments. The increase rate of water users was relative to planting area, while the increase rate of subzones was relative to the crop type and planting area, and water allocation was the comprehensive tradeoff among available water for agriculture, pollution emissions and economic benefits. The proposed model is of great significance for ensuring water quality safety and sustainable development of regional economy in Yanghe River Basin, and has a certain reference value for optimizing allocation of water resources considering water quantity and quality. [ABSTRACT FROM AUTHOR]

Due to the shortage of water resources, serious water pollution, improper use of water and the occupation of agricultural water rights by other industries, China's agriculture will face the risk of water shortage in the future. Due to the amount of water resources in North China is relatively small compared with that in South China, coupled with extensive operating methods and the low level of agricultural irrigation technology, water resources are wasted seriously, the problem of agricultural water shortage in North China is more serious. Optimal allocation of water resources is one of the main measures to alleviate the shortage of agricultural water resources, and is an important means to achieve sustainable use of water resources. Accurate prediction of regional agricultural water consumption is the key to optimal allocation of water resources. Grey model is a method to study “poor information”, “small sample” and uncertainty problems, which is widely used in economics, finance and other fields. The amount of historical data of annual agricultural water consumption is not enough, which is affected by many factors, and has concussion. Therefore, it is suitable to use grey model to predict agricultural water consumption. The oscillation characteristics of agricultural water consumption data series have a certain impact on the prediction accuracy of the model. To resolve these problems, an improved fractional grey prediction model is proposed in this paper. Based on the monotonically decreasing non-negative series which transformed from the oscillation series of the agricultural water consumption, a multi-objective optimization model was constructed according to the two objective functions of “maximum (or minimum) order” and “the best fit of historical data”, which was solved by the improved non-dominated sorting genetic algorithm II (NSGA-II) method. Agricultural water consumption in the test set for the research areas of Tongliao city (42°15′N～45°59′N, 119°14′E～123°43′E), Ordos city (37°35′24″N～40°51′40″N, 106°42′40″E～111°27′20″E) of Inner Mongolia autonomous region and Baoji city(33°35′N-35°06′N,106°18′E-108°03′E) of Shaanxi province was predicted by the grey model (GM(1,1)) model of fractional order reverse accumulation, the order of which was optimized according to the results of the test set fitting. The average error of the prediction was 2.23%,1.77% and 0.31%, respectively. In order to test the performance of the model, the model proposed in this paper was compared with the traditional GM (1,1) model, traditional autoregressive model and the combined model based on the wavelet analysis theory respectively. Among them, the average prediction error of GM(1,1) model for the three research areas is 5.55%, failed detection, and 1.28%. The average prediction error of autoregressive model for the three research areas is 3.34%, 4.17% and 6.49%. The average prediction error of agricultural water consumption in Ordos City of Inner Mongolia Autonomous Region based on the combination model of wavelet analysis theory is 2.87%. The results show that compared with GM(1,1) model, the prediction effect of the model in this paper is better, which depends on the objective function of “the best fitting of historical data” and the objective function of “the largest (or the smallest) order” to reduce the learning degree of the model for noise, because the model in this paper transforms the oscillating data and reduces the uncertainty of the data, so compared with the autoregressive model without data processing, the model in this paper is less affected by data volatility. In this paper, the idea of fractional order “in between” is used to improve the traditional gray model with positive integer as order, which can effectively improve the accuracy of the model. At the same time, the method of reverse accumulation is used to increase the use of new data. On the whole, for the prediction of agricultural water consumption in all research areas, the model in this paper has the minimum error, strong generalization ability and certain practicability, which can provide a basis for the prediction of regional agricultural water consumption and the allocation of agricultural water resources in northern China. [ABSTRACT FROM AUTHOR]

Due to the climate change and human activities, the global ecosystem has changed significantly, which leads to the continuous decline of ecosystem resilience at the regional and even global scale and constrains the sustainable development of ecosystems. The question about how to maintain the sustainable development ability of ecosystem and reduce the influence of uncertainties has attracted extensive attention of scholars at home and abroad. A theory based on ecosystem resilience has provided a framework for solving these problems, but at present, the research of resilience is in the initial stage. Based on this, this paper takes the documents in CNKI database and Web of science database as samples. In CNKI database, the subject is “ecosystem resilience”, and in Web of science core database, the topic is “resilience” & “ecological system” or “marine ecological resilience” or “wetland ecological resilience” or “forest ecological resilience” or “grassland ecological resilience” or “desert ecological resilience” not “social-ecological system” not “urban system” not “community system”. The Histcite citation visualization software is used to select 40 papers with the highest local citation score (LCS), generating citation chronicles of foreign ecosystem resilience literature, with a time span of 1990-2019, so as to analyze the development of resilience research in foreign countries. In the analysis of domestic research hotspot trends, it is mainly to analyze the quantity of keywords appearing at different stages, and to study the evolution of resilience content. At the same time, this paper also uses VOSviewer data processing software to perform co-occurrence and cluster analysis on keywords, and generates a network map of keywords co-occurrence relationship to understand the main research direction of ecosystem resilience at home and abroad. The findings are as follows: 1) On the whole, the research on ecosystem resilience at home and abroad is on the rise in quantity, and the documents are mainly from the United States. The journals published in the field of ecosystem resilience research at home and abroad tend to focus on ecology and resources. Research institutions include comprehensive colleges, and the universities of mining, teacher education, academy of sciences, and some administrative institutions. 2) At the international level, the focus is different at different stages. Judging from the emergent words, the early stage focuses on biodiversity protection and ecosystem stability. After 2010, more attention is paid to the research of practical policies on the resilience of specific ecosystems. According to the results of citation analysis, resilience is researched from theoretical discussion to specific case modeling, and the research object extends from marine ecosystem to forest ecosystem. The results of co-occurrence analysis and cluster analysis of key words show that the research topics of international resilience focus on biodiversity, basic theory, policy management and the stability of marine ecosystem (especially coral reef). 3) Compared with other countries, China's resilience research starts late, and the research direction of ecosystem resilience in China mainly includes three aspects: land reclamation, land investigation and monitoring means, and research on influencing factors. In recent years, social ecosystem resilience, and sustainability and stability of ecosystem have become research hotspots. [ABSTRACT FROM AUTHOR]

The temperature in edible fungi greenhouse has the characteristics of time-variant, nonlinear and multi-coupling, so accurate and effective temperature predictions can effectively help growers adjust the greenhouse environment and prevent edible fungus production and quality decline. Based on the perspective of mining the time-series information in historical temperature data. This paper described the specific steps to realize the MA-ARIMA-GASVR-based hybrid combination method to predict the temperature in the edible fungus greenhouse. Firstly, we assumed that the historical temperature series data of edible fungus greenhouse was a dynamic combination of linear and non-linear components, the historical temperature sequences were decomposed into linear sequences and residual sequences using the moving averages (MA) method. Then, time series analysis was conducted to established the model of the autoregressive integrated moving average (ARIMA) by using linear sequence after the decomposition of the moving averages, and the future trend of linear sequences was predicted by the established model. Afterward, to better fit the relationship between temperature trends and various noises in the environment, the autoregressive integrated moving average model prediction value, the historical residual data and the historical temperature data were employed as the input of the support vector regression (SVR) model, and the genetic algorithm (GA) was used to optimize the parameters of the support vector regression model to improve its performance, the parameters being optimized are penalty parameter and radial basis function kernel parameters in the support vector regression model. Finally, the hybrid model output was the temperature prediction value which was more in line with the actual situation. Moreover, the hybrid method was verified using the experimental data from the edible fungus greenhouse in Beijing. In this paper, a representative edible fungus greenhouse was selected as the experimental object according to the observation time requirements and the time-varying needs of edible fungus greenhouse temperature, which was located in the Daxing District of Beijing. A total of 2 208 measured edible fungus greenhouse temperature data were collected from July 1st, 2019 to September 30th, 2019 during the experiment. The experimental data acquisition device used the JXBS-7001 temperature monitoring sensor was used to automatically collect and record the experimental data. Three sets of sensors were deployed in the edible fungus greenhouse to record the experimental data set which included the average temperature data. We trained the proposed model by using data from July 3rd, 2019 to July 16th, 2019 and forecasted the temperature of the next two days and compared temperature prediction experiments with different models and different time intervals. The results indicated that the MA-ARIMA-GASVR-based hybrid model could better fit the original temperature data, the mean squared error, the mean absolute error and mean absolute percentage error of an hour interval temperature were 0.18, 0.36, 1.34, and three error evaluation indexes all showed that the prediction accuracy of the hybrid method in this paper was better than the single models of support vector regression and support vector regression optimized by genetic algorithm, and it was also superior to the hybrid methods which were not processed by moving averages method or optimized by genetic algorithm. Besides, the mean squared error, the mean absolute error and mean absolute percentage error of 6hours interval temperature were 0.29, 0.52, 1.95. the hybrid method in this paper can satisfy the multi-step prediction within 6 hours, which could provide more time for edible fungus producers to adjust the temperature in the greenhouse. [ABSTRACT FROM AUTHOR]

Hydraulic retarder is a highly efficient auxiliary brake device in vehicle transmission system. Compared with traditional vehicle braking systems, it can improve braking and transmission thereby improving safety in driving. Being able to independently design and manufacture hydraulic retarders is therefore essential to safeguarding development of vehicle industry for a country. The internal pressure pulsation in the hydraulic retarder is one of important factors that could cause vibrations and noise in a vehicle, while there is limited study on how the transient pressure pulsation changes responsively to the impeller angle and liquid filling rate in the transmission system. The purpose of this paper is to bridge this gap by presenting the results of an experiment study on the impact of the impeller angle and charging rate on transient pressure pulsation in a hydraulic retarder during its starting process. We compared three blade inclination angles: 60°, 75° and 90°, and five liquid filling rate: 60%, 70%, 80%, 90% and 100%, and conducted the experiments in a transient pressure pulsation test system equipped with an INV3020 data acquisition system. The test system included a frequency converter, a drive motor, a hydraulic retarder, a high frequency dynamic pressure sensor, a torque speed collector and a booster pump. During the test, the motor speed was adjusted by the inverter, and the liquid filling rate was adjusted by the inlet ball valve and the outlet ball valve in the hydraulic reducer as well as the booster pump to control water-filling of the hydraulic reducer cavity. The amplitude of the pressure pulsation at the axial frequency and the leaf frequency was analyzed to elucidate the effect of the impeller angle and the liquid filling rate. The results showed that the peaks of the pressure pulsation in the hydraulic retarder under different blade inclination angles occurred below 200 Hz, and the pressure pulsation mainly occurred at the leaf frequency and increased with the blade inclination. The pressure pulsation under different liquid filling rate mainly occurred at the leaf frequency. The amplitude of the pressure pulsation decreased as the charging rate increased from 60% to 90%, and then increased steadily when the charging rate further increased from 90% to 100%. Reducing the impeller angle and maintaining the charging rate at 70%-90% can therefore effectively reduce the pressure pulsation. The results presented in this paper unravel the impact of the impeller angle and charging rate on transient pressure pulsation in the hydraulic retarder, and have important implications for optimal design of low-vibration and low-noisy hydraulic retarders. [ABSTRACT FROM AUTHOR]

Manually classifying citrus based on its surface defects is tedious and time-consuming and a new real-time method is proposed in this paper based on the improved SSD deep learning model. In the testing bench of the waxing machine, 2 500 images of a variety of citrus species were taken, of which 2 000 were randomly selected as training set and 500 as testing set. Among them, the method classified 19 507 as normal, 9 097 skin defects and 4 327 mechanically damaged. Considering that traditional methods using near-infrared spectra, support vector machines, HSV and RGB color space model are inefficient to detect surface defects of citrus and can only identify one, we proposed an improved method to calculate the image using the one-stage detection model - SSD-ResNet18. The method gets the feature maps through backbone first, and then predicts the number of boundary boxes from the feature maps before determining the location and category of citrus using confidence and non-maximum suppression. This can detect a batch of citrus. In the proposed method, we used the mAP (mean average precision) as the precision index and the mean detection time as the speed index. Optimization in the proposed method was solved using the SGD (stochastic gradient descent) algorithm. The learning scheduler was based on cosine decay, enabling the learning rate to drop to 0 at the end of the training period. This ensures the lost value during the training period to continuously decline. As the model was stable at the end of the training period, it can be saved at the end of the training for further use. While the VGG16 was used as the original SSD backbone, it needs a multitude of parameters and is hence computationally inefficient. We replaced it with the ResNet18, which is approximately 100 times more efficient than the VGG16. An improved feature map was obtained from the analysis of the effective sensory field of different feature maps and the size of citrus in the map, the anchor in which was obtained using the K-means clustering algorithm from the manual label box. The suitable image resolution for the proposed model was obtained by comparing images taken at five resolutions: 512×512 pixels, 640×640 pixels, 768×768 pixels, 896×896 pixels and 1024×1024 pixels. The results showed that the accuracy of the mAP of SSD-ResNet18 was 87.89%, improving 0.34 percentage points higher than the original SSD. The average detecting time of the SSD-ResNet18 was 20.72 ms, reduced by 436.90% compared to the original SSD's 108.83 ms. The accuracy of the AP of SSD-ResNet18 was 94.72%, 85.79% and 83.17%, respectively, for detecting normal, skin lesion and mechanical damage. We compared MobileNetV3, ESPNetV2, VoVNet39 and ResNet18 as backbones and did not find significant difference between their accuracy, but ResNet18 was 10.52 ms, 16.78 ms and 36.76 ms less than MobileNetV3, ESPNetV2 and VoVNet39 in detection time, respectively. The method proposed in the paper meets the requirement on detecting speed in real-time citrus production line and can effectively classify and detect a multitude of citrus simultaneously. [ABSTRACT FROM AUTHOR]

In order to clarify the rheological mechanical properties of millet grain group during mechanical harvesting and processing, this paper studied the effects of different varieties and different moisture contents on the creep characteristics of millet grain group. In this experiment, different varieties (Jingu 21, Zhangza 10), and different moisture contents (12.10%, 16.05%, 20.00%) of millet grain group were selected as creep experimental factors. The creep characteristics (instantaneous elastic modulus, delayed elastic modulus, relaxation time and viscosity coefficient) of millet seed group were used as experimental indicators, and one-way analysis of variance and multi-factor analysis of variance were performed. Based on the original contents of millet seed group, the target moisture contents of the millet grain group were formulated to be 12.10%, 16.05%, 20.00%. The experiment was conducted on the CMT-6104 universal testing machine, and the special fixture for testing the creep characteristics of millet grain group was designed. Combining the Burgers model, nonlinear programming method was used to optimize search regression parameters, and creep parameters of millet grain group were obtained. The creep curve of millet grain group of different varieties showed that, as time going on, the amount of deformation caused by creep tended to be constant, which reaching the strain saturation. There were differences in the millet grain group creep characteristic curves of the different varieties. The significance test standard was 0.05, and the variance analysis was performed. The results showed that the creep characteristics of millet grain group could be described by the four-element Burgers model, and the creep parameters of millet grain group with different moisture content and different varieties were different. The variety had a significant effect on the delayed elastic modulus of the millet grain group. The delayed elastic modulus of the Jingu 21 millet grain group was higher than that of Zhangza 10. The moisture content had significant effects on the instantaneous elastic modulus, delayed elastic modulus and viscosity coefficient of the millet grain group, which was decreasing while the moisture increasing. The instantaneous elastic modulus of the millet grain group with the water content of 12.10% was significantly higher than the water content of 16.05% and 20.00%. The delayed elastic modulus of the millet grain group with the moisture content of 12.10% was significantly higher than the water content of 20.00%. The instantaneous elastic modulus and the delayed elastic modulus represented elasticity and hardness of the millet grain group, respectively. With the increasing of the moisture content of the millet grain group, the instantaneous elastic modulus and the delayed elastic modulus of millet grain group decreased, resulting in a high increasing of the creep curve. The strain in the screw conveyor of combine harvester was correspondingly larger. In this paper, the creep properties of millet grain group with different varieties and different moisture contents were studied experimentally, which provided theoretical support for low-loss harvesting, processing and storage and parameter optimization of millet. There are many factors affecting the mechanical properties of the material, in addition to the influence of moisture contents and varieties, it is also related to many factors such as temperature, internal molecular structure and chemical composition of the material, et al. The rheological properties of agricultural materials need to be further studied in conjunction with the principles of life sciences. [ABSTRACT FROM AUTHOR]

Aiming at the problem that Weibo text data sparseness is difficult to detect hot topics, this paper proposed a topic detection model based on IDLDA-ITextRank. Firstly, this paper constructed an IDLDA topic text clustering model by introducing the Weibo time series features and word frequency features, and used the model to cluster the text of the same topic into a text set(TS) . Secondly, by using the similarity calculation method which combining editing distance and word vector, it constructed the ITextRank text summary and keyword extraction model to extract the summaries and keywords of the TS. Finally, it used the mutual information of words and left-right information entropy to convert the extracted keywords into key topic phrases, and combined the key topic phrases and summaries to express the topic content. Experiments show that IDLDA model has better clustering effect on topic text than the traditional BTM model and LDA model. The key topic phrases and summaries are better than keywords to express and understand the topics of Weibo. [ABSTRACT FROM AUTHOR]

Pigs like to fight with each other to form a hierarchy relationship in groups. Aggressive behaviors, mostly fighting, are frequently found in intensive pig raising facilities. Strong aggressive behaviors can cause other pigs lack of food and water, growing slowly, wounds, sick and even dead in serious situation. This considerably reduces health and welfare of pigs and further decreases economic benefits of pig industries. Monitoring and recognizing aggressive behaviors among pig group is the first step to manage the aggressive behaviors in group pigs effectively. Traditional human recording method is time-consuming and labor-intensive. This method can’t be used 24 hours a day, 7 days a week. Machine vision technique brings an automatic monitoring method to solve this problem. In this paper, we introduced a new method for aggressive behaviors monitoring based on deep learning. The experiments were held under controlled environments, which were achieved in an environment-controlled chamber designed previously. The details of the chamber were depicted in a published paper written by our research group. Nursery pigs were fed under three different concentration levels of NH3 gas, which were <3.80 mg/m³, 15.18 mg/m³, 37.95 mg/m³, with a suitable temperature of around 27 ℃ and the comfortable humidity between 50%-70%. Each nursery group had six pigs and were weight around 9.6 kg. During each 28 days’ experiment of three concentration levels of NH3, videos were taken from the top of the chamber. An end-to-end network, named 3D CONVNet, was proposed for aggressive behavior recognition of group pigs in this paper, which based on a C3D network and built with 3D convolution kernels. The network structure of the 3D CONVNet was improved in both width and depth dimensions. The number of main convolutional layers was increased to 19, extra batch normalization and dropout layers were added to deepen the network. Furthermore, the multi-scale feature fusion method was introduced to widen the network. This improvement had bettered the performance of the algorithm considerably. To train the 3D CONVNet, 380 aggressive (14 074 frames) and 360 none-aggressive videos (13 040 frames) were chosen from experimental videos recording in experiments of two concertation levels. These videos were randomly divided into training set and validation set, and the ratio of each set is 3:1. Another 556 aggressive videos and 510 none-aggressive videos from the three experimental batches were chosen to build the testing set. There was no overlap among training set, validation set, and testing set. Results showed a total of 981 videos, including aggressive and non-aggressive behaviors, was correctly recognized from the whole 1066 testing videos. The precision of the 3D CONVNet was proved to be 92.03% on testing set. Among them, the precision, recall rate and F1-Score for aggressive behaviors were 94.86%, 89.57%, and 92.14%, respectively. The precision for different NH3 concentration experimental levels were 94.29%, 89.44%, and 85.91%, respectively, which showed the generalization performance of the 3D CONVNet. With the similar heat environments, the 3D CONVNet also showed the good performances under different illumination condition. The comparison with C3D，C3D_1 (19 layers) and C3D_2 (BN) networks resulted in 95.7% on validation set, 43.27 percent higher than the C3D network. The recognition on single image using the 3D CONVNet was only 0.5 s, which was much faster than the other three networks. Therefore, the 3D CONVNet was effective and robust in aggressive behavior recognition among group pigs. The algorithm provides a new method and technique for aggressive behavior auto-monitoring of group pigs and helps improve establishment of auto-monitoring system in pig farms and manage level of pig industry. [ABSTRACT FROM AUTHOR]

To popularize the technology of sugarcane pre-cutting seed, and cultivation with good method, combine with the development of intelligent transverse sugarcane pre-cutting seed-cutting machine, and realize the continuous and dynamic intelligent recognition of sugarcane seed characteristics by sugarcane seed-cutting device, in this study, an intelligent recognition convolution neural network model based on improved YOLOv3 network was established by continuously and dynamically collecting the surface data of the whole sugarcane through the camera built in the black box of the sugarcane cutting machine. The real-time location and recognition of the image features of the whole sugarcane cane nodes in the input recognition system was carried out by the camera inside the system. Compared with the recognition information, the improved network timely updated the sugarcane nodes data, identified and marked the position of the sugarcane nodes, and then got real-time sugarcane nodes information through data processing, which was transmitted to the multi-tool cutting table for real-time cutting. In this paper, based on the improved YOLOv3 network, a sugarcane nodes recognition system was established. The image acquisition was carried out by the camera in the sugarcane cutting system. The video data of sugarcane was collected before the training of this network, and then the image data was processed to establish training set, validation set and test set. The training data sets of different improved models were tested and the best model was selected as the model in this paper. Through the training and test, the measured results showed that the recognition accuracy of the model for sugarcane nodes was 96.89%, the recall rate was 90.64%, average recognition accuracy AP was 90.38%, and the average recognition time of pictures was 28.7 ms. Compared with the original network, the AP was improved by 2.26 percentage point, the accuracy was decreased by 0.61 percentage point, and the recognition time was shortened by 22.8 ms. At present, the recognition of sugarcane nodes still remained in single or basic image processing and recognition, and there was still a lack of fast processing methods for the whole sugarcane image. In this study, we proposed to use the improved YOLOv3 network to recognize and locate sugarcane features, and to establish the recognition model of sugarcane nodes through network training. On the basis of the accuracy and speed of the original network identification and location, the speed of identification, detection and the recognition rate were further improved. The whole sugarcane can be identified and processed quickly in real time, which can meet the needs of various sugarcane seed cutting. Combining with the other parts of the intelligent sugarcane cutting machine system designed by our research group, the whole cutting process can be mechanized and intellectualized, which can greatly improve the quality of sugarcane cutting, reduce the labor intensity and time, and greatly improve the production efficiency. It provides a research basis for the industrialized production of sugarcane pre-cutting and realizes the sugarcane production. Continuous and real-time dynamic identification of sugarcane seeds lays the application foundation for the development of intelligent transverse cutting machine for sugarcane pre-cutting. [ABSTRACT FROM AUTHOR]

At present, when constructing basic farmland protection zone in China, they always pay more attention to the functional quality of basic farmland itself, while ignoring the relationship between basic farmland and supporting infrastructure and ecological land, and lack of awareness of “co-governing, co-construction, and management”. There is an increasingly intensified contradiction between the agricultural production and the development of construction land.The quantity,landscape and ecological systerm of basic farmland protection zone have been affected. In order to solve this main contradiction, this paper analyzes the theoretical framework system of the basic farmland protection community, and builds the basic farmland protection zone community vitality evaluation index system, taking Quzhou county, a typical agricultural county in the Huanghuaihai Plain, as the case zone.According to the research path of the basic farmland protection zone community “factor status analysis-community vitality assessment-damaged living body identification-comprehensive management and protection recommendations”, the community has conducted a more deep research in the three basic time points of 2009, 2013 and 2017. On this basis, the main conclusions of this paper are as follows:1）In view of the problems existing in the current basic farmland protection, on the basis of domestic and foreign research, the meaning of the basic farmland protectionzone community is defined as a community of life for mutual protection includes basic farmland, basic supporting facilities and small ecological space; 2）Based on the theory of life community, the indicators in the evaluation index system of the basic farmland protection community are divided into four aspects: survivability, development, stability and environmental pressure.Then the analytic hierarchy process is used to determine the level of the indicator system weights; 3）The basic farmland protection zone community is relatively stable and fluctuates between Level II and Level III. In 2009, the evaluation level of Quzhou county basic farmland protection zone community vitality was in the state of Grade III. Except Quzhou Town was in Grade II state, and the rest of the towns and villages were in Grade III state. In 2013, the evaluation of Quzhou county basic farmland protection zone community vitality is in the state of II, Quzhou town, Houcun town, Yanqiao township and Dahedao township are in grade II state, and the remaining townships are in Grade III state; in 2017, the evaluation of Quzhou county basic farmland protection zone community vitality is in Level III, except Quzhou town and Dahedao township are in the second-level state, and the rest of the townships are in the state of Grade III; 4)From the perspective of the spatial distribution of towns and villages, the trend of the level of vitality first rises and then declines during the inter-annual period. In 2013, the level of vitality evaluation was the highest in the township level II, and the distribution pattern of townships along the road network with higher vitality level was presented. The research results can provide a strong basis for strengthening the construction of the basic farmland life community knowledge system and comprehensive land improvement. [ABSTRACT FROM AUTHOR]

The silo has the advantages of small area, good sealing conditions, low circulation costs, low cost and etc. It is currently the leading warehouse type of grain storage in China. Most silos still use gravity-based discharging methods, however, arches often form during silo discharging, it causes the pressure that should have been taken from the bottom of the warehouse to the wall of the warehouse. As a result, cracks may form in the silo walls causing serious damage. In addition, when there are workers above the grain surface or when the arch is manually broken, the collapse of the arch will not only result in personal injury or death of the grain surface staff, but also may damage the warehouse structure and facilities in the warehouse. In recent years, with the development of building technology, the silo volume has been continuously increasing, the diameter and height of the silo have also become larger and larger, and the related accidents caused by unloading and arching silos have also become more frequent Silo arch accidents in Daqing 2013, Lanxi 2015, in Heilongjiang province, and in Jing County in 2016 in Anhui province, for example, have resulted in the destruction of some silo facilities and casualties. Existing studies have shown that silo unloading and arching are closely related to the internal and external friction of grain. However, in those studies, a single circular particle is used in the current discrete element simulation. The real contact area of simulated grain is much smaller. As such, the simulation cannot objectively reflect the grain of the unloading process. The frictional situation is also more difficult to restore the mesoscopic dynamic process of unloading grain arching. In this paper, based on the existing PFC3D discrete element program, the micro particle simulation dust with larger viscosity coefficient was added to create an improved particle for the defect that the internal friction force of the circular particle in the discrete element simulation was smaller than that in the real grain. We used geometric methods in the structural mechanics model to determine the contact between the circular particles and derives the force-displacement relationship between the basic units. Based on typical accident cases and laboratory test results, the established particle structure mechanics model was used to simulate the force-displacement relationship between circular large particles and between small round particles and walls in the dynamic process of unloading grain and arching. The displacement map of shear stress and its stress characteristic curve were basically consistent with the experimental results. By using this model, the dynamic meso-mechanism of unloading and arching silos can be objectively reflected. The improved particle structure mechanics model proposed in this paper can not only be used to simulate the unloading and arching mechanism simulation, but also has certain reference value for the simulation of the characteristics of loose particles flow and the mechanism of the interaction between the bulk and the silo wall. [ABSTRACT FROM AUTHOR]

Tractor transmission shaft is one of the components that affect the reliability of the tractor. Compiling load spectrum which can reflect the actual working conditions of high-power tractor drive shaft is of great significance to improve the reliability of the tractor. It is time consuming and expensive to obtain the actual measurement for load spectrum of the whole life cycle, so extrapolated methods are always applied by using the load spectrum of the limited measurement duration. However, the traditional method of rain flow counting and rain basin extrapolation in the process of compiling load spectrum of transmission system has limitations, this paper proposed a POT (peak over threshold) model based time domain extrapolation method for driving shaft load of high-power tractor. Firstly, a set of wireless torque measurement system for tractor transmission shaft is built, which mainly includes strain gauges, batteries, wireless strain nodes and wireless acquisition terminals. The strain gauge is pasted on the proper part of the tractor drive shaft and connected with the wireless torque node. The wireless transmitter is encapsulated in the wireless torque node and transmits the measured torque signal to the wireless receiver synchronously. The test data of the drive shaft under field working conditions are stored in a portable computer connected to a wireless receiver. Then, POT model is established based on extremum theory to extract the peak and valley values of the test data and eliminate the small load cycles, and the intervals of the optimal thresholds are determined by using the mean excess function graph, which are [492, 501] N·m for the upper limit and [325, 334] N·m for the lower limit, respectively. Thirdly, the data in the threshold interval is divided according to a certain gradient, and the fitting effect corresponding to each threshold is preliminarily tested by calculating R-square. The grey relational degree analysis method is used to select the optimal threshold and calculate the grey relational degree of each threshold in the corresponding threshold range. the optimum threshold values 497 N·m for upper limit and 333 N·m for lower limit are achieved by comparing the values of grey correlation degree corresponding to different threshold. The excess threshold data are fitted by generalized Pareto distribution and the CDF and P-P figures are plotted to evaluate the effectiveness of fitting test. The results show that the fitting curve of generalized distribution function can accurately describe the distribution law of extreme samples. Finally, according to the fitted generalized distribution function, the load sequence with the same distribution is generated. The time domain extrapolation of the load is achieved by replacing the original extremum load with the generated sequence. The cumulative frequency curve of the load cycle is developed according to the original load data and the results obtained by the time domain extrapolation method and the rain flow extrapolation method. The results show that the frequency curves obtained by the traditional rain flow extrapolation method and the time domain extrapolation method are consistent. Compared to the original load data, both load extrapolation results have a small amount of large extreme load which did not occur during the test. Therefore, it can be indicated that the load extrapolation method based on the POT model not only increases the frequency of the load cycle, but also extrapolates the load extreme value to a certain extent based on the distribution law of the large extreme load. The accuracy of the time domain extrapolation method is verified. The load time domain extrapolation method developed in this paper has good applicability to the measured loads of high-power tractor transmission shaft in operation conditions. Compared with the rain basin extrapolation method, the load time domain extrapolation method based on POT model can not only obtain the load time domain sequence of arbitrary mileage, but also retain the order of the measured load cycle to a great extent, which can provide reliable data support for the indoor load spectrum loading test of high-power tractor transmission system in the future. [ABSTRACT FROM AUTHOR]

For pressurized water supply systems, excessive installation of air valves will inevitably increase the risk of secondary water hammer especially when the air valve fails. Therefore, reducing redundant air valves is of positive significance whether from the maintenance cost on apparatus or from the possible adverse impact caused by the malfunction. Compared to traditional analytical and numerical methods, the semi-analytical method adopted in this paper could not only ensure the clear and intuitive physical meaning of the research results but also expand the application scope of the analytical method, which was also conducive to the further study of key parameters. In this research, it aimed to find out the primary relationship between the function of air valves and the geometry characteristic of the system. Taking a simplified reservoir-pipe-reservoir system with one air valve installed at the elevated point for example, the research initially employed the basic theory of fluid transients to analyze the water hammer wave propagation process. Since the gas inhaled through the air valve separated the water column as the depressurized pressure arrived at the elevated point, the system was divided into 2 subsystems at the point according to their different wave propagation processes. The semi-analytical formulas of the target parameters such as the duration time of cavity growth and collapse, maximum air pocket volume and extreme pressure spike， were firstly proposed in a frictionless condition. Based on the formulas, it studied the key factors affecting the protective effect of the air valve against the water hammer. The semi-analytical solution indicated that the relative length of the downstream pipe section and the relative elevation of the high point played a leading role in the process of cavity growth and collapse. The effect of friction was later taken into the consideration of the semi-analytical expressions serving to reveal its influences on the system. Numerical simulations established on the method of characteristics, which had been proved to a credible and effective numerical method, were then conducted and compared with the semi-analytical solutions to validate the corresponding expressions with and without friction. The outcomes of the 2 approaches presented a consistent variation tendency with the principle variable. However, deviations still existed particularly when the targeted point had a relatively low elevation. The reasons for above deviations were discussed which probably stemmed from some hypotheses during the derivation of semi-analytic formulas, mainly including the omission of gas storage in the upstream section, linear and averaging treatment of the flow variation process in the derivation step. It could also be speculated from the results that the length and roughness of the downstream pipeline determined the maximum pressure spike and the specific pipeline elevation. The semi-analytical formula proposed in this paper was applicable to the containing-one-air-valve pressurized water supply system, and it required to be discussed in the future research for the complex system containing multiple water hammer protection devices with mutual influence. In spite of the limitations, the semi-analytical formulas still reflected the key factors correctly of the air valve as the protection device against water hammer. The findings are helpful to understand the action mechanism of the air valve in the hydraulic transition and provide references for the research of water hammer protection. [ABSTRACT FROM AUTHOR]

Rational water price policy is of great significance for the efficient use of water resources. However, development processes of water price policy are mostly subjective and few methods are suitable to quantify policy effects. In this study, we focused on irrigation areas with multiple water sources and irrigation techniques. A method based on positive mathematical programming (PMP) was proposed to simulate the impacts of agricultural water price policy on economic benefits of water use, irrigation water consumption, planting land occupation and water-saving irrigation area. This method was applied to address a case in Minqin County, Gansu Province. Under the current water price policy of the study area, the model was calibrated with the actual data in 2015, the absolute value of the difference between the optimal solution of the calibrated model and the actual planting area in the base year didn’t exceed 1.3×10-3 hm2, indicating that the model was reliable to simulate the effect of water price policy. Subsequently, the model was used to simulate the change trend of index with the rise of water price under the volumetric water price policy, the 2-part water price policy and the differential water price policy. The simulation results showed that with the rise of water price, the change trend of index under 3 water price policy was similar: the farmer income reduced, the irrigation water consumption and planting area reduced first and then stabilized, the water-saving irrigation area increased, and the benefit per cubic meter of water increased first and then decreased. Simultaneously, in response to rising water price, farmers tended to reduce the planting area of food crops first and then the area of cash crops to alleviate the loss of income. Based on the simulation results, the water price threshold under different policies and the scope of application of various policies were further analyzed, and the following conclusions were drawn. Under the volumetric water price and the 2-part water price policy, the water price should be between 0.24 yuan/m3-2.10 yuan/m3; under the differential water price policy, the water price should be between 0.24-1.50 yuan/m3; when the metering part of the water price was lower than 2.10 yuan/m3 or higher than 6 yuan/m3, the comprehensive benefit of the differential water price policy was the highest; when the metering part of the water price was between 2.10 yuan/m3 and 6 yuan/m3, the volumetric water price policy was the most ideal; when the differential water price policy was implemented and the metering part of the water price was 1.50 yuan/m3, the benefit per cubic meter of water was the highest. This paper confirmed that the steady increase of water price would not reduce the comprehensive benefits of water use, but increase the benefit per cubic meter of water. The differential water price policy currently implemented in Minqin County was appropriate. In the future, in Minqin County, the water price under the differential water price policy should be increased within the range of 0.24-1.50 yuan/m3. Moreover, during the course of water price rising in the study area, special attentions should be paid to increase investment in water-saving irrigation projects and farmer income subsidy policies should be established under local contexts. Based on the conclusions of this study, various local factors and evaluation indexes such as current water price, farmers' willingness to pay, government financial subsidies, and income from water supply projects, could be comprehensively considered to obtain detailed implementation plan of water price policy. Finally, the method established in this paper was also applicable to other areas with similar problems. [ABSTRACT FROM AUTHOR]

Tractor transmission shaft is one of the components that affect the reliability of the tractor. Compiling load spectrum which can reflect the actual working conditions of high-power tractor drive shaft is of great significance to improve the reliability of the tractor. It is time consuming and expensive to obtain the actual measurement for load spectrum of the whole life cycle, so extrapolated methods are always applied by using the load spectrum of the limited measurement duration. However, the traditional method of rain flow counting and rain basin extrapolation in the process of compiling load spectrum of transmission system has limitations, this paper proposed a POT (peak over threshold) model based time domain extrapolation method for driving shaft load of high-power tractor. Firstly, a set of wireless torque measurement system for tractor transmission shaft is built, which mainly includes strain gauges, batteries, wireless strain nodes and wireless acquisition terminals. The strain gauge is pasted on the proper part of the tractor drive shaft and connected with the wireless torque node. The wireless transmitter is encapsulated in the wireless torque node and transmits the measured torque signal to the wireless receiver synchronously. The test data of the drive shaft under field working conditions are stored in a portable computer connected to a wireless receiver. Then, POT model is established based on extremum theory to extract the peak and valley values of the test data and eliminate the small load cycles, and the intervals of the optimal thresholds are determined by using the mean excess function graph, which are [492, 501] N·m for the upper limit and [325, 334] N·m for the lower limit, respectively. Thirdly, the data in the threshold interval is divided according to a certain gradient, and the fitting effect corresponding to each threshold is preliminarily tested by calculating R-square. The grey relational degree analysis method is used to select the optimal threshold and calculate the grey relational degree of each threshold in the corresponding threshold range. the optimum threshold values 497 N·m for upper limit and 333 N·m for lower limit are achieved by comparing the values of grey correlation degree corresponding to different threshold. The excess threshold data are fitted by generalized Pareto distribution and the CDF and P-P figures are plotted to evaluate the effectiveness of fitting test. The results show that the fitting curve of generalized distribution function can accurately describe the distribution law of extreme samples. Finally, according to the fitted generalized distribution function, the load sequence with the same distribution is generated. The time domain extrapolation of the load is achieved by replacing the original extremum load with the generated sequence. The cumulative frequency curve of the load cycle is developed according to the original load data and the results obtained by the time domain extrapolation method and the rain flow extrapolation method. The results show that the frequency curves obtained by the traditional rain flow extrapolation method and the time domain extrapolation method are consistent. Compared to the original load data, both load extrapolation results have a small amount of large extreme load which did not occur during the test. Therefore, it can be indicated that the load extrapolation method based on the POT model not only increases the frequency of the load cycle, but also extrapolates the load extreme value to a certain extent based on the distribution law of the large extreme load. The accuracy of the time domain extrapolation method is verified. The load time domain extrapolation method developed in this paper has good applicability to the measured loads of high-power tractor transmission shaft in operation conditions. Compared with the rain basin extrapolation method, the load time domain extrapolation method based on POT model can not only obtain the load time domain sequence of arbitrary mileage, but also retain the order of the measured load cycle to a great extent, which can provide reliable data support for the indoor load spectrum loading test of high-power tractor transmission system in the future. [ABSTRACT FROM AUTHOR]

For pressurized water supply systems, excessive installation of air valves will inevitably increase the risk of secondary water hammer especially when the air valve fails. Therefore, reducing redundant air valves is of positive significance whether from the maintenance cost on apparatus or from the possible adverse impact caused by the malfunction. Compared to traditional analytical and numerical methods, the semi-analytical method adopted in this paper could not only ensure the clear and intuitive physical meaning of the research results but also expand the application scope of the analytical method, which was also conducive to the further study of key parameters. In this research, it aimed to find out the primary relationship between the function of air valves and the geometry characteristic of the system. Taking a simplified reservoir-pipe-reservoir system with one air valve installed at the elevated point for example, the research initially employed the basic theory of fluid transients to analyze the water hammer wave propagation process. Since the gas inhaled through the air valve separated the water column as the depressurized pressure arrived at the elevated point, the system was divided into 2 subsystems at the point according to their different wave propagation processes. The semi-analytical formulas of the target parameters such as the duration time of cavity growth and collapse, maximum air pocket volume and extreme pressure spike， were firstly proposed in a frictionless condition. Based on the formulas, it studied the key factors affecting the protective effect of the air valve against the water hammer. The semi-analytical solution indicated that the relative length of the downstream pipe section and the relative elevation of the high point played a leading role in the process of cavity growth and collapse. The effect of friction was later taken into the consideration of the semi-analytical expressions serving to reveal its influences on the system. Numerical simulations established on the method of characteristics, which had been proved to a credible and effective numerical method, were then conducted and compared with the semi-analytical solutions to validate the corresponding expressions with and without friction. The outcomes of the 2 approaches presented a consistent variation tendency with the principle variable. However, deviations still existed particularly when the targeted point had a relatively low elevation. The reasons for above deviations were discussed which probably stemmed from some hypotheses during the derivation of semi-analytic formulas, mainly including the omission of gas storage in the upstream section, linear and averaging treatment of the flow variation process in the derivation step. It could also be speculated from the results that the length and roughness of the downstream pipeline determined the maximum pressure spike and the specific pipeline elevation. The semi-analytical formula proposed in this paper was applicable to the containing-one-air-valve pressurized water supply system, and it required to be discussed in the future research for the complex system containing multiple water hammer protection devices with mutual influence. In spite of the limitations, the semi-analytical formulas still reflected the key factors correctly of the air valve as the protection device against water hammer. The findings are helpful to understand the action mechanism of the air valve in the hydraulic transition and provide references for the research of water hammer protection. [ABSTRACT FROM AUTHOR]

Agricultural machinery is one of the important components of modern agriculture. In recent years, the number of agricultural machinery has continually increased, as well as the fuel consumption caused by agricultural machinery. The fuel consumption of agricultural machinery is directly related to agricultural production costs and the vital interests of farmers. Estimating the fuel consumption of agricultural machinery is of great significance in environmental governance, agricultural machinery operator evaluation, and agricultural cost input. Different from road vehicles, the factors affecting agricultural machinery fuel consumption seem to be more complex. Taking driving conditions for the only consideration cannot accurately predict the fuel consumption of agricultural machineries. Random forest, as a typical representative of ensemble learning, has many applications in various fields and has strong fitting ability for nonlinear data. It is widely used in the research of vehicle fuel consumption prediction. The purpose of this article is to realize the fuel consumption prediction of the grain harvester, WORLD 4LB-150AA, during working in the farmland. Based on the engine operating condition data and driving condition data collected by the harvester CAN(Controller Area Network) bus and GPS (Global Positioning System) terminal, seven indicators are constructed, including engine mean torque, engine mean speed, average speed, mean acceleration, mean deceleration, acceleration standard deviation and deceleration standard deviation. The acquisition frequency of the average data is 1.3 s, and the total number of the records is 130 788. Agricultural machineries that provided the data worked in six provinces including Liaoning, Jilin Province, Shandong, Jiangsu, Zhejiang, and Hubei. At the same time, by calculating the Spearman correlation coefficient, the correlations between seven indicators and fuel consumption were explored. According to China's agricultural divisions, the six provinces are divided into three regions: northeast region, plain region, and hilly region. Then, the fuel consumptions of the same grain harvesters in different regions were analyzed. Above the analysis, the fuel consumption prediction model of the harvester based on Random Forest was established, and compared with the one based on support vector machine. The results showed that the fuel consumption is correlated with all indicators. Among them, the fuel consumption is highly correlated to engine mean torque, engine mean speed and average speed, all with the correlation coefficient above 0.6, followed by mean acceleration, mean deceleration, acceleration standard deviation and deceleration standard deviation, whose correlation coefficients are above 0.4. There are significant differences in the fuel consumption of harvesters working in different regions. Among them, areas with high output per unit area are also relatively high in fuel consumption. Moreover, the Random Forest based model can realize the higher accurate prediction of fuel consumption during harvester operation. The root mean square error RMSE is 0.14, the average absolute error MAE is 0.24, and the coefficient of determination R2 is 0.84. The proposed method in this paper can provide a reference for the optimization of working conditions of agricultural machinery and precise fuel consumption monitoring. [ABSTRACT FROM AUTHOR]

Poverty has always been a worldwide problem, especially for developing countries. The agricultural industry poverty alleviation is one of the most effective methods to improve the ability of the poor, improve the livelihood level of the rural family, and achieve sustainable development in the future. With the introduction and implementation of the "precise poverty alleviation concept" by the Chinese government, analyses of whether agricultural industry's precise poverty alleviation can become an effective way to increase the income of the poor or not, determination of what are the leading factors of the spatial and temporal differentiation of agricultural industry poverty alleviation efficiencies, and further scientifically revealing the poverty alleviation efficiency of the agricultural industry and its mechanism are of vital importance. The previous research mainly focuses on the field of economics, however, in this paper, we used innovative analysis for this research problem from a geospatial perspective using Wanzhou District, a national poverty-stricken county in the Southwestern Mountainous Area with a rate of alleviate poverty of 88% from the year 2013 to 2018 under the government program. The main innovations of this paper included that (1) an indicator system was constructed for factors that affected agricultural industry poverty alleviation efficiency based on the spatial perspective, and the industrial poverty alleviation efficiency was measured by the improved DEA efficiency model, spatial autocorrelation Global Moran’s I model and geo-detector; (2) a framework of action mechanisms between dominant influencing factors and industrial poverty alleviation efficiency was established. The spatial and temporal evolution characteristics of agricultural industry poverty alleviation efficiency, and its dominant influencing factors in 41 townships in the study area in 2013, 2015 and 2018 were further analyzed by the natural breakpoint classification method, Moran index, and geo-detector, and (3) a framework of the dominant factors that affected the efficiency of industrial poverty alleviation was set up, which can be conducive to improving China's anti-poverty model and the mechanism of industrial poverty alleviation, and further provided decision-making suggestions to alleviate the multi-dimensional poverty in the rural areas of the southwestern mountainous area. Moreover, the data in this paper were collected from the Chinese agricultural sector, the township government departments, the poverty alleviation office and the national land department, which was really reliable, the software such as SPSS 25.0，Arcgis10.2 and GeoDa095 were used for analysis. The main conclusions and policy recommendations of this paper were, (1) the industrial poverty alleviation policy had effectively promoted the overall increase in the poverty alleviation efficiency of the county industry in the southwestern mountainous area. Its policy intervention had a positive effect on promoting the poverty alleviation of the poor, the longer the intervention time, the more obvious the efficiency of industrial poverty alleviation, especially in deep poverty towns. (2) The industrial poverty alleviation efficiency showed obvious high-value clusters and low-value clusters. The high-value gathering area was located in the flat dam area along the Yangtze River with abundant water resources and flat terrain. The low-value gathering area was concentrated in the high-altitude mountainous area with a steep slope. (3) The substitution of family and economic factors by policy factors and social factors had become the two main types of factors affecting the efficiency of industrial poverty alleviation in Wanzhou District, and the influence of policy factors was greater than that of social factors. Specifically, with the continuous implementation of the precise poverty alleviation policy, the five major social factors of road density, characteristic planting, labor skill training, agricultural insurance and poverty alleviation credit funds, market participation and human resources level gradually replaced the family economy factor, and became the leading influencing factors for the poverty alleviation efficiency. Finally, this paper offers several feasible suggestions for policy direction and future research according to the research results. [ABSTRACT FROM AUTHOR]

Physiological and biochemical indices play a significant role in the evaluation of wheat storage quality. The changes in the storage environment and time will not only cause the deterioration of wheat quality but also cause significant changes of wheat physiological and biochemical indices. However, the information expressed is quite different, which brings great inconvenience to the analysis of wheat storage quality. To solve this problem, a multi-index analysis model of wheat storage quality was proposed in this paper. The falling number, germination rate, peroxidase, fatty acid, conductivity, reducing sugar were selected. Seven physiological and biochemical indices of malondialdehyde were selected as the key factors in this paper. First, the KMO (kaiser-meyer-olkin) and Bartlett's sphericity method were used to test the wheat index, and it was found that the KMO measure value was 0.807 > 0.7, SIG value was less than 0.001, which indicated that the selected index and the measured data were suitable for factor analysis. Then the correlation of wheat index was calculated by the European similarity coefficient and PCA(principal components analysis). The results showed that the distance between the falling number and fatty acid, reducing sugar value, malondialdehyde and conductivity was small, which indicated that the effect of these five indices on the quality of wheat was the same. Meanwhile, these five indices are very far away from germination rate and peroxidase, indicating that they are different in expressing the quality of wheat. In addition, the distance between the germination rate and peroxidase is very close. It shows that the two indices reflect the same quality in some degree. The sensitivity of fatty acid is the highest (0.186), which indicates that it has the greatest influence on the evaluation result, so this index can be used as the key index to evaluate wheat quality, and the sensitivity of peroxidase (0.160) is the least, and it has the least influence on the evaluation result. After a comprehensive analysis, the peroxidase was eliminated and the other six indices were retained for further calculation. Then, a systematic classification method based on nearest element and square Euclidean distance is used to cluster the wheat data. The classification results show that the wheat sample is composed of three kinds of data with obvious classification characteristics. The primary classification of wheat was obtained by analyzing the distribution of fatty acid data, and the discriminant function was obtained by using the Fisher discriminant method to train wheat data. According to the discriminant function, the center value of excellent wheat was –5.699 9. The center value of medium wheat was 1.316 and the center value of poor wheat was 3.945. By comparing the distance between the value of unknown wheat under this function and the center value of these three kinds of wheat, the unknown wheat classification can be identified. If a batch of wheat has the smallest distance to one center value of these three kinds of wheat, then it would be identified as this classification. The final test analysis shows: The result of the classification of wheat storage quality by the discriminant model in this paper is up to 88.9% in accordance with the classification of an actual reference standard. The analysis model in this paper is basically correct, which can not only provide technical support for the construction of quality evaluation system of stored wheat, but also guide the analysis and discrimination of other grain crops to a certain extent. [ABSTRACT FROM AUTHOR]

For intelligent vehicles and advanced driving assistant systems, real-time and accurate vehicle objects detection and tracking through on-board visual sensors are conducive to discovering potential dangers, and can take timely warning to drivers or measures to control vehicle braking and steering systems to avoid traffic accidents by active safety system. In recent years, vehicle detection based on deep learning has become a research hotspot. Although the deep learning method has made a significant breakthrough in vehicle detection precision, it will lead to high missed detection rate of small vehicle targets and rely on expensive computing resources in visual vehicle detection tasks, which is difficult to achieve in embedded real-time applications. Further analysis shows that the main reason for the above problems is that deep convolution neural network cannot reasonably prune network layer parameters, especially cannot reasonably utilize the shallow semantic information. On the contrary, a series of operations at the lower sampling layers will lead to the loss of vehicle information, especially for the small vehicle objects. Therefore, how to effectively extract and utilize the semantic information of small vehicle objects is a problem to be solved in this paper. On this basis, the problem of pruning network layer parameters reasonably was discussed. For the detection algorithm, on the one hand, based on visual analysis of receptive field of Tiny YOLOV3 network shallow layers, the use of shallow semantic information was enhanced by constructing a shallow feature pyramid structure, on the other hand, the shallow down sampling layer was replaced by convolution layer to reduce the semantic information loss of shallow network layers and increase the shallow layer features of vehicle objects to be extracted. Combine the above 2 aspects, the enhanced Tiny YOLOV3 network was proposed. For the tracking algorithm, because of the high frame rate of the vehicle-mounted camera, assuming that the vehicle objects in the adjacent frame moving uniformly without considering the image information, Kalman filter algorithm was used to track the vehicle target, and its observation position was estimated optimally. The proposed enhanced Tiny YOLO V3 network was trained by using 50 000 images collected by the on-board vehicle camera during the day and night. The training strategy included pre-training model, multi-scale training, batch normalization and data augment methods, which same as Tiny YOLOV3 network. On the vehicle Jetson TX2 embedded platform, 8 groups of comparative experiments were carried out with Tiny YOLOV3 model, including day and night traffic scenes. The experimental results showed that compared with Tiny YOLO V3 model，the mean precision rate of the enhanced Tiny YOLOV3 model proposed in this paper was improved by 4.6%, the mean detection error rate was reduced by 0.5%, the mean missing detection rate was reduced by 7.4%, and the mean time consumption was increased by 43.8 ms/frame without tracking algorithm. After adding the vehicle tracking algorithm, the mean precision rate was improved by 10.6%, the mean detection error rate was reduced by 1.2%, the mean missing detection rate was reduced by 23.6%, and the mean operation speed was 5 times faster than that of the Tiny YOLOV3 model, reaching 30 frame/s. The study provides an important guidance for embedded vehicle detection and tracking algorithm application in intelligent vehicles and advanced driving assistant systems. [ABSTRACT FROM AUTHOR]

In order to understand the current situation of land use layout in the plateau lake basin and explore the optimal allocation of land use space in the plateau lake basin based on urban-agriculture-ecology coordination, the paper takes the Fuxian Lake Basin as an example, based on the 3S technology, and uses the MCR-CLUE-S model to study the structure and layout of town-agriculture-ecological space in 2030 under the four modes of urban construction scenario, cultivated land development scenario, ecological protection scenario and integrated optimization scenario in the Fuxian Lake Basin. Finally, the optimal allocation of land use space under the coordination of town-agriculture-ecological space in Fuxian Lake Basin is proposed. The results show: 1) From 2005 to 2015, in the Fuxian Lake Basin, the urban space is on the rise, the agricultural space is in a downward trend, and the ecological space is rising first and then decreasing. The spatial distribution pattern is basically the same in 10 years. 2) The paper selects important water source, lakeside wetland and large-scale natural forest land as protection sources, 8 resistance factors such as elevation and slope are selected. The minimum cumulative resistance model and the cumulative cost distance model are used to construct the ecological security pattern of the Fuxian Lake Basin, as well as the “source” core area, ecological protection area, ecological margin area, agricultural development area, agricultural fringe area and urban construction area which are conducive to ecological protection. 3) Taking the ecological functional partition area in the ecological security pattern as a constraint, based on the Markov model's calculation of the number of land types, and simulating the town-agriculture-ecological space optimization layout in the urban construction scenario, agricultural development scenario and ecological protection scenario of the Fuxian Lake Basin in 2030 through the CLUE-S model. The urban construction scenario takes into account the current needs of life, and the development of the number and spatial structure of town-agriculture-ecological space led by the development of urban space under the guidance of policies; the agricultural development scenario controls the size of urban space and develops part of the ecological space potential; the area of agricultural space has decreased under the ecological protection scenario, and the urban space area has increased slightly. 4) In the 2030 comprehensive optimization scenario of the Fuxian Lake Basin, seven spatial types are classified according to different land functions: Urban space, agricultural space, ecological space, urban-agricultural space, urban-ecological space, agriculture-ecological space and town-agriculture-ecological space.There are large differences in various types of space: Agriculture - ecological space is minimal, ecological space is maximum. 5) By analyzing the advantages of regional development in four cases and combining with various factors such as cultivated land protection, ecological protection and socioeconomic development in the Fuxian Lake Basin, the study considers that the integrated scenario is more reasonable and more suitable for local development, while the other three single-element scenarios can complement and adjust the implementation of the integrated scenario. The research results provide reference for the decision-making of land space planning and ecological protection strategy in Fuxian Lake Basin. [ABSTRACT FROM AUTHOR]

Ammonia-nitrogen is one of the key parameters to evaluate aquaculture water quality and aquatic ecosystem security. When the ammonia-nitrogen content exceeds 0.2 mg/L, it will cause serious harm to the health of aquatic organisms, and even cause the disease and death of aquaculture objects. However, the pond culture environment is complex and variable. Many factors have affected ammonia-nitrogen concentration, such as dissolved oxygen, nitrite, salinity, blue-green algae, turbidity, pH value, oxidation reduction potential and so on. And these factors are related to each other and exhibit nonlinear changes. In order to master rules of the changes of ammonia nitrogen, a lot of information systems and models were developed to monitor the pond water. But the data acquired by internet of things for aquaculture has a lot of noise due to environmental weather factors and the sensor or network failure. Traditional mechanism methods are different from statistical model, and mainly depend on small amount of data. So traditional mechanism methods are difficult to achieve the desired accuracy using the time sequence data online, and can’t be used in the real time system because of the large amount of data. Some scholars have also carried out lots of research on statistical models such as traditional neural networks in the field of forecasting. But it is difficult to accurately predict the ammonia-nitrogen content because of the model’s local convergence and poor computational efficiency. In this paper, a method for predicting ammonia-nitrogen in pond aquaculture water based on multi-variate deep belief network model optimized by particle swarm optimization (PSO-MDBN) was proposed. The mothed constructs a non-linear deep-seated network structure through self-learning, and has strong data prediction ability and data classification and recognition ability. Firstly, because the ammonia-nitrogen content in pond culture water is affected by various environmental factors, the ammonia-nitrogen ecosystem of pond culture water was analyzed, and the data collection scheme of pond culture environment information was formulated according to the actual conditions of pond equipment and modeling needs. In this part, principal component analysis was used to screen the key influence factors of ammonia nitrogen change as auxiliary variables of the prediction model. Secondly, the wavelet threshold denoising method was used to reduce the data of the selected auxiliary variable data, and the data dimension and improve the reliability of the data. Thirdly, based on the deep belief network theory, a multi-parameter deep belief network model is constructed, and the optimal network structure was determined by experimental methods. Finally, the particle swarm optimization algorithm was used to optimize the deep belief network parameter setting to get the optimal combination of parameters. The experimental results showed that the average percentage error of the proposed method is 0.117 2. Compared with the traditional LSSVM(least squares support vector machine), BPNN (back propagation neural network) and DBNs(deep belief networks) methods, the RMSE of PSO-MDBN is reduced by 0.285 9, 0.214 6 and 0.013 9. In addition, if the number of samples increases, the model error would show a downward trend. Above all, the proposed method of this paper has high prediction accuracy and generalization performance. The research can provide data theoretical basis and parameter support for accurate prediction of ammonia nitrogen content in pond water. [ABSTRACT FROM AUTHOR]

Solar radiation is an important parameter affecting light and thermal environment in solar greenhouse. Accurately obtaining the solar radiation value and variation of the interior wall and ground in the greenhouse can play an important guiding role in greenhouse design, construction, environmental regulation and crop production. On the basis of summarizing the previous models of solar radiation, a relatively perfect solar radiation model of solar greenhouse was established in this paper. Meteorological data, the laws of the earth and the movement of the sun, and the relationship between the sun's rays and the angle of the front roof were all considered in this solar radiation model. Internal radiation change of the greenhouse was analyzed based on the model. The model was verified in typical sunny days. The results showed that there were some differences between the calculated and measured solar radiation values, the MBE(mean bias error) was 63.46 W/m2, the MAE(mean absolute error) was 63.48 W/m2, the RMSE(root mean square error) was 79.18 W/m2 and the coefficient of determination R2 was high, ranged from 0.95 to 0.99. The model can be used to calculate the solar radiation intensity of the wall and the ground of solar greenhouse in any area anytime. Highly accurate analysis of the radiation change and the variation of light transmittance in the greenhouse in different seasons were achieved in this paper. It was found that the light transmittance is more affected by the solar azimuth and the solar elevation angle than by the roof angles at different positions. The solar radiation on the surface of the wall and the ground in greenhouse changes with the seasons. The spring equinox and autumn equinox are the days in which the wall and the ground received the longest solar radiation during the year. At this time, the surface of the wall is roughly equivalent to the solar radiation on the ground. From the spring equinox to the autumn equinox, the ground radiation is higher than the surface radiation, while it is opposite when it comes from the autumnal equinox to the vernal equinox. The daily accumulation of solar radiation on the wall changed little from the winter solstice to the start of spring, decreased from the start of the spring to the summer solstice and rose from the summer solstice to the start of winter. The time from winter to the start of the spring is the main production season in the greenhouse, and the heat accumulation of the wall is also the highest in the whole year. Therefore, it is of great significance to carry out research on the heat storage at this stage. Solar radiation in greenhouses in different regions is mainly affected by latitude. In lower latitudes, the amount of solar radiation in winter is large and the amount of radiation in summer is small. The research results can provide reference and related data for the study of wall heat storage, roof optimization, crop planting and energy balance of envelope in solar greenhouse. In addition, the change rule of solar radiation in greenhouse was analyzed just under clear and cloudless conditions. For the plant canopy shade, radiation under different weather conditions, insulation quilt position, different rear roof incline angles and lengths, azimuth angles, wall inclined angle and other aspects of the solar radiation change inside the greenhouse can be further explored by the method proposed in this paper. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of agricultural electrical equipment and reduce energy consumption, many scholars attempt to estimate the iron loss accurately. The analysis of finite element showed that in rotational electric machines total core loss comprised alternating core loss and rotational core loss. Precise measured value and modeling of rotational core loss in electrical steel sheets and rotating electrical machines is very important to design and optimize the kind of agricultural motor. According to the separated core loss model under alternative excitation, the core loss can be separated into hysteresis loss, eddy current loss and excess loss. As for alternating core loss, the specific core loss with a circular magnetic flux density can also be separated into 3 portions: the rotational hysteresis loss, the rotational classical eddy-current loss and the rotational excess loss. By means of fourier analysis, the rotating core loss model which considered the influence of alternating and rotating magnetic field, skin effect, dynamic hysteresis loop and minor hysteresis loop was proposed in this paper. Actually, the static hysteresis loop and the dynamic hysteresis loop are different, when the flux density is in saturation, the hysteresis loop shape will be changed. In order to consider the complex behavior of dynamic hysteresis, variable coefficient hysteresis loss was used in the model. The classic exponential coefficient were chosen to be substituted in to the 3 parameters polynomial and fitted out the rotational hysteresis loss with the logarithm. Considering the impact of skin effect at high frequencies, eddy-current loss coefficient were corrected in the paper. Minor hysteresis loop generated by the massive harmonic components leaded the inaccurate prediction of the core loss, the influence of minor hysteresis loop was described by the modified coefficient in the improved formulations, and the modified coefficient was related to the ratio of local flux density to flux density amplitude. By applying orthogonal decomposition technology, 2 mutually orthogonal magnetic flux field was used to describe elliptical rotating magnetic field and replace the rotating loss data. Taking an object affected by the elliptical flux density as an example, the applied magnetic field intensity might not be an elliptical vector because of the nonlinear magnetic flux density-magnetic field intensity relationship and magnetic anisotropy, when it was expanded into a fourier series, however, it shown that the higher harmonics of magnetic field intensity did not contribute to the total core loss as long as magnetic flux density only contains the basic components, the total core loss under the elliptical flux was the summation of alternating core loss and rotating core loss. In order to verify the accuracy of the improved model, a new 3D magnetic properties measurement system was used to measure the rotational core loss of electrical sheet steels The 3D excitation structure consisted of 3 orthogonal C-shaped cores, 6 multilayer excitation coils which were wound around core poles, a sensing box with built-in core material was placed in the center of 3D magnetic properties measurement device, 6 thin pieces, named as protective layer of uniform magnetic field were fixed around the specimen to make the measured field more uniform at the surface of specimen. Experimental results showed that compared with the classical model and the improved model considering the single factor of skin effect or hysteresis loops only, the accuracy of core loss calculation value of the proposed model was increased by 25.32% and 9.16%, respectively, especially under the condition of high flux density and high frequency. When the frequency was 50 Hz, compared with the classical model, the accuracy of core loss calculation value of the proposed model was increased by 9.21%, and the accuracy was increased by 39.76% at 200 Hz. The comparison of measured value between alternating core loss and rotational core loss showed that the energy of magnetic domain could not accumulated to the maximum in a fixed direction that would lead to irreversible magnetic domain conversion under alternating excitation, which resulted in the increase of hysteresis loss with the increase of magnetic density. The research results can provide reference for the design and optimization of agricultural electrical equipment. [ABSTRACT FROM AUTHOR]

In order to reasonably determine the manufacturing tolerance range of 2-DOF planar parallel mechanism parts on quadruped robot, the error sensitivity analysis of the leg parallel mechanism and the position precision estimation in the working space were proposed in this paper. The leg mechanism of the quadruped robot analyzed in the paper was composed of a 2-DOF planar parallel mechanism and a RPR mechanism in series. The planar parallel mechanism realized the forward direction of leg lifting and stepping, and the RPR mechanism realized the lateral pendulum movement of leg mechanism, and the coordinate movement of the 2 mechanisms realized the spatial movement of leg mechanism. The advantage of the leg structure was that the planar parallel mechanism could enlarge the stroke of the end position to achieve rapid leg lifting and stride, and the RPR mechanism could achieve lateral swing decoupling motion. In the manufacturing process of the leg mechanism, due to the relatively simple structure of RPR mechanism and the coupling and complexity of parallel mechanism, the manufacturing of 2-DOF planar parallel mechanism was emphatically analyzed. Firstly, the composition of the leg mechanism was introduced, the position equation of 2-DOF planar parallel mechanism was established, an error model of 2-DOF planar parallel mechanism was established by using the full differential theory, the mapping relation of each error source to the end position error was obtained. Secondly, the position error sensitivity model and evaluation index of the mechanism were established. The histogram of each geometric error source on the end position precision under the significance of generalization was drawn according the sensitivity evaluation index, and the influence degree of each error source on the end position precision was revealed. The result showed that the angle error sources had a great influence on the position precision of leg mechanism, the angle error between the rod BC and the X axis was the most sensitive, other angle errors were more sensitive, while the influence of other manufacturing error sources was relatively small. Then, according to the 3s principle and sensitivity evaluation index, the allowable manufacturing tolerances of the parts of each error source were determined. Finally, according to the allowable manufacturing tolerance range, the actual parts were designed and processed. The manufacturing error values of each part were measured by using the three coordinate measuring instrument, and the actual manufacturing errors of each part were obtained. According to the error transfer model and the sensitivity evaluation index, the distribution of the end position precise in the workspace was estimated. At the same time, the representative 6 pose points in the working space of the experimental prototype were measured with the three coordinate measuring instrument, and the end precision estimation was verified by some examples. The result of precision estimation showed that the error value of the lower part of the working space was within the range of 0.18-0.3 mm, and the precision was higher; the error value of the upper part of the working space was within the range of 0.4-1 mm, and the precision was poor; and the precision of the upper part of the working space was worse, and the error of the left upper and top part was up to 1 mm. Therefore, when performing more precise tasks, the trajectory of the foot contacting to ground could be planned in the lower part of the workspace to improve the foot positioning precision. The experiments results showed that the maximum error absolute value between the actual position precision value and the theoretical position precision estimation was 0.003 8 mm, and the minimum error absolute value was 0.001 5 mm, which verifies the correctness of the error transfer model and the position precision estimation, and the validity of the method to determine the manufacturing tolerance of components. The results laid a foundation for the kinematics calibration, error compensation and trajectory planning of the leg mechanism of the quadruped robot. [ABSTRACT FROM AUTHOR]

In order to improve the processing quality and reliability of the cleaning screen of grain combine harvester, it is necessary to eliminate the manufacturing defects in the design and pilot stage, which causing the additional load, , and to locate and eliminate the processing defects by measuring in the production stage. In this paper, a method can locate and calculate the exciting forces act on the connection points of cleaning screen and thresher body by classical transfer path analysis (TPA ) were developed. By measuring and comparing the vibrations of the connection points, it is found that the vibration frequency components were the same and were strongly correlated, so it is impossible to find the main excitation source by analyzing the frequency to locate the manufacturing defects. According to the relationship between exciting force and defect, it is pointed out that there should be major manufacturing defects near the maximum excitation force. The vibration of connection points and a certain observation point of the test bench were measured by using triaxial accelerometers and dynamic signal analyzer , and the characteristics of time domain and time-frequency domain were analyzed. The results showed that although there was no change in the frequency components in the spectrum, the amplitude changed greatly, and the phase changed greatly at different times of each frequencies. The transfer functions of the connection points to each measurement points were detected by using a modal force hammer and vibration measuring devices. Since the phase was unstable, according to the principle that the product of each excitation force and the transfer function (ie, the contribution of the excitation force to the vibration) had the largest vibration response in the same direction, the maximum acceleration amplitude was introduced into the inverse matrix method formula, and the influence of the phase was ignored, and the problem that the ill-conditioned matrix of the transfer function matrix could not be inverted was solved by increasing the measurement point, the generalized inverse matrix of transfer function was calculated by singular value decomposition. The calculated excitation force was optimized by least squares method, and finally the practical incentive calculation formula was derived. In order to verify the validity of the method, the magnitude of the excitation force during normal operation and the contribution to the vibration in Ydirection of the observation point were calculated. The results showed that the sum of contributions was only slightly smaller than the measured acceleration, which was about 84.7%-94.6% of the measured value, the excitation force calculated by this method was basically correct. The keyway clearance was used as a typical defect for the location verification test. It was found that the amplitude of each frequency in the excitation force spectrum near the defect increased significantly (71%-3 271%), while the amplitude of the slot excitation force away from the defect was only slightly added, the effectiveness of the positioning method was verified. The defect localization method proposed in this paper only adds one accelerometer and one measuring instrument to the original cleaning screening assembly quality inspection platform, the vibration excitation force at the connection points between the cleaning screen and the rack mounting could also be roughly calculated by measuring 4-8 acceleration responses. [ABSTRACT FROM AUTHOR]

The coordinated development of Beijing-Tianjin-Hebei has become a major national strategy. Hebei Province in China, is not only the key bearing of the extracted non- capital function and where Xiong’an New District is situated, but also the crucial “short board” for the expected cooperation, demanding urgently to overcome various shortcomings, to undertake industries and to accelerate local development. Previous studies have directly or indirectly defined land development intensity as the ratio of the construction land area to the total area within a region, designating the area of the construction land as the numerator and the regional land area as the denominator. However, due to the constraints of natural conditions, development planning of ecological red line protection and arable land protection, part of the land within a region is not suitable for exploitation and utilization. Taking account of these limitations, in this paper, we adjusted the numerator and the denominator of the original land development intensity and tried to propose the concept of “narrowed threshold of land use intensity”, which was defined as the ratio of the size of regional construction land to the natural land area suitable for construction. The threshold was the theoretical maximum under the red line constraint of ecological and farming policy in the region. The scale of development and construction accounts for the proportion of natural conditions suitable for the construction of land area. Taking Hebei Province as an example, we measured the current status, situation value and threshold of the narrow-scale land development intensity of the province, and proposed the feedback adjustment of the narrow development intensity threshold under the expected correction. Through defining the “narrowed threshold of land use intensity”, we measured the largest demanded area of construction land in Hebei Province under the background of natural growth and industrial transfer, as well as the suitable development and construction area constricted by natural conditions and development planning. The result showed that narrow development intensity threshold in Hebei Province was 52.32%. Under the ideal prediction of safeguarding regional self-development and industrial transfer for construction land use, the province's narrow-scale land development intensity situation value in 2030 was 29.34%, which narrow-scale land development intensity in 2016 current situation was 26.79%, and the adjacent threshold coefficient was 0.56, which in 2016 was 0.51. Facing the relatively limited land use intensity threshold, it was suggested that Hebei Province should take the suitability evaluation of land space development and construction as the foundation, combine the reasonable demand of urbanization and industrialization and the requirements of ecological space protection, rationally arrange the transfer industry from Beijing and Tianjin, implement land agglomeration development and classified protection, as well as optimize the pattern of land space development, in order to provide land resource protection for realizing the optimal utilization and major development of Beijing- Tianjin- Hebei region. Furthermore, this paper provides reference for the implementation of provincial land use planning strategy and regional policy guidance in Hebei Province. [ABSTRACT FROM AUTHOR]

The hydraulic conductivity of frozen soil is not only one of the main factors affecting the speed of moisture migration, but also an important model parameter in a large number of frost heave models. However, the study of the hydraulic conductivity for frozen soil is often described by empirical formulas. The calculation results lack a theoretical basis, and the predicted values of different empirical formulas often have enormous deviations. Therefore, it is debatable to use the empirical formula to predict the hydraulic conductivity coefficient of frozen soil. In order to reveal the process of moisture migration, according to the theory of water film thermodynamics at the ice-water interface, this paper points out that the water flow can be regarded as the Darcy flow under the equivalent pressure control in frozen soil. On this basis, the expression of pore water freezing temperature and pore radius is obtained. The capillary bundle theory is applied to the frozen soil and combined with the soil frozen characteristic curve to give a theoretical model for predicting the hydraulic conductivity of frozen soil, Meanwhile, the function of pore radius probability density is eliminated to make this proposed model convenience to calculate and use. The calculated values of this model are compared with the experimental data and empirical formulas of the predecessors. The results show that the pore freezing temperature decreases with the decrease of pore radius, and the temperature drop rate also increases. The freezing temperature reduction rate is significantly accelerated especially when the pore radius is less than 10-6 m. Considering the unfrozen pore water and the unfrozen water film as the migration channel of moisture, in general, the calculated values of this model agree well with the experimental results and are better than the empirical formula, which proves the rationality of the model. The predicted values of the hydraulic conductivity of frozen soil obtained by three empirical formulas are even different by four orders of magnitude for Qinghai-Tibet silty clay. Although the predicted value obtained by the Fowler formula is closer to the measured value, there is an empirical coefficient in the prediction formula. For the empirical coefficient is often arbitrarily determined in a certain range, and it is no clear physical meaning and calculation procedure, which leads to the reliability of the predicted value from the empirical formula is debatable. At the same time, the power function is used to fit the soil frozen characteristic curve (SFCC) in this model, which tends to infinity near 0 °C. In order to ensure the continuity of the function of the SFCC, the piecewise function is used to describe the soil frozen characteristic curve, but the deviation of the calculated and measured values of the model within 0~-0.0357 °C. Because the model is based on a quasi-steady state process, and the frozen soil is in the stage of intense phase change near 0 °C. Thus, this paper pointed out that the fitting formula of the soil frozen characteristic curve is very important to this model for reducing this deviation near 0 °C. [ABSTRACT FROM AUTHOR]

Researchers have developed various design methods for driving systems and control strategies for electric tractors, as well as performance analysis of key components. However, little attention has been paid to the precise management of torque requests in the top layer in consideration of factors such as the power output restrictions at motor operating temperature limits, battery state-of-charge limits, time-based torque ramp limits, and the speed-dependent torque capability of the motor. In this paper, we developed a driving torque management model on the upper layer of driving systems for electric tractors based on the common functional blocks related to the decision of target torque in electric tractor control. In order to meet the field operation requirements and improve the quality of work, the input signals were calibrated to the desired cruise speed and further converted to the motor target revolving speed. According to the deviation between the actual revolving speed and the target revolving speed, the motor target output torque was calculated to balance the required motor power with the work load. Further considering the impacts on the electric tractor caused by the torque fluctuations during the cruise operation, the motor maximum torque available at the current revolving speed, the influence of the over-temperature of the driving system and the over-discharge of the battery, models of time-based ramp limitation of target torque, motor’s speed-based maximum torque limitation and load reduction protection under extreme conditions were constructed in turn. The electric tractor model consisting of tractor dynamic model, battery model, and electric motor model was also built. A tractor control unit to support the torque demand management model was designed, and a hardware-in-the-loop real-time test platform was built with dSPACE. The parameters in the torque management model were calibrated separately, and the output characteristics of the drive system under traction conditions were tested. The results showed that the actual vehicle speed tracked the expected cruising speed steadily during the traction operation. The tracking error mainly depended on the degree of slip of the driving wheels. When the expected speed changed, the actual vehicle speed smoothly transited to the expected value according to the calibrated climbing rate. During the operation, the model output torque always stayed within the motor torque capacity, and kept a small change rate of not more than 35 N·m/s, which led to more gentle variations of motor torque compared with the original without ramp limitations. When the battery voltage dropped below the over-discharge threshold, the management model scaled down the target torque in time by 10%-27% according to the degree of undervoltage, which therefore kept the battery voltage always above the safe level. The driving torque demand management model built in this paper can provide a technical reference for tractor control unit designs of electric tractors. [ABSTRACT FROM AUTHOR]

Agricultural machine automatic navigation is one of the key technologies in precision agriculture technology system, the in-depth study is important in scientific research, application and social values. In this paper, we investigated the navigation problems in the application of the farm work, including slow on-line speed, bad on-line stability, and poor adaptability of bumpy complex road surface. These problems can be summarized as the speed and stability problems of track tracking in the case of large position deviation or large course deviation. Through the analysis of the work principle and control parameters function of the navigation, a conclusion is made that the correct speed and stability of the position deviation and course deviation of agricultural machinery can be improved by allocating the weight of the position deviation wheel angle decision quantity and course deviation wheel angle decision quantity reasonably. Then in this paper, we developed the navigation control algorithm based on the agricultural machine kinematics model and the pole optimal configuration theory. Because of the small angle linearization of course deviation angle and wheel angle during the deducing, the control law can achieve good control effect only in the ideal straight path tracking control with small position deviation, course deviation, and wheel angle. Based on this, a path tracking control method of aiming pursuit model for agricultural machine navigation was proposed aiming at improving the on-line speed, stability and adaptability to complex road surface of the automatic navigation system of agricultural machine. In this method, we selected a tracking target point on the planning path of agricultural machine ahead, and tracked the target point by controlling the steering wheel angle. The direction of the agricultural machine vehicle center point to the target point was called as the aiming course. The desired steering angle would be larger when the deviation was larger between the course of agricultural machine and the aiming course, with a rapid correction of aiming course deviation to achieve the goal of fast tracking the target path. On the other hand, the desired steering angle would be smaller when the deviation was smaller between the course of agricultural machine and the aiming course, with a stable tracking to aiming path to achieve the goal of stable tracking the target path. In this model, the steering wheel angle was designed to be K times of the aiming course deviation and K was called as control gain. The length of the projection of the agricultural machine vehicle center to the target point vector on the planning path was named as the preview distance. The control gain and the preview distance were two important parameters that affected the control effect of the model. In this paper, there were three steps to prove the feasibility of the method. The control gain K and the preview distance were set up by referring to the result of the pole optimal configuration method based on the kinematic model of agricultural machine. By comparing two methods formula, the position deviation wheel angle decision quantity had a linear relationship with the position deviation in the kinematics model method and the position deviation wheel angle decision quantity had an inverse tangent function relationship with the position deviation. The inverse tangent function relation was more beneficial to maintain proper weight of position deviation wheel angle decision quantity and course deviation wheel angle decision quantity that would make the path tracking control of agricultural machinery navigation more rapid and stable. Simulation analysis results of aiming pursuit model algorithm in different position and different course deviation showed that the proposed method had a fast and stable path tracking performance and good robustness and adaptability to the navigation path tracking. The test results of agricultural machine showed that the control method proposed in this paper had a good effect in the rapid responsibility and line tracking performance. In the case of 0.5, 1, 1.5 m initial position error, the on-line time was 6.8, 8.2, 9.4 s, respectively, the corresponding travelling distance was 6.73, 8.11 and 9.33 m, respectively and the corresponding overshoot was 5.2, 7.0, 8.5 cm, respectively. The maximum error of straight-line path tracking for bumpy uneven field was not more than 4.23 cm, the mean value of the absolute value of the error was 1 cm, and the standard deviation was 1.25 cm, which satisfied the operation requirements of agricultural machine. [ABSTRACT FROM AUTHOR]

The silo has the advantages of small area, good sealing conditions, low circulation costs, low cost and etc. It is currently the leading warehouse type of grain storage in China. Most silos still use gravity-based discharging methods, however, arches often form during silo discharging, it causes the pressure that should have been taken from the bottom of the warehouse to the wall of the warehouse. As a result, cracks may form in the silo walls causing serious damage. In addition, when there are workers above the grain surface or when the arch is manually broken, the collapse of the arch will not only result in personal injury or death of the grain surface staff, but also may damage the warehouse structure and facilities in the warehouse. In recent years, with the development of building technology, the silo volume has been continuously increasing, the diameter and height of the silo have also become larger and larger, and the related accidents caused by unloading and arching silos have also become more frequent Silo arch accidents in Daqing 2013, Lanxi 2015, in Heilongjiang province, and in Jing County in 2016 in Anhui province, for example, have resulted in the destruction of some silo facilities and casualties. Existing studies have shown that silo unloading and arching are closely related to the internal and external friction of grain. However, in those studies, a single circular particle is used in the current discrete element simulation. The real contact area of simulated grain is much smaller. As such, the simulation cannot objectively reflect the grain of the unloading process. The frictional situation is also more difficult to restore the mesoscopic dynamic process of unloading grain arching. In this paper, based on the existing PFC3D discrete element program, the micro particle simulation dust with larger viscosity coefficient was added to create an improved particle for the defect that the internal friction force of the circular particle in the discrete element simulation was smaller than that in the real grain. We used geometric methods in the structural mechanics model to determine the contact between the circular particles and derives the force-displacement relationship between the basic units. Based on typical accident cases and laboratory test results, the established particle structure mechanics model was used to simulate the force-displacement relationship between circular large particles and between small round particles and walls in the dynamic process of unloading grain and arching. The displacement map of shear stress and its stress characteristic curve were basically consistent with the experimental results. By using this model, the dynamic meso-mechanism of unloading and arching silos can be objectively reflected. The improved particle structure mechanics model proposed in this paper can not only be used to simulate the unloading and arching mechanism simulation, but also has certain reference value for the simulation of the characteristics of loose particles flow and the mechanism of the interaction between the bulk and the silo wall. [ABSTRACT FROM AUTHOR]

The silo has the advantages of small area, good sealing conditions, low circulation costs, low cost and etc. It is currently the leading warehouse type of grain storage in China. Most silos still use gravity-based discharging methods, however, arches often form during silo discharging, it causes the pressure that should have been taken from the bottom of the warehouse to the wall of the warehouse. As a result, cracks may form in the silo walls causing serious damage. In addition, when there are workers above the grain surface or when the arch is manually broken, the collapse of the arch will not only result in personal injury or death of the grain surface staff, but also may damage the warehouse structure and facilities in the warehouse. In recent years, with the development of building technology, the silo volume has been continuously increasing, the diameter and height of the silo have also become larger and larger, and the related accidents caused by unloading and arching silos have also become more frequent Silo arch accidents in Daqing 2013, Lanxi 2015, in Heilongjiang province, and in Jing County in 2016 in Anhui province, for example, have resulted in the destruction of some silo facilities and casualties. Existing studies have shown that silo unloading and arching are closely related to the internal and external friction of grain. However, in those studies, a single circular particle is used in the current discrete element simulation. The real contact area of simulated grain is much smaller. As such, the simulation cannot objectively reflect the grain of the unloading process. The frictional situation is also more difficult to restore the mesoscopic dynamic process of unloading grain arching. In this paper, based on the existing PFC3D discrete element program, the micro particle simulation dust with larger viscosity coefficient was added to create an improved particle for the defect that the internal friction force of the circular particle in the discrete element simulation was smaller than that in the real grain. We used geometric methods in the structural mechanics model to determine the contact between the circular particles and derives the force-displacement relationship between the basic units. Based on typical accident cases and laboratory test results, the established particle structure mechanics model was used to simulate the force-displacement relationship between circular large particles and between small round particles and walls in the dynamic process of unloading grain and arching. The displacement map of shear stress and its stress characteristic curve were basically consistent with the experimental results. By using this model, the dynamic meso-mechanism of unloading and arching silos can be objectively reflected. The improved particle structure mechanics model proposed in this paper can not only be used to simulate the unloading and arching mechanism simulation, but also has certain reference value for the simulation of the characteristics of loose particles flow and the mechanism of the interaction between the bulk and the silo wall. [ABSTRACT FROM AUTHOR]

The silo has the advantages of small area, good sealing conditions, low circulation costs, low cost and etc. It is currently the leading warehouse type of grain storage in China. Most silos still use gravity-based discharging methods, however, arches often form during silo discharging, it causes the pressure that should have been taken from the bottom of the warehouse to the wall of the warehouse. As a result, cracks may form in the silo walls causing serious damage. In addition, when there are workers above the grain surface or when the arch is manually broken, the collapse of the arch will not only result in personal injury or death of the grain surface staff, but also may damage the warehouse structure and facilities in the warehouse. In recent years, with the development of building technology, the silo volume has been continuously increasing, the diameter and height of the silo have also become larger and larger, and the related accidents caused by unloading and arching silos have also become more frequent Silo arch accidents in Daqing 2013, Lanxi 2015, in Heilongjiang province, and in Jing County in 2016 in Anhui province, for example, have resulted in the destruction of some silo facilities and casualties. Existing studies have shown that silo unloading and arching are closely related to the internal and external friction of grain. However, in those studies, a single circular particle is used in the current discrete element simulation. The real contact area of simulated grain is much smaller. As such, the simulation cannot objectively reflect the grain of the unloading process. The frictional situation is also more difficult to restore the mesoscopic dynamic process of unloading grain arching. In this paper, based on the existing PFC3D discrete element program, the micro particle simulation dust with larger viscosity coefficient was added to create an improved particle for the defect that the internal friction force of the circular particle in the discrete element simulation was smaller than that in the real grain. We used geometric methods in the structural mechanics model to determine the contact between the circular particles and derives the force-displacement relationship between the basic units. Based on typical accident cases and laboratory test results, the established particle structure mechanics model was used to simulate the force-displacement relationship between circular large particles and between small round particles and walls in the dynamic process of unloading grain and arching. The displacement map of shear stress and its stress characteristic curve were basically consistent with the experimental results. By using this model, the dynamic meso-mechanism of unloading and arching silos can be objectively reflected. The improved particle structure mechanics model proposed in this paper can not only be used to simulate the unloading and arching mechanism simulation, but also has certain reference value for the simulation of the characteristics of loose particles flow and the mechanism of the interaction between the bulk and the silo wall. [ABSTRACT FROM AUTHOR]