Search

Purpose: Prediction of soil particle content is essential for soil texture classification, soil management and agricultural production. This study aimed to achieve high-accuracy predictions of soil particle content in the Ogan-Kucha River Oasis using hyperspectral data and environmental variables. Materials and methods: We collected 62 representative surface soil samples (depth: 0–10 cm), and conducting indoor soil particle content and spectral measurements. The relationship between environmental variables and soil particle content was analyzed using the Boruta algorithm, and seven three-band spectral indices (TBIs) were constructed using an optimal band algorithm. By integrating environmental covariates and TBIs, soil particle inversion models were developed using the extreme learning machine (ELM), backpropagation neural networks (BP), neural networks optimized with the sparrow search algorithm (SSA-BP), and neural networks optimized with the sparrow search algorithm enhanced by Sine chaos mapping (Sine-SSA-BP). Results and discussion: The results demonstrated that (1) the Boruta algorithm identified key environmental covariates that affect specific soil particle components; (2) there was significant variation in the correlation between different TBIs and soil particle content, with absolute correlation coefficients ranging from 0.225 to 0.852; (3) the estimation models established by the four machine learning algorithms performed well in predicting soil particle content, particularly for silt (R2: 0.664–0.858, RMSE: 11.107–17.128) and clay (R2: 0.444–0.857, RMSE: 0.550–1.405), for which higher accuracy was achieved; and (4) compared with the traditional ELM (R2: 0.422–0.664), BP (R2: 0.487–0.673) and SSA-BP models (R2: 0.625–0.777), the Sine-SSA-BP model showed a significant improvement in prediction accuracy, with the highest R2 reaching 0.858. Conclusion: Compared to the traditional ELM, BP and SSA-BP models, the Sine-SSA-BP model significantly excelled in predicting soil particle content, offering innovative insights and robust support for soil texture classification and management. [ABSTRACT FROM AUTHOR]

As computer graphics technology continues to advance, Collision Detection (CD) has emerged as a critical element in fields such as virtual reality, computer graphics, and interactive simulations. CD is indispensable for ensuring the fidelity of physical interactions and the realism of virtual environments, particularly within complex scenarios like virtual assembly, where both high precision and real-time responsiveness are imperative. Despite ongoing developments, current CD techniques often fall short in meeting these stringent requirements, resulting in inefficiencies and inaccuracies that impede the overall performance of virtual assembly systems. To address these limitations, this study introduces a novel algorithm that leverages the capabilities of a Backpropagation Neural Network (BPNN) to optimize the structural composition of the Hybrid Bounding Volume Tree (HBVT). Through this optimization, the research proposes a refined Hybrid Hierarchical Bounding Box (HHBB) framework, which is specifically designed to enhance the computational efficiency and precision of CD processes. The HHBB framework strategically reduces the complexity of collision detection computations, thereby enabling more rapid and accurate responses to collision events. Extensive experimental validation within virtual assembly environments reveals that the proposed algorithm markedly improves the performance of CD, particularly in handling complex models. The optimized HBVT architecture not only accelerates the speed of collision detection but also significantly diminishes error rates, presenting a robust and scalable solution for real-time applications in intricate virtual systems. These findings suggest that the proposed approach offers a substantial advancement in CD technology, with broad implications for its application in virtual reality, computer graphics, and related fields. [ABSTRACT FROM AUTHOR]

To address the problem that the whale optimization algorithm tends to fall into the local optimum and fails to maintain a balance between exploration and exploitation, an elitist whale optimization algorithm with the nonlinear parameter (EWOANP) is proposed in this paper. An elitist strategy based on the random Cauchy mutation is used in the shrinking encircling mechanism to increase the chance of escaping the local optimum. Cleverly, the strategy is to generate mutation solutions based on the random Cauchy mutation, after which the better population is selected to proceed to the next iteration. Then, a nonlinear parameter is used in the logarithmic spiral mechanism to balance exploration and exploitation. Various numerical optimization experiments are performed based on the IEEE CEC2020 benchmark suite and compared with eleven other algorithms. The results show that EWOANP outperforms most competitors in numerical optimization. Finally, the backpropagation neural network is optimized by EWOANP to build a prediction model for the sulfur content in the molten iron. The experimental results based on production data indicate that the proposed prediction model has a relatively small fluctuation in errors. Compared to the other seven competitors, the proposed model has a better prediction performance with RMSE=0.001457$$ RMSE=0.001457 $$ and R2$$ {R}^2 $$=0.916619. [ABSTRACT FROM AUTHOR]

Complex processing parameters need to be adjusted for expected qualities in injection molding processing. Once the process is abnormal, it is essential to spend time and human work on fault diagnosis. In this study, we focus on fault diagnosis of injection molding processing parameters for polylactic acid/glass fiber composites. The injection molding processing parameters include the melt temperature, injection speed, packing pressure, packing time, and cooling time. The qualities include tensile strength, hardness, impact strength, and flexure strength. When processing parameters deviate from the optimal process condition, the multivariate statistical control chart monitors downgraded qualities. The machine is operated at the optimal process conditions to generate normal samples and the corresponding four qualities of data are chosen as the historical data. Hotelling's T 2 is used to calculate the upper control limit (UCL) from the historical data to detect abnormal samples. If the T 2 value exceeds the UCL, the corresponding sample is considered abnormal. Then, the residuals of qualities for abnormal samples are obtained by a residual control chart. They are chosen as the feature values for the backpropagation neural network (BPNN) to identify the abnormal processing parameters. The experimental results proved that the BPNN can achieve a 100% recognition rate for single-factor abnormal samples. For the single-/double-factor mixture, the accuracy rate of double-factor classification can reach 97.44%. This proposed study has the advantage of high stability, being non-destructive, high precision, and low cost, and can be widely promoted in injection molding industries. [ABSTRACT FROM AUTHOR]

Traditional statistical models present limitations in capturing the complexity of advanced manufacturing processes. To address this challenge, the integration of the iteratively weighted least squares (IWLS) method in the training of a backpropagation neural network (BPNN) model is proposed. This combination allows for the application of generalized linear model (GLM) statistical inference, forming a hybrid learning approach that enhances both predictive accuracy and model interpretability. The hybrid approach provides robust statistical support, identifying critical factors in manufacturing processes and offering a comprehensive tool for model evaluation and an in-depth understanding of process dynamics. The applicability of the method is demonstrated through the analysis of two manufacturing processes and identifying their key factors. The results of the deviance analysis and hypothesis testing highlight metal temperature and solidification time as crucial factors in the die casting process and input voltage as a crucial factor in the TIG welding process. This method underscores its practical value by improving efficiency and competitiveness in the manufacturing industry, providing detailed and accurate evaluations of process factors. [ABSTRACT FROM AUTHOR]

Accurate prediction of borrowing volume of library books is conducive to the decision-making of the managers. This study briefly introduces the backpropagation neural network (BPNN) algorithm used to predict the borrowing volume of university libraries. The factor analysis method and genetic algorithm were employed to optimize the BPNN algorithm to improve its prediction performance. The book borrowing records of 2022 from Handan College Library were considered the subject of simulation experiments. The designed algorithm was compared with the extreme gradient boosting and traditional BPNN algorithms in the experiments. The results showed that average borrowing time, book lending ratio, book return ratio, and average grade of borrowers could be used as the input features of BPNN. The improved BPNN algorithm demonstrated faster convergence and a smaller error during training. The borrowing volume predicted by the improved BPNN algorithm closely matched the actual volume, and the increase in prediction time did not lead to a significant change in the prediction error. [ABSTRACT FROM AUTHOR]

• The indicators were collected based on three levels of situation awareness (SA). • The drivers' SA assessment system was built using grey correlation analysis. • The constructed backpropagation neural network model can effectively predict SA. Based on combining the relevant studies on situation awareness (SA), this paper integrated multiple indicators, including eye movement, electroencephalogram (EEG), and driving behavior, to evaluate SA. SA is typically divided into three stages: perception, understanding and prediction. This paper used eye movement indicators to represent perception, EEG indicators to represent understanding, and driving behavior indicators to represent prediction. After identifying indicators for evaluating SA, a driving simulation experiment was designed to collect data on the indicators. 41 subjects were recruited to participate in the investigation, and the experimenter collected data from each subject in a total of 9 groups. After removing 4 groups of invalid data, 365 groups of valid data were finally obtained. The grey correlation analysis was used to optimize the SA indicators, and 10 SA evaluation indicators were finally determined. There were the average fixation duration, the nearest neighbor index, pupil area, the percentage power spectral density values of the 3 rhythmic waves (θ, α, β), rhythmic wave energy combination parameters (α / θ), mean speed, SD of speed and acceleration. Taking the optimized 10 indicators as input and the SA scores as output, a backpropagation neural network model with a topological structure of 10-8-1 was constructed. 75% of the data were randomly selected for model training, and the final network training's mean square error was 0.0025. Using the remaining 25% of data for verification, the average absolute error and average relative error of the predicted results are 0.248 and 0.046, respectively. This showed that the model was effective, and it was feasible to evaluate the SA by using the data of eye movement, EEG and driving behavior parameters. [ABSTRACT FROM AUTHOR]

Investigating the optimal laser processing parameters for industrial purposes can be time-consuming. Moreover, an exact analytic model for this purpose has not yet been developed due to the complex mechanisms of laser processing. The main goal of this study was the development of a backpropagation neural network (BPNN) with a grey wolf optimization (GWO) algorithm for the quick and accurate prediction of multi-input laser etching parameters (energy, scanning velocity, and number of exposures) and multioutput surface characteristics (depth and width), as well as to assist engineers by reducing the time and energy require for the optimization process. The Keras application programming interface (API) Python library was used to develop a GWO-BPNN model for predictions of laser etching parameters. The experimental data were obtained by adopting a 30 W laser source. The GWO-BPNN model was trained and validated on experimental data including the laser processing parameters and the etching characterization results. The R2 score, mean absolute error (MAE), and mean squared error (MSE) were examined to evaluate the prediction precision of the model. The results showed that the GWO-BPNN model exhibited excellent accuracy in predicting all properties, with an R2 value higher than 0.90. [ABSTRACT FROM AUTHOR]

Lung cancer is the main cause of cancer-related deaths worldwide. Due to lack of obvious clinical symptoms in the early stage of the lung cancer, it is hard to distinguish between malignancy and pulmonary nodules. Understanding the immune responses in the early stage of malignant lung cancer patients may provide new insights for diagnosis. Here, using high-through-put sequencing, we obtained the TCRβ repertoires in the peripheral blood of 100 patients with Stage I lung cancer and 99 patients with benign pulmonary nodules. Our analysis revealed that the usage frequencies of TRBV, TRBJ genes, and V-J pairs and TCR diversities indicated by D50s, Shannon indexes, Simpson indexes, and the frequencies of the largest TCR clone in the malignant samples were significantly different from those in the benign samples. Furthermore, reduced TCR diversities were correlated with the size of pulmonary nodules. Moreover, we built a backpropagation neural network model with no clinical information to identify lung cancer cases from patients with pulmonary nodules using 15 characteristic TCR clones. Based on the model, we have created a web server named “Lung Cancer Prediction” (LCP), which can be accessed at http://i.uestc.edu.cn/LCP/index.html.

Wind power has unpredictable, intermittent traits due to meteorological conditions and environmental factors. Large-scale grid integration of wind energy will undoubtedly challenge system stability. This study developed a fireworks algorithmbackpropagation (FWA-BP) neural network model to forecast wind power using wind speed, direction, and power as model inputs. Optimization of the BP network weights and thresholds occurred through the fireworks algorithm. Compared to a standard BP network, the FWA-BP model yielded improved prediction accuracy seen through a lower mean squared error. This implies that the approach introduced in this paper significantly enhances global search capabilities, prediction accuracy, and speed. It contributes to enhancing the reliability of the power system, optimizing resource allocation, and improving wind power scheduling, with substantial potential and economic significance. [ABSTRACT FROM AUTHOR]

Comprehensive evaluation of reservoirs is an important link in gas reservoir exploration and development. The evaluation of tight carbonate reservoirs often focuses on the characteristics of porosity and permeability, ignoring the important factor of fractures, also the quantitative evaluation of reservoirs is relatively few. It is difficult to identify fractures and evaluate the reservoir factors qualitatively and quantitatively. Herein, the sedimentary microfacies and microporosity of the tight carbonate reservoir of the Ma55 submember in the eastern Sulige area are comprehensively studied by casting thin section, rock physical property, and capillary pressure test data. The backpropagation (BP) neural network algorithm is used to identify and predict fractures. Finally, through the analytic hierarchy process, the above reservoir influencing factors are modeled and quantitatively analyzed for reservoir evaluation. The results show that the highest probability of fracture development in the central and northwest areas of the study area can reach 0.92. The accuracy of the BP neural network model in identifying cracks can reach 80%, which is reliable and effective compared with the conventional logging identification method. Reservoirs can be classified into four types according to their quality. The synthetic weights of porosity, permeability and fracture development probability are 0.2, 0.2 and 0.216 respectively, which are the three most important evaluation parameters. This study improves the accuracy of fracture identification and prediction of tight reservoirs in comprehensive reservoir evaluation, which provides guidance and scheme for more detailed exploration and development of tight carbonate reservoirs. [ABSTRACT FROM AUTHOR]

In this paper, the hybrid electric vehicle (HEV) energy management optimization method is proposed based on deep learning (DL) model predictive control. Through empirical research combined with the questionnaire survey, this article not only provides a new perspective and practical basis but also improves the efficiency and accuracy of the model by improving the relevant algorithms. The study first analyzes the importance of HEV energy management and reviews the existing literature. Then, the optimization method of HEV energy management based on the deep learning model is introduced in detail, including the composition of energy management for hybrid electric vehicles, the structure and working principle of the deep learning model, especially the backpropagation neural network (BPNN) and the convolutional neural network (CNN), and the steps of application of deep learning in energy management. In the experimental part, questionnaire data from 1,500 consumers were used to design the HEV energy management optimization scheme, and consumers' attitudes and preferences towards HEV energy optimization were discussed. The experimental results show that the proposed model can predict HEV energy consumption under different road conditions (urban roads, highways, mountain areas, suburban areas, and construction sites), and the difference between the average predicted energy consumption and the actual energy consumption is between 0.1KWH and 0.3KWH, showing high prediction accuracy. In addition, the deep learning-based energy management strategy outperforms traditional control strategies in terms of fuel consumption (6.2 L/100 km), battery charge and discharge times (814), battery life, and CO2 emissions, significantly improving the efficiency of HEV energy. These results demonstrate the great potential and practical application value of deep learning models in the optimization of energy management of HEVs, helping to drive the development of more sustainable and efficient transportation systems. [ABSTRACT FROM AUTHOR]

The purpose of this study is to put forward a feature extraction and pattern recognition method for the flow noise signal of natural gas pipelines in view of the complex situation brought by the rapid development and expansion of urban natural gas infrastructure in China, especially in the case that there are active and abandoned pipelines, metal and nonmetal pipelines, and natural gas, water and power pipelines coexist in the underground of the city. Because the underground situation is unknown, gas leakage incidents caused by natural gas pipeline rupture occur from time to time, posing a threat to personal safety. Therefore, the motivation of this study is to provide a feasible method to accelerate the aging, renewal and transformation of urban natural gas pipelines to ensure the safe operation of urban natural gas pipeline network and promote the high-quality development of urban economy. Through the combination of experimental test and numerical simulation, this study establishes a database of urban natural gas pipeline flow noise signals, and uses principal component analysis (PCA) to extract the characteristics of flow noise signals, and develops a mathematical model for feature extraction. Then, a classification and recognition model based on backpropagation neural network (BPNN) is constructed, which realizes the detection and recognition of convective noise signals. The research results show that the theoretical method based on acoustic feature analysis provides guidance for the orderly and safe construction of urban natural gas pipeline network and ensures its safe operation. The research conclusion shows that through the simulation analysis of 75 groups of gas pipeline flow noise under different working conditions. Combined with the experimental verification of ground flow noise signals, the feature extraction and pattern recognition method proposed in this study has a recognition accuracy of up to 97% under strong noise background, which confirms the accuracy of numerical simulation and provides theoretical basis and technical support for the detection and recognition of urban gas pipeline flow noise. [ABSTRACT FROM AUTHOR]

In order to reduce the position errors of the Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) integrated navigation system during GPS denial, this paper proposes a method based on the Particle Swarm Optimization–Back Propagation Neural Network (PSO-BPNN) to replace the GPS for positioning. The model relates the position information, velocity information, attitude information output by the SINS, and the navigation time to the position errors between the position information output by the SINS and the actual position information. The performance of the model is compared with the BPNN through an actual ship experiment. The results show that the PSO-BPNN can obviously reduce the position errors in the case of GPS signal denial. [ABSTRACT FROM AUTHOR]

Interior design is a complex system that includes many elements, such as user needs, spatial structure, material selection, color matching, lighting layout, etc. The relationships between them are complex and intricate. How to design space according to user needs is one of the most challenging issues in interior design. A distributed 3D interior design system based on artificial intelligence algorithms was adopted to address this issue. The system divided indoor spatial information into several subsets. It assigned subsets to various professional designers based on user needs, and generated multiple design schemes through artificial intelligence algorithms, which were then screened, modified, and optimized by each professional designer. In this way, designers from different professional backgrounds can personalized design the space according to their own understanding, thereby avoiding issues such as information fragmentation and low efficiency in traditional interior design. The results showed that the distributed 3D interior design system based on artificial intelligence algorithms can improve the overall performance from an average of 82.68% to 94.94%, effectively avoiding the problem of information fragmentation. [ABSTRACT FROM AUTHOR]

In this study, limestone samples from a coal mine in the North China region were selected for analysis. High Pressure Mercury Intrusion (HPMI) and Scanning Electron Microscopy (SEM) experiments were conducted to explore the impact of pore characteristics and fractal dimension of limestone on permeability. Additionally, regression analysis and a Backpropagation Neural Network (BPNN) were employed to predict permeability. The results of this study reveal that the pore-throat distribution of the limestone samples is non-uniform, indicating significant heterogeneity. The difference of pressure curve morphology affects the permeability. Utilizing multivariate regression analysis, a relationship was established between permeability and parameters such as mean radius, porosity, and fractal dimension. Furthermore, the BP neural network was effectively employed to predict permeability values, with small discrepancies between predicted and measured values. This study establishes a link between microstructural attributes and macroscopic permeability providing a robust theoretical foundation for permeability assessment and engineering applications pertaining to limestone. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (Chl-a) concentration monitoring is very important for managing water resources and ensuring the stability of marine ecosystems. Due to their high operating efficiency and high prediction accuracy, backpropagation (BP) neural networks are widely used in Chl-a concentration inversion. However, BP neural networks tend to become stuck in local optima, and their prediction accuracy fluctuates significantly, thus posing restrictions to their accuracy and stability in the inversion process. Studies have found that metaheuristic optimization algorithms can significantly improve these shortcomings by optimizing the initial parameters (weights and biases) of BP neural networks. In this paper, the adaptive nonlinear weight coefficient, the path search strategy "Levy flight" and the dynamic crossover mechanism are introduced to optimize the three main steps of the Artificial Ecosystem Optimization (AEO) algorithm to overcome the algorithm's limitation in solving complex problems, improve its global search capability, and thereby improve its performance in optimizing BP neural networks. Relying on Google Earth Engine and Google Colaboratory (Colab), a model for the inversion of Chl-a concentration in the coastal waters of Hong Kong was built to verify the performance of the improved AEO algorithm in optimizing BP neural networks, and the improved AEO algorithm proposed herein was compared with 17 different metaheuristic optimization algorithms. The results show that the Chl-a concentration inversion model based on a BP neural network optimized using the improved AEO algorithm is significantly superior to other models in terms of prediction accuracy and stability, and the results obtained via the model through inversion with respect to Chl-a concentration in the coastal waters of Hong Kong during heavy precipitation events and red tides are highly consistent with the measured values of Chl-a concentration in both time and space domains. These conclusions can provide a new method for Chl-a concentration monitoring and water quality management for coastal waters. [ABSTRACT FROM AUTHOR]

The fused deposition molding (FDM) process can manufacture complex molded parts based on molten filaments. The quality of parts molded by this process is measured primarily by tensile strength, flexural strength, and surface roughness. Due to the complexity and variety of FDM process parameters, there is a nonlinear variation relationship between the combination of process parameters and the quality of molded parts, which makes the accurate and stable prediction of quality particularly important. To this end, an improved whale optimization algorithm combined with a neural network is proposed to predict the quality of FDM molded parts and make up for the shortcomings of the traditional methods with low prediction accuracy and instability. Based on the FDM production process, four process parameters, namely, printing layer thickness, nozzle temperature, chamber temperature, and filling interval, were selected as input variables of the model. The prediction model was obtained by optimizing the hyperparameters of the neural network using the improved whale algorithm. A four-factor three-level experimental method was used to obtain experimental data on the tensile strength, bending strength, and surface roughness of the styles. The accuracy and stability of the model were verified according to Monte Carlo cross-validation based on the above data set. The prediction results of model CIWOA–BP are compared with those of model WOA–BP and BP in a comparison experiment. The results show that the FDM part quality prediction method based on the CIWOA–BP model has higher prediction accuracy and stability. [ABSTRACT FROM AUTHOR]

Abstract To address the problem that the whale optimization algorithm tends to fall into the local optimum and fails to maintain a balance between exploration and exploitation, an elitist whale optimization algorithm with the nonlinear parameter (EWOANP) is proposed in this paper. An elitist strategy based on the random Cauchy mutation is used in the shrinking encircling mechanism to increase the chance of escaping the local optimum. Cleverly, the strategy is to generate mutation solutions based on the random Cauchy mutation, after which the better population is selected to proceed to the next iteration. Then, a nonlinear parameter is used in the logarithmic spiral mechanism to balance exploration and exploitation. Various numerical optimization experiments are performed based on the IEEE CEC2020 benchmark suite and compared with eleven other algorithms. The results show that EWOANP outperforms most competitors in numerical optimization. Finally, the backpropagation neural network is optimized by EWOANP to build a prediction model for the sulfur content in the molten iron. The experimental results based on production data indicate that the proposed prediction model has a relatively small fluctuation in errors. Compared to the other seven competitors, the proposed model has a better prediction performance with RMSE=0.001457 and R2=0.916619.

With the increasing scale of distribution network at distribution time, its complexity grows geometrically, and its fault diagnosis becomes more and more difficult. Aiming at the slow convergence and low accuracy of traditional backpropagation neural network in dealing with single-phase ground faults, the study proposes a backpropagation neural network based on improved particle swarm optimization. The model optimizes the weights and acceleration constants of the particle swarm algorithm by introducing dynamic coefficients to enhance its global and local optimization seeking ability. It is also applied in optimizing the parameters of backpropagation neural network and constructing the routing model and ranging model for fault diagnosis about distributed energy distribution network. The simulation results revealed that the maximum absolute error of the improved method is 0.08. While the maximum absolute errors of the traditional backpropagation neural network and the particle swarm optimized backpropagation neural network were 0.65 and 0.10, respectively. The fluctuation of the relative errors of the research method was small under different ranges of measurements. At 8.0 km, the minimum relative error was 0.39% and the maximum relative error was 2.81%. The results show that the improved method proposed in the study significantly improves the accuracy and stability of fault diagnosis and localization in distribution networks and is applicable to complex distribution network environments. The method has high training efficiency and fault detection capability and provides an effective tool for distribution network fault management.

Abstract Comprehensive evaluation of reservoirs is an important link in gas reservoir exploration and development. The evaluation of tight carbonate reservoirs often focuses on the characteristics of porosity and permeability, ignoring the important factor of fractures, also the quantitative evaluation of reservoirs is relatively few. It is difficult to identify fractures and evaluate the reservoir factors qualitatively and quantitatively. Herein, the sedimentary microfacies and microporosity of the tight carbonate reservoir of the Ma55 submember in the eastern Sulige area are comprehensively studied by casting thin section, rock physical property, and capillary pressure test data. The backpropagation (BP) neural network algorithm is used to identify and predict fractures. Finally, through the analytic hierarchy process, the above reservoir influencing factors are modeled and quantitatively analyzed for reservoir evaluation. The results show that the highest probability of fracture development in the central and northwest areas of the study area can reach 0.92. The accuracy of the BP neural network model in identifying cracks can reach 80%, which is reliable and effective compared with the conventional logging identification method. Reservoirs can be classified into four types according to their quality. The synthetic weights of porosity, permeability and fracture development probability are 0.2, 0.2 and 0.216 respectively, which are the three most important evaluation parameters. This study improves the accuracy of fracture identification and prediction of tight reservoirs in comprehensive reservoir evaluation, which provides guidance and scheme for more detailed exploration and development of tight carbonate reservoirs.

Abstract In response to the issues of traditional backpropagation (BP) neural networks in state of charge (SOC) estimation, including easy convergence to local optima, slow convergence speed, and low accuracy, this paper proposes a novel adaptive crossover mutation strategy and dynamic sparrow search algorithm to optimize BP networks' initial values and thresholds (ACMSSA‐BP). The proposed method is based on the sparrow search algorithm, where the number of producers and scroungers is adjusted through an adaptive factor. This improvement effectively transitions the search process from extensive full exploration to localized fine‐tuning search. In the position update phase of the producers, crossover mutation and dynamic search strategies are introduced to increase the diversity of good populations, prevent the algorithm from converging to local optima, and maintain its local search capability in the later stage. Using real transportation data from coal mining flame‐proof tracked vehicles, we applied correlation theory to extract model feature parameters and constructed a training data set to estimate the SOC. The results of both static and dynamic validation experiments have indicated that the ACMSSA‐BP method has delivered impressive performance in predicting SOC, as reflected in the mean absolute error, root mean squared error, and mean absolute percentage error values of less than 1.5%, 1.5%, and 1.6%, respectively. Compared with BP, SSA‐BP, CMSSA‐BP, PSO‐BP, and NARX_NN methods, the ACMSSA‐BP approach demonstrates enhanced accuracy in estimation, significant robustness, and impressive generalization capabilities.

Self-oscillatory systems have great utility in energy harvesting, engines, and actuators due to their ability to convert ambient energy directly into mechanical work. This characteristic makes their design and implementation highly valuable. Due to the complexity of the motion process and the simultaneous influence of multiple parameters, computing self-oscillatory systems proves to be challenging, especially when conducting inverse parameter design. To simplify the computational process, a combined approach o0f Random Forest (RF) and Backpropagation Neural Network (BPNN) algorithms is employed. The example used is a self-rotating skipping rope made of liquid crystal elastomer (LCE) fiber and a mass block under illumination. Numerically solving the governing equations yields precise solutions for the rotation frequency of the LCE skipping rope under various system parameters. A database containing 138,240 sets of parameter conditions and their corresponding rotation frequencies is constructed to train the RF and BPNN models. The training outcomes indicate that RF and BPNN can accurately predict the self-rotating skipping rope frequency under various parameters, demonstrating high stability and computational efficiency. This approach allows us to discover the influences of distinct parameters on the rotation frequency as well. Moreover, it is capable of inverse design, meaning it can derive the corresponding desired parameter combination from a given rotation frequency. Through this study, a deeper understanding of the dynamic behavior of self-oscillatory systems is achieved, offering a new approach and theoretical foundation for their implementation and construction. [ABSTRACT FROM AUTHOR]

The use of fly ash (FA)-based geopolymer concrete as a low-carbon and eco-friendly substitute to Portland cement concrete has gained attention in recent years. However, accurately predicting its compressive strength remains a challenge due to the complex chemical and physical interactions involved in the geopolymerization process. In this research, three machine learning models, namely backpropagation neural network (BPNN), random forest regression (RFR), and k-nearest neighbors (KNN), were employed to predict the compressive strength of FA-based geopolymer concrete. The models were trained, validated, and tested using a dataset that considered the chemical composition, mix proportions, and pre-curing conditions of the concrete. The performance of each model was assessed utilizing various metrics, including the coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). The results indicated that the BPNN model gave the best results with an R2 value of 0.948 relative to RFR and KNN with R2 values of 0.927 and 0.911, respectively. The permutation feature importance (PFI) index revealed that the coarse aggregate content, SiO2 content in FA, and NaOH concentration were found to have the greatest impact on the compressive strength of the FA-based geopolymer concrete. [ABSTRACT FROM AUTHOR]

Ecological concrete has excellent water and air permeability, which not only is conducive to plant growth but also allows surface water to infiltrate underground and intercept pollutants. The performance of ecological concrete is largely determined by the nature of the raw materials and their relative content. Therefore, mastering the optimal design method for the mix proportion of ecological concrete is crucial to achieving good performance. In the current research, there is a lack of systematic intelligent decision-making models for predicting performance and optimizing mix proportions. In this paper, four factors, namely mechanical properties, water permeability, decontamination properties, and planting properties of ecological concrete, were considered when evaluating the comprehensive performance of ecological concrete. The evaluation was conducted using the analytic hierarchy process (AHP). The gray wolf optimizer (GWO) was introduced to enhance the backpropagation (BP) neural network, and an optimization model for finding the optimal ecological concrete mix proportion was established. The optimal mix proportion of two types of typical ecological concrete, one for filtration and one for plant growth, was discussed. The results indicate that the AHP-GWO-BP model calculates the optimal mixing proportion of filtration ecological concrete as follows: The diameter of coarse aggregate is 10–15 mm, with adsorbed coarse aggregate accounting for 49.7%, a component ratio is 118%, the water–cement ratio should be 28.7%, and the silica fume mix ratio should be 32.1%. According to the given parameters, the performance test of ecological concrete is conducted, with a coarse aggregate size of 12 mm. The results showed that under these parameters, the compressive strength was 12.3 MPa, the flexural strength was 3.35 MPa, the water permeability coefficient was 14.87 cm s−1, the porosity was 27.23%, the removal rate of total nitrogen was 80.56%, the removal rate of total phosphorus was 67.33%, the pH was 9.16, and the plant dry weight was 9.37 g. The optimal mix proportion of the planting ecological concrete is as follows: The diameter of the coarse aggregate is 20–25 mm, the adsorbed coarse aggregate accounts for 49.7%, its component ratio is 138%, the water–cement ratio should be 27.3%, and the silica fume mix ratio should be 34.1%.Highlights: An orthogonal test optimization method based on AHP-GWO-BP neural network Two types of multi-aggregate ecological concrete utilizing multiple adsorbent materials as coarse aggregates Simulation and prediction of various aspects of the performance of ecological concrete under different factors [ABSTRACT FROM AUTHOR]

Copyright of Wool Textile Journal is the property of National Wool Textile Science & Technology Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An advanced driver-assistance system (ADAS) is critical to driver–vehicle-interaction systems. Driving behaviour modelling and control significantly improves the global performance of ADASs. A haptic assistive system assists the driver by providing a specific torque on the steering wheel according to the driving–vehicle–road profile to improve the steering control. However, the main problem is designing a compensator dealing with the high-level uncertainties in different driving scenarios with haptic driver assistance, where different personalities and diverse perceptions of drivers are considered. These differences can lead to poor driving performance if not properly accounted for. This paper focuses on designing a data-driven model-free compensator considering various driving behaviours with a haptic feedback system. A backpropagation neural network (BPNN) models driving behaviour based on real driving data (speed, acceleration, vehicle orientation, and current steering angle). Then, the genetic algorithm (GA) optimises the integral time absolute error (ITEA) function to produce the best multiple PID compensation parameters for various driving behaviours (such as speeding/braking, lane-keeping and turning), which are then utilised by the fuzzy logic to provide different driving commands. An experiment was conducted with five participants in a driving simulator. During the second experiment, seven participants drove in the simulator to evaluate the robustness of the proposed combined GA proportional-integral-derivative (PID) offline, and the fuzzy-PID controller applied online. The third experiment was conducted to validate the proposed data-driven controller. The experiment and simulation results evaluated the ITAE of the lateral displacement and yaw angle during various driving behaviours. The results validated the proposed method by significantly enhancing the driving performance. [ABSTRACT FROM AUTHOR]

The combination of information technology and machine learning fuels the rapid evolution of today's educational landscape. Revolutions in both fields and a common goal of improving education drive this transformative journey. In a time when resources and information are more readily available than ever, traditional teaching strategies are changing to include digital tools that meet the various needs of students. For a teaching services support system (TSS), this paper proposed a novel machine learning-based model that utilizes the powerful backpropagation (BP) neural network, well known for its machine learning and data analysis capabilities. Increasing the effectiveness of online learning is the primary objective of the TSS, which focuses on contributing to a complete learning environment and promoting self-directed learning. This work closely examines the construction and functionality of the BP neural network within the TSS, contribution visions into input–output mechanisms, activation functions, and weight coefficients. This novel approach can bring concerning a digital age educational revolution by increasing the effectiveness and caliber of online learning, rekindling students' enthusiasm for learning, and making the best use of teaching resources. In addition, the research explores the field of data-mining-based online teaching support services in Open Universities. It clarifies the system's architecture, which includes virtual teaching modules, resource management, and user authentication. Machine learning methods such as adaptive genetic algorithms and BP neural networks optimize the system's architecture. Experimental results achieved remarkable BP neural network accuracy and stability, significantly enhancing instructional quality and student engagement. The results show an impressive 83.4% accuracy, outperforming traditional methods such as SVM, KNN, DT, RF, and XGBoost. This demonstrates how learning outcomes and productivity can be enhanced by incorporating machine learning into education. [ABSTRACT FROM AUTHOR]

The Eddy Current Damper (ECD) is a velocity-based passive energy dissipation device that can be used for structural vibration control. This study aims to reveal the damper behavior and determine the optimal parameters of the ECDs applied to a suspension bridge suffering from bi-directional earthquakes. The influence of the bi-directional vibration interaction on the damping force is theoretically investigated. An optimization method is proposed combining the Sobol Sequence sampling method, the Coefficient of Variation (COV) method, and the Backpropagation neural network (BPNN) algorithm. A numerical model of a long-span suspension bridge with bi-directional ECDs is established and taken as a case study to verify the feasibility of the proposed optimization method. The results indicate that the ECD system with bi-directional interaction will produce a larger maximum force and less energy dissipation than one without bi-directional interaction. The proposed optimization method possesses the characteristics of objectivity, accuracy, and effectiveness. The obtained optimal bi-directional ECDs can significantly reduce the longitudinal and transverse vibration. Failing to consider bi-directional effects will lead to an overestimation of longitudinal damper parameters and an underestimation to transverse ones, as well as ignoring the influence of damper length on its mechanical performance. [ABSTRACT FROM AUTHOR]

Summary: High‐precision state of charge (SOC) estimation is essential for battery management systems (BMSs). In this paper, a new SOC estimation method is proposed. The dual Kalman filter algorithm and backpropagation neural network (particle swarm optimization ‐ backpropagation neural network ‐ double extended Kalman filter [PSO‐BPNN‐DEKF]) are combined to estimate and correct the SOC of lithium‐ion batteries, in which the initial weight and threshold of the BPNN are optimized by particle swarm optimization algorithm. Based on the second‐order RC equivalent circuit model, parameter identification is carried out using the adaptive forgetting factor least squares (AFFRLS) method. Online parameter updates and SOC estimation are realized by DEKF algorithm. Then, the trained PSO‐BPNN is used to predict the SOC estimation error in real time, and the SOC estimation value is corrected by adding prediction errors. The SOC estimates before and after correction under Beijing Dynamic Stress Test (BBDST), dynamic stress test (DST), and hybrid pulse power characterization (HPPC) were compared. Under BBDST, DST, and HPPC tests, the maximum errors of the corrected SOC estimates are 0.0107, 0.0090, and 0.0147, respectively. The root mean square error (RMSE) of the corrected SOC estimates decreased by 94.02%, 83.18%, and 88.03%, respectively, compared with the extended Kalman filtering (EKF). The mean absolute error (MAE) of the corrected SOC estimates remained around 0.1% for all the BBDST dynamic operating conditions at different temperatures. The experimental results demonstrate the accuracy, effectiveness, and temperature adaptability of the proposed algorithm for SOC estimation under complex conditions of lithium‐ion batteries. [ABSTRACT FROM AUTHOR]

In response to the issues of traditional backpropagation (BP) neural networks in state of charge (SOC) estimation, including easy convergence to local optima, slow convergence speed, and low accuracy, this paper proposes a novel adaptive crossover mutation strategy and dynamic sparrow search algorithm to optimize BP networks' initial values and thresholds (ACMSSA‐BP). The proposed method is based on the sparrow search algorithm, where the number of producers and scroungers is adjusted through an adaptive factor. This improvement effectively transitions the search process from extensive full exploration to localized fine‐tuning search. In the position update phase of the producers, crossover mutation and dynamic search strategies are introduced to increase the diversity of good populations, prevent the algorithm from converging to local optima, and maintain its local search capability in the later stage. Using real transportation data from coal mining flame‐proof tracked vehicles, we applied correlation theory to extract model feature parameters and constructed a training data set to estimate the SOC. The results of both static and dynamic validation experiments have indicated that the ACMSSA‐BP method has delivered impressive performance in predicting SOC, as reflected in the mean absolute error, root mean squared error, and mean absolute percentage error values of less than 1.5%, 1.5%, and 1.6%, respectively. Compared with BP, SSA‐BP, CMSSA‐BP, PSO‐BP, and NARX_NN methods, the ACMSSA‐BP approach demonstrates enhanced accuracy in estimation, significant robustness, and impressive generalization capabilities. [ABSTRACT FROM AUTHOR]

Purpose: With the continuous advancement of modern agriculture and urbanization, soil quality assessment has been considered an important guarantee for sustainable agricultural development. Despite the availability of numerous methods for assessing soil quality, little emphasis has been paid to comprehensive studies on soil quality in greenhouse agriculture. This study aims to construct a comprehensive evaluation model of greenhouse agricultural soil quality, including soil nutrition and heavy metal pollution, to better assess greenhouse soil quality. Materials and methods: In this study, the concentrations of eight heavy metals, five soil nutrients, and nine soil-available microelements in 300 greenhouses were measured. Genetic algorithm–backpropagation (GA-BP) neural networks and backpropagation (BP) neural networks were used to construct a comprehensive soil quality evaluation model, and the soil quality of the greenhouse in the study area was evaluated based on soil nutrients and heavy metal pollution. Results: The results showed that the prediction accuracy of both models exceeded 85%. However, constructed utilizing the genetic algorithm–backpropagation (GA-BP), the evaluation model can be more effective in assessing soil quality, with an accuracy of 96.1%. In this study, the soil quality was categorized into eight levels: IA, IB, IC, IIA, IIB, IIC, IIIA, and IIIB. 80.6% of the samples were IIA and IIB, suggesting that the soil quality of greenhouse planting sheds in this research area was poor, with severe heavy metal pollution, although soil nutrients were relatively sufficient. Conclusions: This study holds significance for assessing soil quality in greenhouse agriculture and improving agricultural scientific management. [ABSTRACT FROM AUTHOR]

With the continuous improvement of China’s current technology level, the Internet of Things (IoT) technology is also increasingly widely used in various fields. IoT is a network composed of electronic components, software, sensors, etc. With the development of IoT technology, users’ demand for remote control equipment is increasingly urgent. In this article, the genetic algorithm–backpropagation neural network (GA–BPNN) algorithm was deeply studied by combining genetic algorithm (GA) and backpropagation neural network (BPNN) algorithm, and its application in network security evaluation was discussed. This article mainly studied the security and remote control technology of computer network and applied it to intelligent agriculture. In the evaluation performance test of the GA–BPNN algorithm, the evaluation accuracy of GA, BPNN, and GA–BPNN under simple difficulty was 99.58, 99.15, and 99.92%, respectively, and the evaluation accuracy under difficulty was 96.72, 96.47, and 98.88%, respectively. This proved the effectiveness of the GA–BPNN algorithm. In addition, in the application of smart agriculture, through the experimental data and concentration change data, it can be seen that the use of air-driven CO2{\text{CO}}_{2} (carbon dioxide) for concentration replenishment can effectively adjust the requirements of crops on the concentration of CO2{\text{CO}}_{2}. Through the analysis of the system’s light compensation system, it was proved that the system has a good structure and remarkable light compensation effect; it can replenish light for crops in time, and the PLC control structure has good working performance. The correctness of the system design was proved.

The purpose of this study is to put forward a feature extraction and pattern recognition method for the flow noise signal of natural gas pipelines in view of the complex situation brought by the rapid development and expansion of urban natural gas infrastructure in China, especially in the case that there are active and abandoned pipelines, metal and nonmetal pipelines, and natural gas, water and power pipelines coexist in the underground of the city. Because the underground situation is unknown, gas leakage incidents caused by natural gas pipeline rupture occur from time to time, posing a threat to personal safety. Therefore, the motivation of this study is to provide a feasible method to accelerate the aging, renewal and transformation of urban natural gas pipelines to ensure the safe operation of urban natural gas pipeline network and promote the high-quality development of urban economy. Through the combination of experimental test and numerical simulation, this study establishes a database of urban natural gas pipeline flow noise signals, and uses principal component analysis (PCA) to extract the characteristics of flow noise signals, and develops a mathematical model for feature extraction. Then, a classification and recognition model based on backpropagation neural network (BPNN) is constructed, which realizes the detection and recognition of convective noise signals. The research results show that the theoretical method based on acoustic feature analysis provides guidance for the orderly and safe construction of urban natural gas pipeline network and ensures its safe operation. The research conclusion shows that through the simulation analysis of 75 groups of gas pipeline flow noise under different working conditions. Combined with the experimental verification of ground flow noise signals, the feature extraction and pattern recognition method proposed in this study has a recognition accuracy of up to 97% under strong noise background, which confirms the accuracy of numerical simulation and provides theoretical basis and technical support for the detection and recognition of urban gas pipeline flow noise.

As the issues of social sustainability and the aging of population becomes increasingly severe, Autonomous Driving technology is increasingly being seen as an important issue for future travel. At present, the Human Machine Interface (HMI) of Autonomous Driving system has certain difficulties for middle-aged and elderly users, which further affects their perception of cars status and operation experience. Therefore, in order to design an elderly-oriented HMI design of Autonomous Driving cars that meets the Kansei needs of middle-aged and elderly users, a design flow based on Kansei Engineering/ Rough Set Theory/ Backpropagation Neural Network is proposed. The HMI of Autonomous Driving cars is taken as an example in this paper. Under the framework of Kansei Engineering research, Kansei intention analysis is carried out. Factor Analysis is used to reduce dimension and cluster the collected Kansei words. By using the morphological analysis method, the HMI samples are deconstructed into 14 different design features. The attribute reduction algorithm in Rough Set Theory is used to identify the key design features of HMI that have important influence on the elderly-oriented level. Backpropagation Neural Network is used to establish the mapping model between the Kansei intention of middle-aged and elderly users and the key design features of HMI. The mapping model demonstrates good fit as the errors between the predicted and actual values in the 4 types of kansei semantic evaluation tests are all less than 5%. So that it could meet the needs of middle-aged and elderly users and obtain the design combination with the highest Kansei average value. Based on the experimental results, it is found that the optimal emotion of middle-aged and elderly users could be obtained by overall color type three, mode of speed display type three, font display of rotate speed type three, reminding color of turning type four and mode of fuel indicator display one in the morphological deconstruction table. The research results show that the elderly-oriented HMI design constructed by Kansei Engineering/ Rough Set Theory/ Backpropagation Neural Network can meet the sentimental needs of middle-aged and elderly users and can provide a reference example for relevant elderly-oriented design.

Abstract Background This study aimed to apply the backpropagation neural network (BPNN) to develop a model for predicting multidrug-resistant organism (MDRO) infection in critically ill patients. Methods This study collected patient information admitted to the intensive care unit (ICU) of the Affiliated Hospital of Qingdao University from August 2021 to January 2022. All patients enrolled were divided randomly into a training set (80%) and a test set (20%). The least absolute shrinkage and selection operator and stepwise regression analysis were used to determine the independent risk factors for MDRO infection. A BPNN model was constructed based on these factors. Then, we externally validated this model in patients from May 2022 to July 2022 over the same center. The model performance was evaluated by the calibration curve, the area under the curve (AUC), sensitivity, specificity, and accuracy. Results In the primary cohort, 688 patients were enrolled, including 109 (15.84%) MDRO infection patients. Risk factors for MDRO infection, as determined by the primary cohort, included length of hospitalization, length of ICU stay, long-term bed rest, antibiotics use before ICU, acute physiology and chronic health evaluation II, invasive operation before ICU, quantity of antibiotics, chronic lung disease, and hypoproteinemia. There were 238 patients in the validation set, including 31 (13.03%) MDRO infection patients. This BPNN model yielded good calibration. The AUC of the training set, the test set and the validation set were 0.889 (95% CI 0.852–0.925), 0.919 (95% CI 0.856–0.983), and 0.811 (95% CI 0.731–0.891), respectively. Conclusions This study confirmed nine independent risk factors for MDRO infection. The BPNN model performed well and was potentially used to predict MDRO infection in ICU patients.

Kebutuhan energi listrik mengalami peningkatan seiring dengan berjalannya waktu. Faktor-faktor yang mempengaruhi peningkatan tersebut, diantaranya yaitu laju pertumbuhan penduduk, permintaan kebutuhan listrik dalam berbagai sektor seperti rumah tangga, industri, bisnis, social atau public dan juga kenaikan PDRB suatu wilayah. PLN sebagai perusahaan penyedia listrik harus memikirkan hal tersebut agar terjadi keseimbangan supply dan demand. Salah satu cara yang dapat dilakukan oleh PLN yaitu memprakirakan kebutuhan energi listrik di masa yang akan datang. Metode Backpropagation Neural Network (Jaringan Syaraf Tiruan) dan metode Ekstrapolasi Linier digunakan untuk memprakirakan kebutuhan listrik di wilayah Provinsi Jawa Tengah dari tahun 2022-2027. Hasil dari penelitian ini menunjukkan pada metode Backpropagation Neural Network arsitektur jaringan terbaik menggunakan model fungsi pembelajaran LEARNGDM, fungsi pelatihan TRAINGDX dengan 30 neuron yang menghasilkan nilai MAPE atau nilai rata-rata error dengan data aktual PLN terkecil yaitu 8,4%. Sedangkan pada metode Ekstrapolasi Linier prakiraan yang dihasilkan menunjukkan nilai MAPE atau nilai rata-rata error dengan data aktual PLN sebesar 10,7%.

Fiber-reinforced plastic (FRP) is prone to invisible damage caused by low-velocity impact (LVI) during service. The structural health monitoring system is of great significance for damage monitoring and maintenance of composite materials. In this study, four fiber Bragg grating sensors were employed to collect the time domain strain signals of composite materials subjected to LVIs. Furthermore, a numerical simulation model was established to rapidly obtain impact signal dataset. The signal arrival time, peak time, and peak amplitude were selected as signal features, and the backpropagation neural network was successfully applied to determine the location and energy of LVIs. To address the issue of peak feature extraction in the strain signal processing, a genetic algorithm-based sliding window peak detection optimization method was proposed, which significantly improved the final prediction accuracy. The experimental results indicated that within a position range of 300 mm × 300 mm, the average positioning error can reach 5.1 mm; and in an energy range of 0.5–1 J, the average energy prediction error can reach 0.030 J. The proposed method achieved accurate identification of the LVI location and energy for FRP.

Pillars are key structural components in coal mining. The safety requirements of underground coal mines are non-negotiable. Accurately classifying the areas of pillar stability helps ensure safety in coal mines. This study aimed to classify new pillar stability categories and their stability areas. The multiclass support vector machine (SVM) method was implemented with two types of kernel functions (polynomial and radial basis function (RBF) kernels) on pillar stability data with four new categories: failed or intact, either with or without an appropriate safety factor. This classification uses three basic ensemble learning models: Artificial Neural Network-Backpropagation Rectified Linear Unit, Artificial Neural Network-Backpropagation Exponential Linear Unit, and Artificial Neural Network-Backpropagation Gaussian Error Linear Unit. The results with four data proportions and ten experiments had an average accuracy and standard deviation of 92.98% and 0.56%-1.64% respectively. The accuracies of the multiclass SVM method using the polynomial kernel and the RBF kernel with Bayesian parameter optimization to classify the areas of pillar stability were 91% and 92%, respectively. The multiclass SVM method with the RBF kernel captured 96.6% of potentially dangerous pillars. The visualization of classification areas showed that areas with intact pillars may also have failed pillars. [ABSTRACT FROM AUTHOR]

The tropospheric delay is a major error source for space geodetic techniques, and the performance of its modeling is significantly limited due to the high spatiotemporal variability of the moisture in the lower atmosphere. In this study, global modeling of the tropospheric zenith wet delay (ZWD) was realized based on surface meteorological data obtained from radiosondes and Global Positioning System (GPS) radio occultation (RO) measurements through the random forest (RF) and backpropagation neural network (BPNN) regression analysis. The modeling performance was further validated based on two kinds of global atmospheric profiles for the year 2020. Our results show that the ZWD modeling accuracy gained by two machine learning regression approaches is significantly improved by taking into account surface meteorological parameters, especially the surface water vapor pressure when compared to the Global Pressure and Temperature 3 (GPT3) model. When surface meteorological data are available, the RF-B model yields ZWD estimations with an overall agreement of 3.1 cm in comparison with the sounding profiles and 2.4 cm in contrast to the GPS RO atmospheric profiles. The RF-B is superior to other models based on surface meteorological parameters for ZWD calculation, e.g., the accuracy improves by 21.8–23.8% against the approach by Saastamoinen and 7–12.2% against the formula by Askne and Nordius. [ABSTRACT FROM AUTHOR]

This study investigates the factors influencing the permeability and water storage capabilities of Recycled Graded Crushed Stone Layer (RGCSL) materials, which are crucial for constructing sustainable "sponge cities". The research focuses on how aggregate characteristics, such as particle size and filling sequence, affect the porosity structure of RGCSL and, consequently, its permeability and water storage performance. The findings reveal significant impacts of these factors on material performance, leading to the development of a performance prediction model based on the principle of superposition and backpropagation neural networks. The model's efficacy was validated through simulation experiments, indicating that the water storage capacity of recycled sand is significantly higher than that of coarse aggregates, with the model achieving an accuracy of 89.1%. This study is vital for advancing environmental restoration and sustainable urban development. [ABSTRACT FROM AUTHOR]

In nuclear power systems, centrifugal pumps often need to operate under extreme conditions. However, accurately determining the cavitation status of centrifugal pumps under such extreme conditions is challenging. To improve the recognition accuracy of the three statuses of non-cavitation, incipient cavitation, and severe cavitation while improving the anti-interference capability of the monitoring system, this study extracted cavitation features from centrifugal pumps' motor current and vibration signals under three different operational conditions. It fused the features using feature-level multi-source information fusion (MSIF) based on the backpropagation neural network (BPNN) or support vector machine (SVM) to construct a cavitation status recognition model and analyzed the results to compare with those of recognition without information fusion. The results show that, compared with one signal source, MSIF can significantly improve the recognition accuracy of cavitation statuses. Combined current and pump casing axial monitoring based on the BPNN is the optimal scheme, with an overall recognition accuracy of 97.3% for all operational conditions, compared to 73.9% for the single current signal and 89.3% for the single casing axial vibration signal. These research results can guide the monitoring of cavitation statuses in practical engineering, as well as timely intervention at incipient cavitations to reduce structural damage to centrifugal pumps and prolong service life. [ABSTRACT FROM AUTHOR]

Tool condition monitoring (TCM) systems are essential in milling operations to guarantee the product's quality, and when they are paired with indirect measuring techniques, such as vibration or acoustic emission sensors, the monitoring can happen without sacrificing productivity. Some more advanced techniques in tool wear estimation are based on supervised machine learning algorithms, like several other applications in Industry 4.0's context; however, a satisfactory performance can be obtained with simple techniques and low computational power. This work focuses on an application of tool wear estimation using a simple backpropagation neural network in a milling dataset. Statistical techniques, i.e., the mean, variance, skewness, and kurtosis, were used as features that were extracted from indirect measurements from vibration and acoustic emission sensors' data in a real milling testbench dataset containing multiple experiments with sensor data and a direct measure of the flank wear (VB) in most instances. The data were preprocessed, specifically to acquire clean and normalized values for the neural network training, assuming that the VB measure would be the target variable used to predict tool wear; all incomplete samples without a VB measure, as well as outliers, were removed beforehand. The train and test subsets were chosen randomly after making sure that the maximum values of every variable were represented in the training subset. A multiple topology approach was implemented to test the configurations of multiple backpropagation neural networks to determine the most suitable one based on two performance criteria, i.e., the mean absolute percent error (MAPE) and variance. Although only a simple backpropagation algorithm was used, the results were adequate to demonstrate a balance between accuracy and computational resource usage. [ABSTRACT FROM AUTHOR]