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Schizopygopsis younghusbandi is an endemic fish of Tibet characterized by slow growth. Artificial stock enhancement was applied to rebuild the natural population of S. younghusbandi in recent years. However, the optimal growth temperature and thermal tolerance of S. younghusbandi has not been studied, which restricts the production of S. younghusbandi fingerling for stock enhancement. The purpose of this paper is to determine the growth, critical thermal maximum (CTMax), lethal thermal maximum (LTMax) and acclimation response ratio (ARR) of S. younghusbandi juveniles (body weight 5.7 ± 1.2 g) at three acclimation temperature levels (10, 15, 20°C). The results showed that acclimation temperature significantly affected the growth, CTMax, LTMax and ARR of the experimental fish. Largest final weight (7.5 ± 2.3 g) was recorded in 15°C group. At a heating rate of 1°C/30 min, CTMax ranged from 30.98 to 32.01°C and LTMax ranged from 31.76 to 32.31°C in the three acclimation temperatures. Schizopygopsis younghusbandi had lower ARR value (0.097) than most other fish species. Low ARR value indicates that S. younghusbandi may have narrower thermal tolerance range and weaker acclimation ability to global warming. For successful aquaculture of S. younghusbandi juveniles, temperature should be maintained around 15°C.

Heart failure (HF) is a disease that is harmful to human health. Recent advances in machine learning yielded new techniques to train deep neural networks, which resulted in highly successful applications in many pattern recognition tasks such as object detection and speech recognition. To improve the diagnostic accuracy of HF staging, this study evaluates the performance of deep learning-based models on combined features for its categorization. We proposed a novel deep convolutional neural network-Recurrent neural network (CNN-RNN) model for automatic staging of heart failure diseases in real-time and dynamically. We employed the data segmentation and data augmentation pre-processing dataset to make the classification performance of the proposed architecture better. Specifically, this paper use convolutional neural network (CNN) as a feature extractor instead of training the entire network to extract the characteristics of the electrocardiogram (ECG) signals and form a feature set. We combine the above feature set with other clinical features, feed the combined features to RNN for classification, and finally obtain 5 classification results. Experiments shows that the CNN-RNN model proposed in this paper achieved an accuracy of 97.6%, the sensitivity of 96.3%, specificity of 97.4% and proportion of 97.1% for two seconds of ECG segments. We obtained an accuracy, sensitivity, specificity and proportion of 96.2%, 96.9%, 95.7%, and 94.3% respectively for five seconds of ECG duration. The model can be used as an aid to help clinicians confirm their diagnosis.

The problem of land ecological security directly threatens the sustainable development of many regions, and exploring the spatio-temporal characteristics of land ecological security is helpful for analyzing the land ecological patterns between regions and over time. Based on the interpretation of remote sensing image data for Tianjin in 1980, 2000, 2010 and 2019, supported by software such as ArcGIS and GeoDa, the changes of land use in the study area are calculated by using the land use dynamic degree. Then, the land ecological security index and spatial autocorrelation analysis methods are used to study the spatial correlations and internal heterogeneity of land ecological security in each district in Tianjin. The results show that: (1) The land use of Tianjin has changed dramatically in the past 40 years: the building land has been expanding, while the farmland and barren have been shrinking continuously. (2) The overall level of land ecological security is in the high security area, and Jizhou District is the highest, while the Central areas and Binhai are low. (3) The spatial heterogeneity of land ecological security is not obvious, but the spatial agglomeration is strong.

By comparing the outpatient volume of pediatric patients and the outpatient volume of respiratory tract infections in Chongqing Maternal and Child Health Care Hospital from 2017 to June 2020, the changes in the outpatient volume of pediatric patients and the outpatient volume of respiratory tract infections during COVID-19 were summarized and analyzed, so as to provide basis for disease prevention.

Highlights • Depression, anxiety, and somatic symptoms in hospital workers were 30.2%, 20.7%, and 46.2% • 6.5% hospital workers reported suicidal or self-harm ideation. • Female, single, and low-level educational background were risk factors of psychological impact. • Epidemic-related attitudes and behaviors are associated with stress, support, and SSI. • County hospital workers suffered more psychological effects, stress, and SSI., Background There was an outbreak of COVID-19 towards the end of 2019 in China, which spread all over the world rapidly. The Chinese healthcare system is facing a big challenge where hospital workers are experiencing enormous psychological pressure. This study aimed to (1) investigate the psychological status of hospital workers and (2) provide references for psychological crisis intervention in the future. Method An online survey was conducted to collect sociodemographic features, epidemic-related factors, results of PHQ-9, GAD-7, PHQ-15, suicidal and self-harm ideation (SSI), and the score of stress and support scales. Chi-square test, t-test, non-parametric, and logistic regression analysis were used to detect the risk factors to psychological effect and SSI. Results 8817 hospital workers participated in this online survey. The prevalence of depression, anxiety, somatic symptoms, and SSI were 30.2%, 20.7%, 46.2%, and 6.5%, respectively. Logistic regression analysis showed that female, single, Tujia minority, educational background of junior or below, designated or county hospital, need for psychological assistance before or during the epidemic, unconfident about defeating COVID-19, ignorance about the epidemic, willingness of attending parties, and poor self-rated health condition were independent factors associated with high-level depression, somatic symptom, and SSI among hospital workers (P

Accurately predicting distant metastasis is very important for esophageal squamous cell carcinoma (ESCC) patients, as it is a key evidence to determine the degree of this disease. Traditional metastasis predictions are made by doctors manually, which is time and labor consuming. In recent years, some researchers try to predict ESCC distant metastasis using machine learning techniques. However, they ignored the factor that the problem of predicting ESCC distant metastasis is a highly imbalanced task. In this study, we try to build a new imbalanced learning based ESCC distant metastasis predicting scheme using blood cell analysis testing results. We first extract effective features from blood cell analysis testing data. And then we use over-sampling method to generate new positive instances (instances with ESCC distant metastasis). Finally, we apply k-nearest neighbor (k-NN) algorithms to predict unknown instances. Experimental results show that our method is effective in cases with few positive instances.

Purpose Since 2000, China, along with the USA, UK, France, Japan and many other developed countries have drawn up new blueprints for the development of a marine economy. At present, international marine economics research has entered into a new period of development, and the research methods of ocean econometrics are becoming more complex and mature. The purpose of this paper is to review the progress of international marine econometrics research and gives the development direction of marine econometrics. Design/methodology/approach The Web of Science core collection database was utilized, harvesting data from 1996 to May 2018, measuring the marine economy research from 1,489 articles as its sample, using CiteSpace visualization analysis tools. Findings Mapping the knowledge map from annual international marine economic metrology, literature identification, keywords, involving disciplines and related journals, countries (regions) and research and analyzing the research status of reveals the research frontiers of international marine economy measurement (learning) by using CiteSpace. Originality/value The conceptions and characteristics of marine econometrics are defined and analyzed, and the theoretical method of marine econometrics is sorted out. Mapping the knowledge diagram of marine econometrics and discussing the research status of international marine economics, and clarifying the existing problems, future opportunities and challenges of international marine econometrics research.

Integrable nanophotodiode devices have attracted much research interest in recent years because of their potential applications in all-optical computing and optical communication systems. We propose a new optical diode design scheme. We use genetic algorithms (GAs) to design an optical diode, which has a device footprint of only 2.5 × 2.5 µ m 2 . These devices designed by GA have the ability to achieve high-efficiency unidirectional transmission. Simulations show the forward transmission efficiency can reach higher than 65% for a Gaussian beam between the wavelengths of 1400 and 1600 nm, and the peak transmission efficiency reaches 75%. The transmission contrast at the design wavelength between 1500 and 1600 nm is higher than 90%, which meets the requirements of high unidirectionality, wide operational bandwidth, and small scale. The devices have more advantages for optical diodes compared with structures designed by photonic crystals and gratings. The application of this scheme provides a new idea for the design and research of all-optical diodes in the field of optical communication.

The study tries to estimate the trade potentials might be raised by comparing the current maritime route of One Belt One Road initiative and new maritime route in particular with the China-Pakistan Corridor as a result of the changing freight costs, shipping period and distance between European Union (EU), Middle East, North African countries (MENA) and China. To estimate trade potentials, qualitative and descriptive statistical methods will be used to compare the real and predicted trade. The results reveal that China and the EU, MENA can increase trade potentials and show that average predicted freight costs, shipping period and distance between China and EU are decreased by $1,545, 16.3 days and 10,084 km respectively, and the average predicted freight costs, shipping period and distance between China and the MENA are decreased by $1,695, 17.2 days and 10,477 km respectively.

In recent years, as a new form of public transport, the modern tram has been the rapidly developed and widely applicated at home and abroad. In order to ensure the safe operation of the modern tram, the groove rail damage block is used to explore suitable method of flaw detection. To solve the problem, finite element software was used to model the modern tramway structure, internal groove rail stress changes was analysised, the appropriate internal damage distribution was determined, combined with the corresponding national standard, a new groove rail damage test block was put forward, to determine the appropriate modern tram methods of flaw detection provide detection standard.

Inkjet printing technology has advantages of energy saving and environmental protection, as well as personalized on-demand printing. Inkjet digital printing is a pressure free printing method, its quality defects is different from other printing method,such as offset printing and gravure printing. The analysis shows that the main types of inkjet digital printing defects include color rendition, paper flatness, graphic edge sharpness, back sticks, dirty ink dots and white lines. By analyzing the specific characteristics of the above inkjet printing quality defects, the possible causes and the solutions to the quality defects of inkjet printing are given.

Effect of rearing temperature on growth and thermal tolerance of Schizothorax (Racoma) kozlovi Nikolsky larvae and juveniles was investigated. The fish (start at 12d post hatch) were reared for nearly 6 months at five constant temperatures of 10, 14, 18, 22 and 26°C. Then juvenile fish being acclimated at three temperatures of 14, 18 and 22°C were chosen to determine their critical thermal maximum (CTMax) and lethal thermal maximum (LTMax) by using the dynamic method. Growth rate of S. kozlovi larvae and juveniles was significantly influenced by temperature and fish size, exhibiting an increase with increased rearing temperature, but a decline with increased fish size. A significant ontogenetic variation in the optimal temperatures for maximum growth were estimated to be 24.7°C and 20.6°C for larvae and juveniles of S. kozlovi, respectively. The results also demonstrated that acclimation temperature had marked effects on their CTMax and LTMax, which ranged from 32.86°C to 34.54°C and from 33.79°C to 34.80°C, respectively. It is suggested that rearing temperature must never rise above 32°C for its successful aquaculture. Significant temperature effects on the growth rate and thermal tolerance both exhibit a plasticity pattern. Determination of critical heat tolerance and optima temperature for maximum growth of S. kozlovi is of ecological significance in the conservation and aquaculture of this species.

A simulation model consisting of wind speed, wind turbine and AA-CAES (advanced adiabatic compressed air energy storage) system is developed in this paper, and thermodynamic analysis on energy conversion and transfer in hybrid system is carried out. The impacts of stable wind speed and unstable wind speed on the hybrid system are analyzed and compared from the viewpoint of energy conversion and system efficiency. Besides, energy conversion relationship between wind turbine and AA-CAES system is investigated on the basis of process analysis. The results show that there are several different forms of energy in hybrid system, which have distinct conversion relationship. As to wind turbine, power coefficient determines wind energy utilization efficiency, and in AA-CAES system, it is compressor efficiency that mainly affects energy conversion efficiencies of other components. The strength and fluctuation of wind speed have a direct impact on energy conversion efficiencies of components of hybrid system, and within proper wind speed scope, the maximum of system efficiency could be expected.

Summary The food preferences of paddlefish (Polyodon spathula) in polyculture with bighead carp (Aristichthys nobilis) are unknown. Three consecutive analyses of gastrointestinal contents of paddlefish and bighead carp were conducted at 3d, 11d and 19d after introduction into the non-fed ponds. The objectives of this study were to test for competitive interactions between bighead carp and paddlefish, and to document their preferred prey when the two species have a limited food resource. The results showed that paddlefish and bighead carp are basically competitive in their diets. When they were cultured in one pond at the same density, paddlefish fed more quickly and efficiently, depleting the food resources for bighead carp; however, bighead carp were able to switch and trap small food items with the decrease in available food resources. Moreover, larger zooplankton abundance declined in ponds after fish were introduced.

As the penetration of renewable energy sources (RES) into energy market is becoming increasingly evident, it is urgent to deal with the problem of fluctuations of RES. Compressed Air Energy Storage (CAES), as one of energy storage technologies aiming at this problem, has excellent characteristics of energy storage and utilization, but its dependence on fossil fuels makes CAES less attractive. In order to avoid the use of fuels, Advanced Adiabatic Compressed Air Energy Storage (AA-CAES), which is an optimized CAES system, is designed to capture and reuse the compressed air heat. In this paper, four different air storage chamber models are established and the characteristics of charge and discharge process are analyzed based on the theory of thermodynamics. Meanwhile, the thermodynamic effects of different air storage chamber models on AA-CAES system are examined. Results show that the difference of air storage chamber models can result in different characteristics of charge and discharge process which may affect the working stability of AA-CAES system. Besides, the influence degree of the number of stages on system efficiency and other parameters change with air storage chamber models.

With the increasing penetration of renewable energy into energy market, it is urgent to solve the problem of fluctuations of renewable energy sources (RES). Energy storage technology is regarded as one method to cope with the unstable nature of RES. One of these technologies is compressed air energy storage (CAES), which is a modification of the basic gas turbine technology. Electric power supplied by CAES can meet peak-load requirement of electric utility systems. Because there is heat waste in the existing CAES systems during compression process, fossil fuels are used to improve the expansion work to generate peak power. In order to avoid the use of fuels and keep high efficiency of system, CAES system with thermal energy storage (TES) is designed to capture and reuse the compressed air heat. This paper uses a thermodynamic model of a CAES system with TES to analyze the effect of TES on system efficiency. Besides, this paper evaluates the influence of temperature and pressure on the utilization of heat in TES. Results show that even when power efficiency reaches maximum, there is still a proportion of thermal energy left in TES for other use. Meanwhile, the utilization of heat in TES can be affected by pressure in the air storage chamber. With appropriate selection of pressure limits, the utilization of compressed air heat can be optimized.

This paper examines the relationship between foreign direct investment (FDI), economic growth and financial development in Cabo Verde for the period 1987-2014.The methodology involves the use of bound test approach to cointegration (ARDL) as well as the ECM-Granger causality analysis. The bound test indicated that there is a long-run relationship when the variable GDP and FDI are the dependent variable. Moreover, the results indicated that FDI has a positive effect on the economic growth in Cabo Verde. It also found a bidirectional causality between FDI and economic growth, i.e. FDI granger causes GDP and GDP granger cause FDI. Thus, we concluded that higher levels of FDI inflow mean higher levels of economic growth and vice versa for the economy of Cabo Verde. Furthermore, we found that both economic growth and domestic credit to private sector are important factors in stimulating the FDI into the country. These results found are important for the country policymakers to take appropriate measures to enhance and improve the condition for FDI inflow in Cabo Verde.

A new formal solution for the multiple scattering of plane harmonic waves by a group of arbitrary configuration tubular piles in an elastic total space is derived. Each order of scattering satisfies prescribed boundary conditions at the interface of tubular piles, which is delivered as the sum of incident and scattering waves. The first order performs the scattering wave by each scattered pile and the subsequent orders resulted from the excitation of each pile of first order of scattering from the remaining tubular piles. Advanced scattering orders can be regarded as the same manners. Several series of scattering coefficients are figured out with the aids of addition theorem so that the exact steady-state solution for the scattered displacement and stress is obtained. Particularly, when internal diameter of tubular piles tends to be infinitely small, it degenerates to a solid pile problem. By imposing the normalized displacement amplitudes and transmissibility indices, the influences of specific parameters such as scattering orders, internal and external diameter ratio of piles, pile material rigidity, position and distances between tubular pile and pile rows, and pile numbers are discussed. Certain recommended conclusions have been drawn as the guidelines of practical engineering design for discontinuous barrier of tubular piles.

Conventional polynomial interpolation methods produce images with blurred edges, while edge-directed interpolation methods make enlarged images with good quality edges but with detail distortion in the non-edge portion. A new method for constructing a fitting surface to image data is presented. Unlike existing methods which produce enlarged images using image data as interpolation data, the new method constructs the fitting surface using the image data as constraints to reverse the sampling process for improving the fitting precision. To remove the zigzagging artifact, for each pixel and its nearby region, the edge information is used to determine the quadratic polynomial which approximates the original scene with a quadratic polynomial precision. Comparison results of the new method with other methods are included.

Purpose: To evaluate the performances for detection of IgM and IgG antibodies to Orientia. tsutsugamushi (Ot) using a gold conjugate-based rapid diagnostic test (RDT). Materials and Methods: The RDT employing mixture recombinant 56-kDa proteins of O. tsutsugamushi and the mIFA assay was performed on 33 patients from Fujian and Yunnan province respectively and 94 positive sera (36 from Hainan province and 58 from Jiangsu province) from convalescent stages of the patients with scrub typhus respectively and 82 negative sera from healthy farmers from Anhui province and Beijing City respectively in 2009. A comparison of the RDT and mIFA assay was performed by using the c2 test and the P level of ≤0.05 was considered to be significant. Results: Among these 94 positive sera from convalescent stages of the illness and 82 sera from control farmers, the specificity of RDT was 100% for both IgM and IgG tests. In 33 cases with scrub typhus, 5 cases were positively detected earlier by RDT than by mIFA for the IgM test, and 2 cases were positive for the IgG test. The sensitivities of RDT were 93.9% and 90.9% for IgM and IgG, respectively. Considering IgM and IgG together, the sensitivity was 100%. The geometric mean titre (GMT) of IFA and the RDT assay in diluted sera from confirmed cases were 1:37 versus 1:113 respectively (P

A novel scheme on reality preserving image encryption is proposed in this paper. The Hilbert transform and the double random phase encoding are utilized in the proposed method. The encrypted image is real-valued without data expanding, which can benefit the digital processing of images by electronic computers where speed of computation is important. The image is processed in the plane which can be regarded as different fractional Fourier domain. In addition, the proposed scheme can carry out the encryption and the decryption in the same structure, which makes it convenient to implement the encryption in practical application. Simulations are presented to verify the validity of this novel approach.

Aiming at data sharing between Geomagic and SolidWorks, the article gives some methods dealing with data in point phase, polygon phase and shape phase. Meanwhile, the article takes an example which shows its input methods and efforts in different way from Geomagic to SolidWorks.

Accurate building cooling load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Hourly cooling load forecasting is a difficult work as the load at a given point is dependent not only on the load at the previous hour but also on the load at the same hour on the previous day. In this paper, a novel short-term cooling load forecasting approach is presented by conjunctive use of fuzzy C-mean clustering algorithm and fuzzy support vector machines (FSVMs). According to the similarity degree of input samples, the training samples are clustered by means of the homogenous characteristic, and then we apply a fuzzy membership to each input point such that different input points can make different contributions to the learning of decision surface. The results of experiment indicate that the proposed method can be used as an attractive and effective means for short-term cooling load forecasting.

This paper introduces the design of smart cars based on 16-bit microcontroller MC9S12XS128, which is provided by Freescale Semiconductor Company. The controller is also combined with the steering gear to make up path identification. Its speed control system uses a CCD camera to track the black line through edge detection. In addition, it uses MOSFET to set up H bridges driving circuit. Omron rotary encoder is used for speed feedback. Finally, it is proved that this smart car system can run on the track line steadily.

Forecasting agriculture water demand is significant to optimize confirmation of water resources. In this study, we introduce a hybrid model which combines rough set theory and least square support vector machine to forecast the agriculture irrigation water demand. Through a certain district agriculture irrigation water demand dataset experiment, we have proved that the reduction feature set exacted by RST has good subject-independence and intrinsic good separability. Weighted LS-SVM predictor demonstrated promising prediction accuracy, better generalization ability and more rapid execution speed than most of the all benchmarking methods listed in this study.

Accurate building cooling load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Hourly cooling load forecasting is a difficult work as the load at a given point is dependent not only on the load at the previous hour but also on the load at the same hour on the previous day. So the accuracy of forecasting is influenced by many unpredicted factors. Support vector machine (SVM) is a novel type of learning machine, which has been successfully employed to solve nonlinear regression and time series problems. The key problem of SVM is the selection of SVM free parameters. In this paper, it is proposed a new optimal model, which is based on Stimulated Annealing Particle Swarm Optimization Algorithm (SAPSO) that combines the advantages of PSO algorithm and SA algorithm. The strong searching ability of SA was employed to PSO algorithm to avoid the premature convergence with better stability and astringency. The SA based PSO algorithm was used to optimize the free parameters of SVM model. The study used the proposed model to forecast load of cooling load system. The numerical simulation results demonstrate that the accuracy of SA-PSO based SVM model outperforms that of the traditional SVM load forecasting model.

Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) is a powerful tool for modeling the inputs and output(s) of complex and nonlinear systems. However, parameters determination for a SVR model is competent to the forecasting accuracy. Several evolutionary algorithms, such as genetic algorithms and simulated annealing algorithms have been used to the parameters selection, however, these algorithms often suffer the problem of being trapped in local optimum. In this paper, a novel building cooling load forecasting approach combining support vector regression (SVR) and Ant colony algorithm (ACO) is proposed. ant colony (ACO) optimization was developed to optimize three parameters of SVR, including penalty parameter C, insensitive loss function e and kernel function σ. SVR constructs hyperplane in high dimension space and fits the data in non-linear form. Normalized Mean square error (NMSE) of fitting result is used as target of ant colony optimization. ACO finds the best parameters which correspond to the NMSE. The results showed that the proposed approach, by comparing with back-propagation neural network model, was an efficient way to model building cooling load with good predictive accuracy. ACO and SVR provide a useful tool for maximizing the combustion efficiency of cooling load. Also, the method can be easily extended to other applications.

Mobile robotics has been used widely in education as a learning tool, as it provides a motivating and interesting tool to perform laboratory experiments within the context of mechatronics, electronics, computer, and control. The robot innovative platform made it possible for the students to practice and learn many necessary skills. A tracing algorithm about machine vision intelligent tracing System based on CCD camera was described. The proposed system is comprised of a modular robot and the microcontroller XS128 as tools to design and implement the intelligent tracing system. The objective of this machine vision system is to implement an intelligent tracing system, which can track a black line as fast and stable as possible. It is student-oriented and career-oriented. The encouraging results demonstrate that students learn to design and give creative solution to given problems from an interdisciplinary point of view.

Accurate building cooling load forecasting is the precondition for the optimal control and energy saving operation of HVAC systems. Many forecasting approaches such as artificial neural network (ANN), support vector machine (SVM), autoregressive integrated moving average (ARIMA) and grey model, have been proposed in the field of building cooling load prediction. However, none of them has enough accuracy to satisfy the practical demand. Therefore, a novel hybrid predictor integrating seasonal ARIMA and Support Vector Regression (SVR) is presented to forecast building cooling load. SARIMA is suitable for linear prediction and SVR is suitable for nonlinear prediction. Experimental results demonstrate that the proposed hybrid building cooling load forecasting model can be an effective way to improve forecasting accuracy achieved by either of the models used separately.

In this paper, a novel building cooling load forecasting approach combining kernel principal component analysis (KPCA) and support vector machine (SVM) is proposed. KPCA is an improved PCA, which possesses the property of extracting optimal features by adopting a nonlinear kernel function method. The original inputs are firstly transformed into nonlinear principal components using KPCA. These new features are then used as the inputs of SVR to solve the load forecasting problem. The theoretical analysis and the simulation results show that KPCA can efficiently extract the nonlinear feature of initial data. KPCA-SVR has powerful learning ability, good generalization ability and low dependency on sample data compared with PCA-SVR and SVR. It also indicates that the integration of KPCA and SVR forecast cooling load effectively and can be used in building cooling load prediction.

In this paper, a novel approach combining kernel principal component analysis (KPCA) and least square support vector machine (LSSVM) is proposed for HVAC fan machinery status monitoring and fault diagnosis, which combines KPCA for fault feature extraction and multiple SVMs (MSVMs) for identification of different fault sources. KPCA is used as a preprocessor of LSSVM, which maps the original input feature into a higher dimension feature space through a nonlinear map, the principal components are then found in the higher dimension feature space. Then the hyperparameters of LSSVM are optimized by particle swarm optimization. Then we compared the accuracies of the hybrid KPCA-LSSVM mode with other artificial intelligence (BPNN and fixed-SVM). The experimental results showed that KPCA based on LS-SVM has a higher correct recognition rate, and a faster computational speed.

A novel approach to HVAC fan machinery fault diagnosis based on combination of artificial neuron network and D-S evidence theory is presented in this paper. Firstly, the system pre-processes the acquisition data from multi sensor, then makes pattern classifies observations based on BP neural network. Secondly, the output values of BPNN are directly taken as the basic probability assignment of the proposition in the frame of discernment. Therefore realizing the objective treatment of the basic probability assignments and avoiding the complexity of constituting the basic probability distribution function. The experiment results demonstrate that the reliability and accuracy to apply the fusion BPNN results with the D-S evidence theory are higher than the independent BPNN diagnosis.

Many aspects should be considered to train the undergraduates’ mechanical innovation design ability. In this paper, the ways of training university students’ mechanical innovation design ability are discussed as follows: The environment of training university students’ mechanical innovation design ability should be built by changing philosophy of teaching. The training programs which meet the requirement of mechanical engineering specialists and the characteristic classroom teaching mode should be worked out to construct new teaching system. The teaching management system and the experiment teaching methods should be reformed to construct new experiment teaching platform of mechanical foundation. Students are organized actively to take part in the nationwide mechanical innovation design competition and the ability of mechanical innovation design and engineering realization is promoted greatly.

A number of different forecasting methods have been proposed for cooling load forecasting including historic method, real-time method, time series analysis, and artificial neural networks (ANN), but accuracy and time efficiency in prediction are a couple of contradictions to be hard to resolve for real-time traffic information prediction. In order to improve time efficiency of prediction, a new hourly cooling load prediction model and method based on Least Square Support Vector Machine (LS-SVM) is proposed in this paper. A comparison of the performance of LSSVM with back propagation neural network (BPNN) is carried out. Experiments results demonstrate that LSSVM can achieve better accuracy and generalization than the BPNN, the LSSVM predictor can reduce significantly both relative mean errors and root mean squared errors of cooling load.

In this paper, the structural feature and forming technology of two automobile engine covers are analyzed. The mould structure with two cavities is designed and the working process of the injection mould is discussed. The mould structure is complicated. Ten core-pulling mechanisms are designed reasonably in one mould and demoulding of the plastic parts is realized. Many inserts and standard parts are used in the injection mould, so the economical efficiency of the injection mould is well and the injection mould can be maintained conveniently. The injection mould is already in mass production. Industrial practice proves that the action of the core-pulling mechanisms, ejector mechanisms and resetting mechanisms is stable and reliable. The plastic parts produced can meet the customers' technical requirements.

A novel approach used for Printed Circuit Board (PCB) defect inspection is presented in this paper. This image processing approach that developed in Lab Windows/CVI can be used to determine whether PCB components are put in the correct location, and matches areas of inspection to a template of valid components. The experimental result shows that the proposed approach can search the correct components in PCB image with a given template, therefore it can be used in an automatic optical inspection for on line inspection.

Last decade saw a rapid development of science and technology, which provides better educational environment and conditions for both learners and teachers. Advanced educational technology attracts more and more interests. Mobile robotics provides a motivating and innovative platform for performing laboratory experiments regarding interdiscipline including mechatronics, electronics, microcomputer and control. This paper reports the innovative educational environment of innovative laboratory built on intelligent robot. This innovative laboratory focuses on innovative educational software and technology for use in education. Through innovative design process, the intelligent modular robot platform constructs innovative learning environment through which students learn not only knowledge and skills but also how to collaborate with group members. Collaborative learning based on innovative laboratory helps to promote quality-oriented education and students' all-around development. It is student-oriented and career-oriented. Results indicate that the innovative laboratory not only imparting knowledge and skills to students but also developing their creativity, collaboration, practicing skill, innate ability and personality. It creates an environment in which teachers enjoy teaching and students enjoy learning and thus makes enjoyable education possible.

In this paper, a novel HVAC fan machinery fault recognition method combining kernel principal component analysis (KPCA) and support vector machine (SVM) is proposed. KPCA is an improved PCA, which possesses the property of extracting optimal features by adopting a nonlinear kernel function method. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. An integrated method is applied for HVAC fan machinery status monitoring and fault diagnosis, which combines KPCA for fault feature extraction and multiple SVMs (MSVMs) for identification of different fault sources. The experimental results show that KPCA based on LS-SVM has a higher correct recognition rate, and a faster computational speed.

Due to the rapid technological developments in image/video capturing, huge data storage, video compression and networking, huge amount of video data are produced each day all over the world. Finding effective ways to store, index and retrieve these video remains a hot researching area. It is especially important for the producers/editors of television programs, since to keep track of the 1000's of short video clips that are used to make a film without help from the computer is unimaginable. Typically 25 hours worth of material is required for a 50-minute show. For this reason, the producers/editors must be able to store these video clips efficiently, and be able to quickly locate and retrieve the necessary clips when editing. The ways to store and index the video clips with the extracted features, and the ways to retrieve them quickly and correctly on the user’s query, are introduced in this paper.

Genetic Algorithms (GAs) have demonstrated to be effective procedures for solving multi criterion optimization problems. These algorithms mimic models of natural evolution and have the ability to adaptively search large spaces in near-optimal ways. One direct application of GAs is in the area of evolutionary robotics, but standard GAs have some drawbacks such as time-consuming and premature convergence. A novel robot path planning method based on Adaptive Niche Genetic Algorithm (ANGA) is first presented in this paper. To make ANGA more effective, the fitness evaluation with multi criterions is designed to fit feasible and infeasible paths. The adaptive crossover and mutation operators are trimmed to the path planning problem. The experiment results demonstrate that AGNA based path planer has more adaptability, displaying near-optimal paths in different configurations of the environment with obstacle than the standard GAs.

As the worldpsilas aged population grows many governments are looking to remote healthcare monitoring solutions. With the recent active research activities in the area of Wireless Sensor Networks (WSN), the development and realization of a light-weighted, low-cost and wireless home healthcare monitor has become feasible. A novel embedded remote healthcare system based on a commercially available wireless biomedical sensor networks is proposed in this paper. The sensor node device utilizes MSP430 microcontroller and operates on TinyOS operating system. The wireless communication between the sensor nodes and the care base-station used IEEE 802.15.4/Zigbee standard. The biophysical signal from elder is detected and radioed to a care base-station for monitoring.

Recent years have shown impressive growth in the development of pervasive healthcare systems, which are primarily being designed to provide emergency and preventive healthcare to citizens anywhere/anytime using wired and wireless mobile technologies. This paper presents an e-healthcare framework with pervasive sensor network for the disabled and elderly people. A home healthcare server collects user health information and sends the data in case of emergency to a doctor or guardian in order to achieve a pervasive intelligent healthcare system. Beyond e-healthcare service, as a primitive application for pervasive healthcare service using mobile Internet, we studied the real-time health-monitoring service for the disabled and elderly people with an inexpensive and effective Web server. Preliminary results are presented in the context of a larger future integration with the pervasive healthcare framework.

Mobile robotics has been used widely in education as a learning tool, as it provides a motivating and interesting tool to perform laboratory experiments within the context of mechatronics, electronics, microcomputer, and control. Lego Mindstorms has been successfully applied at introductory level. However, the number of I/O ports and the processing capacities is very limited and thus is inappropriate for advanced classes at universities. This paper presents the concepts of our modular security and patrol robot with the characteristics of scalability, modularity, flexibility and easy operation. The robot innovative platform made it possible for the students to practice and learn many necessary skills, like collaboration, cognitive skills, self-confidence, perception, and spatial understanding. Interdisciplinary innovative education based on modular robot platform focuses on developing creativity, collaboration, practicing skill, innate ability and personality instead of just imparting knowledge. It is student-oriented and career-oriented. The encouraging results demonstrate that students learn to design and give creative solution to given problems from an interdisciplinary point of view.

Wireless sensor networks (WSN) are a nascent technology that builds upon the recent decadepsilas advances in electrical and mechanical engineering including wireless communications, low-power embedded systems, MEMS-sensor design, network architectures and instrumentation applications. These networks promise a means by which to better monitor and understand environmental monitoring, precision agriculture and public health. The nature of WSNs motivates collaboration among researchers from diverse disciplines to leverage their skills to conduct meaningful experiments. An ongoing innovative education practice has been investigating the effectiveness of WSNs for monitoring environment signals in modern agriculture. Several undergraduate students were recruited as research assistants and given the responsibility of implementing and deploying a complete WSN solution. Using wireless sensor networks as a motivating technology, this paper documents our early experiences developing and deploying a WSN in the field. The novel aspect of this innovative practice was provided with a hands-on experience in which interdisciplinary team work, technical communications and hardware design is emphasized. Future undergraduate research areas are explored with suggestions on how WSN topics can permeate into undergraduate education.

User demands and technological advances are moving us closer to the pervasive computing vision. The home of the future will include networked appliances that publish the functions they offer as intelligent middleware services. In the conventional home network systems, a powerful centralized server controls all electric home appliances connected to provide value-added integrated services. However, when the number of the appliances increases and the appliances become more sophisticated, the conventional architecture would suffer from problems in superfluous resources, flexibility, scalability and reliability. This paper presents Service Oriented Architecture as a new architecture to solve the problems. We propose a two-layered design of an appliance, including device and service layers. The device layer corresponds to the physical device of the appliance controlled by vendor-specific interfaces. The service layer exhibits features of the device as self-contained services accessible via device-independent interfaces. Thus, the appliances are loosely coupled at the service layer without any centralized server. This enables more flexible, robust and load-balanced integrated services.

Modular, self-reconflgurable robots are showing the promise of robustness, great versatility and low cost. Based on the unique characters of reconfigurable robot, modular mechanical and electrical structure and control method are discussed in detail. Innovative control system architecture of modular and reconfigurable robot is presented in this paper. The hybrid control architecture is composed of the centralized supervisory controller and distributed module controllers. The centralized controller is implemented on Motorola PowerPC using RT-Linux Operating system and each module controller is implemented on DSP real time processor. The proposed architecture has been implemented on a modular mobile robot with an arm manipulator, and the results show validity and expandability of our proposed control architecture.

With the development of society and economy, more and more high buildings and large mansions come forth. The task to maintain public security become heavier, it is imperative to seek an automatic patrol solution. This paper describes how to construct a security and patrol robot system based on modular and object oriented approach. A novel controller WinCon-8k running on the real time Windows CE operation system is applied to the robot design. The robot equipped with stereovision and friendly man-robot interface can finish tasks such as navigation and maintaining the public security. At last, a cooperative supermarket providing an extraordinary test bed permits to establish meaningful statistics over 3 months (from September 12 to December 15). The experiment results show the good performance of the robot.