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Tennis is a set of sports and entertainment and a sports activity, since 2014, tennis in China has been another rapid development. With the development of economy and technology, tennis training mode has been further optimized and reformed. At present, tennis training robot is the mainstream way to train athletes. However, there are some defects in the current tennis training robots, such as the low accuracy of human motion real-time evaluation, and the lack of stability. Therefore, this paper puts forward the related research on the real-time evaluation algorithm of human motion in tennis training robots, hoping to make up for the deficiency in this field. The research of this paper is mainly divided into four parts. The first part is to analyze the current situation of technology research in this field and put forward the idea of this paper by analyzing the shortcomings of the existing technology. The second part is the related basic theory research; this part deeply studies the core theory of tennis training and intelligent training robot, which provides a theoretical basis for the realization of the optimization scheme. The third part is the design and implementation of a real-time human motion evaluation optimization algorithm for tennis training robots. At the end of the paper, that is, the fourth part, through the way of field test and investigation, further proves the superiority of the improved real-time evaluation algorithm of human movement. The algorithm has good stability and accuracy and can meet the existing tennis training requirements. [ABSTRACT FROM AUTHOR]

School enterprise cooperation is of great strategic significance to China's manufacturing industry. The Party Central Committee has repeatedly proposed to strengthen the integration of industry and education and deepen the cooperation between schools and enterprises. In recent years, experts and scholars at home and abroad have been from different perspectives, although they have achieved fruitful results, there are also problems such as insufficient attention to theoretical research, relatively backward research, narrow research scope, and so on. In view of this phenomenon, this paper will study a new school-enterprise cooperation mechanism based on the improved decision tree algorithm. The research of this paper is divided into three parts. First, after analyzing the advantages and disadvantages of the algorithm, the algorithm of the decision tree is improved, which makes the improved algorithm more suitable for the field of school-enterprise cooperation. Then, based on cloud computing and intelligence, this paper establishes a new model of school-enterprise cooperation platform, which solves some problems of data management and information exchange in school-enterprise cooperation. Finally, in order to make the cooperation mechanism of this paper better used in practice, this paper builds an online and offline hybrid training base, hoping to make the cooperation between schools and enterprises closer through the training base. In order to test the effect of the cooperation model, this paper takes school as the experimental model. After investigation and research, it is believed that thanks to the school-enterprise cooperation mechanism in this paper, the cooperative enterprise of school has been greatly improved in the past three years, and the willingness of enterprises to cooperate has become more and more strong. No matter students, teachers, or enterprises are reaping huge benefits under this cooperation mechanism, it is a suit for extensive promotion The school-enterprise cooperation mechanism of Guangyun. [ABSTRACT FROM AUTHOR]

The global economy appears the trend of anti-globalization under the influence of COVID-19. Based on the input-output table of lead database from 2006 to 2020, this paper divides the factors that affect the development of financial industry in China, the United States and Russia into six aspects: price, intermediate input, household consumption, government consumption, export and import. ADGA-BP neural network model is proposed in this paper, which is based on six aspects of price, intermediate input, consumer, government consumption, export and import. The intermediate input is decomposed from the perspective of industrial structure to study the interrelationship between financial industry and other industries in the three countries. The results show that the intermediate input is the main factor in the development of financial industry in the three countries, but the source industries of the intermediate input are not the same; the two factors of household consumption and price are closely related to the development of financial industry in the three countries, and they all play a role in promoting China, while the relationship between household consumption and the United States and between price and Russia is reverse; Government consumption only has a significant impact on Russia; from the perspective of mutual influence, the mutual investment between the financial industry of China and the United States is relatively large, while the relationship between the Russian financial industry and the two countries is relatively weak. It shows that under the background of covid-19, the development of financial industry is affected. [ABSTRACT FROM AUTHOR]

The unbalanced development strategy makes the regional development unbalanced. Therefore, in the development process, resources must be effectively utilized according to the level and characteristics of each region. Considering the resource and environmental constraints, this paper measures and analyzes China's green economic efficiency and green total factor productivity. Moreover, by expounding the characteristics of high-dimensional data, this paper points out the problems of traditional clustering algorithms in high-dimensional data clustering. This paper proposes a density peak clustering algorithm based on sampling and residual squares, which is suitable for high-dimensional large data sets. The algorithm finds abnormal points and boundary points by identifying halo points, and finally determines clusters. In addition, from the experimental comparison on the data set, it can be seen that the improved algorithm is better than the DPC algorithm in both time complexity and clustering results. Finally, this article analyzes data based on actual cases. The research results show that the method proposed in this paper is effective. [ABSTRACT FROM AUTHOR]

Background: How to accurately predict the occurrence of contamination in the fermentation process of Chlortetracycline? How to prompt field operators to take effective measures in time? This is a difficult problem that the fermentation process of Chlortetracycline has not been solved well.Objective: The aim of this paper is to effectively predict whether the fermentation process of Chlortetracycline is contaminated or not.Methods: A Gaussian process regression soft sensor modeling method with real time integration learning is studied in depth by combining two local learning strategies, namely just-in-time learning (JITL) method and integrated learning method, and a multi-model weighted Gaussian process regression (MWGPR) soft sensor modeling method based on real-time integration learning is proposed in the paper. This soft sensing method was used to study the relationship between the viscosity of fermentation broth and the contamination in fermentation process. A soft-sensing model based on the viscosity of fermentation broth for predicting the signs of contamination is established.Results: The validity of this method is verified by field data. The experimental results demonstrate that the soft sensing model proposed in this paper can effectively determine whether the fermentation broth is infected by hybrid bacteria.Conclusions: The method proposed in this paper is innovative and practical so that field operators can issue early warning and take effective measures. [ABSTRACT FROM AUTHOR]

Since the BP neural network has poor performance and unstable learning rate in the maximum power point tracking (MPPT) algorithm of photovoltaic (PV) system, an adaptive particle swarm optimization BP neural network-fuzzy control PV MPPT algorithm (APSO-BP-FLC) is proposed in this paper. First, the inertia weight, learning factor and acceleration factor of particle swarm optimization (PSO) are self-updating, and the mutation operator is adopted to initialize the position of each particle. Second, the APSO algorithm is used to update the optimal weight threshold of BP neural network, where the input layer is irradiation and temperature, and the output layer is the maximum power point (MPP) voltage. Third, the fuzzy logical control (FLC) is employed to adjust the duty cycle of Boost converter. The inputs of FLC are voltage difference and duty ratio D(n-1) at the previous time, and the output is duty ratio D(n). Moreover, D(n-1) is optimized by |dP/dU| to improve the search range of FLC. The irradiation, temperature and MPP voltage of PV cell are adopted as the datasets for simulation in a city in Shaanxi province, China. Simulation results show that the proposed MPPT algorithm is superior to the APSO-BP, FLC and perturbation and observation (P&O) algorithm with tracking performance, steady state oscillation rate and efficiency. In addition, the efficiency of proposed MPPT algorithm is improved by 0.37%, 6.2%, and 6.8% as compared to APSO-BP, FLC and P&O algorithm. [ABSTRACT FROM AUTHOR]

At present, the teaching of architectural art in China is still relatively traditional, and there are still some problems in the actual teaching. Based on this, this study combines the Naive Bayesian classification algorithm with the fuzzy model to construct a new architectural art teaching model. In teaching, the Naive Bayesian classification algorithm generates only a small number of features for each item in the training set, and it only uses the probability calculated in the mathematical operation to train and classify the item. Moreover, by combining the fuzzy model, the materials needed for architectural art teaching can be quickly generated, and the teaching principles and implementation strategies of architectural art are summarized. In addition, this paper proposes an attribute weighted classification algorithm combining differential evolution algorithm with Naive Bayes. The algorithm assigns weights to each attribute based on the Naive Bayesian classification algorithm and uses differential evolution algorithm to optimize the weights. The research shows that the method proposed in this paper has certain effect on the optimization of architectural art teaching mode. [ABSTRACT FROM AUTHOR]

Decision-makers usually have a variety of unsure situations in the environment of group decision-making. In this paper, we resolve this difficulty by constructing two-stage stochastic integrated adjustment deviations and consensus models (iADCMs). By introducing the minimum cost consensus models (MCCMs) with costs direction constraints and stochastic programming, we develop three types of iADCMs with an uncertainty of asymmetric costs and initial opinions. The factors of directional constraints, compromise limits and free adjustment thresholds previously thought to affect consensus separately are considered in the proposed models. Different from the previous consensus models, the resulting iADCMs are solved by designing an appropriate L-shaped algorithm. On the application in the negotiations on Grains to Green Programs (GTGP) in China, the proposed models are demonstrated to be more robust. The proposed iADCMs are compared to the MCCMs in an asymmetric costs context. The contrasting outcomes show that the two-stage stochastic iADCMs with no-cost threshold have the smallest total costs. Moreover, based on the case study, we give a sensitivity analysis of the uncertainty of asymmetric adjustment cost. Finally, conclusion and future research prospects are provided. [ABSTRACT FROM AUTHOR]

The problem of the optimal three-level location allocation of transfer center, processing factory and distribution center for supply chain network under uncertain transportation cost and customer demand are studied. We establish a two-stage fuzzy 0-1 mixed integer optimization model, by considering the uncertainty of the supply chain. Given the complexity of the model, this paper proposes a modified hybrid second order particle swarm optimization algorithm (MHSO-PSO) to solve the resulting model, yielding the optimal location and maximal expected return of supply chain simultaneously. A case study of clothing supply chain in Shanghai of China is then presented to investigate the specific influence of uncertainties on the transfer center, clothing factory and distribution center three-level location. Moreover, we compare the MHSO-PSO with hybrid particle swarm optimization algorithm and hybrid genetic algorithm, to validate the proposed algorithm based on the computational time and the convergence rate. [ABSTRACT FROM AUTHOR]

Unlike the other fuzzy neural networks, in this paper the fuzzy system and the neural network are separated, and they are corresponding to structure identification and parameter identification. The fuzzy model is generated automatically by on-line clustering method and fuzzy support vector machines. This fuzzy model is not updated, but its modeling error is compensated by a neural network. The neural network acts as the parameter identification model. The benefits of this new method are the structure is simple, and the physical meanings of each part of the model are clear. For the neural compensator, a new stable and fast training algorithm is proposed. Finally, this modeling method is successfully applied to model a magnetic tube recovery ratio in a metal company in China. [ABSTRACT FROM AUTHOR]

Track irregularity is the main source of vibration excitation of vehicle-track weighing system, which has an important impact on the safety, stability and comfort of train operation, and it is also the main factor limiting the speed of train operation. Higher requirements are put forward for track smoothness with the rapid development of China's railways. Therefore, it has a great theoretical and practical significance to study the relationship between track irregularity and random vibration of vehicle-track cooker-in system and the evaluation method of track smoothness. In this paper, a model construction method is proposed based on machine learning fuzzy logic control and neural network algorithm to analyze the high frequency dynamic characteristics of ballastless track wheel-rail vehicle system. Firstly, a numerical analysis model of high frequency dynamic characteristics of ballastless track wheel-rail vehicle system is established by using linear discrete elastic-yellow damper element connection model; Secondly, the Rough Set Block Neural Network is introduced to optimize the dynamic characteristic analysis model, and the intelligent model of model optimization analysis is established. Finally, the validity of the proposed algorithm is verified by simulation experiments of practical examples. [ABSTRACT FROM AUTHOR]

Presents a series of articles on artificial intelligence research activities being carried out in China. Method for rule extraction from ensembles of neural networks; Approach to multilevel data summarization by employing the rule-plus-exception model in cognitive science; Development of a technique by employing a stepwise greedy algorithm for certain data sets with medium size.

Abstract: In this paper, a two-dimensional numerical calculation algorithm for the water quality modeling in the Hengyang City section of the Xiangjiang River is researched considering the effect of the Dayuandu navigational key project. The research river is winding and has two branches resulted from an isle. The numerical calculation algorithm for the water quality modeling is set up on the basis of applying topographic map of the river course and the finite element method. The calculation result for the water quality modeling includes the concentration fields for various pollutants. The numerical calculation algorithm for the water quality modeling set up in this paper can he applied to shallow river with similar topographically complicated river course. Key words: two-dimensional river; water quality; numerical calculation algorithm. [ABSTRACT FROM AUTHOR]

A care/clinical pathway defines a standardized care process for a specific patient group, which consists of clinical goals, activities, data attributes, and constraints describing temporal dependencies and data preconditions of the activities. The constraints, which are the key elements to represent the best practices, are difficult to define due to the variations in different regions and populations. In this paper, we propose an approach to discover temporal and data constraints that are correlated with clinical outcomes for care pathways. For each activity of interest, we extract a set of associated event-condition-action (ECA) rules from electronic medical records (EMR) to represent the temporal and data preconditions of the activity, by using our modified association rule mining algorithm. Then the best ECA rule that is significantly more likely to lead to a positive outcome is translated into the constraint on the activity. The approach has been applied to real-world EMR, and discovered meaningful constraints for different groups of type 2 diabetes patients, which can be used to provide decision support during individual patient care.

Basketball has always been a relatively hot sport. However, the level of basketball in China does not maintain the synchronous development trend with competitive sports, which can be seen from the achievements of various international competitions. Many basketball players have retired due to sports injuries. How to avoid and delay the occurrence of injuries to the maximum extent, and make the best competitive state to get the longest time is an urgent problem to be solved in the current basketball training and competition process. Therefore, how to reduce sports damage in basketball sports has become a crucial problem. The artificial neural network algorithm is widely used in complex system hardware fault detection, medical diagnosis, medical image processing and other complex task, to classify and forecast, and achieved good results. But in the use of the sports injury risk prevention is very limited, in sports injury risk early warning research, predecessors to sports injury factors made a lot of research and the qualitative model was established, but no quantitative evaluation research, and artificial neural network algorithm has good performance in complex system classification and prediction, so the artificial neural network algorithm is applied to sports injury risk early warning study is a very meaningful work, can carry on the accurate to the athlete sports injury risk assessment. Using RBF neural network to achieve dimensional reduction preprocessing of high-dimensional data not only has sufficient theoretical basis, but also it is more superior. Based on the optimization study of RBF neural network algorithm, we study the data-based feature selection RBF neural network, and apply it in the high-dimensional multi-objective optimization decision space and pare to quality and disadvantages prediction. Through the evaluation of the test sample, the early warning model achieves ideal results, so it is feasible to apply to the sports injury risk warning. [ABSTRACT FROM AUTHOR]

In recent years, electricity stealing has been repeatedly prohibited, and as the methods of stealing electricity have become more intelligent and concealed, it is growing increasingly difficult to extract high-dimensional data features of power consumption. In order to solve this problem, a correlation model of power-consumption data based on convolutional neural networks (CNN) is established. First, the original user signal is preprocessed to remove the noise. The user signal with a fixed signal length is then intercepted and the parallel class labelled. The segmented user signals and corresponding labels are input into the convolutional neural network for training, and the trained convolutional neural network is then used to detect and classify the test user signals. Finally, the actual steal leak dataset is used to verify the effectiveness of this algorithm, which proves that the algorithm can effectively carry out anti–-electricity stealing by warning of abnormal power consumption behavior. There are lots of line traces on the surface of the broken ends which left in the cable cutting case crime scene along the high-speed railway in China. The line traces usually present nonlinear morphological features and has strong randomness. It is not very effective when using existing image-processing and three-dimensional scanning methods to do the trace comparison, therefore, a fast algorithm based on wavelet domain feature aiming at the nonlinear line traces is put forward to make fast trace analysis and infer the criminal tools. The proposed algorithm first applies wavelet decomposition to the 1-D signals which picked up by single point laser displacement sensor to partially reduce noises. After that, the dynamic time warping is employed to do trace feature similarity matching. Finally, using linear regression machine learning algorithm based on gradient descent method to do constant iteration. The experiment results of cutting line traces sample data comparison demonstrate the accuracy and reliability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

China has now become the country with the most anti-dumping lawsuits in the world, and the trade protection of anti-dumping measures has become a huge obstacle to the sustainable development of China's foreign trade. In view of the current situation of anti-dumping in the United States, this study combines BP neural network to construct an anti-dumping early warning model. In order to predict the longer-term future based on the existing database, the BP neural network should be used to predict the indicators in the existing index database, and then the predicted warning indicator system is used as the input layer to warn the future police. Moreover, this study conducts research on the performance of the algorithm based on the actual case analysis. The research shows that the algorithm has certain effects and can provide theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

Background: Scientific management methods are urgently needed to balance the demand and supply of heath care services in Chinese hospitals. Operating theatre is the bottleneck and costliest department. Therefore, the surgery scheduling is crucial to hospital management.Objective: To increase the utilization and reduce the cost of operating theatre, and to improve surgeons' satisfaction in the meantime, a practical surgery scheduling which could assign the operating room (OR) and surgeon for the surgery and sequence surgeries in each OR was provided for hospital managers.Methods: Surgery durations were predicted by fitting the distributions. A two-step mixed integer programming model considering surgery duration uncertainty was proposed, and sample average approximation (SAA) method was applied to solve the model.Results: Durations of various surgeries were log-normal distributed respectively. Numerical experiments showed the model and method could get good solutions with different sample sizes.Conclusions: Real-life constraints and duration uncertainty were considered in the study, and the model was also very applicable in practice. Average overtime of each OR was reducing and tending to be stable with the number of surgeons increasing, which is a discipline for OR management. [ABSTRACT FROM AUTHOR]

Background: There are two major classes of cardiac tissue models: the ionic model and the FitzHugh-Nagumo model. During computer simulation, each model entails solving a system of complex ordinary differential equations and a partial differential equation with non-flux boundary conditions. The reproducing kernel method possesses significant applications in solving partial differential equations. The derivative of the reproducing kernel function is a wavelet function, which has local properties and sensitivities to singularity. Therefore, study on the application of reproducing kernel would be advantageous.Objective: Applying new mathematical theory to the numerical solution of the ventricular muscle model so as to improve its precision in comparison with other methods at present.Methods: A two-dimensional reproducing kernel function inspace is constructed and applied in computing the solution of two-dimensional cardiac tissue model by means of the difference method through time and the reproducing kernel method through space.Results: Compared with other methods, this method holds several advantages such as high accuracy in computing solutions, insensitivity to different time steps and a slow propagation speed of error. It is suitable for disorderly scattered node systems without meshing, and can arbitrarily change the location and density of the solution on different time layers.Conclusions: The reproducing kernel method has higher solution accuracy and stability in the solutions of the two-dimensional cardiac tissue model. [ABSTRACT FROM AUTHOR]