Correlation analysis of structural characteristics of table tennis players' hitting movements and hitting effects based on data analysis

Chen, Yanke; Li, Lan; Li, Xiaodong

doi:10.1016/j.entcom.2023.100610

Entertainment Computing

2024

DOI: 10.1016/j.entcom.2023.100610

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Correlation analysis of structural characteristics of table tennis players' hitting movements and hitting effects based on data analysis

Yanke Chen,

Lan Li,

Xiaodong Li

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2024

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Cited by 3 publications

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Location strategy for logistics distribution centers utilizing improved whale optimization algorithm

Chen

2024

Journal of Intelligent Systems

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An enhanced whale optimization algorithm is introduced to address the challenges associated with the logistics distribution center location problem, aiming to overcome the traditional heuristic algorithm’s susceptibility to local optima and enhance the overall efficiency of the logistics system. This improvement involves the integration of stochastic sinusoidal inertia weights and a variational strategy to augment the global search capability and convergence accuracy of the conventional algorithm. The results of experimental simulations indicate that the upgraded whale algorithm exhibits superior computational performance compared to other heuristic algorithms. Furthermore, it demonstrates the ability to effectively compute optimal distribution center locations, thereby enhancing the delivery efficiency of the logistics distribution system.

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Location strategy for logistics distribution centers utilizing improved whale optimization algorithm

Chen

2024

Journal of Intelligent Systems

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Table tennis stroke technique and fitness improvement based on strength training

Zhuang,

Li,

Zhuang

2024

Applied Mathematics and Nonlinear Sciences

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Table tennis is regarded as the national ball of China, and in the actual process of competition, athletes should have both high technical and tactical levels and good physical fitness. This paper focuses on the changes in hitting skills and physical fitness of table tennis players after strength training. In order to establish a scientific training system and improve training efficiency, this paper designs a physical fitness monitoring method using big data technology. The monitoring method initially uses the backpropagation algorithm to process the physical fitness data of the athletes. To reduce the computational amount of frequent item sets, it is recommended to use the Apriori algorithm combined with the DC_Apriori algorithm for data mining on processed physical fitness data. Finally, the physical fitness training results analyzed by this fitness monitoring method were synthesized to develop a reasonable strength training program for table tennis players. The athletes were tested for changes in table tennis hitting skills and physical fitness before and after 4 weeks of strength training. By comparing with the athletes who underwent traditional physical training, it was found that the average score of table tennis batting skills of the strength training group based on big data analysis was significantly higher than the average score of the traditional physical training group. Comparative analysis of athletes’ physical fitness from four aspects: speed, strength, sensitivity, and endurance. Strength training based on big data analysis can significantly improve the physical fitness quality of table tennis players.

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Research on optimization of table tennis hitting action based on image recognition technology

2024

Applied Mathematics and Nonlinear Sciences

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In the process of table tennis batting action analysis, the analysis system that relies on modern digital image processing technology plays an increasingly important role. The new GoogleNet model, as a kind of auxiliary means, because of its intuitive, rapid, and other characteristics, can be very good to find out and optimize the drawbacks of batting action in the process of the game and help learners to master the batting essentials quickly. In this paper, the batch regression algorithm is used to transform the images of players’ batting actions during table tennis games, and the AM-Softmax algorithm in the Softmax classifier is used to construct the New GoogleNet model to build the batting action recognition and analysis model that contains both temporal and spatial flows. The main conclusions are as follows: the accuracy of the test results for forehand and left-handed ball attacks based on the New GoogleNet model is as high as 92% and 90%. Forehand and left-handed ball rolling accuracy was 87.5% and 85%, respectively. The optical flow optimization method resulted in a 0.4% and 1.4% increase in the accuracy of the experiments. In the two-stream fusion method with thresholds s=99% and s=1, the accuracy of optimization of the hitting action reached 89.8% and 91.4%, respectively. The accuracy in the averaging method was 95.9% when the optical flow threshold s=1. When the optical flow threshold s = 99% is used in the downscaling method, the accuracy is 93.5%. The results of this paper are of great significance for the recognition of batting movements during table tennis matches and the optimization and improvement of batting movements.

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Correlation analysis of structural characteristics of table tennis players' hitting movements and hitting effects based on data analysis

Cited by 3 publications

References 20 publications

Location strategy for logistics distribution centers utilizing improved whale optimization algorithm

Location strategy for logistics distribution centers utilizing improved whale optimization algorithm

Table tennis stroke technique and fitness improvement based on strength training

Research on optimization of table tennis hitting action based on image recognition technology

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