Cognitive neural mechanism of sports competition pressure source

Zhou, Yucheng; Zhou, Feifei

doi:10.1515/tnsci-2019-0025

Cited by 7 publications

(8 citation statements)

References 13 publications

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“…For the detection of multiscale athletes in sports videos, literature [ 20 ] proposes an automatic detection method based on CNN. The literature [ 21 ] describes the function realization and concept of AM in detail, as well as the drawbacks of having to use a binary input signal in practice. By combining the advantages of strong nonlinear processing of BP networks with the advantages of fitting the network structure reasonably, the network structure can be widely used in practice.…”

Section: Related Workmentioning

confidence: 99%

Sports Video Athlete Detection Based on Associative Memory Neural Network

Yang

2022

Computational Intelligence and Neuroscience

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Aiming at the detection of athletes in sports videos, an automatic detection method based on AMNN is proposed. The background image from the image sequence is obtained, the moving area is extracted, and the color information of pixels to extract the green stadium from the background image is used. In order to improve the accuracy of athletes’ detection, the texture similarity measurement method is used to eliminate the shadow in the movement area, the morphological method is used to eliminate the cracks in the area, and the noise outside the stadium is removed according to the stadium information. Combined with the images of nonathletes, a training set is constructed to train the NN classifier. For the input image frames, image pyramids of different scales are constructed by subsampling and the positions of several candidate athletes are detected by NN. The center of gravity of candidate athletes is calculated, a representative candidate athlete is obtained, and then, the final athlete position through a local search process is determined. Experiments show that the system can accurately detect the motion shape of moving targets, can process images in real time, and has good real-time performance.

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Section: Related Workmentioning

confidence: 99%

Sports Video Athlete Detection Based on Associative Memory Neural Network

Yang

2022

Computational Intelligence and Neuroscience

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“…At the same time, a cognitive neuroscience evaluation system in sports competitions is constructed by using the clustering algorithm and recommendation algorithm [1]. eir proposal is only aimed at the evaluation of cognitive nerves, and the stressors may also be reflected in the psychological and physical aspects, so they have a certain one sidedness [2]. Wang et al proposed a clustering algorithm for fast searching and finding density peaks, which is a clustering algorithm for fast finding clustering centers.…”

Section: Relevant Contentsmentioning

confidence: 99%

Application of Clustering and Recommendation Algorithm in Sports Competition Pressure Source

Zhang

Guo

2022

Scientific Programming

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With the vigorous development of China’s sports industry, the rules and number of events are increasing, and the competition pressure on the playground is also increasing. The increase of competition pressure will bring many negative effects to athletes. In order to relieve the pressure of athletes in sports competition and eliminate the negative significance of pressure to athletes, this paper mainly introduces the clustering algorithm of sports source and competition. The clustering algorithm uses the similarity of attributes between data objects to calculate the clustering structure of fractional clustering. In this paper, the original data of sports competition pressure are obtained through the questionnaire survey, using clustering and recommendation algorithms to calculate and analyze the original data, the data utilization rate is as high as 98%, and the analysis efficiency is as high as 97%. Dividing athletes into three categories, the magnitude and source of stress are analyzed, respectively, and application methods are recommended according to their respective stress distributions, so as to assist psychologists in the diagnosis, and the corresponding height is 80%; this enables athletes to receive good counseling advice and remain mentally healthy.

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“…Small changes in single-layer input will affect the results of the latter layer. Whether the initial weight value is set randomly or empirically, the weight difference is very large before and after learning [18]. Considering a small number of weights, there may be many outliers for the required feature activation initially.…”

Section: Deep Learning Algorithmmentioning

confidence: 99%

Standardized Management System of Sports Events Based on Deep Learning Algorithm

2021

IJST

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Sports industry has become a new economic growth point, and standardized management of sports events plays an important role in promoting the development of sports industry. Only the scientific and efficient operation of the project can achieve a win-win situation for both economy and society. Sports have typical characteristics of project management, and the development of modern high-level sports events needs more advanced and scientific project management mode and system. Based on the deep learning algorithm, this paper constructs and analyzes the sports project management system, compares and optimizes the deep learning algorithm, and evaluates the satisfaction of track and field competition and the maturity of the management system in a university. The final result shows that the error of the optimized PPO algorithm is the smallest, with a stable mean difference of 1.34, and the simulated score of the optimized management system maturity is 9.17, which is 0.91 points higher than the original one.

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Cognitive neural mechanism of sports competition pressure source

Cited by 7 publications

References 13 publications

Sports Video Athlete Detection Based on Associative Memory Neural Network

Sports Video Athlete Detection Based on Associative Memory Neural Network

Application of Clustering and Recommendation Algorithm in Sports Competition Pressure Source

Standardized Management System of Sports Events Based on Deep Learning Algorithm

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