VAE-Stega: Linguistic Steganography Based on Variational Auto-Encoder

Yang, Zhongliang; Zhang, Siyu; Hu, Yuting; Hu, Zhiwen; Huang, Yudong

doi:10.1109/tifs.2020.3023279

Cited by 124 publications

(53 citation statements)

References 36 publications

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“…e key to martial arts routine training methods lies in the prediction of martial arts routines. is section proposes an improved multilayer recurrent neural network [17][18][19][20][21][22]. In addition, the big data of lactate determination will assist the neural network in predicting.…”

Section: Methodsologymentioning

confidence: 99%

Martial Arts Routine Training Method Based on Artificial Intelligence and Big Data of Lactate Measurement

Han

Huo

Li³

2021

Journal of Healthcare Engineering

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As a traditional Chinese sport, competitive martial arts routines have a long history. The competition rules are the unified norms and standards formulated for sports competitions. They are a yardstick for referees to judge the technical level and competitive ability of athletes and an essential basis for coaches during training. In particular, the new rules increase the difficulty of martial arts routines training and score, improve the balance movement of various groups, highlight the action specifications, increase the proportion of the score, and strengthen the scoring measures for the performance level. Subsequently, this puts higher requirements for the exceptional technical level of routine athletes. Therefore, it is vital to formulate scientific martial arts systematic training methods. This paper considers the above problem and current popular artificial intelligence technology and constructs a neural network algorithm to solve it. In addition, since lactic acid is a good monitoring indicator of the training load intensity and effect of martial arts routine exercises, this article also considers extensive lactate measurement data to construct martial arts systematic training methods. Through simulations, our experimental verification and the obtained results demonstrate the effectiveness of the proposed algorithm.

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Section: Methodsologymentioning

confidence: 99%

Martial Arts Routine Training Method Based on Artificial Intelligence and Big Data of Lactate Measurement

Han

Huo

Li³

2021

Journal of Healthcare Engineering

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“…e human eye often cannot accurately judge the movement posture of the ball [1], and often there are problems that the ball's falling point cannot be accurately recognized and the movement track cannot be retained. e use of machine learning techniques to identify and locate the ball and computer vision [4][5][6][7][8][9] and deep learning techniques [10][11][12][13][14] to reconstruct and display the ball's trajectory is a futuristic application. It results in instant playback systems in table tennis matches due to merging technology and sports.…”

Section: Introductionmentioning

confidence: 99%

Falling-Point Recognition and Scoring Algorithm in Table Tennis Using Dual-Channel Target Motion Detection

Yang

Chang

Chen

2021

Journal of Healthcare Engineering

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In table tennis, the ball has numerous characteristics of high speed, small size, and changeable trajectory. Due to these characteristics, the human eye often cannot accurately judge the ball’s movement and position, leading to the problem of precise detection of the ball’s falling point and movement tracking. In sports, the use of machine learning for locating and detecting the ball and the use of deep learning for reconstructing and displaying the ball’s trajectories are considered futuristic technologies. Therefore, this paper proposes a novel algorithm for identifying and scoring points in table tennis based on dual-channel target motion detection. The proposed algorithm consists of multiple input channels to jointly learn different features of table tennis images. The original image is used as the input of the first channel, and then the Sobel operator is used to extract the first-order derivative feature of the original image, which is used as the input of the second channel. The table tennis feature information from the two channels is then fused and sent to the 3D neural network module. The fully connected layer is used to identify the table tennis ball’s drop point, compare it with a standard drop point, calculate the error distance, and give a score. We also constructed a data set and conducted experiments. The experimental results show that the method in this paper is effective in sports.

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“…e proposed approach firstly introduces the core architecture, working principle, and related ecosystem of the Hadoop platform and then introduces the basic ideas of the data mining theory of big data analysis and the common algorithms of data mining are outlined. Finally, the principle of convolutional neural network [26][27][28][29][30][31] and the two-branch algorithm proposed in this paper are explained. Figure 2 shows the overall framework of the proposed approach.…”

Section: Methodsmentioning

confidence: 99%

Injury Risk Prediction of Aerobics Athletes Based on Big Data and Computer Vision

Zhu

Zhang

Sun

et al. 2021

Scientific Programming

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In recent years, competitive aerobics has been rapidly popularized and developed, and the level of sports skills has also been greatly improved. The performance of some events has gradually approached and reached the advanced level. Therefore, it is vital to invest in the quantitative analysis and cross-disciplinary comprehensive research of aerobics performance and related factors. This paper adopts big data analysis technology and computer vision technology based on convolutional neural network, according to the related theories of sports biomechanics and computer image recognition, to establish a loss risk prediction model for aerobics athletes. The approach firstly has used technology of big data analysis for analyzing the characteristics of competitive aerobics sports data. Secondly, the approach combines the convolutional neural network to visually recognize the aerobics sports images and establish a two-branch prediction model. Finally, the output can be fused to accurately diagnose and evaluate the level of physical fitness development of aerobics athletes, the focus and goal of training content are clarified, and the scientific degree of aerobics training is improved. The study can help injury risk prediction of aerobic athletes based on applications of big data and computer vision.

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VAE-Stega: Linguistic Steganography Based on Variational Auto-Encoder

Cited by 124 publications

References 36 publications

Martial Arts Routine Training Method Based on Artificial Intelligence and Big Data of Lactate Measurement

Martial Arts Routine Training Method Based on Artificial Intelligence and Big Data of Lactate Measurement

Falling-Point Recognition and Scoring Algorithm in Table Tennis Using Dual-Channel Target Motion Detection

Injury Risk Prediction of Aerobics Athletes Based on Big Data and Computer Vision

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