Integration g-C3N4 nanotubes and Sb2MoO6 nanoparticles: Impressive photoactivity for tetracycline degradation, Cr (VI) reduction, and organic dyes removals under visible light

Hemmati-Eslamlu, Paria; Habibi‐Yangjeh, Aziz; Asadzadeh‐Khaneghah, Soheila; Chand, Hushan; Krishnan, Venkata

doi:10.1016/j.apt.2021.05.007

Cited by 28 publications

(4 citation statements)

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“…Experimental results show that the proposed algorithm can avoid the loss of some important information and further increase the randomness of the dataset. At the same time, the model performance Sports training dataset Top-2 Top-6 Literature [13] 70.78 73.29 Literature [18] 74.41 79.54 Literature [19] 77.73 78.98 Literature [20] 82.56 84.24 Literature [21] 81 under different values of parameter α is verified. e proposed method achieves a balance in recognition accuracy, computation amount, and parameter number, which is more attractive than other mainstream methods and more suitable for application scenarios with limited computing resources and requirements on recognition accuracy.…”

Section: Discussionmentioning

confidence: 87%

Section: Experimental Results and Analysismentioning

confidence: 99%

“…As shown in Table 4, compared with reference [13,[18][19][20], reference [21] reduced the number of references by order of magnitude. Although the recognition accuracy was higher than the reference [13,18,19], it was lower than the reference [20]. Compared with the literature [21], the algorithm in this paper is not only lower in the number of parameters than the literature [21] but also significantly higher in recognition accuracy.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Compared with other mainstream methods, it is more attractive and suitable for application scenarios with limited computing resources and requirements on recognition accuracy. Literature [18] Literature [19] Literature [20] Literature [21] Literature [22] Proposed CS/% SS/% Lterature [19] Lterature [20] Lterature [21] Figure 6: Comparison of convergence of different algorithms. is paper also constructs a real motion training pose video data set.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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A Deep Learning Method for Intelligent Analysis of Sports Training Postures

Sun

2022

Computational Intelligence and Neuroscience

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With the further research of artificial intelligence technology, motion recognition technology is widely used in posture analysis of sports training. However, the interference of light, Angle, and distance in real life makes the existing model unable to focus on the expression of human movements. Aiming at the above problems, this paper proposes a motion training attitude analysis method based on a multiscale spatiotemporal graph convolution network. Firstly, the spatiotemporal image of the skeleton is constructed, and then the convolution operation is performed on the spatiotemporal image of the skeleton. Finally, the convolution results are linearly weighted and fused to capture the characteristics of action types with different time lengths. At the same time, the algorithm increases the processing of some important information loss and increases the randomness of the data set. Experimental results show that the proposed algorithm can adapt to the behavior changes of different complexity, and the model performance and recognition accuracy are significantly improved.

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