Search results for: Yizhi Wu

Authors: Yisong Cong, Mingming Guo, Xiaozan Wang, Yizhi Zhang, Qingqing Yuan

Abstract:

This study is to test the correlation between the Physical Fitness (PF) of middle school students and their performance and sports grade of table tennis. Methods: 398 middle school students from Shanghai Tongji No.2 Middle School participated in the study, including 232 boys and 166 girls. Each participant participated in the Chinese Students’ Physical Fitness Test (CSPFT), including BMI, 50-meter running, vital capacity, sitting body flexion, standing long jump, 800-meter running, 1000-meter running, pull-ups, and sit-ups. Test scores were converted to a percentage score according to the CSPFT guidelines. In addition, each student participated in the Nation Junior Table Tennis grade test, and completed the table tennis sports grade assessment of 1-9. Results: There is a significant positive correlation between the scores of multiple PF tests and the total scores of table tennis, such as BMI (r = 0.15, p < 0.01), standing long jump (r = 0.15, p < 0.05), 800-meter run (r = 0.02, p <0.01); The scores of multiple PF are positively correlated with table tennis grade, such as vital capacity (r = 0.1, p < 0.01) and 50-meter running (r = 0.18, p < 0.05). At the same time, the sit-ups performance showed a significant negative correlation with the table tennis performance (r = -0.08, p < 0.01); There is no significant correlation between the other PF indicators and the performance and grade of table tennis. Conclusions: This study shows that there is a corresponding relationship between some PF indicators of middle school students and their table tennis performance and table tennis grade，but the specific form and reason of the relationship need to be further explored.

Keywords: middle school students, physical fitness, table tennis grade, table tennis performance

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Authors: Yizhi Zhang, Xiaozan Wang, Mingming Guo, Pengpeng Li

Abstract:

Introduction: The purpose of this study is to examine the effectiveness of after-school football training in improving the physical fitness of primary school students, so as to provide corresponding suggestions for carrying out after-school football training in primary schools. Methods: A total of 72 students from the experimental primary school of Mouping district, Yantai city, Shandong province, participated in this experiment. The experiment was conducted for two semesters. During the experiment period, the experimental group conducted one-hour football training after school from Monday to Thursday afternoon every week, and two hours of football training on Saturday morning every week. The control group conducted sports teaching and extracurricular activities as usual without other intervention. Before and after the experiment, both the experimental group and the control group underwent physical fitness tests according to the physical fitness test standards of Chinese students, including lung capacity, 50-meter run, one-minute skipping rope, sitting forward flexor, and one-minute sit-ups. The test results were all converted to the 100-point system according to the scoring standards. Results: (1) Before the experiment, there was no significant difference between the experimental group and the control group in various physical fitness indicators (p > 0.05). (2) After the experiment, the lung capacity score (T = 3.108, p < 0.05), the 50-meter run score (T = 6.593, p < 0.05), the skipping score (T = 9.227, p < 0.05), the sitting forward flexor score (T = 3.742, p < 0.05), and the sit-up score (T = 5.210, p < 0.05) of the experimental group were significantly higher than that of the control group. Conclusion: This study shows that the physical fitness of primary school students can be improved by football training in their spare time. It is suggested to carry out after-school football training activities in primary schools so as to effectively improve the physical fitness of pupils.

Keywords: after-school football training, physical fitness, primary school students, school sports

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Authors: Kaleem Kashif, Tayyaba Anam, Yizhi Wu

Abstract:

This paper introduces a methodology for advancing Italian speech vowels landmark detection within the distinctive feature-based speech recognition domain. Leveraging the legacy tool 'xkl' by integrating combined convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the study presents a comprehensive enhancement to the 'xkl' legacy software. This integration incorporates re-assigned spectrogram methodologies, enabling meticulous acoustic analysis. Simultaneously, our proposed model, integrating combined CNNs and RNNs, demonstrates unprecedented precision and robustness in landmark detection. The augmentation of re-assigned spectrogram fusion within the 'xkl' software signifies a meticulous advancement, particularly enhancing precision related to vowel formant estimation. This augmentation catalyzes unparalleled accuracy in landmark detection, resulting in a substantial performance leap compared to conventional methods. The proposed model emerges as a state-of-the-art solution in the distinctive feature-based speech recognition systems domain. In the realm of deep learning, a synergistic integration of combined CNNs and RNNs is introduced, endowed with specialized temporal embeddings, harnessing self-attention mechanisms, and positional embeddings. The proposed model allows it to excel in capturing intricate dependencies within Italian speech vowels, rendering it highly adaptable and sophisticated in the distinctive feature domain. Furthermore, our advanced temporal modeling approach employs Bayesian temporal encoding, refining the measurement of inter-landmark intervals. Comparative analysis against state-of-the-art models reveals a substantial improvement in accuracy, highlighting the robustness and efficacy of the proposed methodology. Upon rigorous testing on a database (LaMIT) speech recorded in a silent room by four Italian native speakers, the landmark detector demonstrates exceptional performance, achieving a 95% true detection rate and a 10% false detection rate. A majority of missed landmarks were observed in proximity to reduced vowels. These promising results underscore the robust identifiability of landmarks within the speech waveform, establishing the feasibility of employing a landmark detector as a front end in a speech recognition system. The synergistic integration of re-assigned spectrogram fusion, CNNs, RNNs, and Bayesian temporal encoding not only signifies a significant advancement in Italian speech vowels landmark detection but also positions the proposed model as a leader in the field. The model offers distinct advantages, including unparalleled accuracy, adaptability, and sophistication, marking a milestone in the intersection of deep learning and distinctive feature-based speech recognition. This work contributes to the broader scientific community by presenting a methodologically rigorous framework for enhancing landmark detection accuracy in Italian speech vowels. The integration of cutting-edge techniques establishes a foundation for future advancements in speech signal processing, emphasizing the potential of the proposed model in practical applications across various domains requiring robust speech recognition systems.

Keywords: landmark detection, acoustic analysis, convolutional neural network, recurrent neural network

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