scholarly journals Academic Influence of China’s Sports Social Discipline Based on Bibliometrics

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiuyun Cui
Keyword(s):  
Feature Extraction ◽  
Statistical Analysis ◽  
Semantic Feature ◽  
Physical And Mental Health ◽  
Analysis Model ◽  
Characteristic Quantity ◽  
Quantitative Calculation ◽  
Relative Closeness ◽  
Social Discipline ◽  
Academic Influence

Background. To accurately evaluate sports social discipline’s academic influence in China, a model of academic influence evaluation of sports social discipline in China based on bibliometrics is proposed. Objective. A statistical model of the academic influence of sports social discipline is constructed, the word frequency statistics method is used to measure the literature, and the semantic characteristic quantity of the sports social discipline academic influence is extracted, using the literature method and logical analysis method to analyze sports’ social value in the Internet era from healthy sports. The combination of sports and medical treatment can comprehensively promote physical and mental health. Methods. The dual semantic feature decomposition method is used to analyze sports social discipline’s academic influence. The statistical analysis model of sports social discipline academic influence is established. The principles of fuzzy pattern recognition include the principle of maximum membership degree and proximity degree. Results. The comprehensive relative closeness of the distribution of literature statistics on sports and social disciplines’ academic influence is constructed. The linear programming analysis of literature statistics is carried out using the standardized grid computing method. Conclusions. The combination of association rule feature extraction and semantic feature extraction is used to realize the quantitative calculation of literature statistics and academic influence. The simulation results show that the statistical analysis of the academic influence of sports and social discipline by this method is accurate, and the level of confidence is high.

Management of Guidance and Counseling on Learning Discipline

2020 ◽  
Vol 10 (2) ◽  
pp. 60-74
Author(s):  
Muhammad Syauqi Mubarok
Keyword(s):  
Statistical Analysis ◽  
Descriptive Analysis ◽  
Field Studies ◽  
Guidance And Counseling ◽  
Analysis Method ◽  
Vocational High School ◽  
Analysis Model ◽  
Survey Techniques ◽  
Analysis Technique ◽  
Path Analysis Model

This article aims to examine and describe the influence of guidance and counseling management on learning discipline. The method used in this research is descriptive analysis method using survey techniques. Data collection techniques that used are documentation studies and field studies. Moreover, the data analysis technique that has been used to answer the research hypothesis is statistical analysis with a path analysis model. The location of the study was at the Ciledug Vocational High School Al-Musaddadiyah Garut, with 85 respondents taking part in the survey. The results of the discussion show that guidance and counseling management has a positive and significant effect on the discipline of learning

scholarly journals An Efficient Module for Instance Segmentation Based on Multi-Level Features and Attention Mechanisms

2021 ◽  
Vol 11 (3) ◽  
pp. 968
Author(s):  
Yingchun Sun ◽  
Wang Gao ◽  
Shuguo Pan ◽  
Tao Zhao ◽  
Yahui Peng
Keyword(s):  
Feature Extraction ◽  
Spatial Structure ◽  
Semantic Feature ◽  
Semantic Features ◽  
Segmentation Method ◽  
Spatial Dimensions ◽  
Feature Pyramid ◽  
Multi Level ◽  
High Level ◽  
Instance Segmentation

Recently, multi-level feature networks have been extensively used in instance segmentation. However, because not all features are beneficial to instance segmentation tasks, the performance of networks cannot be adequately improved by synthesizing multi-level convolutional features indiscriminately. In order to solve the problem, an attention-based feature pyramid module (AFPM) is proposed, which integrates the attention mechanism on the basis of a multi-level feature pyramid network to efficiently and pertinently extract the high-level semantic features and low-level spatial structure features; for instance, segmentation. Firstly, we adopt a convolutional block attention module (CBAM) into feature extraction, and sequentially generate attention maps which focus on instance-related features along the channel and spatial dimensions. Secondly, we build inter-dimensional dependencies through a convolutional triplet attention module (CTAM) in lateral attention connections, which is used to propagate a helpful semantic feature map and filter redundant informative features irrelevant to instance objects. Finally, we construct branches for feature enhancement to strengthen detailed information to boost the entire feature hierarchy of the network. The experimental results on the Cityscapes dataset manifest that the proposed module outperforms other excellent methods under different evaluation metrics and effectively upgrades the performance of the instance segmentation method.

Improving Emotion Analysis for Speech-Induced EEGs Through EEMD-HHT-Based Feature Extraction and Electrode Selection

2021 ◽  
Vol 12 (2) ◽  
pp. 1-18
Author(s):  
Jing Chen ◽  
Haifeng Li ◽  
Lin Ma ◽  
Hongjian Bo
Keyword(s):  
Feature Extraction ◽  
Statistical Analysis ◽  
Orbitofrontal Cortex ◽  
Mental States ◽  
Emotion Detection ◽  
Emotional Speech ◽  
Eeg Signals ◽  
Cognitive Response ◽  
Extensive Analysis ◽  
Electrode Selection

Emotion detection using EEG signals has advantages in eliminating social masking to obtain a better understanding of underlying emotions. This paper presents the cognitive response to emotional speech and emotion recognition from EEG signals. A framework is proposed to recognize mental states from EEG signals induced by emotional speech: First, speech-evoked emotion cognitive experiment is designed, and EEG dataset is collected. Second, power-related features are extracted using EEMD-HHT, which is more accurate to reflect the instantaneous frequency of the signal than STFT and WT. An extensive analysis of relationships between frequency bands and emotional annotation of stimulus are presented using MIC and statistical analysis. The strongest correlations with EEG signals are found in lateral and medial orbitofrontal cortex (OFC). Finally, the performance of different feature set and classifier combinations are evaluated, and the experiments show that the framework proposed in this paper can effectively recognize emotion from EEG signals with accuracy of 75.7% for valence and 71.4% for arousal.

scholarly journals Correction to: A failure case study of tunnel water inrush source identified by statistical analysis model

2021 ◽  
Vol 14 (20) ◽  
Author(s):  
Li-Gang Yuan ◽  
Xiao-Li Li ◽  
Xin Li ◽  
Yi-Lin Yu ◽  
Li-Guang Chen ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Water Inrush ◽  
Analysis Model

Image Recognition Technology Based on Artificial Intelligence

2013 ◽  
Vol 347-350 ◽  
pp. 3537-3540
Author(s):  
Hai Yun Lin ◽  
Yu Jiao Wang ◽  
Jian Chun Cai
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Semantic Feature ◽  
Current Image ◽  
Semantic Feature Extraction

In respect of the classification of current image retrieval technology and the existing issues, the paper put forward a method designed for image semantic feature extraction based on artificial intelligence. The new method has solved the tough problem of image semantic feature extraction, by fusing fuzzy logic, genetic algorithm and artificial neural network altogether, which greatly improved the efficiency and accuracy of image retrieval.

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