Research on athlete skipping surface electromyography and energy consumption based on principal component analysis of wavelet packet

2020 ◽  
pp. 1-11
Author(s):  
Yanan Yu
Keyword(s):  
Principal Component Analysis ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Signal Acquisition ◽  
Analysis Model ◽  
Principal Component Analysis Model ◽  
Linear Discriminant ◽  
Emg Signal

EMG signal acquisition is mostly used in medical research. However, it has not been applied in athletes’ sports state recognition and body state detection, and there are few related studies at present. In order to promote the application of EMG signal acquisition in sports, this study combined with the actual needs of athletes to construct an EMG signal acquisition system that can collect athletes’ motion status. At the same time, in order to improve the effect of EMG signal acquisition, a wavelet packet principal component analysis model is proposed. In addition, in order to ensure the recognition efficiency of athletes’ motion state, this paper uses linear discriminant analysis method as the motion recognition assistant algorithm. Finally, this paper judges the performance of this research model by setting up comparative experiments. The research shows that the wavelet packet principal component analysis model performance is significantly better than the traditional algorithm, and the recognition rate for some subtle motions is also high. In addition, this study provides a theoretical reference for the application of EMG signals in the sports industry.

scholarly journals Fault Data Detection of Traffic Detector Based on Wavelet Packet in the Residual Subspace Associated with PCA

2019 ◽  
Vol 9 (17) ◽  
pp. 3491 ◽  
Author(s):  
Xiaolu Li ◽  
Xi Zhang ◽  
Peng Zhang ◽  
Guangyu Zhu
Keyword(s):  
Principal Component Analysis ◽  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Data Detection ◽  
Analysis Model ◽  
Traffic Detector

To improve the accuracy and efficiency of fault data identification of traffic detectors is crucial in order to decrease the probability of unexpected failures of the intelligent transportation system (ITS). Since convolutional fault data recognition based on traffic flow three-parameter law has a poor capability for multiscale of fault data, PCA (principal component analysis) is adopted for traffic fault data identification. This paper proposes the fault data detection models based on the PCA model, MSPCA (multiscale principal component analysis) model and improved MSPCA model, respectively. In order to improve the recognition rate of traffic detectors’ fault data, the improved MSPCA model combines the wavelet packet energy analysis and PCA to achieve traffic detector data fault identification. On the basis of traditional MSPCA, wavelet packet multi-scale decomposition is used to get detailed information, and principal component analysis models are established on different scale matrices, and fault data are separated by wavelet packet energy difference. Through case analysis, the feasibility verification of traffic flow data identification method is carried out. The results show that the improved method proposed in this paper is effective for identifying traffic fault data.

Study on the Effect Factors of Non-Perormance Loan Ratio of Chinese Commerical Banks

2011 ◽  
Vol 50-51 ◽  
pp. 728-732
Author(s):  
Ping Li ◽  
Ming Ying Zhuo ◽  
Li Chao Feng ◽  
Rui Zhang
Keyword(s):  
Principal Component Analysis ◽  
Commercial Banks ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Analysis Model ◽  
Principal Component Analysis Model ◽  
Effect Factors ◽  
Financial Indicator ◽  
Commerical Banks

Non-performance loan ratio is one of the important assessment criteria of the security of credit assets. It is also an important financial indicator to evaluate the general strength of commercial banks. Using principal component analysis method and statistical software SPSS16.0 and based on the non-performance loan ratio and relative data of some commercial banks in China in 2007, this paper provided a principal component analysis model for the non-performance loan ratio of China’s commercial banks. The factors that affect the non-performance loan ratio were refined in this paper. Finally, the characteristics of effect factors of each bank were analyzed and compared in detail.

scholarly journals Perceived difficulties and complications in learners of phacoemulsification: A principal component analysis model

2019 ◽  
Vol 67 (2) ◽  
pp. 213 ◽  
Author(s):  
Rohit Saxena ◽  
Sagnik Sen ◽  
Mukesh Patil ◽  
Atul Kumar ◽  
SreelakshmiP Amar ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Model ◽  
Principal Component Analysis Model

scholarly journals Integration of Two-Dimensional Kernel Principal Component Analysis Plus Two-Dimensional Linear Discriminant Analysis with Convolutional Neural Network for Finger Vein Recognition

2021 ◽  
Vol 38 (4) ◽  
pp. 1181-1187
Author(s):  
Zhitao Gao ◽  
Jianxian Cai ◽  
Yanan Shi ◽  
Li Hong ◽  
Fenfen Yan ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Two Dimensional ◽  
Linear Discriminant ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

High complexity and low recognition rate are two common problems with the current finger vein recognition methods. To solve these problems, this paper integrates two-dimensional kernel principal component analysis (K2DPCA) plus two-dimensional linear discriminant analysis (2DLDA) (K2DPCA+2DLDA) into convolutional neural network (CNN) to recognize finger veins. Considering the row and column correlations of the finger vein image matrix and the classes of finger vein images, the authors adopted K2DPCA and 2DLDA separately for dimensionality reduction and extraction of nonlinear features in row and column directions, producing a dimensionally reduced compressed image without row or column correlation. Taking the dimensionally reduced compressed image as the input, the CNN was introduced to learn higher-level features, making finger vein recognition more accurate and robust. The public dataset of Finger Vein USM (FV-USM) Database was adopted for experimental verification. The results show that the proposed approach effectively overcome the common defects of original image feature extraction: the insufficient feature description, and the redundancy of information. When the training reached 120 epochs, the model basically realized stable convergence, with the loss approaching zero and the recognition rate reaching 97.3%. Compared with two-directional two-dimensional Fisher principal component analysis ((2D)2FPCA), our strategy, which integrates K2DPCA+2DLDA with CNN, achieved a very high recognition rate of finger vein images.

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