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.

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.

Feature Extraction Technique of Acoustic Target Based on Wavelet Packet Energy and Principal Component Analysis

2012 ◽  
Vol 532-533 ◽  
pp. 687-691 ◽  
Author(s):  
Yong Lin Lü ◽  
Zheng Hua Zi
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Energy Distribution ◽  
Correlation Coefficient ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Data Set ◽  
Acoustic Target

A feature extraction method based on wavelet packet energy distribution and correlation coefficient has been put forward to recognize the different acoustic targets in this paper. In view of the characteristics of acoustic target, we employed principal component analysis (PCA) to compress data set of the features extracted based on wavelet packet energy distribution and correlation coefficient. The results have been inputted into the neural network as eigenvectors for pattern recognition. Simulation results indicate that the method suggested in this paper have a recognition rate better than 8% with only wavelet packet energy method, thus verifying its effectiveness .

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.

A Hybrid Approach for Facial Expression Recognition Using Extended Local Binary Patterns and Principal Component Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 1-25
Author(s):  
Gopal Krishan Prajapat ◽  
Rakesh Kumar
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Recognition ◽  
Distance Method

Facial feature extraction and recognition plays a prominent role in human non-verbal interaction and it is one of the crucial factors among pose, speech, facial expression, behaviour and actions which are used in conveying information about the intentions and emotions of a human being. In this article an extended local binary pattern is used for the feature extraction process and a principal component analysis (PCA) is used for dimensionality reduction. The projections of the sample and model images are calculated and compared by Euclidean distance method. The combination of extended local binary pattern and PCA (ELBP+PCA) improves the accuracy of the recognition rate and also diminishes the evaluation complexity. The evaluation of proposed facial expression recognition approach will focus on the performance of the recognition rate. A series of tests are performed for the validation of algorithms and to compare the accuracy of the methods on the JAFFE, Extended Cohn-Kanade images database.

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