Application of time-frequency principal component analysis to text-independent speaker identification

2002 ◽  
Vol 10 (6) ◽  
pp. 371-378 ◽  
Author(s):  
I. Magrin-Chagnolleau ◽  
G. Durou ◽  
F. Bimbot
Keyword(s):  
Principal Component Analysis ◽  
Speaker Identification ◽  
Principal Component ◽  
Component Analysis ◽  
Time Frequency

scholarly journals Fault diagnosis of bearing based on the kernel principal component analysis and optimized k-nearest neighbour model

2017 ◽  
Vol 36 (4) ◽  
pp. 354-365 ◽  
Author(s):  
Shaojiang Dong ◽  
Tianhong Luo ◽  
Li Zhong ◽  
Lili Chen ◽  
Xiangyang Xu
Keyword(s):  
Principal Component Analysis ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Frequency Domain Method ◽  
Nearest Neighbour ◽  
Swarm Optimization ◽  
Time Frequency

Aiming to identify the bearing faults level effectively, a new method based on kernel principal component analysis and particle swarm optimization optimized k-nearest neighbour model is proposed. First, the gathered vibration signals are decomposed by time–frequency domain method, i.e., local mean decomposition; as a result, the product functions decomposed from the original signal are derived. Then, the entropy values of the product functions are calculated by Shannon method, which will work as the input features for k-nearest neighbour model. The kernel principal component analysis model is used to reduce the dimension of the features, and then the k-nearest neighbour model which was optimized by the particle swarm optimization method is used to identify the bearing fault levels. Case of test and actually collected signal are analysed. The results validate the effectiveness of the proposed algorithm.

scholarly journals Forcing variables in simulation of transpiration of water stressed plants determined by principal component analysis

2016 ◽  
Vol 30 (4) ◽  
pp. 431-445
Author(s):  
Angelica Durigon ◽  
Quirijn de Jong van Lier ◽  
Klaas Metselaar
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Specific Humidity ◽  
Leaf Angle ◽  
Prediction Errors ◽  
Angle Distribution ◽  
Time Frequency ◽  
Soil Dryness

AbstractTo date, measuring plant transpiration at canopy scale is laborious and its estimation by numerical modelling can be used to assess high time frequency data. When using the model by Jacobs (1994) to simulate transpiration of water stressed plants it needs to be reparametrized. We compare the importance of model variables affecting simulated transpiration of water stressed plants. A systematic literature review was performed to recover existing parameterizations to be tested in the model. Data from a field experiment with common bean under full and deficit irrigation were used to correlate estimations to forcing variables applying principal component analysis. New parameterizations resulted in a moderate reduction of prediction errors and in an increase in model performance. Agsmodel was sensitive to changes in the mesophyll conductance and leaf angle distribution parameterizations, allowing model improvement. Simulated transpiration could be separated in temporal components. Daily, afternoon depression and long-term components for the fully irrigated treatment were more related to atmospheric forcing variables (specific humidity deficit between stomata and air, relative air humidity and canopy temperature). Daily and afternoon depression components for the deficit-irrigated treatment were related to both atmospheric and soil dryness, and long-term component was related to soil dryness.

scholarly journals Combination of principal component analysis and time-frequency representations of multichannel vibration data for gearbox fault detection

2016 ◽  
Vol 18 (4) ◽  
pp. 2167-2175 ◽  
Author(s):  
Radoslaw Zimroz ◽  
Jacek Wodecki ◽  
Pawel Stefaniak ◽  
Jakub Obuchowski ◽  
Agnieszka Wylomanska
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Time Frequency ◽  
Vibration Data

Myoelectric signal classification based on S transform and two-directional two-dimensional principal component analysis

2017 ◽  
Vol 40 (7) ◽  
pp. 2387-2395 ◽  
Author(s):  
Yi Ji ◽  
Hong-Bo Xie
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Computational Cost ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
S Transform ◽  
Efficiency And Effectiveness

Time-frequency representiation has been intensively employed for the analysis of biomedical signals. In order to extract discriminative information, time-frequency matrix is often transformed into a 1D vector followed by principal component analysis (PCA). This study contributes a two-directional two-dimensional principal component analysis (2D2PCA)-based technique for time-frequency feature extraction. The S transform, integrating the strengths of short time Fourier transform and wavelet transform, is applied to perform the time-frequency decomposition. Then, 2D2PCA is directly conducted on the time-frequency matrix rather than 1D vectors for feature extraction. The proposed method can significantly reduce the computational cost while capture the directions of maximal time-frequency matrix variance. The efficiency and effectiveness of the proposed method is demonstrated by classifying eight hand motions using 4-channel myoelectric signals recorded in health subjects and amputees.

scholarly journals Rolling Bearing Reliability Assessment via Kernel Principal Component Analysis and Weibull Proportional Hazard Model

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Fengtao Wang ◽  
Xutao Chen ◽  
Bosen Dun ◽  
Bei Wang ◽  
Dawen Yan ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Frequency Domain ◽  
Reliability Assessment ◽  
Principal Component ◽  
Component Analysis ◽  
Performance Degradation ◽  
Assessment Model ◽  
Kernel Principal Component Analysis ◽  
Engineering Project ◽  
Time Frequency

Reliability assessment is a critical consideration in equipment engineering project. Successful reliability assessment, which is dependent on selecting features that accurately reflect performance degradation as the inputs of the assessment model, allows for the proactive maintenance of equipment. In this paper, a novel method based on kernel principal component analysis (KPCA) and Weibull proportional hazards model (WPHM) is proposed to assess the reliability of rolling bearings. A high relative feature set is constructed by selecting the effective features through extracting the time domain, frequency domain, and time-frequency domain features over the bearing’s life cycle data. The kernel principal components which can accurately reflect the performance degradation process are obtained by KPCA and then input as the covariates of WPHM to assess the reliability. An example was conducted to validate the proposed method. The differences in manufacturing, installation, and working conditions of the same type of bearings during reliability assessment are reduced after extracting relative features, which enhances the practicability and stability of the proposed method.

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