ACTIVE CONTROL OF FLEXIBLE STRUCTURES USING PRINCIPAL COMPONENT ANALYSIS IN THE TIME DOMAIN

2002 ◽  
Vol 253 (3) ◽  
pp. 545-569 ◽  
Author(s):  
A.S. AL-DMOUR ◽  
K.S. MOHAMMAD
Keyword(s):  
Principal Component Analysis ◽  
Active Control ◽  
Time Domain ◽  
Flexible Structures ◽  
Principal Component ◽  
Component Analysis ◽  
The Time Domain

scholarly journals Vector optical-chirp-chain Brillouin optical time-domain analyzer based on complex principal component analysis

2020 ◽  
Vol 28 (20) ◽  
pp. 28831
Author(s):  
Jingdong Zhang ◽  
Hua Zheng ◽  
Haoting Wu ◽  
Nan Guo ◽  
Guolu Yin ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Time Domain ◽  
Principal Component ◽  
Component Analysis ◽  
Optical Time

Robust wideband time‐reversal using time‐domain principal component analysis

2006 ◽  
Vol 120 (5) ◽  
pp. 3007-3008
Author(s):  
W. J. Higley ◽  
Philippe Roux ◽  
W. S. Hodgkiss ◽  
W. A. Kuperman ◽  
Heechun Song ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Time Domain ◽  
Time Reversal ◽  
Principal Component ◽  
Component Analysis

scholarly journals Temperature extraction in Brillouin optical time-domain analysis sensors using principal component analysis based pattern recognition

2017 ◽  
Vol 25 (14) ◽  
pp. 16534 ◽  
Author(s):  
Abul Kalam Azad ◽  
Faisal Nadeem Khan ◽  
Waled Hussein Alarashi ◽  
Nan Guo ◽  
Alan Pak Tao Lau ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Time Domain ◽  
Principal Component ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Component Analysis ◽  
Optical Time

Broadband terahertz time-domain spectroscopy of drugs-of-abuse and the use of principal component analysis

The Analyst ◽  
2009 ◽  
Vol 134 (8) ◽  
pp. 1658 ◽  
Author(s):  
Andrew D. Burnett ◽  
Wenhui Fan ◽  
Prashanth C. Upadhya ◽  
John E. Cunningham ◽  
Michael D. Hargreaves ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Time Domain ◽  
Drugs Of Abuse ◽  
Principal Component ◽  
Component Analysis ◽  
Terahertz Time Domain Spectroscopy ◽  
Time Domain Spectroscopy ◽  
Broadband Terahertz

Bearing Condition Recognition Based on Kernel Principal Component Analysis and Genetic Programming

2013 ◽  
Vol 397-400 ◽  
pp. 1282-1285 ◽  
Author(s):  
Wen Bin Liu ◽  
Yu Xin He ◽  
Hua Qing Wang ◽  
Jian Feng Yang
Keyword(s):  
Principal Component Analysis ◽  
Genetic Programming ◽  
Principal Component ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Initial Symptom ◽  
The Time Domain ◽  
Condition Recognition

In order to extract the fault feature validity in early fault diagnosis, method based on kernel principal component analysis and genetic programming (GP) is presented. The time domain features of the vibration signal are extracted and the initial symptom parameters (SP) are constructed. Then the combination to the initial SPs is carried on to optimize and build composite characteristics by GP. Through kernel principal component analysis (KPCA), the nonlinear principal component of the original characteristics is produced. Finally, the nonlinear principal components are selected as the feature subspace to classify the conditions of rolling bearing. Meanwhile, the within-class and among-class distance is introduced to compare and analyze the bearing condition recognition effect by using KPCA and GP plus KPCA separately. Experimental results show that the features extracted by kernel principal component analysis and genetic programming perform better ability in identifying the working states of the rolling bearing.

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