Human identification on the basis of gait analysis using Kohonen self-organizing mapping technique

2016 ◽  
Author(s):  
Sagor Chandro Bakchy ◽  
Md. Rabiul Islam ◽  
Abu Sayeed
Keyword(s):  
Gait Analysis ◽  
Human Identification ◽  
Mapping Technique ◽  
Self Organizing

Degradation assessment of ball bearings utilizing curvilinear component analysis

2018 ◽  
Vol 233 (3) ◽  
pp. 714-730 ◽  
Author(s):  
Prashant Tiwari ◽  
SH Upadhyay
Keyword(s):  
Feature Space ◽  
Component Analysis ◽  
Performance Degradation ◽  
Quantization Error ◽  
Mapping Technique ◽  
Nonlinear Mapping ◽  
Ball Bearings ◽  
Self Organizing Map ◽  
Feature Vectors ◽  
Self Organizing

The performance degradation assessment of ball bearings is of great importance to increase the efficiency and the reliability of rotating mechanical systems. The large dimensionality of feature space introduces a lot of noise and buries the potential information about faults hidden in the feature data. This paper proposes a novel health assessment method facilitated with two compatible methods, namely curvilinear component analysis and self-organizing map network. The novelty lies in the implementation of a vector quantization approach for the sub-manifolds in the feature space and to extract the fault signatures through nonlinear mapping technique. Curvilinear component analysis is a nonlinear mapping tool that can effectively represent the average manifold of the highly folded information and further preserves the local topology of the data. To answer the complications and to accomplish reliability and accuracy in bearing performance degradation assessment, the work is carried out with following steps; first, ensemble empirical mode decomposition is used to decompose the vibration signals into useful intrinsic mode functions; second, two fault features i.e. singular values and energy entropies are extracted from the envelopes of the intrinsic mode function signals; third, the extracted feature vectors under healthy conditions, further reduced with curvilinear component analysis are used to train the self-organizing map model; finally, the reduced test feature vectors are supplied to the trained self-organizing map and the confidence value is obtained. The effectiveness of the proposed technique is validated on three run-to-failure test signals with the different type of defects. The results indicate that the proposed technique detects the weak degradation earlier than the widely used indicators such as root mean square, kurtosis, self-organizing map-based minimum quantization error, and minimum quantization error-based on the principal component analysis.

Human identification based on gait analysis

2007 ◽  
Author(s):  
Sungjun Hong ◽  
Heesung Lee ◽  
Imran Fareed Nizami ◽  
Sung-Je An ◽  
Euntai Kim
Keyword(s):  
Gait Analysis ◽  
Human Identification
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