Exploring the behavior of Caenorhabditis Elegans by using a self-organizing map and hidden Markov model

2012 ◽  
Vol 60 (4) ◽  
pp. 604-612 ◽  
Author(s):  
Seung-Ho Kang ◽  
Sang-Hee Lee ◽  
Tae-Soo Chon
Keyword(s):  
Caenorhabditis Elegans ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Self-Organizing Hidden Markov Model Map (SOHMMM)

2013 ◽  
Vol 48 ◽  
pp. 133-147 ◽  
Author(s):  
Christos Ferles ◽  
Andreas Stafylopatis
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing

Bearing performance degradation assessment based on topological representation and hidden Markov model

2020 ◽  
pp. 107754632094663
Author(s):  
Ran Wang ◽  
Jihao Jin ◽  
Xiong Hu ◽  
Jin Chen
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Performance Indicator ◽  
Hidden Markov ◽  
General Structure ◽  
Feature Space ◽  
Performance Degradation ◽  
Assessment Model ◽  
Self Organizing Map ◽  
Topological Representation

Bearing performance degradation assessment is essential to avoid abrupt machinery breakdown. However, background noise, outliers, and other interferences in the monitoring data may restrict the accuracy and stability of bearing performance degradation assessment in practical applications. In this study, a bearing performance degradation assessment method based on the topological representation and hidden Markov model is proposed. To construct a robust and representative feature space, the topological representations, specifically, topological meshes of the original features are obtained by self-organizing map, which can represent the general structure of the original feature space and eliminate outliers and other interferences. Then, the weight vectors of topological meshes are used as degradation features. Finally, the hidden Markov model is adopted as the assessment model to evaluate the bearing performance degradation tendency and detect the initial degradation effectively. To validate the effectiveness and superiority of the proposed method, two experimental datasets are analyzed. Compared with peer methods, the performance indicator curve of the proposed method presents a more smooth and accurate degradation tendency than comparative methods. Moreover, initial degradation can be identified accurately.

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