A novel method for splice sites prediction using sequence component and hidden Markov model

2016 ◽  
Author(s):  
Elham Pashaei ◽  
Alper Yilmaz ◽  
Mustafa Ozen ◽  
Nizamettin Aydin
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Splice Sites ◽  
Novel Method

Online Degradation Assessment and Adaptive Fault Detection Using Modified Hidden Markov Model

2010 ◽  
Vol 132 (2) ◽  
Author(s):  
Seungchul Lee ◽  
Lin Li ◽  
Jun Ni
Keyword(s):  
Tool Wear ◽  
Markov Model ◽  
Hidden Markov Model ◽  
State Space ◽  
Manufacturing Industry ◽  
Failure Modes ◽  
Hidden Markov ◽  
Statistical Process ◽  
New States ◽  
Novel Method

Online condition monitoring and diagnosis systems play an important role in the modern manufacturing industry. This paper presents a novel method to diagnose the degradation processes of multiple failure modes using a modified hidden Markov model (MHMM) with variable state space. The proposed MHMM is combined with statistical process control to quickly detect the occurrence of an unknown fault. This method allows the state space of a hidden Markov model to be adjusted and updated with the identification of new states. Hence, the online degradation assessment and adaptive fault diagnosis can be simultaneously obtained. Experimental results in a turning process illustrate that the tool wear state can be successfully detected, and previously unknown tool wear processes can be identified at the early stages using the MHMM.

scholarly journals A Novel Method for Decoding Any High-Order Hidden Markov Model

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Fei Ye ◽  
Yifei Wang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
High Order ◽  
Optimal State ◽  
First Order ◽  
State Sequence ◽  
Novel Method

This paper proposes a novel method for decoding any high-order hidden Markov model. First, the high-order hidden Markov model is transformed into an equivalent first-order hidden Markov model by Hadar’s transformation. Next, the optimal state sequence of the equivalent first-order hidden Markov model is recognized by the existing Viterbi algorithm of the first-order hidden Markov model. Finally, the optimal state sequence of the high-order hidden Markov model is inferred from the optimal state sequence of the equivalent first-order hidden Markov model. This method provides a unified algorithm framework for decoding hidden Markov models including the first-order hidden Markov model and any high-order hidden Markov model.

A novel method for day-ahead solar power prediction based on hidden Markov model and cosine similarity

2019 ◽  
Vol 24 (7) ◽  
pp. 4991-5004
Author(s):  
Khatereh Ghasvarian Jahromi ◽  
Davood Gharavian ◽  
Hamidreza Mahdiani
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Solar Power ◽  
Hidden Markov ◽  
Cosine Similarity ◽  
Power Prediction ◽  
Novel Method

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

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions
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