Driver behavior modeling near intersections using Hidden Markov Model based on genetic algorithm

2016 ◽  
Author(s):  
Seifemichael B. Amsalu ◽  
Abdollah Homaifar
Keyword(s):  
Genetic Algorithm ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Driver Behavior ◽  
Behavior Modeling ◽  
Model Based

An unsupervised Hidden Markov Model-based system for the detection and classification of blue whale vocalizations off Chile

Bioacoustics ◽  
2019 ◽  
Vol 29 (2) ◽  
pp. 140-167 ◽  
Author(s):  
Susannah J. Buchan ◽  
Rodrigo Mahú ◽  
Jorge Wuth ◽  
Naysa Balcazar-Cabrera ◽  
Laura Gutierrez ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Blue Whale ◽  
Model Based

A hybrid hidden Markov model towards fault detection of rotating components

2016 ◽  
Vol 23 (19) ◽  
pp. 3175-3195 ◽  
Author(s):  
Ayan Sadhu ◽  
Guru Prakash ◽  
Sriram Narasimhan
Keyword(s):  
Fault Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Time Scale ◽  
Hidden Markov ◽  
Energy Operator ◽  
Fault Classification ◽  
Multiple Faults ◽  
Condition Indicators ◽  
Model Based

A robust hybrid hidden Markov model-based fault detection method is proposed to perform multi-state fault classification of rotating components. The approach presented in this paper enhances the performance of the standard hidden Markov model (HMM) for fault detection by performing a series of pre-processing steps. First, the de-noised time-scale signatures are extracted using wavelet packet decomposition of the vibration data. Subsequently, the Teager Kaiser energy operator is employed to demodulate the time-scale components of the raw vibration signatures, following which the condition indicators are calculated. Out of several possible condition indicators, only relevant features are selected using a decision tree. This pre-processing improves the sensitivity of condition indicators under multiple faults. A Gaussian mixing model-based hidden Markov model (HMM) is then employed for fault detection. The proposed hybrid HMM is an improvement over traditional HMM in that it achieves better separation of the feature space leading to more robust state estimation under multiple fault states and measurement noise scenarios. A simulation employing modulated signals and two experimental validation studies are presented to demonstrate the performance of the proposed method.

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