scholarly journals Research on identification method of heavy vehicle rollover based on hidden Markov model

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 479-485 ◽  
Author(s):  
Zhiguo Zhao ◽  
Yeqin Wang ◽  
Xiaoming Hu ◽  
Yukai Tao ◽  
Jinsheng Wang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Early Warning ◽  
Hidden Markov ◽  
The State ◽  
Lateral Acceleration ◽  
Heavy Vehicle ◽  
General Strategy ◽  
Identification Method ◽  
Vehicle Rollover

AbstractAiming at the problem of early warning credibility degradation as the heavy vehicle load and its center of gravity change greatly; the heavy vehicle rollover state identification method based on the Hidden Markov Model (HMM, is introduced to identify heavy vehicle lateral conditions dynamically in this paper. In this method, the lateral acceleration and roll angle are taken as the observation values of the model base. The Viterbi algorithm is used to predict the state sequence with the highest probability in the observed sequence, and the Markov prediction algorithm is adopted to calculate the state transition law and to predict the state of the vehicle in a certain period of time in the future. According to combination conditions of Double lane change and steering, applying Trucksim and Matlab trained hidden Markov model, the model is applied to the online identification of heavy vehicle rollover states. The identification results show that the model can accurately and efficiently identify the vehicle rollover state, and has good applicability. This study provides a novel method and a general strategy for active safety early warning and control of vehicles, which has reference significance for the application of the Hidden Markov theory in collision, rear-end and lane departure warning system.

scholarly journals A Sequence-Based Damage Identification Method for Composite Rotors by Applying the Kullback–Leibler Divergence, a Two-Sample Kolmogorov–Smirnov Test and a Statistical Hidden Markov Model

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 690 ◽  
Author(s):  
Angelos Filippatos ◽  
Albert Langkamp ◽  
Pawel Kostka ◽  
Maik Gude
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Composite Structures ◽  
Damage Identification ◽  
Hidden Markov ◽  
Structural Dynamic ◽  
Identification Method ◽  
Leibler Divergence ◽  
Kolmogorov Smirnov ◽  
Smirnov Test

Composite structures undergo a gradual damage evolution from initial inter-fibre cracks to extended damage up to failure. However, most composites could remain in service despite the existence of damage. Prerequisite for a service extension is a reliable and component-specific damage identification. Therefore, a vibration-based damage identification method is presented that takes into consideration the gradual damage behaviour and the resulting changes of the structural dynamic behaviour of composite rotors. These changes are transformed into a sequence of distinct states and used as an input database for three diagnostic models, based on the Kullback–Leibler divergence, the two-sample Kolmogorov–Smirnov test and a statistical hidden Markov model. To identify the present damage state based on the damage-dependent modal properties, a sequence-based diagnostic system has been developed, which estimates the similarity between the present unclassified sequence and obtained sequences of damage-dependent vibration responses. The diagnostic performance evaluation delivers promising results for the further development of the proposed diagnostic method.

scholarly journals Text segmentation by language

2016 ◽  
Vol 14 (38) ◽  
pp. 63-72 ◽  
Author(s):  
Robin Cabeza Ruiz
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
The State ◽  
Text Segmentation ◽  
State Of Art

There are two approaches for text segmentation by language: first, assuming that language changes happen in the “border” between sentences (never within a sentence); second, assuming that language changes can happen anyplace in the text. This work presents methods for both types of text’s segmentation by languages. On the first proposal, the text is initially segmented by sentence, then the language of each sentence is obtained; the second proposal is an adaptation of hidden Markov model to this task. Both cases, according to results obtained in experimental proofs, exceed the state of art.

scholarly journals A Dependent Hidden Markov Model of Credit Quality

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Małgorzata Wiktoria Korolkiewicz
Keyword(s):  
Markov Chain ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Credit Rating ◽  
Hidden Markov ◽  
Credit Ratings ◽  
The State ◽  
Credit Quality ◽  
The Em Algorithm ◽  
Rating Process

We propose a dependent hidden Markov model of credit quality. We suppose that the "true" credit quality is not observed directly but only through noisy observations given by posted credit ratings. The model is formulated in discrete time with a Markov chain observed in martingale noise, where "noise" terms of the state and observation processes are possibly dependent. The model provides estimates for the state of the Markov chain governing the evolution of the credit rating process and the parameters of the model, where the latter are estimated using the EM algorithm. The dependent dynamics allow for the so-called "rating momentum" discussed in the credit literature and also provide a convenient test of independence between the state and observation dynamics.

Approximating the Variance of the Conditional Probability of the State of a Hidden Markov Model

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
David O Siegmund ◽  
Benjamin Yakir
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Change Point ◽  
Conditional Distribution ◽  
Hidden Markov ◽  
The State ◽  
Change Point Analysis ◽  
Score Statistic ◽  
Hidden Markov Chain ◽  
Point Analysis

In a hidden Markov model, one "estimates" the state of the hidden Markov chain at t by computing via the forwards-backwards algorithm the conditional distribution of the state vector given the observed data. The covariance matrix of this conditional distribution measures the information lost by failure to observe directly the state of the hidden process. In the case where changes of state occur slowly relative to the speed at which information about the underlying state accumulates in the observed data, we compute approximately these covariances in terms of functionals of Brownian motion that arise in change-point analysis. Applications in gene mapping, where these covariances play a role in standardizing the score statistic and in evaluating the loss of noncentrality due to incomplete information, are discussed. Numerical examples illustrate the range of validity and limitations of our results.

scholarly journals A research on early warning method of Distribution Network Cyber Physical System

2021 ◽  
Vol 248 ◽  
pp. 02054
Author(s):  
Feng Chen ◽  
Wei Wei Xu ◽  
Zong Heng Wang ◽  
Tao Yang ◽  
Hong Yang Huang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Early Warning ◽  
Physical System ◽  
Hidden Markov ◽  
Distribution Network ◽  
Development Trend ◽  
Cyber Physical System ◽  
Stable Operation ◽  
Time Accuracy

The high integration of cyber and physics is the development trend of intelligent distribution network in the future, but the cyber system not only supports the stable operation of the physical system, also brings some security risks to the cyber physical system of distribution network. Aiming at the requirements of real-time, accuracy, efficiency and other characteristics of distribution network monitoring, this paper proposes an early warning method of distribution network cyber physical system based on Hidden Markov model. Firstly, the online monitoring and early warning system architecture of distribution network information physical system is proposed, and then the early warning method of distribution network cyber physical system based on Hidden Markov model is established. Finally, an example is given to verify that the proposed strategy can accurately and efficiently early warn the fault.

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