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Your search keyword '"Nakajima, Shin"' showing total 3 results
Start Over Author "Nakajima, Shin" Topic hidden markov model
3 results on '"Nakajima, Shin"'

1. Efficient Online Analysis of Accidental Fault Localization for Dynamic Systems using Hidden Markov Model

Author

Ge, Ning, Nakajima, Shin, Pantel, Marc, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), and National Institute of Informatics - NII (JAPAN)

Subjects
Hidden Markov model, Online analysis, Architectures Matérielles, Cryptographie et sécurité, Génie logiciel, Accidental fault localization, Modélisation et simulation, Systèmes embarqués, Interface homme-machine, Simulation
Abstract

This paper proposes a novel approach to do online analysis of accidental fault localization for dynamic systems by using Hidden Markov Model (HMM). By introducing reasonable and appropriate abstraction of complex system, HMM is used to represent the fault and no-fault states of system's components and system's behaviour. The HMM is parametrized to be statistically equivalent to real system's behaviour. Inspired by the principles of Fault Tree Analysis and maximum entropy in Bayesian probability theory, we propose the algorithms to estimate HMM's parameters, instead of learning, because in real systems the learning data for accidental fault is difficult to obtain. We design a specific test bed to generate large quantity of test cases, and give out the experimental results to assess the accuracy and efficiency. Meanwhile, we apply the approach to a simple helicopter control system case study, and give out convincing results.

Published
2013

2. Online diagnosis of accidental faults for real-time embedded systems using a hidden Markov model.

Author

Ge, Ning, Nakajima, Shin, and Pantel, Marc

Subjects
*EMBEDDED computer systems, *REAL-time control, *HIDDEN Markov models, *FAULT-tolerant computing, *COMPUTER simulation
Abstract

This article proposes an approach for the online analysis of accidental faults for real-time embedded systems using hidden Markov models (HMMs). By introducing reasonable and appropriate abstraction of complex systems, HMMs are used to describe the healthy or faulty states of system’s hardware components. They are parametrized to statistically simulate the real system’s behavior. As it is not easy to obtain rich accidental fault data from a system, the Baum–Welch algorithm cannot be employed here to train the parameters in HMMs. Inspired by the principles of fault tree analysis and the maximum entropy in Bayesian probability theory, we propose to compute the failure propagation distribution to estimate the parameters in HMMs and to adapt the parameters using a backward algorithm. The parameterized HMMs are then used to online diagnose accidental faults using a vote algorithm integrated with a low-pass filter. We design a specific test bed to analyze the sensitivity, specificity, precision, accuracy and F1-score measures by generating a large amount of test cases. The test results show that the proposed approach is robust, efficient and accurate. [ABSTRACT FROM AUTHOR]

Published
2015
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3. Hidden Markov model based automated fault localization for integration testing.

Author

Ning Ge, Nakajima, Shin, and Pantel, Marc

Abstract

Integration testing is an expensive activity in software testing, especially for fault localization in complex systems. Model-based diagnosis (MBD) provides various benefits in terms of scalability and robustness. In this work, we propose a novel MBD approach for the automated fault localization in integration testing. Our method is based on Hidden Markov Model (HMM) which is an abstraction of system's component to simulate component's behaviour. The core of this method is a fault localization algorithm that gives out the set of suspect faulty components and a backward algorithm that calculates the matching degree between the HMM and the real system to evaluate the confidence degree of the localization conclusion. The proposed method is evaluated on a specific test bed and is applied to a simple helicopter control system case study. [ABSTRACT FROM PUBLISHER]

Published
2013
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