HMM-Based Abnormal Behaviour Detection Using Heterogeneous Sensor Network

Aliakbarpour, Hadi; Khoshhal, Kamrad; Quintas, João; Mekhnacha, Kamel; Ros, Julien; Andersson, Maria; Dias, Jorge

doi:10.1007/978-3-642-19170-1_30

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…To further verify the effectiveness of this method, aging state was recognized using the SVM and hidden Markov Model (HMM) and taking 27 original partial discharge signals and 10 principle component factors as characteristic quantities. The results are shown in Table .…”

Section: Instance Analysismentioning

confidence: 99%

Analysis of fault of insulation aging of oiled paper of a large‐scale power transformer and the prediction of its service life

Xie

2019

IEEJ Transactions Elec Engng

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Insulation aging of oiled paper is a common malfunction of transformers. It is of great value to recognize the insulation aging status of oiled paper and predict the service life of transformers using appropriate methods. In this study, an aging state recognition method based on partial discharge was proposed. Partial discharge signals were collected, the feature values were obtained through analysis of the principle component factor, and the state was identified by using a support vector machine. A Weibull distribution‐based method was proposed for the prediction of service life, and the residual life of transformers was determined using failure rate function. Through analysis, it was found that the fault analysis method had an accuracy rate of 80.55% in the recognition of the aging state, which was higher than that of Hidden Markov Model (HMM). In the case analysis, the accuracy rate of the method in recognizing the aging state of 100 transformers reached 82.06%. The accuracy of the failure rate function was also verified in the prediction of service life. This study provides some new ideas for the effective recognition of insulation aging of oiled paper in transformers and the prediction of service life. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Instance Analysismentioning

confidence: 99%

Analysis of fault of insulation aging of oiled paper of a large‐scale power transformer and the prediction of its service life

Xie

2019

IEEJ Transactions Elec Engng

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“…An observation sequence of human activity is a finite string from Σ ∗ denoted by

\overset{O}{false} = o_{normal1} o_{normal2} \dots o_{T}

. These temporal transition constraints between different motions are acquired by statistics in HMM and Bayesian Network methods [7, 13]. …”

Section: Recurrent Transformation Modelmentioning

confidence: 99%

“…In this study, 50 features that are widely used in related articles [2–7, 13, 15] are chosen for candidate selection, like mean, variance, interquartile range, signal magnitude area (SMA), and so on. However, the number of features applied in one classifier is not the best.…”

Section: Recurrent Transformation Modelmentioning

confidence: 99%

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Recurrent Transformation of Prior Knowledge Based Model for Human Motion Recognition

Zhang

et al. 2018

Computational Intelligence and Neuroscience

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Motion related human activity recognition using wearable sensors can potentially enable various useful daily applications. So far, most studies view it as a stand-alone mathematical classification problem without considering the physical nature and temporal information of human motions. Consequently, they suffer from data dependencies and encounter the curse of dimension and the overfitting issue. Their models are hard to be intuitively understood. Given a specific motion set, if structured domain knowledge could be manually obtained, it could be used for better recognizing certain motions. In this study, we start from a deep analysis on natural physical properties and temporal recurrent transformation possibilities of human motions and then propose a useful Recurrent Transformation Prior Knowledge-based Decision Tree (RT-PKDT) model for recognition of specific human motions. RT-PKDT utilizes temporal information and hierarchical classification method, making the most of sensor streaming data and human knowledge to compensate the possible data inadequacy. The experiment results indicate that the proposed method performs superior to those adopted in related works, such as SVM, BP neural networks, and Bayesian Network, obtaining an accuracy of 96.68%.

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“…Human-machine interaction (HMI) technologies and corresponding cognitive capabilities of agent systems have seen many developments in the last few decadessee [3] for an extensive survey. Solutions for multisensory active perception and attention allocation have greatly evolved [4], as has multimodal human emotion and dialogue analysis and human-like emotion and dialogue synthesis [5] and also human behavior analysis [6,7]. In summary, we identified that existing cyber-physical systems that have been developed recently in order to provide assistance in domestic, professional and public environments are based on closed architectures, thus being operational only in specific contexts of usage, equipment and data.…”

Section: Related Workmentioning

confidence: 99%

Auto-Adaptive Interactive Systems for Active and Assisted Living Applications

Quintas¹,

Menezes

Dias

2016

Technological Innovation for Cyber-Physical Systems

Self Cite

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The objective of this work is of improving the efficacy, acceptance, adaptability and overall performance of Human-Machine Interaction (HMI) applications using a context-based approach. In HMI, we aim to define a general human model that may lead to principles and algorithms allowing more natural and effective interaction between humans and artificial agents. This is paramount for applications in the field of Active and Assisted Living (AAL). The challenge of user acceptance is of vital importance for future solutions, and still one of the major reasons for reluctance to adopt cyber-physical systems in this domain. Our hypothesis is that, we can overcome limitations of current interaction functionalities by integrating contextual information to improve algorithms accuracy when performing under very different conditions and to adapt interfaces and interaction patterns according user intentions and emotional states.

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HMM-Based Abnormal Behaviour Detection Using Heterogeneous Sensor Network

Cited by 5 publications

References 17 publications

Analysis of fault of insulation aging of oiled paper of a large‐scale power transformer and the prediction of its service life

Analysis of fault of insulation aging of oiled paper of a large‐scale power transformer and the prediction of its service life

Recurrent Transformation of Prior Knowledge Based Model for Human Motion Recognition

Auto-Adaptive Interactive Systems for Active and Assisted Living Applications

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