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Machine condition recognition via hidden semi-Markov model
Computers & Industrial Engineering, 2021Abstract In intelligent manufacturing systems, machines are subject to condition deterioration.Identifying machine condition is crucial for making practical decisions in production management. This paper studies the machine condition recognition problem in wafer fabrication.
Wenhui Yang, Lu Chen
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Weibull partition models with applications to hidden semi-Markov models
2017 International Joint Conference on Neural Networks (IJCNN), 2017We develop the Weibull partition model (WPM), which defines a novel nonparametric stochastic process over distributions of partitions of sequential data, aiming at directly modeling the boundaries of segments comprising the sequence. The Weibull partition model employs a Dirichlet process mixture with a Weibull kernel.
Youwei Lu, Shogo Okada, Katsumi Nitta
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A hierarchical hidden semi-Markov model for modeling mobility data
Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014Ubiquity of portable location-aware devices and popularity of online location-based services, have recently given rise to the collection of datasets with high spatial and temporal resolution. The subject of analyzing such data has consequently gained popularity due to numerous opportunities enabled by understanding objects’ (people and animals, among ...
Mitra Baratchi +4 more
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Hidden Semi-Markov Models for Semantic-Graph Language Modeling
Journal of the Franklin InstituteSemantic communication is expected to play a critical role in reducing traffic load in future intelligent large-scale sensor networks. With advances in Machine Learning (ML) and Deep Learning (DL) techniques, design of semantically-aware systems has become feasible in recent years.
Sadik Yagiz Yetim +2 more
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Online identification of hidden Semi-Markov models
3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the, 2004Hidden Markov models (HMM) are a powerful tool in signal modelling. In an HMM, the probability that signal leaves a state is constant, and hence the duration that signal stays in each state has an exponential distribution. However, this exponential density is not appropriate for a large class of physical signals.
M. Azimi, P. Nasiopoulos, R.K. Ward
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Using Hidden Semi-Markov Model for learning behavior in smarthomes
2015 IEEE International Conference on Automation Science and Engineering (CASE), 2015Within the framework of demographic changes and the constraint of permanent growth of the elderly number in European population (i.e. France) which the health sector is confronted to, the detection of abnormal behaviors in the supervisory framework has known a particular interest during these last years.
Arnaud Paris +4 more
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Reconstructing Individual Activity Trajectories by Hidden Semi-Markov Model
2018 26th International Conference on Geoinformatics, 2018The individual trajectory of human activity collected through crowdsourcing implies the characteristic regularity of individual activity. There are always data gaps, and it introduces negative influence of individual activity pattern analyses. By extracting the internal regularity of known data, the incomplete relationship between location and time can
Zixuan Han +3 more
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The Centisecond Two Levels Hidden Semi Markov Model (CTLHSMM)
International Symposium on Parallel Computing in Electrical Engineering (PARELEC'06), 2006A major deficiency of standard Hidden Markov Models (HMM) is that both the spectral and the prosodic feature are uniformly processed. To combine more efficiently the prosodic cues with the acoustic ones, a segmental two levels Hidden Markov Model has been recently studied by suaudeau [Suaudeau 94].
Abdellah Yousfi, Abdelouafi Meziane
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On robust estimation of hidden semi-Markov regime-switching models
Annals of Operations ResearchzbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shanshan Qin, Zhenni Tan, Yuehua Wu
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Adaptive Training for Hidden Semi-Markov Model
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2006Junichi Yamagishi, Takao Kobayashi
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