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Hidden semi-Markov model for anomaly detection
Applied Mathematics and Computation, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xiaobin Tan, Hongsheng Xi
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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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Implementation of hidden semi-Markov models
, 2011 One of the most frequently used concepts applied to a variety of engineering and scientific studies over the recent years is that of a Hidden Markov Model (HMM). The Hidden semi-Markov model (HsMM) is contrived in such a way that it does not make any premise of constant or geometric distributions of a state duration.
Dasu, Nagendra Abhinav
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Hidden semi-Markov model based speech synthesis
Interspeech 2004, 2004In the present paper, a hidden-semi Markov model (HSMM) based speech synthesis system is proposed. In a hidden Markov model (HMM) based speech synthesis system which we have proposed, rhythm and tempo are controlled by state duration probability distributions modeled by single Gaussian distributions.
Heiga Zen +4 more
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Hidden semi-Markov models for electricity load disaggregation
ACM SIGMETRICS Performance Evaluation Review, 2019This paper assesses the performance of a technique for estimating the power consumption of individual devices based on aggregate consumption. The new semi-Markov technique, outperforms pure hidden Markov models on the REDD dataset. The technique also exploits information from transients to eliminate a substantial fraction of the observed ...
Yung Fei Wong +2 more
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Offline and online identification of hidden semi-Markov models
IEEE Transactions on Signal Processing, 2005We present a new signal model for hidden semi-Markov models (HSMMs). Instead of constant transition probabilities used in existing models, we use state-duration-dependant transition probabilities. We show that our modeling approach leads to easy and efficient implementation of parameter identification algorithms.
Mehran Azimi +2 more
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On efficient Viterbi decoding for hidden semi-Markov models
2008 19th International Conference on Pattern Recognition, 2008We present algorithms for improved Viterbi decoding for the case of hidden semi-Markov models. By carefully constructing directed acyclic graphs, we pose the decoding problem as that of finding the longest path between specific pairs of nodes. We consider fully connected models as well as restrictive topologies and state duration conditions, and show ...
Ritendra Datta +2 more
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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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