Results 221 to 230 of about 116,234 (262)

Weibull partition models with applications to hidden semi-Markov models

2017 International Joint Conference on Neural Networks (IJCNN), 2017
We 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
openaire   +1 more source

Using Hidden Semi-Markov Model for learning behavior in smarthomes

2015 IEEE International Conference on Automation Science and Engineering (CASE), 2015
Within 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.
Paris, Arnaud, Arbaoui, Selma, Cislo, Nathalie, El Amraoui, Adnen, Ramdani, Nacim   +4 more
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The Prediction of Remaining Useful Life of Aluminum Reduction Cells Based on Improved Hidden Semi-Markov Model

JOM, 2023
Jiarui Cui, Chengguo Su, Xiangquan Li, Ruoyu Huang, Qun Yan, Bin Cao, Qing Li, Jiashan Cui   +7 more
semanticscholar   +1 more source

Human-robot collaboration empowered by hidden semi-Markov model for operator behaviour prediction in a smart assembly system

Journal of manufacturing systems, 2022
Chiu-Hsiang Lin, Kung-Jeng Wang, A. A. Tadesse, B. H. Woldegiorgis   +3 more
semanticscholar   +1 more source

Reliability modeling with hidden Markov and semi-Markov chains

2013 IEEE Integration of Stochastic Energy in Power Systems Workshop (ISEPS), 2013
Abstract form only given. Semi-Markov processes and Markov renewal processes represent a class of stochastic processes that generalize Markov and renewal processes. As it is well known, for a discrete-time (respectively continuous-time) Markov process, the sojourn time in each state is geometrically (respectively exponentially) distributed. In the semi-
openaire   +2 more sources

Online identification of hidden Semi-Markov models

3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the, 2004
Hidden 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.
Panos Nasiopoulos, Rabab K. Ward, Maryam Azimi   +2 more
openaire   +2 more sources

Residual useful life prognosis of equipment based on modified hidden semi-Markov model with a co-evolutional optimization method

Computers & industrial engineering, 2023
Qinming Liu, Wenyi Liu, Ming Dong, Zhinan Li, Yihan Zheng   +4 more
semanticscholar   +1 more source

Fault prognosis of HVAC air handling unit and its components using hidden-semi Markov model and statistical process control

Energy and Buildings, 2021
Ying Yan, Jun Cai, Tao Li, Wan Zhang, Liangliang Sun   +4 more
semanticscholar   +1 more source

A new multimode process monitoring method based on a hierarchical Dirichlet process—Hidden semi-Markov model with application to the hot steel strip mill process

, 2021
Jie Dong, Chi Zhang, Kai-xiang Peng
semanticscholar   +1 more source

Online Tool Wear Monitoring Via Hidden Semi-Markov Model With Dependent Durations

IEEE Transactions on Industrial Informatics, 2018
K. Zhu, Tongshun Liu
semanticscholar   +1 more source