The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Shubin Si - One of the best experts on this subject based on the ideXlab platform.
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semi Markov Process based integrated importance measure for multi state systems
IEEE Transactions on Reliability, 2015Co-Authors: Shubin SiAbstract:Importance measures in reliability engineering are used to identify weak components of a system and signify the roles of components in contributing to proper functioning of the system. Recently, an integrated importance measure (IIM) has been proposed to evaluate how the transition of component states affects the system performance based on the probability distributions and transition rates of component states. In the system operation phase, the bathtub curve presents the change of the transition rate of component states with time, which can be described by three different Weibull distributions. The behavior of a system under such distributions can be modeled by the semi-Markov Process. So, based on the reported IIM equations of component states, this paper studies how the transition of component states affects system performance under the semi-Markov Process. This measure can provide useful information for preventive actions (such as monitoring enhancement, construction improvement, etc.), and provide support to improve system performance. Finally, a simple numerical example is presented to illustrate the utilization of the proposed method.
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Research of predictive maintenance for deteriorating system based on semi-Markov Process
2009 16th International Conference on Industrial Engineering and Engineering Management, 2009Co-Authors: Ning Wang, Shudong Sun, Shubin Si, Jingyao LiAbstract:The paper proposes a predictive maintenance model for the deteriorating system with semi-Markov Process, and presents a method to determine the best inspection and maintenance policy together. Furthermore, the phase-type (PH) algorithm is put forward to measure the transition probability matrix analytical tractability. The results of numerical simulation show that the model and algorithm are effective in improving maximal availability while optimizing the inspection rate. And it is also found that when the deterioration is the same at each failure stage, the optimal policy obtained by semi-Markov decision Process with the phase-type approach (PSMDP) is a dynamic threshold scheme whose threshold value relates to the inspection rate.
Sabrina Mulinacci - One of the best experts on this subject based on the ideXlab platform.
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mixing and moments properties of a non stationary copula based Markov Process
Communications in Statistics-theory and Methods, 2019Co-Authors: Fabio Gobbi, Sabrina MulinacciAbstract:AbstractWe provide conditions under which a non-stationary copula-based Markov Process is geometric β-mixing and geometric ρ-mixing. Our results generalize some results of Beare who considers the s...
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beta mixing and moments properties of a non stationary copula based Markov Process
arXiv: Statistics Theory, 2017Co-Authors: Fabio Gobbi, Sabrina MulinacciAbstract:This paper provides conditions under which a non-stationary copula-based Markov Process is $\beta$-mixing. We introduce, as a particular case, a convolution-based gaussian Markov Process which generalizes the standard random walk allowing the increments to be dependent.
Stuart C. Schwartz - One of the best experts on this subject based on the ideXlab platform.
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Object Tracking by Finite-State Markov Process
2007 IEEE International Conference on Acoustics Speech and Signal Processing - ICASSP '07, 2007Co-Authors: Lan Dong, Stuart C. SchwartzAbstract:The general problem of object tracking can be modeled as a Markov Process and solved by computing probability distributions of the possible object states, followed by MAP estimation. This paper presents a new framework for the efficient estimation of the probability distribution of the states. In contrast to particle filters, where the possible states are numerous and random, we limit the possible states to a finite candidate set which is guaranteed with high probability to contain the true state of the object. After the problem is reduced to a finite-state Markov Process (FSM), forward filtering is used to estimate the distribution of the object state. Moreover, the Viterbi algorithm can also be used to estimate the most likely state sequence. We test the new framework by both these methods and compare the tracking results. Experimental results show the effectiveness and efficacy of the proposed algorithm.
Fabio Gobbi - One of the best experts on this subject based on the ideXlab platform.
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mixing and moments properties of a non stationary copula based Markov Process
Communications in Statistics-theory and Methods, 2019Co-Authors: Fabio Gobbi, Sabrina MulinacciAbstract:AbstractWe provide conditions under which a non-stationary copula-based Markov Process is geometric β-mixing and geometric ρ-mixing. Our results generalize some results of Beare who considers the s...
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beta mixing and moments properties of a non stationary copula based Markov Process
arXiv: Statistics Theory, 2017Co-Authors: Fabio Gobbi, Sabrina MulinacciAbstract:This paper provides conditions under which a non-stationary copula-based Markov Process is $\beta$-mixing. We introduce, as a particular case, a convolution-based gaussian Markov Process which generalizes the standard random walk allowing the increments to be dependent.
Fatih Kamisli - One of the best experts on this subject based on the ideXlab platform.
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Intra Prediction Based on Markov Process Modeling of Images
IEEE Transactions on Image Processing, 2013Co-Authors: Fatih KamisliAbstract:In recent video coding standards, intraprediction of a block of pixels is performed by copying neighbor pixels of the block along an angular direction inside the block. Each block pixel is predicted from only one or few directionally aligned neighbor pixels of the block. Although this is a computationally efficient approach, it ignores potentially useful correlation of other neighbor pixels of the block. To use this correlation, a general linear prediction approach is proposed, where each block pixel is predicted using a weighted sum of all neighbor pixels of the block. The disadvantage of this approach is the increased complexity because of the large number of weights. In this paper, we propose an alternative approach to intraprediction, where we model image pixels with a Markov Process. The Markov Process model accounts for the ignored correlation in standard intraprediction methods, but uses few neighbor pixels and enables a computationally efficient recursive prediction algorithm. Compared with the general linear prediction approach that has a large number of independent weights, the Markov Process modeling approach uses a much smaller number of independent parameters and thus offers significantly reduced memory or computation requirements, while achieving similar coding gains with offline computed parameters.
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Intra prediction based on Markov Process modeling of images
2013 IEEE International Conference on Image Processing, 2013Co-Authors: Fatih KamisliAbstract:In our previous work, we proposed a new approach to intra prediction, in which we model image pixels with a separable first-order Markov Process. The used Markov Process is separable and therefore the developed method was only applied to intra prediction with vertical, horizontal and DC modes. In this paper, we extend our previous work by developing intra prediction methods based on non-separable Markov models and apply them to intra prediction along any angular direction. Compared to general linear prediction approaches, in which each block pixel is predicted using a weighted sum of all neighbor pixels of the block, the proposed approach uses much fewer independent parameters and thus offers reduced memory or computation requirements, while achieving similar coding gains.
