A parameter estimation algorithm of the continuous hidden Markov model isintroduced and the rigorous proof of its convergence is also included. The algorithm uses theViterbi algorithm instead of K-means clustering use...A parameter estimation algorithm of the continuous hidden Markov model isintroduced and the rigorous proof of its convergence is also included. The algorithm uses theViterbi algorithm instead of K-means clustering used in the segmental K-means algorithm to determineoptimal state and branch sequences. Based on the optimal sequence, parameters are estimated withmaximum-likelihood as objective functions. Comparisons with the traditional Baum-Welch and segmentalK-means algorithms on various aspects, such as optimal objectives and fundamentals, are made. Allthree algorithms are applied to face recognition. Results indicate that the proposed algorithm canreduce training time with comparable recognition rate and it is least sensitive to the training set.So its average performance exceeds the other two.展开更多
Modeling non coding background sequences appropriately is important for the detection of regulatory elements from DNA sequences. Based on the chi square statistic test, some explanations about why to choose higher ...Modeling non coding background sequences appropriately is important for the detection of regulatory elements from DNA sequences. Based on the chi square statistic test, some explanations about why to choose higher order Markov chain model and how to automatically select the proper order are given in this paper. The chi square test is first run on synthetic data sets to show that it can efficiently find the proper order of Markov chain. Using chi square test, distinct higher order context dependences inherent in ten sets of sequences of yeast S.cerevisiae from other literature have been found. So the Markov chain with higher order would be more suitable for modeling the non coding background sequences than an independent model.展开更多
Markov random fields(MRF) have potential for predicting and simulating petroleum reservoir facies more accurately from sample data such as logging, core data and seismic data because they can incorporate interclass re...Markov random fields(MRF) have potential for predicting and simulating petroleum reservoir facies more accurately from sample data such as logging, core data and seismic data because they can incorporate interclass relationships. While, many relative studies were based on Markov chain, not MRF, and using Markov chain model for 3D reservoir stochastic simulation has always been the difficulty in reservoir stochastic simulation. MRF was proposed to simulate type variables(for example lithofacies) in this work. Firstly, a Gibbs distribution was proposed to characterize reservoir heterogeneity for building 3-D(three-dimensional) MRF. Secondly, maximum likelihood approaches of model parameters on well data and training image were considered. Compared with the simulation results of MC(Markov chain), the MRF can better reflect the spatial distribution characteristics of sand body.展开更多
The burst error of wireless channels and the two-state Markov wireless model are analyzed. Based on this model and the coding modes of the video encoder,the channel distortion of inter-coding and intra-coding due to b...The burst error of wireless channels and the two-state Markov wireless model are analyzed. Based on this model and the coding modes of the video encoder,the channel distortion of inter-coding and intra-coding due to burst error is deduced. Then we propose a novel intra refresh scheme in rate-distortion (R-D) framework. This scheme optimizes the error resilience and coding efficiency of wireless video transmission system. It can also stop error propagation and reduce channel distortion effectively. Simulations under different channel conditions verify the improvements of the proposed scheme with respect to error resilience for wireless video communication.展开更多
Along with the development of motion capture technique, more and more 3D motion databases become available. In this paper, a novel approach is presented for motion recognition and retrieval based on ensemble HMM (hidd...Along with the development of motion capture technique, more and more 3D motion databases become available. In this paper, a novel approach is presented for motion recognition and retrieval based on ensemble HMM (hidden Markov model) learning. Due to the high dimensionality of motion’s features, Isomap nonlinear dimension reduction is used for training data of ensemble HMM learning. For handling new motion data, Isomap is generalized based on the estimation of underlying eigen- functions. Then each action class is learned with one HMM. Since ensemble learning can effectively enhance supervised learning, ensembles of weak HMM learners are built. Experiment results showed that the approaches are effective for motion data recog- nition and retrieval.展开更多
With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available fro...With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available from IoT. Information can be analyzed to learn user intentions and automatically provide the appropriate services. However, existing service recommendation models typically do not consider the services that are unavailable in a user's living environment. In order to address this problem, we propose a series of semantic models for SH devices. These semantic models can be used to infer user intentions. Based on the models, we proposed a service recommendation probability model and an alternative-service recommending algorithm. The algorithm is devoted to providing appropriate alternative services when the desired service is unavailable. The algorithm has been implemented and achieves accuracy higher than traditional Hidden Markov Model(HMM). The maximum accuracy achieved is 68.3%.展开更多
In this paper,we present a new self-similar traffic shaping mechanism and investigate its effect on the output traffic characteristics and network performance under self-similar traffic.Simulation results show that ou...In this paper,we present a new self-similar traffic shaping mechanism and investigate its effect on the output traffic characteristics and network performance under self-similar traffic.Simulation results show that our proposed mechanism can not only effectively reduce the burstiness of input traffic,but also perform better than the non-traffic shaping scheme in the terms of packet-loss rate and blocking probability.展开更多
A semi-Markov model is proposed for congestion in weaving section at right exit with two lanes due to the method of cells modeling. The relationship among the arriving rate, turning-right rate, giving-way rate and the...A semi-Markov model is proposed for congestion in weaving section at right exit with two lanes due to the method of cells modeling. The relationship among the arriving rate, turning-right rate, giving-way rate and the congestion states is hence formulated by using the generating function. The results are illustrated that the weak interruption on traffic flow may leads to heavy congestion in road network although the capacity of main way and exit is definitely greater than the traffic flow.展开更多
文摘A parameter estimation algorithm of the continuous hidden Markov model isintroduced and the rigorous proof of its convergence is also included. The algorithm uses theViterbi algorithm instead of K-means clustering used in the segmental K-means algorithm to determineoptimal state and branch sequences. Based on the optimal sequence, parameters are estimated withmaximum-likelihood as objective functions. Comparisons with the traditional Baum-Welch and segmentalK-means algorithms on various aspects, such as optimal objectives and fundamentals, are made. Allthree algorithms are applied to face recognition. Results indicate that the proposed algorithm canreduce training time with comparable recognition rate and it is least sensitive to the training set.So its average performance exceeds the other two.
文摘Modeling non coding background sequences appropriately is important for the detection of regulatory elements from DNA sequences. Based on the chi square statistic test, some explanations about why to choose higher order Markov chain model and how to automatically select the proper order are given in this paper. The chi square test is first run on synthetic data sets to show that it can efficiently find the proper order of Markov chain. Using chi square test, distinct higher order context dependences inherent in ten sets of sequences of yeast S.cerevisiae from other literature have been found. So the Markov chain with higher order would be more suitable for modeling the non coding background sequences than an independent model.
基金Project(2011ZX05002-005-006)supported by the National "Twelveth Five Year" Science and Technology Major Research Program,China
文摘Markov random fields(MRF) have potential for predicting and simulating petroleum reservoir facies more accurately from sample data such as logging, core data and seismic data because they can incorporate interclass relationships. While, many relative studies were based on Markov chain, not MRF, and using Markov chain model for 3D reservoir stochastic simulation has always been the difficulty in reservoir stochastic simulation. MRF was proposed to simulate type variables(for example lithofacies) in this work. Firstly, a Gibbs distribution was proposed to characterize reservoir heterogeneity for building 3-D(three-dimensional) MRF. Secondly, maximum likelihood approaches of model parameters on well data and training image were considered. Compared with the simulation results of MC(Markov chain), the MRF can better reflect the spatial distribution characteristics of sand body.
文摘The burst error of wireless channels and the two-state Markov wireless model are analyzed. Based on this model and the coding modes of the video encoder,the channel distortion of inter-coding and intra-coding due to burst error is deduced. Then we propose a novel intra refresh scheme in rate-distortion (R-D) framework. This scheme optimizes the error resilience and coding efficiency of wireless video transmission system. It can also stop error propagation and reduce channel distortion effectively. Simulations under different channel conditions verify the improvements of the proposed scheme with respect to error resilience for wireless video communication.
基金Project supported by the National Natural Science Foundation of China (Nos. 60533090 and 60525108), the National Basic Research Program (973) of China (No. 2002CB312101), and the Science and Technology Project of Zhejiang Province (Nos. 2005C13032 and 2005C11001-05), China
文摘Along with the development of motion capture technique, more and more 3D motion databases become available. In this paper, a novel approach is presented for motion recognition and retrieval based on ensemble HMM (hidden Markov model) learning. Due to the high dimensionality of motion’s features, Isomap nonlinear dimension reduction is used for training data of ensemble HMM learning. For handling new motion data, Isomap is generalized based on the estimation of underlying eigen- functions. Then each action class is learned with one HMM. Since ensemble learning can effectively enhance supervised learning, ensembles of weak HMM learners are built. Experiment results showed that the approaches are effective for motion data recog- nition and retrieval.
基金supported by the National Key Research and Development Program(No.2016YFB0800302)
文摘With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available from IoT. Information can be analyzed to learn user intentions and automatically provide the appropriate services. However, existing service recommendation models typically do not consider the services that are unavailable in a user's living environment. In order to address this problem, we propose a series of semantic models for SH devices. These semantic models can be used to infer user intentions. Based on the models, we proposed a service recommendation probability model and an alternative-service recommending algorithm. The algorithm is devoted to providing appropriate alternative services when the desired service is unavailable. The algorithm has been implemented and achieves accuracy higher than traditional Hidden Markov Model(HMM). The maximum accuracy achieved is 68.3%.
基金Supported by the National Natural Science Foundation of China(No.60132020 ,60302026)
文摘In this paper,we present a new self-similar traffic shaping mechanism and investigate its effect on the output traffic characteristics and network performance under self-similar traffic.Simulation results show that our proposed mechanism can not only effectively reduce the burstiness of input traffic,but also perform better than the non-traffic shaping scheme in the terms of packet-loss rate and blocking probability.
基金This project is Supported by National Natural Science Foundation of China（70131160744, 70521001 and NCET-04-0173）
文摘A semi-Markov model is proposed for congestion in weaving section at right exit with two lanes due to the method of cells modeling. The relationship among the arriving rate, turning-right rate, giving-way rate and the congestion states is hence formulated by using the generating function. The results are illustrated that the weak interruption on traffic flow may leads to heavy congestion in road network although the capacity of main way and exit is definitely greater than the traffic flow.
