• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1036-1057
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• 2013

Wireless body area network (WBAN) is a promising candidate for future health monitoring system. Nevertheless, the path to mature solutions is still facing a lot of challenges that need to be overcome. Energy efficient scheduling is one of these challenges given the scarcity of available energy of biosensors and the lack of portability. Therefore, researchers from academia, industry and health sectors are working together to realize practical solutions for these challenges. The main difficulty in WBAN is the uncertainty in the state of the monitored system. Intelligent learning approaches such as a Markov Decision Process (MDP) were proposed to tackle this issue. A Markov Decision Process (MDP) is a form of Markov Chain in which the transition matrix depends on the action taken by the decision maker (agent) at each time step. The agent receives a reward, which depends on the action and the state. The goal is to find a function, called a policy, which specifies which action to take in each state, so as to maximize some utility functions (e.g., the mean or expected discounted sum) of the sequence of rewards. A partially Observable Markov Decision Processes (POMDP) is a generalization of Markov decision processes that allows for the incomplete information regarding the state of the system. In this case, the state is not visible to the agent. This has many applications in operations research and artificial intelligence. Due to incomplete knowledge of the system, this uncertainty makes formulating and solving POMDP models mathematically complex and computationally expensive. Limited progress has been made in terms of applying POMPD to real applications. In this paper, we surveyed the existing methods and algorithms for solving POMDP in the general domain and in particular in Wireless body area network (WBAN). In addition, the papers discussed recent real implementation of POMDP on practical problems of WBAN. We believe that this work will provide valuable insights for the newcomers who would like to pursue related research in the domain of WBAN.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.40-48
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• 2007

This paper presents a novel texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields in multiscale Bayesian framework. Multiscale wavelet coefficients are used as input for the neural networks. The output of the neural network is modeled as a posterior probability. Texture classification at each scale is performed by the posterior probabilities from MLP networks and MAP (maximum a posterior) classification. Then, in order to obtain the more improved segmentation result at the finest scale, our proposed method fuses the multiscale MAP classifications sequentially from coarse to fine scales. This process is done by computing the MAP classification given the classification at one scale and a priori knowledge regarding contextual information which is extracted from the adjacent coarser scale classification. In this fusion process, the MRF (Markov random field) prior distribution and Gibbs sampler are used, where the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. The proposed segmentation method shows better performance than texture segmentation using the HMT (Hidden Markov trees) model and HMTseg.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.663-667
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• 2010

A simple approach to semantic document-retrieval is to measure document similarity based on the bag-of-words representation, e.g., cosine similarity between two document vectors. However, such a syntactic method hardly considers the semantic similarity between documents, often producing semantically-unsound search results. We circumvent such a problem by combining supervised machine learning techniques with ontology information based on Markov logic. Specifically, Markov logic networks are learned from similarity-tagged documents with an ontology representing the diverse relationship among words. The learned Markov logic networks, the ontology, and the training documents are applied to the semantic document-retrieval task by inferring similarities between a query document and the training documents. Through experimental evaluation on real world question-answering data, the proposed method has been shown to outperform the simple cosine similarity-based approach in terms of retrieval accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.5
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• pp.805-814
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• 1994

In this paper, we introduce a new method which is available for analyzing the throughput of the packet radio network by using the auxiliary Markov transient matrix with a failure state and a success state. And we consider the effect of symbol error for the network state(X, R) consisted of the number of transmitting PRU X and receiving PRU R. We examine the packet radio network of a continuous time Markov chain model, and the direct sequence binary phase shift keying CDMA radio channel with hard decision Viterbi decoding and bit-by-bit changing spreading code. For the unslotted distributed multi-hop packet radio network, we assume that the packet error due to a symbol error of radio channel has Poisson process, and the time period of an error occurrence is exponentially distributed. Through the throughputs which are found as a function of radio channel parameters, such as the received signal to noise ratio and chips of spreading code per symbol, and of network parameters, such as the number of PRU and offered traffic rate, it is shown that this composite analysis enables us to combine the Markovian packet radio network model with a coded DS/BPSK CDMA radio channel.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.2
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• pp.58-70
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• 2007

The purpose of this study is to predict future land use using Landsat images through assessing the effect of spatial scale and resolution in applying CA (Cellular Automata)-Markov technique. The scale for areas ranging from $31.26km^2$ to $84.48km^2$ showed about 11% difference of overall accuracies. Among the five spatial resolutions (10m, 30m, 50m, 100m, 150m), 30m resolution showed the best result in the prediction of area and spatial distribution of urban areas. Based on the results, the 2004 land use by CA-Markov was predicted using 1996 and 2001 land use data and compared with the 2004 land use by maximum likelihood classification. After that, future land uses of 2030, 2060 and 2090 were predicted and the results showed a tendency of gradual increase in urban area and high decrease in forest area.

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.27-33
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• 2010

This paper applies Bayesian Markov inferred localization techniques for determining ZigBee mobile device's position. To evaluate its accuracy, we compare it with conventional technique, map-based localization. While the map-based localization technique referring to database of predefined locations and their RSSI data, the Bayesian Markov inferred localization is influenced by changes of time, direction and distance. All determinations are drawn from the estimation of Received Signal Strength (RSS) using ZigBee modules. Our results show the relationship between RSSI and distance in indoor ZigBee environment and higher localization accuracy of Bayesian Markov localization technique. We conclude that map-based localization is not suitable for flexible changes in indoors because of its predefined condition setup and lower accuracy comparing to distance-based Markov Chain inference localization system.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.88-88
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• 2011

수자원에서 일강수량 모의기법은 다양한 목적으로 활용되고 있으며 기본적으로 수공구조물 설계 및 수자원계획을 수립하기 위한 입력 자료로서 이용된다. 수자원계획은 장기적인 목적을 가지고 수행되는 것이 일반적이며 우리가 목표로 하는 장기간의 일강수량자료의 획득이 어렵기 때문에 단기간의 일강수량자료를 장기 모의하여 이용하게 된다. 일강수량을 모의하는데 있어서 강수계열의 단기간의 기억(memory)을 활용한 Markov Chain 모형이 가장 일반적이며, 기존 Markov Chain 모형을 통한 일강수량 모의에서 발생하는 가장 큰 문제점은 극치강수량을 재현하기 어렵다는 점이다. 이러한 문제점으로 인해 수자원 계획을 수립하는데 있어서 불확실성을 가중시키고 있다. 특히 일강수량 모의기법을 통해서 추정되는 빈도강수량의 과소추정으로 인해 수공구조물 설계 시에 신뢰성을 확보하는 데 문제점이 있다. 이러한 점에서 본 연구에서는 기존 Markov Chain 모형에서 일강수량에 평균적인 특성과 극치특성을 동시에 재현할 수 있도록 불연속 Kernel-Pareto Distribution 기반에 일강수량모의기법을 개발하였다. 한강유역의 3개 강수지점에 대해서 기존 Markov Chain 모형과 본 연구에서 제안한 방법을 적용한 결과 여름의 일강수량 모의 시 1차모멘트인 평균과 2-3차 모멘트 모두 효과적으로 재현하지 못하는 문제점이 나타났다. 그러나 본 연구에서 제안한 불연속 Kernel-Pareto 분포형 기반 Markov Chain 모형은 여름의 일강수량 모의 시 강수계열의 평균적인 특성뿐만 아니라 표준편차 및 왜곡도의 경우에도 관측치의 통계특성을 매우 효과적으로 재현하는 것으로 나타났다. 본 연구에서 제시한 방법론은 전체적으로 기존 Markov Chain 모형에 비해 극치강수량을 재현하는데 유리한 기법으로 판단되며, 또한 극치강수량을 일반강수량으로부터 분리하여 모의함으로서 평균 및 중간값 등 낮은 차수에 모멘트 등 일강수량에 전체적인 분포특성을 더욱 효과적으로 모의할 수 장점을 확인하였다.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.6
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• pp.79-94
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• 2018

The purpose of this study is to specify a probabilistic tracking mechanism for customer luxury purchase implemented by hidden Markov model, Bayesian inference, customer satisfaction and net promoter score. In this paper, we have designed a probabilistic model based on customer's actual data containing purchase or non-purchase states by tracking the SPC chain : customer satisfaction -> customer referral -> purchase/non-purchase. By applying hidden Markov model and Viterbi algorithm to marketing theory, we have developed the statistical model related to probability theories and have found the best purchase pattern scenario from customer's purchase records.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.99-100
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• 2014

• Proceedings of the Korea Water Resources Association Conference
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• 1998.05a
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• pp.133-139
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• 1998