• 한국통신학회논문지
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• 제41권8호
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• pp.924-927
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• 2016

In this paper, we consider a cumulative distribution function (CDF)-based opportunistic scheduling for downlink transmission in a cellular network consisting of a base station and multiple mobile stations. We present a closed-form formula for the average starvation period of each mobile station (i.e., the length of the time interval between two successive scheduling points of a mobile station) over Markov time-varying channels. Based on our formula, we investigate the starvation period of the CDF-based scheduling for various system parameters.

• Journal of Information Processing Systems
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• 제11권4호
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• pp.616-629
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• 2015

A mobile terminal will expect a number of handoffs within its call duration. In the event of a mobile call, when a mobile node moves from one cell to another, it should connect to another access point within its range. In case there is a lack of support of its own network, it must changeover to another base station. In the event of moving on to another network, quality of service parameters need to be considered. In our study we have used the Markov decision process approach for a seamless handoff as it gives the optimum results for selecting a network when compared to other multiple attribute decision making processes. We have used the network cost function for selecting the network for handoff and the connection reward function, which is based on the values of the quality of service parameters. We have also examined the constant bit rate and transmission control protocol packet delivery ratio. We used the policy iteration algorithm for determining the optimal policy. Our enhanced handoff algorithm outperforms other previous multiple attribute decision making methods.

• Industrial Engineering and Management Systems
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• 제14권2호
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• pp.210-219
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• 2015

A classification task requires an exponentially growing amount of computation time and number of observations as the variable dimensionality increases. Thus, reducing the dimensionality of the data is essential when the number of observations is limited. Often, dimensionality reduction or feature selection leads to better classification performance than using the whole number of features. In this paper, we study the possibility of utilizing the Markov blanket discovery algorithm as a new feature selection method. The Markov blanket of a target variable is the minimal variable set for explaining the target variable on the basis of conditional independence of all the variables to be connected in a Bayesian network. We apply several Markov blanket discovery algorithms to some high-dimensional categorical and continuous data sets, and compare their classification performance with other feature selection methods using well-known classifiers.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1043-1050
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• 2022

Markov chains and Monte Carlo Simulation were applied to account for the probabilistic nature of pavement deterioration over time using data collected in the field. The primary purpose of this study was to evaluate pavement network performance of Western Australia (WA) by applying the existing pavement management tools relevant to WA road construction networks. Two approaches were used to analyze the pavement networks: evaluating current pavement performance data to assess WA State Road networks and predicting the future states using past and current pavement data. The Markov chains process and Monte Carlo Simulation methods were used to predicting future conditions. The results indicated that Markov chains and Monte Carlo Simulation prediction models perform well compared to pavement performance data from the last four decades. The results also revealed the impact of design, traffic demand, and climate and construction standards on urban pavement performance. This study recommends an appropriate and effective pavement engineering management system for proper pavement design and analysis, preliminary planning, future pavement maintenance and rehabilitation, service life, and sustainable pavement construction functionality.

• 한국콘텐츠학회논문지
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• 제16권4호
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• pp.224-230
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• 2016

최근 인터넷 멀티미디어 서비스의 폭발적인 증가에 따라 급증하는 데이터 트래픽을 효율적으로 전송하기 위한 콘텐츠 중심 네트워크에 대한 연구가 국내외적으로 활발하다. 콘텐츠 중심 네트워크는 기존의 호스트 중심의 전송방식에서 벗어나 콘텐츠를 중심으로 데이터 트래픽을 전송하는 네트워킹 방법이다. 본 논문에서는 기존의 시뮬레이션과 달리 수리적인 접근 방법을 이용하여 두 개 라우터로 구성된 콘텐츠 중심 네트워크의 캐싱 성능을 분석한다. 통신 네트워크의 확률적 상황을 고려하여 라우터가 두 개인 콘텐츠 중심 네트워크의 캐싱시스템을 이차원의 연속시간 마코프체인으로 모형화한다. 전이율행렬의 구조를 분석하여 캐싱 성능치의 수치해를 유도하고 다양한 수치 예제를 제시한다.

• 대한음성학회지:말소리
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• 제62호
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• pp.113-131
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• 2007

It is reported that the segmental feature based recognition system shows better results than conventional feature based system in the previous studies. On the other hand, the various studies of combining neural network and hidden Markov models within a single system are done with expectations that it may potentially combine the advantages of both systems. With the influence of these studies, tandem approach was presented to use neural network as the classifier and hidden Markov models as the decoder. In this paper, we applied the trend information of segmental features to tandem architecture and used posterior probabilities, which are the output of neural network, as inputs of recognition system. The experiments are performed on Auroral database to examine the potentiality of the trend feature based tandem architecture. From the results, the proposed system outperforms on very low SNR environments. Consequently, we argue that the trend information on tandem architecture can be additionally used for traditional MFCC features.

• 한국통신학회논문지
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• 제36권5A호
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• pp.473-484
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• 2011

본 논문에서는 LTE와 WLAN이 존재하는 이종 네트워크 환경에서 네트워크 선택과 자원 예약 기반의 호 수락 제어 기법을 결합한 통합 호 수락 제어 기법을 제시하고 성능을 분석한다. 이를 위해 LTE 와 WLAN이 중첩된 네트워크 환경에서 단말이 네트워크를 선택할 때의 네트워크 결정률과 자원예약기법이 적용된 통합 호 수락 제어시스템을 제안하고 마코프 체인(Markov Chain) 모델링을 통해 제안된 시스템의 성능을 분석한다. 본 논문에서는 성능지표로서 LTE와 WLAN 각 네트워크 신규호 차단률(New call Blocking Probability), 핸드오프호의 절단률(Handoff call droppng Probability), RB 사용률(Resource Block utilization)을 사용한다. 본 논문의 분석결과로서 통합 호 수락 제어 기법을 적용한 시스템이 적용되지 않은 시스템에 비해 나은 성능을 보였고, 통함 호 수락 제어 기법을 적용할 경우에는 전체의 10%를 자원예약비율로 선택하는 것이 적합함을 보였다. 본 논문의 결과는 향후 LTE와 WLAN의 중첩된 네트워크 환경에서 자원예약기법이 적용된 통합 호 수락 제어 시스템 도입 시에 참고할 만한 자원예약률의 가이드라인을 제시할 수 있을 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5631-5652
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• 2019

The existing defense strategy selection methods based on game theory basically select the optimal defense strategy in the form of mixed strategy. However, it is hard for network managers to understand and implement the defense strategy in this way. To address this problem, we constructed the incomplete information stochastic game model for the dynamic analysis to predict multi-stage attack-defense process by combining Bayesian game theory and the Markov decision-making method. In addition, the payoffs are quantified from the impact value of attack-defense actions. Based on previous statements, we designed an optimal defense strategy selection method. The optimal defense strategy is selected, which regards defense effectiveness as the criterion. The proposed method is feasibly verified via a representative experiment. Compared to the classical strategy selection methods based on the game theory, the proposed method can select the optimal strategy of the multi-stage attack-defense process in the form of pure strategy, which has been proved more operable than the compared ones.

• 한국지하수토양환경학회지:지하수토양환경
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• 제20권3호
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• pp.51-64
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• 2015

In the present study, we develop two history matching techniques based on Markov chain Monte Carlo method where radial basis function and Gaussian distribution generated by unconditional geostatistical simulation are employed as the random walk transition kernels. The Bayesian inverse methods for aquifer characterization as the developed models can be effectively applied to the condition even when the targeted information such as hydraulic conductivity is absent and there are transient hydraulic head records due to imposed stress at observation wells. The model which uses unconditional simulation as random walk transition kernel has advantage in that spatial statistics can be directly associated with the predictions. The model using radial basis function network shares the same advantages as the model with unconditional simulation, yet the radial basis function network based the model does not require external geostatistical techniques. Also, by employing radial basis function as transition kernel, multi-scale nested structures can be rigorously addressed. In the validations of the developed models, the overall predictabilities of both models are sound by showing high correlation coefficient between the reference and the predicted. In terms of the model performance, the model with radial basis function network has higher error reduction rate and computational efficiency than with unconditional geostatistical simulation.

• 경영정보학연구
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• 제24권1호
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• pp.1-19
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• 2022

온라인 사용자들이 소셜 미디어상에 올린 온라인 리뷰 속 숨겨진 감정을 분석하는 감성분석은 소셜미디어의 확산에 힘입어 많은 관심을 받고 있다. 본 연구는 기존 연구들과 차별화된 방법으로 감성분석을 시도하기 위하여 베이지안 네트워크에 기반한 감성 분석 모델을 제안한다. 모델에는 MBFS(Markov Blanket-based Feature Selection)가 속성 선택 기법으로 사용된다. MBFS의 성과를 실증적으로 증명하기 위하여 소셜미디어인 Yelp의 리뷰 데이터를 활용하였다. 벤치마킹 속성 선택 기법으로는 상관관계기반 속성 선택, 정보획득 속성 선택, 획득비율 속성 선택을 사용하였다. 한편, 해당 속성선택방법을 토대로 4개의 머신러닝 알고리즘을 이용하여 분류성과를 비교하였다. 나아가 MBFS로 선택된 속성들 간 인과관계를 확인하고자 베이지안 네트워크를 통해 What-if 분석을 실시하였다. 본 연구에서 택한 머신러닝 분류기는 베이지안 네트워크 기반의 TAN (Tree Augmented Naive Bayes), NB (Naive Bayes), S-Spouses(Sons & Spouses), A-markov (Augmented Markov Blanket)이다. 성과분석 결과 본 연구에서 제안한 MBFS 방법이 정확도, 정밀도, F1점수 측면에서 벤치마킹 방법보다 더 우수한 성과를 나타내었다.