• 한국수자원학회논문집
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• 제48권12호
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• pp.969-979
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• 2015

본 연구에서는 가뭄의 특성분석에 유리하며, 확률론적 접근이 가능한 은닉 마코프 모델(HMM) 기반의 가뭄 분석 기법을 적용하였다. HMM 기반의 가뭄의 심도뿐만 아니라 지속시간을 동시에 평가할 수 있도록 코플라 함수 기반의 이변량 가뭄빈도해석 기법을 도입하여 우리나라의 2015년 가뭄 빈도를 평가하였다. 가뭄빈도분석 결과 최근 40년 자료를 기준으로 영동지방에 비해 영서지방이 전체적으로 가뭄이 발생할 경우 가뭄의 심도가 큰 것으로 평가되었다. 심한가뭄의 발생 비율의 경우에 철원의 경우 10%를 상회하는 등 임진강 유역에서 상대적으로 심한가뭄의 발생비율이 크다는 것을 확인할 수 있었다. 한강유역 일부지점에서는 2014/2015년의 가뭄 지속기간 및 심도의 결합재현기간이 1,000년이 넘는 가뭄이 발생하고 있는 것으로 평가되었다. 특히 북한강 및 임진강 유역에 심한 가뭄이 발생하고 있으며 전반적으로 100년 이상의 기왕최대가뭄을 나타내고 있는 것으로 판단되었다.

• 산업경영시스템학회지
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• 제46권4호
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• pp.281-293
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• 2023

This study addresses the optimal design methodology for switching between active electronically scanned array (AESA) radar operating modes to easily select the necessary information to reduce pilots' cognitive load and physical workload in situations where diverse and complex information is continuously provided. This study presents a procedure for defining a hidden Markov chain model (HMM) for modeling operating mode changes based on time series data on the operating modes of the AESA radar used by pilots while performing mission scenarios with inherent uncertainty. Furthermore, based on a transition probability matrix (TPM) of the HMM, this study presents a mathematical programming model for proposing the optimal structural design of AESA radar operating modes considering the manipulation method of a hands on throttle-and-stick (HOTAS). Fighter pilots select and activate the menu key for an AESA radar operation mode by manipulating the HOTAS's rotary and toggle controllers. Therefore, this study presents an optimization problem to propose the optimal structural design of the menu keys so that the pilot can easily change the menu keys to suit the operational environment.

• 전기학회논문지P
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• 제58권2호
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• pp.164-171
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• 2009

This paper presents new learning algorithm of dynamic Bayesian networks (DBN) by means of constrained least square (LS) estimation algorithm and gradient descent method. First, we propose constrained LS based parameter estimation for a Markov chain (MC) model given observation data sets. Next, a gradient descent optimization is utilized for online estimation of a hidden Markov model (HMM), which is bi-linearly constructed by adding an observation variable to a MC model. We achieve numerical simulations to prove its reliability and superiority in which a series of non stationary random signal is applied for the DBN models respectively.

• 한국정보과학회논문지:소프트웨어및응용
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• 제28권1호
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• pp.46-51
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• 2001

2D로 확장한 HMM은 다수 제안되었지만 엄밀한 의미에 있어서 2D HMM이라고 하기에 부족한 점이 많다. 본 논문에서는 기존의 랜덤 필드 모형이 아닌 새로운 2D HMM을 제안한다. 상하 및 좌우 방향의 causal chain 관계를 가정하고 완전한 격자 형성 조건을 두어 2D HMM의 평가, 매개 변수를 추정하는 알고리즘을 제시하였다. 각각의 알고리즘은 동적 프로그래밍과 최우 추정법에 근거한 것이다. 변수 추정 알고리즘은 반복적으로 이루어지며 국소 최적치에 수렴함을 보였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권4호
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• pp.285-294
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• 2007

In this paper, we investigate a novel online estimation algorithm for dynamic Bayesian network(DBN) parameters, given as conditional probabilities. We sequentially update the parameter adjustment rule based on observation data. We apply our algorithm to two well known representations of DBNs: to a first-order Markov Chain(MC) model and to a Hidden Markov Model(HMM). A sliding window allows efficient adaptive computation in real time. We also examine the stochastic convergence and stability of the learning algorithm.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.41-44
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• 2005

Cytokines play a crucial role in the immune and inflammatory responses. But because of the high evolutionary rate of these proteins, the similarity between different members of their family is very low, which makes the identification of novel members of cytokines very difficult. According to this point, a new bioinformatic strategy to identify novel cytokine of the short-chain and long-chain 4${\alpha}$ helix cytokine using hidden markov model (HMM) is proposed in the paper. As a result, two motifs were created on the two train data sets, which were used to search three different databases. In order to improve the result, a strict criterion is established to filter the novel cytokines in the subject proteins. Finally, according to their E-value, scores and the criterion, four subject proteins are predicted to be possible novel cytokines for each family respectively.

• Journal of Multimedia Information System
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• 제8권1호
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• pp.31-34
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• 2021

The classical HMM is defined by a parameter triple �� = (��, A, B), where each parameter represents a collection of probability distributions: initial state, state transition and output distributions in order. This paper proposes a new stationary parameter e = (e1, e2, …, eN) where N is the number of states and et = P(|xt = i, y) for describing how an input pattern y ends in state xt = i at time t followed by nothing. It is often said that all is well that ends well. We argue here that all should end well. The paper sets the framework for the theory and presents an efficient inference and training algorithms based on dynamic programming and expectation-maximization. The proposed model is applicable to analyzing any sequential data with two or more finite segmental patterns are concatenated, each forming a context to its neighbors. Experiments on online Hangul handwriting characters have proven the effect of the proposed augmentation in terms of highly intuitive segmentation as well as recognition performance and 13.2% error rate reduction.

• 한국멀티미디어학회논문지
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• 제9권4호
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• pp.414-420
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• 2006

온라인 문자 인식 기술은 PDA, 타블릿 PC 등 많은 새로운 응용에서 사용되고 있으나, 인식 기술은 아직 이러한 첨단 도구들을 자연스럽게 이용하기에는 못 미치는 실정이다. 또한 최근 많이 사용되는 은닉 마르코프 모델(HMM)은 입력패턴을 전체 표준패턴과 비교함으로써 많은 기억장소와 계산량을 필요로 하는 단점을 지니고 있다. 따라서 본 논문에서는 더욱 효율적으로 온라인 문자 인식을 가능하게 하는 방법을 제안한다. 본 연구에서는 전처리 단계를 거쳐 학습 데이터와 인식 데이터의 체인코드를 생성하고, 인식 단계에서 입력 데이터에 주성분 분석(PCA) 기법을 적용하여 데이터의 차원을 줄여 문자를 인식한다. 제안하는 방법의 타당성은 실험을 통해서 검증한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권9호
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• pp.2285-2301
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• 2012

Peer-to-Peer (P2P) networks are becoming more and more popular in video content delivery services, such as Video on Demand (VoD). Scalability feature of P2P allows a higher number of simultaneous users at a given server load and bandwidth to use stream service. However, the quality of service (QoS) in these networks is difficult to be guaranteed because of the free-riding problem that nodes download the recourses while never uploading recourses, which degrades the performance of P2P VoD networks. In this paper, a distributed trust model is designed to reduce node's free-riding phenomenon in P2P VoD networks. In this model, the P2P network is abstracted to be a super node hierarchical structure to monitor the reputation of nodes. In order to calculate the reputation of nodes, the Hidden Markov Model (HMM) is introduced in this paper. Besides, a distinction algorithm is proposed to distinguish the free-riders and malicious nodes. The free-riders are the nodes which have a low frequency to free-ride. And the malicious nodes have a high frequency to free-ride. The distinction algorithm takes different measures to response to the request of these two kinds of free-riders. The simulation results demonstrate that this proposed trust model can improve QoS effectively in P2P VoD networks.