• Journal of the Korea Society for Simulation
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• v.33 no.1
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• pp.19-26
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• 2024

The phase-type, PH, distribution is defined as the time to absorption into a terminal state in a continuous-time Markov chain. As the PH distribution includes family of exponential distributions, it has been widely used in stochastic models. Since the PH distribution is represented and generated by an initial probability vector and a generator matrix which is called the Markovian representation, we need to find a vector and a matrix that are consistent with given set of moments if we want simulate a PH distribution. In this paper, we propose an approach to simulate a PH distribution based on distribution function which can be obtained directly from moments. For the simulation of PH distribution of order 2, closed-form formula and streamlined procedures are given based on the Jordan decomposition and the minimal Laplace transform which is computationally more efficient than the moment matching methods for the Markovian representation. Our approach can be used more effectively than the Markovian representation in generating higher order PH distribution in queueing network simulation.

• Journal of the Earthquake Engineering Society of Korea
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• v.5 no.6
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• pp.37-46
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• 2001

In this research, a controller design method based on optimization is proposed that can satisfy constraints on maximum responses of building structures subject to around excitation modeled by partially stochastic process. The class of controllers to be optimized is restricted to LQR. Weighting matrix on controlled outputs is used as design variable. Objective function, constraint functions and their gradients are computed by the parameterization of control gain with Riccati matrix. Full state feedback controllers designed by proposed optimization method satisfy various design objectives and their necessary maximum control forces are computed for the production of actuator. LQG controllers composed of Kalman filter and LQR designed by proposed method perform well with little deterioration. So it is possible to design output feedback controllers satisfying constraints on various maximum responses of structures.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.164-171
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• 2006

Recently Digital multimedia broadcasting (DMB) has been available as a commercial service. The users sometimes have difficulty in finding their preferred multimedia contents and need to spend a lot of searching time finding them. They are even very likely to miss their preferred contents while searching for them. In order to solve the problem, we need a method for recommendation users preferred only minimum information. We propose an algorithm and a system for recommending users' preferred contents using preference transition probability from user's usage history. The system includes four agents: a client manager agent, a monitoring agent, a learning agent, and a recommendation agent. The client manager agent interacts and coordinates with the other modules, the monitoring agent gathers usage data for analyzing the user's preference of the contents, the learning agent cleans the gathered usage data and modeling with state transition matrix over time, and the recommendation agent recommends the user's preferred contents by analyzing the cleaned usage data. In the recommendation agent, we developed the recommendation algorithm using a user's preference transition probability for the contents. The prototype of the proposed system is designed and implemented on the WIPI(Wireless Internet Platform for Interoperability). The experimental results show that the recommendation algorithm using a user's preference transition probability can provide better performances than a conventional method.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.433-435
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• 2005

임베디드 시스템은 다수의 디바이스를 컨트롤하여 시스템의 목적을 수행한다. 최근 임베디드 시스템의 요구사항이 증가함에 따라 하나의 임베디드 소프트웨어가 컨트롤하는 디바이스의 종류가 다양해지고 수도 증가하는 추세이다. 다수의 디바이스를 가지고 있는 임베디드 시스템에서 시스템의 신뢰도는 각 디바이스의 신뢰도에 많은 영향을 받는다. 본 논문은 임베디드 시스템의 신뢰도를 측정하기 위해서 통계적 신뢰도 측정 방법 중 한 가지인 마르코프 체인을 이용한 방법을 제안한다. 마르코프 체인은 여러 분야에서 복잡한 시스템을 단순화하여 모델링하고 과거의 변화를 토대로 미래를 예측할 수 있는 방법을 제공한다. 또한 전체 시스템의 확률을 행렬로 계측할 수 있는 방법을 가지고 있어 특정 부분의 확률이 전체 시스템의 확률에 미치는 영향을 산술적으로 계산할 수 있는 장점을 가지고 있다. 본 논문에서 제안하는 임베디드 소프트웨어 마르코프 체인은 테스트 대상 소스를 분석하여 디바이스를 컨트롤하는 루틴과 에러를 핸들링하는 루틴, 일반적인 루틴으로 나누어 각각을 상태로 정의한다. 정의한 각 상태간의 전이는 통계적으로 측정한 디바이스 신뢰도를 확률로 표현한다. 마르코프 체인을 이용하여 임베디드 시스템의 신뢰도를 측정하기 위한 시스템은 소스 분석기와 신뢰도 측정기로 나누어 설계한다. 소스 분석기는 테스트 대상이 되는 소스와 디바이스 드라이버 라이블러리 테이블을 입력으로 하고 소프트웨어의 마르코프 체인을 출력으로 한다 마르코프 체인은 행렬로 표현하고 연산하여 시스템의 신뢰도를 측정한다. 제안하는 시스템의 신뢰도 측정 방법은 부분이 가지고 있는 신뢰도가 전체 신뢰도에 미치는 영향을 산술적으로 측정할 수 있어 시스템이 요구하는 신뢰도에 접근할 수 있는 방법과 근거를 제공하는 장점이 있다.소시키는 장점을 갖는다.것으로 조사되었으며 40대 이상의 연령층은 점심비용으로 더 많은 지출을 하고 있는 것으로 나타났다. 4) 끼니별 한식에 대한 선호도는 아침식사의 경우가 가장 높았으며, 이는 40대와 50대에서 높게 나타났다. 점심 식사로 가장 선호되는 음식은 중식, 일식이었으며 저녁 식사에서 가장 선호되는 메뉴는 전 연령층에서 일식, 분식류 이었으며, 한식에 대한 선택 정도는 전 연령층에서 매우 낮게 나타났다. 5) 각 연령층에서 선호하는 한식에 대한 조사에서는 된장찌개가 전 연령층에서 가장 높은 선호도를 나타내었고, 김치는 40대 이상의 선호도가 30대보다 높게 나타났으며, 흥미롭게도 30세 이하의 선호도는 30대보다 높게 나타났다. 그 외에도 떡과 죽에 대한 선호도는 전 연령층에서 낮게 조사되었다. 장아찌류의 선호도는 전 연령대에서 낮았으며 특히 30세 이하에서 매우 낮게 조사되었다. 한식의 맛에 대한 만족도 조사에서는 연령이 올라갈수록 한식의 맛에 대한 만족도는 낮아지고 있었으나, 한식의 맛에 대한 만족도가 높을수록 양과 가격에 대한 만족도는 높은 경향을 나타내었다. 전반적으로 한식에 대한 선호도는 식사 때와 식사 목적에 따라 연령대 별로 다르게 나타나고 있으나, 선호도는 성별이나 세대에 관계없이 폭 넓은 선호도를 반영하고 있으며, 이는 대학생들을 대상으로 하는 연구 등에서도 나타난바 같다. 주 5일 근무제의 확산과 초 중 고생들의 토요일 휴무와 더불어 여행과 엔터테인먼트산업은 더욱 더 발전을 거듭하고 있으며, 외식은 여행과 여가 활동의 필수적인 요소로써 그 역할을 일조하고 있다. 이와 같은 여가시간의 증가는 독신자들에게는 좀더 많은 여유시간을 가족을 이루고 있는 가족구성원들에게는 가족과의 유대를 강화하는 휴식과 오락의 소비 트렌드를 창출시켰

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.143-151
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• 2010

Fingerprint classification is a step to increase the efficiency of an 1:N fingerprint recognition system and plays a role to reduce the matching time of fingerprint and to increase accuracy of recognition. It is difficult to classify fingerprints, because the ridge pattern of each fingerprint class has an overlapping characteristic with more than one class, fingerprint images may include a lot of noise and an input condition is an exceptional case. In this paper, we propose a novel approach to design a stochastic model and to accomplish fingerprint classification using a directional characteristic of fingerprints for an effective classification of various qualities. We compute the directional value by searching a fingerprint ridge pixel by pixel and extract a directional characteristic by merging a computed directional value by fixed pixels unit. The modified Markov model of each fingerprint class is generated using Markov model which is a stochastic information extraction and a recognition method by extracted directional characteristic. The weight list of classification model of each class is decided by analyzing the state transition matrixes of the generated Markov model of each class and the optimized value which improves the performance of fingerprint classification using GA (Genetic Algorithm) is estimated. The performance of the optimized classification model by GA is superior to the model before the optimization by the experiment result of applying the fingerprint database of various qualities to the optimized model by GA. And the proposed method effectively achieved fingerprint classification to exceptional input conditions because this approach is independent of the existence and nonexistence of singular points by the result of analyzing the fingerprint database which is used to the experiments.

• Journal of Wind Energy
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• v.14 no.1
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• pp.37-43
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• 2023

In this study, synthetic time series wind data was generated numerically using a second-order Markov chain. One year of wind data in 2020 measured by the AWS on Wido Island was used to investigate the statistics for measured wind data. Both the transition probability matrix and the cumulative transition probability matrix for annual hourly mean wind speed were obtained through statistical analysis. Probability density distribution along the wind speed and autocorrelation according to time were compared with the first- and the second-order Markov chains with various lengths of time series wind data. Probability density distributions for measured wind data and synthetic wind data using the first- and the second-order Markov chains were also compared to each other. For the case of the second-order Markov chain, some improvement of the autocorrelation was verified. It turns out that the autocorrelation converges to zero according to increasing the wind speed when the data size is sufficiently large. The generation of artificial wind data is expected to be useful as input data for virtual digital twin wind turbines.

• The Journal of the Acoustical Society of Korea
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• v.19 no.3
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• pp.35-44
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• 2000

In this paper, we propose a new trajectory model for characterizing segmental features and their interaction based upon a general framework of hidden Markov models. Each segment, a sequence of vectors, is represented by a trajectory of observed sequences. This trajectory is obtained by applying a new design matrix which includes transitional information on contiguous frames, and is characterized as a polynomial regression function. To apply the trajectory to the segmental HMM, the frame features are replaced with the trajectory of a given segment. We also propose the likelihood of a given segment and the estimation of trajectory parameters. The obervation probability of a given segment is represented as the relation between the segment likelihood and the estimation error of the trajectories. The estimation error of a trajectory is considered as the weight of the likelihood of a given segment in a state. This weight represents the probability of how well the corresponding trajectory characterize the segment. The proposed model can be regarded as a generalization of a conventional HMM and a parametric trajectory model. The experimental results are reported on the TIMIT corpus and performance is show to improve significantly over that of the conventional HMM.