• 한국공작기계학회논문집
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• 제13권2호
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• pp.81-87
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• 2004

This paper deals with a new approach to tolerance optimization problems. Optimal tolerance allotment problems can be formulated as stochastic optimization problems. Most schemes to solve the stochastic optimization problems have been found to exhibit difficulties in multivariate integration of the probability density function. As a typical example of stochastic optimization the optimal tolerance allotment problem has the same difficulties. In this stochastic model, manufacturing system is represented by Gauss-Markov stochastic process and the manufacturing unit availability is characterized for realistic optimization modeling. The new algorithm performed robustly for a large deviation approximation. A significant reduction in computation time was observed compared to the results obtained in previous studies.

• Journal of the Korean Data and Information Science Society
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• 제26권2호
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• pp.495-504
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• 2015

The Gaussian geostatistical model has been widely used for modeling spatial data. However, this model suffers from a severe difficulty in computation because inference requires to invert a large covariance matrix in evaluating log-likelihood. In addressing this computational challenge, three strategies have been employed: likelihood approximation, lower dimensional space approximation, and Markov random field approximation. In this paper, we reviewed statistical approaches attacking the computational challenge. As an illustration, we also applied integrated nested Laplace approximation (INLA) technology, one of Markov approximation approach, to real data to provide an example of its use in practice dealing with large spatial data.

• 한국군사과학기술학회지
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• 제2권2호
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• pp.131-139
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• 1999

An easy and efficient method is proposed for a computation of reliability of preprocessing filters in the fire control system when the sensor data are frequently unreliable depending on the operation environment. It computes state transition probability matrix after modeling filter states as a Markov process, and computing false alarm and detection probability of each filter state under the given sensor failure probability. It shows that two important indices such as distributed state probability and error variance can be derived easily for a reliability assessment of the given sensor fusion system.

• 대한원격탐사학회지
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• 제17권4호
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• pp.335-344
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• 2001

Texture is extensively studied in a variety of image processing applications such as image segmentation and classification because it is an important property to perceive regions and surfaces. This paper focused on the analysis and synthesis of textured single and multiband images using Markov Random Field model considering the existent spatial correlation. Especially, for multiband images, the cross-channel correlation existing between bands as well as the spatial correlation within band should be considered in the model. Although a local interaction is assumed between the specified neighboring pixels in MRF models, during the maximization process, short-term correlations among neighboring pixels develop into long-term correlations. This result in exhibiting phase transition. In this research, the role of temperature to obtain the most probable state during the sampling procedure in discrete Markov Random Fields and the stopping rule were also studied.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.488-488
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• 2015

Intermittent streamflow is common phenomenon in arid and semi-arid regions. To manage water resources of intermittent streamflows, stochactic simulation data is essential; however the seasonally stochastic modeling for intermittent streamflow is a difficult task. In this study, using the periodic Markov chain model, we simulate intermittent monthly streamflow for occurrence and the periodic gamma autoregressive and copula models for amount. The copula models were tested in a previous study for the simulation of yearly streamflow, resulting in successful replication of the key and operational statistics of historical data; however, the copula models have never been tested on a monthly time scale. The intermittent models were applied to the Colorado River system in the present study. A few drawbacks of the PGAR model were identified, such as significant underestimation of minimum values on an aggregated yearly time scale and restrictions of the parameter boundaries. Conversely, the copula models do not present such drawbacks but show feasible reproduction of key and operational statistics. We concluded that the periodic Markov chain based the copula models is a practicable method to simulate intermittent monthly streamflow time series.

• 대한조선학회논문집
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• 제40권5호
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• pp.43-52
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• 2003

Two methods which estimate manoeuvring derivatives in the model of hydrodynamic force and moment acting on a manoeuvring ship using sea trial data were compared. One is the widely used parameter estimation method by using the Extended Kalman Filter (EKF), which estimates state variables of linearized state space model at every instant after dealing with the coefficients as the augmented state variables. The other one is the Estimation-Before-Modeling (EBM) technique, so called the two-step method. In the first step, hydrodynamic force of which dynamic model is assumed the third-order Gauss-Markov process is estimated along with motion variables by the EKF and the modified Bryson-Frazier smoother. Then, in the next step, manoeuvring derivatives are identified through the regression analysis. If the exact structure of hydrodynamic force could be known, which was an ideal case, the EKF method would be regarded as being more superior compared to the EBM technique. However the EBM technique was more robust than the EKF method from a realistic point of view where the assumed model structure was slightly different from the real one.

• 한국정보과학회논문지:정보통신
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• 제29권6호
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• pp.674-684
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• 2002

기존 침입탐지시스템에서는 구현의 용이성 때문에 오용침입탐지 기법이 주로 사용되었지만, 새로운 침입에 대처하기 위해서는 궁극적으로 비정상행위탐지 기법이 요구된다. 그 중 HMM기법은 생성메커니즘을 알 수 없는 이벤트들을 모델링하고 평가하는 도구로서 다른 침입탐지기법에 비해 침입탐지율이 높은 장점이 있다. 하지만 높은 성능에 비해 정상행위 모델링 시간이 오래 걸리는 단점이 있는데, 본 논문에는 실제 해킹에 사용되고 있는 다양한 침입패턴을 분석하여 권한이동시의 이벤트 추출방법을 이용한 모델링 기법을 제안하였고 이를 통하여 모델링 시간과 False-Positive 오류를 줄일 수 있는 지 평가해 보았다. 실험결과 전체 이벤트 모델링에 비해 탐지율이 증가하였고 시간 또한 단축됨을 알 수 있었다.

• Nuclear Engineering and Technology
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• 제51권2호
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• pp.444-452
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• 2019

To assess the availability of a nuclear power plant's dynamic systems, it is necessary to consider the impact of dynamic interactions, such as components, software, and operating processes. However, there is currently no simple, easy-to-use tool for assessing the availability of these dynamic systems. The existing method, such as Markov chains, derives an accurate solution but has difficulty in modeling the system. When using conventional fault trees, the reliability of a system with dynamic characteristics cannot be evaluated accurately because the fault trees consider reliability of a specific operating configuration of the system. The dynamic reliability graph with general gates (DRGGG) allows an intuitive modeling similar to the actual system configuration, which can reduce the human errors that can occur during modeling of the target system. However, because the current DRGGG is able to evaluate the dynamic system in terms of only reliability without repair, a new evaluation method that can calculate the availability of the dynamic system with repair is proposed through this study. The proposed method extends the DRGGG by adding the repair condition to the dynamic gates. As a result of comparing the proposed method with Markov chains regarding a simple verification model, it is confirmed that the quantified value converges to the solution.

• 한국도로학회논문집
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• 제4권4호
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• pp.1-12
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• 2002

포장유지관리체계는 신설포장의 공용 이후 포장 유지보수를 실시함에 있어 기술적으로 타당하고 경제적으로 효율적인 보수전락을 적용하는 것을 목표로 한다. 이를 위해서는 신뢰성 있는 포장공용성 예측모델을 필요로 한다. 본 연구에서는 마르코프 체인 이론에 기초한 확률적 포장공용성 예측 시스템을 제안하고, 아스팔트 포장으로의 적용상 문제점 등을 기술하였다. 본 연구 결과로서 아스팔트 포장의 공용성 예측을 위한 포장상태 전이행렬을 정의하였으며, 정량적인 포장공용 수명평가 결과를 제시하였다.

• 대한토목학회논문집
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• 제31권3B호
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• pp.277-284
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• 2011

기존 Markov Chain 모형을 통한 일강수량 모의에서 가장 큰 문제점은 극치강수량을 재현하기 어렵다는 점이다. 이러한 문제점으로 인해 수자원계획을 수립하는데 있어서 불확실성을 가중시키고 있다. 특히 일강수량 모의기법을 통해서 추정되는 빈도강수량의 과소추정으로 인해 수공구조물 설계 시에 신뢰성을 확보하는데 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 판단된다. 또한 극치강수량을 일반강수량으로부터 분리하여 모의함으로서 평균 및 중간값 등 낮은 차수에 모멘트 등 일강수량에 전체적인 분포특성을 더욱 효과적으로 모의할 수 장점을 확인할 수 있었다.