• 한국정보통신학회논문지
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• 제12권12호
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• pp.2253-2258
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• 2008

산화물 반도체 감지막이 동작온도에 따라 감응특성을 가지는 마이크로 흐름센서를 설계하기 위해서 통계적 수법에 기초한 Markov 체인 MCM을 이용하여 기초방정식을 정식화하고 마이크로 소자의 열 전달특성을 해석하였다. 계산 결과를 통하여 기존 유한차분법이 가지는 계산 정밀도와 차이가 없음을 확인하였다. 본 논문에서 제안한 Markov 체인 MCM을 활용하면 다양한 마이크로 소자의 열전달 특성과 같은 물리적 특성을 해석하고 설계하는데 유용할 것으로 판단된다.

• Bulletin of the Korean Chemical Society
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• 제16권12호
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• pp.1153-1162
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• 1995

The conformational details of β-lactose are investigated through molecular dynamics simulations in conjunction with the adiabatic potential energy map. The adiabatic energy map generated in vacuo contains five local minima. The lowest energy structure on the map does not correspond to the structure determined experimentally by NMR and the X-ray crystallography. When aqueous solvent effect is incorporated into the energy map calculation by increasing the dielectric constant, one of the local minima in the vacuum energy map becomes the global minimum in the resultant energy map. The lowest energy structure of the energy map generated in aquo is consistent with the one experimentally determined. Molecular dynamics simulations starting from those fivelocal minima on the vacuum energy map reveal that conformational transitions can take place among various conformations. Molecular dynamics simulations of the lactose and ricin B chain complex system in a stochastic boundary indicate that the most stable conformation in solution phase is bound to the binding site and that there are conformational changes in the exocyclic region of the lactose molecule upon binding.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2017년도 학술발표회
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• pp.409-409
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• 2017

유역단위에서 수문모델링을 수행함에 있어 토양수분은 물수지 관점에서 매우 중요한 인자로 고려된다. 더욱이, 최근 발생빈도가 커지고 있는 가뭄을 효과적으로 평가하고 예측하는 데에도 활용성이 매우 큰 것으로 인식되고 있다. 이러한 중요성에도 불구하고, 가용자료의 부족, 자료의 부정확성 등으로 인해 실제 유역모델링을 수행하는데 있어 활용도는 매우 적다. 이러한 점에서 본 연구에서는 동질성이 확보된 유역단위를 기준으로 다지점의 토양수분 자료를 추계학적으로 모의할 수 있는 기법을 개발하고자 한다. 토양함수자료는 지속성(persistence)이 매우 큰 특징을 가진다. 즉, 상태의 지속성이 크며 메모리가 오랫동안 유지된다는 점에서 추계학적 모의가 가능할 것으로 판단된다. 이러한 지속성을 이용함과 동시에 토양함수를 다양한 상태로 분리하고 이들 상태들간의 천이확률을 효과적으로 모의할 수 있다면 관측 토양함수 자료의 통계적 특성 재현이 가능하다. 본 연구에서는 용담댐 유역에 대해서 개발된 모형을 적용하고 활용성을 검토하고자 한다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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• pp.723-732
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• 2020

A theoretical calculation model for ship stern bearings with large hull deformation is established and validated theoretically and experimentally. A hull simulation model is established to calculate hull deformations corresponding to the reaction force of stern bearings under multi-factor and multi-operating conditions. The results show that in the condition of wave load, hull deformation shows randomness; the aft stern tube bearing load obeys the Gaussian distribution and its value increases significantly compared with the load under static, and the probability of aft stern tube bearing load greater than 1 is 65.7%. The influence laws and levels between hull deformation and bearing reaction force are revealed, and suggestions for ship stern bearing specifications are proffered accordingly.

• Structural Engineering and Mechanics
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• 제76권5호
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• pp.631-641
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• 2020

In the present work, an approach for the multiple time probabilistic characterization of the response of linear structural systems subjected to random non-Gaussian processes is presented. Its fundamental property is working directly on the multiple time probability density functions of the actions and of the response. This avoids of passing through the evaluation of the response statistical moments at multiple time or correlations, reducing the computational effort in a consistent measure. This approach is the extension to the multiple time case of a previously published dynamic Probability Transformation Method (PTM) working on a single evolution of the response statistics. The application to some simple examples has revealed the efficiency of the method, both in terms of computational effort and in terms of accuracy.

• 방사성폐기물학회지
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• 제20권2호
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• pp.133-149
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• 2022

The design of a wooden impact limiter equipped to a transportation cask for radioactive materials was optimized. According to International Atomic Energy Agency Safety Standards, 9 m drop tests should be performed on the transportation cask to evaluate its structural integrity in a hypothetical accident condition. For impact resistance, the size of the impact limiter should be properly determined for the impact limiter to absorb the impact energy and reduce the impact force. Therefore, the design parameters of the impact limiter were optimized to obtain a feasible optimal design. The design feasibility criteria were investigated, and several objectives were defined to obtain various design solutions. Furthermore, a probabilistic approach was introduced considering the uncertainties included in an engineering system. The uncertainty of material properties was assumed to be a random variable, and the probabilistic feasibility, based on the stochastic approach, was evaluated using reliability. Monte Carlo simulation was used to calculate the reliability to ensure a proper safety margin under the influence of uncertainties. The proposed methodology can provide a useful approach for the preliminary design of the impact limiter prior to the detailed design stage.

• Earthquakes and Structures
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• 제22권4호
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• pp.421-430
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• 2022

This paper addresses the stochastic control problem of robots within the framework of parameter uncertainty and uncertain noise covariance. First of all, an open circle deterministic trajectory optimization issue is explained without knowing the unequivocal type of the dynamical framework. Then, a Linear Quadratic Gaussian (LQG) controller is intended for the ostensible trajectory-dependent linearized framework, to such an extent that robust hereditary NN robotic controller made out of the Kalman filter and the fuzzy controller is blended to ensure the asymptotic stability of the non-continuous controlled frameworks. Applicability and performance of the proposed algorithm shown through simulation results in the complex systems which are demonstrate the feasible to improve the performance by the proposed approach.

• Communications for Statistical Applications and Methods
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• 제29권6호
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• pp.629-640
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• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• Ocean Systems Engineering
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• 제13권3호
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• pp.225-245
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• 2023

In the present paper, a numerical and experimental analysis for a wave energy converter under extreme environmental conditions is carried out. After the definition of design waves, including a 100-year return period stochastic sea state and a deterministic rogue wave condition, a numerical analysis using potential theory and a RANS equations solver are compared with experiments carried out at the Seakeeping Basin at the Technical University of Berlin. Results are discussed with special emphasis on the limits of potential theory methods for the evaluation of extreme wave conditions and the use of the presented methodology for early design stages.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.226-238
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• 2024

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.