• Journal of Environmental Impact Assessment
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• v.15 no.6
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• pp.435-441
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• 2006

For the calibration of rainfall-runoff model, automatic calibration methods are used instead of manual calibration to obtain the reliable modeling results. When mathematical programming techniques such as linear programming and nonlinear programming are applied, there is a possibility to arrive at the local optimum. To solve this problem, genetic algorithm is introduced in this study. It is very simple and easy to understand but also applicable to any complicated mathematical problem, and it can find out the global optimum solution effectively. The objective of this study is to develope a parameter optimization program that integrate a genetic algorithm and a rainfall-runoff model. The program can calibrate the various parameters related to the runoff process automatically. As a rainfall-runoff model, SWMM is applied. The automatic calibration program developed in this study is applied to the Jangcheon watershed flowing into the Youngrang Lake that is in the eutrophic state. Runoff surveys were carried out for two storm events on the Jangcheon watershed. The peak flow and runoff volume estimated by the calibrated model with the survey data shows good agreement with the observed values.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.651-654
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• 2008

In finite field operations based on $GF(2^m)$, additions and subtractions are easily implemented. On the other hand, multiplications and divisions require mathematical elaboration of complex equations. There are two dominant way of approaching the solutions of finite filed operations, normal basis approach and polynomial basis approach, each of which has both benefits and weakness respectively. In this study, we adopted the mathematically feasible polynomial basis approach and suggest the optimization techniques of finite field operations based of mathematical principles.

• Journal of the Ergonomics Society of Korea
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• v.19 no.1
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• pp.11-22
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• 2000

In this research report, we are presenting several optimization models for cognitive systems by using stimulus-response matrix (S-R Matrix). Stimulus-response matrices are widely used for tabulating results from various experiments and cognition systems design in which the recognition and confusability of stimuli. This paper is relevant to analyze the optimization/mathematical programming models. The weakness and restrictions of the existing models are resolved by generalization considering average confusion of each subset of stimuli. Also, clustering strategies are used in the extended model to obtain centers of cluster in terms of minimal confusion as well as the character of each cluster.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.10
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• pp.832-835
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• 2003

The objective of this study is to investigate the possibility of chemical process synthesis purely based on mathematical programming when given an objective, feed conditions, product specifications, and model equations for available process units. A method based on a state space approach is proposed, and applied to an example problem with a reactor, a heat exchanger, and a separator. The results indicate that a computer can automatically synthesize an optimal process without any heuristics or expertise in process design provided that global optimization techniques are improved to be suitable for large problems.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1870-1873
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• 2004

The objective of this study is to investigate the possibility of chemical process synthesis purely based on mathematical programming when given an objective, feed conditions, product specifications, and model equations for available process units. A method based on a state space approach is proposed, and applied to an example problem with a reactor, a heat exchanger, and a separator. The results indicate that a computer can automatically synthesize an optimal process without any heuristics or expertise in process design provided that global optimization techniques are improved to be suitable for large problems.

• Bulletin of the Korean Mathematical Society
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• v.55 no.4
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• pp.1303-1315
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• 2018

For principal component analysis (PCA) to efficiently analyze large scale matrices, it is crucial to find a few singular vectors in cheaper computational cost and under lower memory requirement. To compute those in a fast and robust way, we propose a new stochastic method. Especially, we adopt the stochastic variance reduced gradient (SVRG) method [11] to avoid asymptotically slow convergence in stochastic gradient descent methods. For that purpose, we reformulate the PCA problem as a unconstrained optimization problem using a quadratic penalty. In general, increasing the penalty parameter to infinity is needed for the equivalence of the two problems. However, in this case, exact penalization is guaranteed by applying the analysis in [24]. We establish the convergence rate of the proposed method to a stationary point and numerical experiments illustrate the validity and efficiency of the proposed method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.97-100
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• 2001

제조 기업의 가치사슬 최적화를 위한 전략적, 운영상 의사결정 문제는 수리적 모델을 이용한 DSS의 효과적인 활용을 통하여 해결 될 수 있다. 의사결정 프로세스는 필연적으로 공급사슬의 여러 성과 목표와 관련 조직간의 Trade-off 및 연계관계(Interaction)가 고려되므로 복수의 DSS 활용이 필요하게 된다. 이와 관련하여 본 논문에서는 공급 사슬 전체의 최적화를 위한 다수의 전략적 목표 및 의사결정 프로세스, 연계된 수리적 모델들을 정의하고, 관련 조직 및 성과 지표 별 부분적 최적화(Local Optimality)를 지양하고 전체최적화 (Global Optimality)를 달성하기 위한 DSS Logic을 철강산업 프로세스를 대상으로 수리적 모델들의 분할(Decomposition) 및 통합개념을 통하여 제시하였다.

• Journal of the Korean Mathematical Society
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• v.57 no.5
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• pp.1079-1101
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• 2020

This work is concerned with a geometric inverse problem in fluid mechanics. The aim is to reconstruct an unknown obstacle immersed in a Newtonian and incompressible fluid flow from internal data. We assume that the fluid motion is governed by the Stokes-Brinkmann equations in the two dimensional case. We propose a simple and efficient reconstruction method based on the topological sensitivity concept. The geometric inverse problem is reformulated as a topology optimization one minimizing a least-square functional. The existence and stability of the optimization problem solution are discussed. A topological sensitivity analysis is derived with the help of a straightforward approach based on a penalization technique without using the classical truncation method. The theoretical results are exploited for building a non-iterative reconstruction algorithm. The unknown obstacle is reconstructed using a levelset curve of the topological gradient. The accuracy and the robustness of the proposed method are justified by some numerical examples.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.2
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• pp.14-35
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• 1991

The purpose of this study is to develop an efficient exact algorithm for the problem of scheduling n in dependent jobs on m unequal parallel processors to minimize makespan. Efficient solutions are already known for the preemptive case. But for the non-preemptive case, this problem belongs to a set of strong NP-complete problems. Hence, it is unlikely that the polynomial time algorithm can be found. This is the reason why most investigations have bben directed toward the fast approximate algorithms and the worst-case analysis of algorithms. Recently, great advances have been made in mathematical theories regarding Lagrangean relaxation and the subgradient optimization procedure which updates the Lagrangean multipliers. By combining and the subgradient optimization procedure which updates the Lagrangean multipliers. By combining these mathematical tools with branch-and-bound procedures, these have been some successes in constructing pseudo-polynomial time algorithms for solving previously unsolved NP-complete problems. This study applied similar methodologies to the unequal parallel processor problem to find the efficient exact algorithm.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.2
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• pp.13-35
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• 1991

The purpose of this study is to develop an efficient exact algorithm for the problem of scheduling n in dependent jobs on m unequal parallel processors to minimize makespan. Efficient solutions are already known for the preemptive case. But for the non-preemptive case, this problem belongs to a set of strong NP-complete problems. Hence, it is unlikely that the polynomial time algorithm can be found. This is the reason why most investigations have bben directed toward the fast approximate algorithms and the worst-case analysis of algorithms. Recently, great advances have been made in mathematical theories regarding Lagrangean relaxation and the subgradient optimization procedure which updates the Lagrangean multipliers. By combining and the subgradient optimization procedure which updates the Lagrangean multipliers. By combining these mathematical tools with branch-and-bound procedures, these have been some successes in constructing pseudo-polynomial time algorithms for solving previously unsolved NP-complete problems. This study applied similar methodologies to the unequal parallel processor problem to find the efficient exact algorithm.