• Journal of the Korean Institute of Gas
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• v.20 no.2
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• pp.66-71
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• 2016

Nowadays, a number of sensors which are placed in industrial complex are monitoring areas involving chemical leak and other faults. However, even in the presence of the sensors, chemical leaks, sometimes involving huge amount of chemicals, continuously led to big losses in the industrial complex. In most industries, sensor installation has been performed using past experience or using senor manufacturers' guideline; which leads to poor performance of the installed sensor grid. Therefore, we investigate an optimal placement methodology of point sensors for rapid detention and response when chemical leaks happen. This research suggests a generalized formulation suitable for the optimized decision making of minimizing number of sensors to be placed and increasing the fraction of covered scenarios under assumption of negligible effect of other structures. The proposed method has been verified for suitable performance for simple leak scenario simulations, by achieving the safety objectives and guaranteeing safe process operations.

• Journal of the Korean Geographical Society
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• v.48 no.3
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• pp.387-401
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• 2013

Redistricting of National Assembly Election has a significant effect on the results of election because it has a strong tendency to be performed with political intentions rather than the equivalent representativeness of population and region. This paper focuses on proposing an alternative for restricting of National Assembly Election in terms of implementation, that is, an procedural and systematic approach, not allowing for political or arbitrary intervention. A spatial optimization model conforming with criteria for political redistricting such as population equality, contiguity, and spatial compactness is developed and applied to Yongin City where are some controversy over the redistricting of the 19th National Assembly. Modeling results show that it is possible to derive National Assembly Election districts based on the information of basic spatial units without political consideration or arbitrary intervention. In addition, The districts derived from the model improved population equality compared with the existing districts.

• Journal of the Korea Computer Graphics Society
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• v.29 no.1
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• pp.13-22
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• 2023

In this paper, we present a method for estimating full 3D shapes from given 2D silhouette images of human bodies. Because the silhouette only consists of the partial information on the true shape, it is an ill-posed problem. To address the problem, we use the statistical human shape space obtained from the existing large 3D human shape database. The method consists of three steps. First, we extract the boundary pixels and their appropriate normal vectors from the input silhouette images. Then, we initialize the correspondences of each pixel to the vertex of the statistically-deformable 3D human model. Finally, we numerically optimize the parameters of the statistical model to fit best to the given silhouettes. The viability and the robustness of the method is demonstrated with various experiments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.571-578
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• 2012

In this paper, we propose two main points. The first is the constraint for logic synthesis, and the second is an efficient logic synthesis method. Logic synthesis is a process to obtain the gate-level netlist from RTL (register transfer level) codes using logic mapping and optimization with the specified constraints. The result of logic synthesis is tightly dependent on constraint and logic synthesis method. Since the size and timing can be dramatically changed by these, we should precisely consider them. In this paper, we present the considering items in the process of logic synthesis by using our experience and experimental results. The proposed techniques was applied to a circuit with the hardware resource of about 650K gates. The synthesis time for the hybrid method was reduced by 47% comparing the bottom-up method and It has better timing property about slack than top-down method.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.1-10
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• 2001

Neural networks we increasingly being seen as an addition to the statistics toolkit which should be considered alongside both classical and modern statistical methods. Neural networks are usually useful for classification and function estimation. In this paper we concentrate on function estimation using neural networks with weight decay factor The use of weight decay seems both to help the optimization process and to avoid overfitting. In this type of neural networks, the problem to decide the number of hidden nodes, weight decay parameter and iteration number of learning is very important. It is called the optimization of weight decay neural networks. In this paper we propose a automatic optimization based on genetic algorithms. Moreover, we compare the weight decay neural network automatically learned according to automatic optimization with ordinary neural network, projection pursuit regression and support vector machines.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.496-496
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• 2012

Sputtering은 박막의 품질(부착력, 밀도, 균일도등)이 우수하고 대면적 증착이 용이하여 반도체, 디스플레이, MEMS기술등과 같은 첨단산업에서 널리 이용되고 있는 증착방법이다. 일반적인 평판형 스퍼터건은 전계와 자계가 직교하는 Target의 일부영역에서만 스퍼터링 현상이 발생하게 되어 증착물질의 사용효율이 20~30% 정도로 좋지 못하고 스퍼터링 되지않는 부분에서는 재증착 현상에 의한 파티클 발생을 유발하여 Substrate에 손상을 입혀 박막의 질을 떨어뜨리게 된다. 본 연구에서는 이러한 문제점들의 물리적 현상의 진단 및 최적화를 위해 Particle-In-Cell (PIC)시뮬레이션을 이용하여 그 특성들을 알아보았다. 인가전압, 압력, 증착물질과 기판사이의 거리를 변화시켜 자기장이 포함된 Paschen curve를 그렸다. 전기장만이 포함된 시스템에서의 Paschen curve는 이미 공식으로 알려져 있으며 마그네트론 스퍼터링의 시스템에서 Paschen curve와 비교하여 보다 낮은 압력에서 플라즈마가 형성할 수 있는 것을 확인하였다. 또한 Target에 충돌하는 아르곤이온의 양, 에너지 분포, 각도의 분포 등을 관찰하였는데, 대부분의 아르곤이온은 압력이 증가할수록 에너지가 큰 경향성을 가지며 입사각도는 Target에 보다 수직으로 충돌하는 경향을 볼 수 있었다. 증착물질과 기판사이의 거리의 변화에 대해서는 이온 특성의 변화는 없었다.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.227-249
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• 2003

Prediction of corporate failure using past financial data is a well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as model construction process. Irrespective of the efficiency of a teaming procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network model. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables fur neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.3
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• pp.465-479
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• 2020

Regardless of the domestic and foreign governments/companies, SOC (Security Operation Center) has operated 24 hours a day for the entire year to ensure the security for their IT infrastructures. However, almost all SOCs have a critical limitation by nature, caused from heavily depending on the manual analysis of human agents with the text-based monitoring architecture. Even though, in order to overcome the drawback, technologies for a comprehensive visualization against complex cyber threats have been studying, most of them are inappropriate for the security monitoring in large-scale networks. In this paper, to solve the problem, we propose a novel visual approach for intuitive threats monitoring b detecting suspicious IP address, which is an ultimate challenge in cyber security monitoring. The approach particularly makes it possible to detect, trace and analysis of suspicious IPs statistically in real-time manner. As a result, the system implemented by the proposed method is suitably applied and utilized to the real-would environment. Moreover, the usability of the approach is verified by successful detecting and analyzing various attack IPs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.786-789
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• 2007

교차-개발환경을 기반으로 한 임베디드소프트웨어 개발은 일반적인 데스크톱 컴퓨터에서의 개발방법론 및 도구와의 차이점이 발생한다. 이러한 문제점들로 인해 난이도가 높은 임베디드 소프트웨어 애플리케이션을 쉽게 개발 해주는 기술에 대한 수요가 확산됨에 따라 다양한 기종과 규격의 임베디드소프트웨어 개발환경에 최적화된 시험검증시스템이 절실히 필요하다. 본 논문은 내장형 시스템 개발자가 쉽고 편리하게 원하는 GUI 형태의 결과 분석도구를 생성할 수 있도록 하기 위한 프로파일 로그 분석 방법을 제안한다. 제안하는 로그 분석 방법에 의한 API를 통해 개발자나 사용자는 자신의 취향에 맞는 GUI 형태의 결과 분석 도구를 쉽고 빠르게 생성하여 내장형 소프트웨어 개발의 효율성을 높일 수 있으며, 고가의 해외 개발도구의 수입대체 효과를 가져와 관련 산업 발전에 크게 기여할 것으로 기대 된다. 또한 국내 시장의 활성화를 통하여 개발업체간 상호교류를 통하여 보다 나은 국내 산업 시장을 형성하여 기존 임베디드 산업의 경쟁력을 강화하고 고난도의 응용 S/W의 개발과 시험 검증을 용이하게 할 수 있어 넓은 신 시장 창출 효과를 불러올 수 있다.

• Proceedings of the Korea Society for Simulation Conference
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• 2002.05a
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• pp.43-49
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• 2002

본 연구는 첨단 신호 시스템 알고리즘의 최적해를 구하는 문제를 기호적 시뮬레이션 기법으로 해결하기 위한 방법론을 제시한다. 최근 지능형 교통 시스템의 일환으로 최적화 된 교통신호를 생성하기 위한 신호제어기법들이 많이 개발되었다. 하지만 이러한 신호제어기법은 복잡한 교통환경에서 신호제어 변수간의 다양한 상호작용의 모든 해를 제공할 수 없는 한계를 지닌다. 한편 기호적 시뮬레이션 기법은 발생 가능한 모든 사건과 시간관계를 자동 생성시킴으로써 동적으로 변화하는 다양한 교통환경에 대해서 신호제어 변수간의 모든 시간관계를 추론해 낼 수 있는 장점을 지닌다. 하지만 기호적 시뮬레이션을 이용한 모델링에 있어서 교통량과 같은 양적인 요소들의 기호적 표현에는 어려움이 따른다. 따라서 본 논문에서는 교통량과 같은 양적인 요소들을 시간에 따른 변화량으로 해석하여 첨단 신호 시스템 알고리즘의 최적해를 구하는 문제에 접근한다. 이를 위해 국내 첨단 신호 시스템을 대상으로 신호제어 전략에 필요한 양적 요소를 검토하고, 이러한 양적 요소를 시간에 따른 변화량으로 해석하여 모델링 하고, 기호적 시뮬레이션 실험을 수행하여 최적신호 제어 알고리즘을 생성한다.