• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.6
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• pp.1204-1208
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• 2012

According to the rapid development of cell phone and camera industry, all of electronic products obtain small-size, high-performance and variety. Therefore, PCB also obtains high-integration, multi-layers and high-specification rapidly. Among the digital cameras which are continuously evolved and developed, the market of mirror-less camera and hybrid digital camera is continuously increased because of the customer's request for small-size and weight lightening, except for DSLR camera. Therefore, the difficulty of PCB design is gradually increased and design L/T is longer according to the high-specifications, low-current and high-performance components. This thesis suggests the method to reduce L/T for PCB design applied to the reduction of digital camera's developing period and manufacturing period.

• Computational Structural Engineering
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• v.8 no.2
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• pp.123-132
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• 1995

Genetic Algorithms(GA), which are based on the theory of natural evolution, have been evaluated highly for their robust performances. Traditional GA has mostly used binary code for representing design variable. The binary code GA has many difficulties to solve optimization problems with continuous design variables because of its large computer core memory size, inefficiency of its computing time, and its bad performance on local search. In this paper, a real code GA is proposed for dealing with the above problems. So, new crossover and mutation processes of GA are developed to use continuous design variables directly. The results of read code GA are compared with those of binary code GA for several single and multiple objective optimization problems. As a result of comparisons, it is found that the performance of the real code GA is better than that of the binary code GA, and concluded that the real code GA developed here can be used for the general optimization problem.

• CDE review
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• v.21 no.1
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• pp.7-12
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• 2015

본 글을 통해 학생들과 함께 수행한 다양한 디지털 디자인 모델을 기반으로 5가지의 디지털 패브리케이션 방식을 적용하여 여러 실제 인테리어패널의 제작 과정 및 그 결과물들을 요약적으로 소개하였다. 해당 과정은 기존의 분리된 설계와 제작 및 시공이 아닌, 설계-제작 통합적인 접근방법을 활용함으로써 학생들로 하여금 디지털 디자인과 패브리케이션의 효과를 극대화 할 수 있음을 체득하도록 하였다. 패브리케이션 기기 및 재료 등의 차이점에 따라 실제 구현된 모델과 원래의 디지털 모델과의 형상적인 차이점은 아직까지 한계점으로 남을 수 있지만, 비정형적으로 생성된 디지털 디자인의 형상이 직접적으로 실현이 가능하다는 점에서 의의가 있다고 할 수 있다. 또한 패러매트릭 모델링 기법이나 실제 제작 경험이 거의 없는 학생들을 대상으로 개별적인 교육이 아닌 설계-제작 통합적인 교육을 실시함으로써 각 요소들이 어떻게 유기적으로 관련되어 디지털 디자인 과정이나 패브리케이션과정에서 상호 보완적으로 중요한지 직접 체득할 수 있도록 하였다는데 의의가 있다. 본 글에서 소개한 재료를 깎는 등의 방식인 "마이너스"의 방식이 아닌 3D프린팅과 같이 재료를 이용해 만들어가는 "플러스"적인 방식 또한 해당 사례에서 수행되었으며 마찬가지로 다양한 특성을 지니고 있으나, 본 글에서는 전자의 방식에 대해 주로 논하였다. 본 글에서 다룬 제작기법뿐만 아니라 3D프린팅 기술의 발전과 보급에 따라 디지털 패브리케이션 역시 다양하게 진보하고 있으며, 팹랩 등을 통한 학교에서의 건축설계 교육도 다양하게 진화하고 있다. 본 사례 및 기타 실험적인 교육과 선도적인 여러 시도를 통해서, 디지털 디자인과 패브리케이션 영역을 확장시켜나가고 해당 기술의 발전에 적극적으로 보조할 수 있는 교육과정을 수립하고 이행하는데 일조 할 수 있기를 희망한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1-3
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• 2022

코로나 19 팬데믹으로 인한 4차 산업혁명이 앞당겨지고 초현실, 초실감, 초연결 사회 등의 키워드가 대두되고 언택트(Untact) 시대가 일상생활에 불가항력적으로 접목되면서 몰입감과 흥미성, 유희성을 목적으로 한 콘텐츠들이 다양하게 활성화되었다. 특히, 메타버스(Metaverse) 플랫폼이 빠르게 진화하면서 물리적 현실과 디지털 가상성을 융합한 다중 사용자에게 다양한 경험이 가능한 서비스가 주목받고 있다. 메타버스는 다중 사용자 플랫폼에서 원활한 커뮤니케이션과 대규모 멀티플레이가 가능한 온라인 비디오 게임, 오픈 게임 월드, AR 협업 공간과 호환되는 사회적, 몰입감이 있는 VR(Virtual Reality) 플랫폼 서비스 중 하나이다. 우리는 메타버스 플랫폼 개발 시 필요로 하는 요소기술 및 3D 데이터 획득을 통한 VR 콘텐츠를 설계하고 구현하고자 한다. 1) 콘텐츠 시나리오 및 사용자 인터랙션 설계; 2) 볼류메트릭 캡처를 활용한 실사 기반의 동적(Dynamic) 데이터 생성; 3) 컴퓨터 그래픽 기반 가상공간 설계 및 정적(Static)데이터 요소기술 개발; 4) 가상 현실 공간 데이터와 볼류메트릭 캡처의 실사 데이터 정합; 5) 공간 데이터 시각화 생성 및 모듈형 공간 이동 제어 기능 개발; 최종적으로 6) VR 환경에서의 콘텐츠 구현을 목표로 한다.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.4 s.346
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• pp.58-66
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• 2006

This paper proposes an FSS(Frequency Selective Surface) design system that automatically derives design parameters minimally specified by engineers. The proposed system derives optimal design parameters through theory of electromagnetic scattering on FSS, database implemented from real data obtained from practically manufactured FSS, and GA(Genetic Algorithm) for optimizing design parameters. The system, at the first step, searches the best matching FSS within preconstructed DB with given characteristics specified by operators, and then sets initial genes from the searched FSS parameters. GA iterates the optimization process until the system finds the FSS design parameters that matches the characteristics specified by operators. The theory for the electromagnetic scattering on FSS is verified by comparing the simulation results with real data obtained by measuring system composed of horn antenna and receiver. The process for manufacturing the FSS is also included in the paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.259-259
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• 2003

Genetic algorithms using a Process of genetic evolution of an organism are appropriate for hard problems that have not been solved by any deterministic method. Up to now, the controller design method has been made with the frequency dependent specification but the design method with the time specification has gotten little progress. In this paper, we study the controller design to satisfy the performance of a plant using the generalized Manabe standard form. When dealing with a controller design in the case of two parameter configurations, there are some situations that neither a known pseudo inverse technique nor the inverse method can be applicable. In this case, we propose two methods of designing a controller by the gradient algorithm and the new pseudo inverse method so that the desired closed polynomials are either equalized to or approximated to the designed polynomial. Design methods of the proposed controller are implemented in Java.

• Journal of Korean Society of Steel Construction
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• v.19 no.2
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• pp.171-179
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• 2007

The greenhouse discrete optimum design program was developed using discrete optimum algorithm based on the genetic algorithm. The basic search method for the optimum design is the genetic algorithm, which is known to be very efficient for discrete optimization. In this paper, the objective function was the weight of the greenhouse structures and the constraints were the limits state design method. The design variables were galvanized steel pipes for plastic housing KSD 3760. Objective criteria were presented for the design of economic greenhouse structure and evaluation of its stability. The standardizations of greenhouse structure were used, as well as the normalization of greenhouse-related materials. Design examples were given to show the applicability of the optimum design using the discrete optimum algorithm based on the genetic algorithm of this study.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.3
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• pp.68-76
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• 2000

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Futhermore, a series of conventional techniques such as the pole placement and the optimal control based on the local linearizations have narrow stabilizable regions. At the same time, the fine tunings of their gain parameters are also troublesome. Thus, in this paper, an Evolving Neural Network Controller(ENNC) which its structure and its connection weights are optimized simultaneously by Real Variable Elitist Genetic Algorithm(RVEGA) was presented for stabilization of an IP system with nonlinearity. This proposed ENNC was described by a simple genetic chromosome. And the deletion of neuron, the according to the various flag types. Therefore, the connection weights, its structure and the neuron types in the given ENNC can be optimized by the proposed evolution strategy. And the proposed ENNC was implemented successfully on the ADA-2310 data acquisition board and the 80586 microprocessor in order to stabilize the IP system. Through the simulation and experimental results, we showed that the finally acquired optimal ENNC was very useful in the stabilization control of IP system.

• Journal of the Korean Geotechnical Society
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• v.35 no.4
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• pp.27-35
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• 2019

This study confirmed the applicability to the field of geotechnical engineering for relatively complicated space and many target design variables in back analysis. The Sharan's equation and the Blum's method were used for the tunnel field and the retaining wall as a model for the multi-variate problem of geotechnical engineering. Optimization methods are generally divided into a deterministic method and a stochastic method. In this study, Simulated Annealing Method (SA) was selected as a deterministic method and Differential Evolution Algorithm (DEA) and Particle Swarm Optimization Method (PSO) were selected as stochastic methods. The three selected optimization methods were compared by applying a multi-variate model. The problem of deterministic method has been confirmed in the multi-variate back analysis of geotechnical engineering, and the superiority of DEA can be confirmed. DEA showed an average error rate of 3.12% for Sharan's solution and 2.23% for Blum's problem. The iteration number of DEA was confirmed to be smaller than the other two optimization methods. SA was confirmed to be 117.39~167.13 times higher than DEA and PSO was confirmed to be 2.43~6.91 times higher than DEA. Applying a DEA to the multi-variate back analysis of geotechnical problems can be expected to improve computational speed and accuracy.

• Journal of the Society of Naval Architects of Korea
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• v.31 no.4
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• pp.147-156
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• 1994

Though optimization method had been used for long time for the optimal design of ship structure, design variables in the most cases were assumed to be continuous real values or it was not easy to solve the mixed integer optimum design problems using the conventional optimization methods. Thus, it was often tried to use various initial starting points to locate the best optimum paint and to use special method such as branch and bound method to handle the discrete design variables in the optimization problems. Sometimes it had succeed, but the essential problems for dealing with the local optimum and discrete design variables was left unsolved. Hence, in this paper, Genetic Algorithms adopting the biological evolution process is applied to the ship structural design problem where the integer values for the number of stiffen design variables or the discrete values for the plate thickness variables would be more preferable in order to find out their effects on the final optimum design. Through the numerical result comparisons, it was found that Genetic Algorithm could always yield the global optimum for the discrete and mixed integer structural optimization problem cases even though it takes more time than other methods.