• 산업경영시스템학회지
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• 제34권3호
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• pp.24-34
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• 2011

An Unmanned Aerial Vehicle (UAV) is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are an attractive alternative for many scientific and military organizations. UAVs can perform operations that are considered to be risky or uninhabitable for human. UA V s are currently employed in many military missions such as reconnaissance, surveillance, enemy radar jamming, decoying, suppression of enemy air defense (SEAD), fixed and moving target attack, and air-to-air combat. UAVs also are employed in a number of civilian applications such as monitoring ozone depletion, inclement weather, traffic congestion, and taking images of dangerous territory. For accomplishing the UAV's missions, guarantee of survivability should be preceded. The main objective of this study is to suggest a mathematical programming model and a $A^*PS$-PGA (A-star with Post Smoothing-Parallel Genetic Algorithm) for an UAV's path planning to maximize survivability. A mathematical programming model is composed by using MRPP (Most Reliable Path Problem) and TSP (Traveling Salesman Problem). A path planning algorithm for UAV is applied by transforming MRPP into SPP (Shortest Path Problem).

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
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• pp.343-346
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• 2008

휴머노이드 로봇이 넘어질 경우, 충격에 의한 손상이 발생할수 있다. 이를 최소화하는 넘어짐 자세의 생성을 위하여 개선된 PGA 기반의 탐색기법을 제안한다. 다목적함수를 고려한 군집 간 이주방식의 효율적 조합을 통해 넘어짐 충격을 최소화하는 각 관절 궤적을 구할수 있도록 하였다. 제안된 기법의 검증을 위하여 Sony QRIO 로봇에 대해서 ODE 기반의 Webots 시뮬레이션을 이용하여 실험을 수행하였다.

• 전자공학회논문지CI
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• 제41권2호
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• pp.9-22
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• 2004

본 논문에서는 NP-Complete 부류에 속하는 다중 디스크 데이터 배치 문제를 해결하기 위한 병렬 유전자 알고리즘을 제안한다. 이 문제는 디스크 입출력 처리의 병렬성이 극대화되도록 Binary Cartesian Product File의 데이터 블록들을 디스크어레이에 배치하는 방식을 찾는 것이다. 이 문제를 해결하기 위하여 제안되었던 DAGA 방식은 순차 유전자 알고리즘(Genetic Algorithm)으로서, 이전에 제안되었던 다른 방식에 비해 디스크 수에 대한 제약을 없애면서도 우수한 결과를 제공함을 보여 주었으나 시뮬레이션 시간이 너무 커서 큰 용량의 데이터 구성에 대한 시뮬레이션을 어렵게 하는 문제점이 있었다. 본 논문에서는 DAGA의 시뮬레이션 시간 단축을 위한 방식으로서, 2차원 토러스(2-Dimension Torus) 기반 병렬 유전자 알고리즘(ParaDAGA)을 제안한다. ParaDAGA는 분산 객체 모형을 기반으로 설계되었으며, 단일 프로세서 시스템에서 구현된 병렬처리 컴퓨터 시뮬레이터에서 수행되도록 구현하였다. 시뮬레이션 연구를 통하여, ParaDAGA의 시뮬레이션 변수 값이 결과에 주는 영향을 분석하였고, ParaDAGA 방식이 DAGA 방식에 비해 우수한 결과를 제공할 수 있는지를 실험하였다. 실험 결과는 ParaDAGA 방식이 순차 알고리즘인 DAGA보다 알고리즘 수행 시간 뿐 아니라, 찾아낸 결과도 우수함을 보여준다.

• 전기학회논문지
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• 제58권12호
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• pp.2520-2528
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• 2009

In this paper, we proposed tracking detection methodology using information granulation-based fuzzy radial basis function neural networks (IG-FRBFNN). According to IEC 60112, tracking device is manufactured and utilized for experiment. We consider 12 features that can be used to decide whether tracking phenomenon happened or not. These features are considered by signal processing methods such as filtering, Fast Fourier Transform(FFT) and Wavelet. Such some effective features are used as the inputs of the IG-FRBFNN, the tracking phenomenon is confirmed by using the IG-FRBFNN. The learning of the premise and the consequent part of rules in the IG-FRBFNN is carried out by Fuzzy C-Means (FCM) clustering algorithm and weighted least squares method (WLSE), respectively. Also, Hierarchical Fair Competition-based Parallel Genetic Algorithm (HFC-PGA) is exploited to optimize the IG-FRBFNN. Effective features to be selected and the number of fuzzy rules, the order of polynomial of fuzzy rules, the fuzzification coefficient used in FCM are optimized by the HFC-PGA. Tracking inference engine is implemented by using the LabVIEW and loaded into embedded system. We show the superb performance and feasibility of the tracking detection system through some experiments.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.104-106
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• 2007

In this paper, we propose an approach to design of optimized Cascade controller for Rotary Inverted Pendulum system using Hierarchical Fair Competition-based Genetic Algorithm(HFCGA). GAs may get trapped in a sub-optimal region of the search space thus becoming unable to find better quality solutions, especially for very large search space. The Parallel Genetic Algorithms(PGA) are developed with the aid of global search and retard premature convergence. HFCGA is a kind of multi-populations of PGA. In this paper, we design optimized Cascade controller by HFCGA for Rotary Inverted Pendulum system that is nonlinear and unstable. Cascade controller comprise two feedback loop, parameters of controller optimize using HFCGA. Then designed controller evaluate by apply to the real plant.

• 한국경영과학회지
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• 제27권2호
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• pp.97-109
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• 2002

Manufacturing environments in the real world are subject to many sources of change and uncertainty, such as new job releases, job cancellations, a chance in the processing time or start time of some operation. Thus, the realistic scheduling method should Properly reflect these dynamic environment. Based on the release times of jobs, JSSP (Job Shoe Scheduling Problem) can be classified as static and dynamic scheduling problem. In this research, we mainly consider the dynamic JSSP with continually arriving jobs. The goal of this research is to develop an efficient scheduling method based on GA (Genetic Algorithm) to address dynamic JSSP. we designed scheduling method based on SGA (Sing1e Genetic Algorithm) and PGA (Parallel Genetic Algorithm) The scheduling method based on GA is extended to address dynamic JSSP. Then, This algorithms are tested for scheduling and rescheduling in dynamic JSSP. The results is compared with dispatching rule. In comparison to dispatching rule, the GA approach produces better scheduling performance.

• 산업공학
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• 제25권3호
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• pp.365-375
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• 2012

An Unmanned Aerial Vehicle (UAV) is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are an attractive alternative for many scientific and military organizations. UAVs can perform operations that are considered to be risky or uninhabitable for humans. UAVs are currently employed in many military missions and a number of civilian applications. For accomplishing the UAV's missions, guarantee of survivability should be preceded. The main objective of this study is to suggest a mathematical programming model and a $A^*PS$_PGA (A-star with Post Smoothing_Parallel Genetic Algorithm) for Multiple UAVs's path planning to maximize survivability. A mathematical programming model is composed by using MRPP (Most Reliable Path Problem) and MTSP (Multiple Traveling Salesman Problem). After transforming MRPP into Shortest Path Problem (SPP),$A^*PS$_PGA applies a path planning for multiple UAVs.

• 제어로봇시스템학회논문지
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• 제15권3호
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• pp.280-285
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• 2009

This paper introduced an automatic generation method of falling motions for humanoid robots to minimize a damage. The proposed approach used a PGA based optimization technique to find a set of joint trajectories which minimize a damage of the falling over and down. Injection-migration PGA technique is introduced and compared with EMO and various migration topologies. To verify the proposed method, experiments for falling motions were executed for Sony QRIO robot in Webots simulation environments.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2007년도 춘계학술대회 학술발표 논문집 제17권 제1호
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• pp.341-344
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• 2007

본 논문에서는 계층적 공정 경쟁 개념을 병렬 유전자 알고리즘에 적용하여 계층적 공정 경쟁 기반 병렬유전자 알고리즘 (Hierarchical Fair Competition Genetic Algorithm: HFCGA)을 구현하였을 뿐만 아니라 실수코딩 유전자 알고리즘(Real-Coded Genetic Algorithm: RCGA)에서 좋은 성능을 갖는 산술교배(Arithmetic crossover), 수정된 단순교배(modified simple crossover) 그리고 UNDX(unimodal normal distribution crossover)등의 다양한 교배연산자들을 적용, 분석함으로써 개선된 병렬 유전자 알고리즘을 제안하였다. UNDX연산자는 다수의 부모(multiple parents)를 이용하여 부모들의 기하학적 중심(geometric center)에 근접하게 정규분포를 이루며 생성된다. 본 논문은 UNDX를 이용한 HFCGA모델을 구현하고 함수파라미터 최적화 문제에 많이 쓰이는 함수들에 적용시킴으로써 그 성능의 우수성을 증명 한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.2097-2098
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• 2006

In this study, we introduce the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA). HFCGA is a kind of multi-populations of Parallel Genetic Algorithms(PGA), and it is used for structure optimization and parameter identification of fuzzy set model. It concerns the fuzzy model-related parameters as the number of input variables, a collection of specific subset of input variables, the number of membership functions, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.