유전알고리즘을 이용한 모델추종형 퍼지제어기 설계

황기현,문경준,김문수,원태현 동의공업대학 1998 論文集 Vol.24 No.1

In this paper, a reference model following control system using a fuzzy logic controller (FLC) is proposed. A reference model whose response does not overshoot and has a fast rise time is designed. A FLC is designed to follow as close as possible the response of the reference model. The proposed design method has the robustness and the optimal tracking property under modeling error, disturbance and parameter perturbations. The proposed designing method is compared using a PD controller throughout the experiment. The effectiveness of the proposed designing method is verified through computer simulation and experiments for the second-order and the first-order reference model.

[논문]병렬 유전 알고리즘을 이용한 발전기 기동정지계획

김형수,문경준,박준호 釜山大學校生産技術硏究所 2003 生産技術硏究所論文集 Vol.62 No.-

본 논문에서는 병렬 유전 알고리즘을 이용한 발전기 기동정지계획을 제안한다. 최소 기동 및 정지 시간 등과 같은 다양한 발전기의 제약조건을 만족시키면서 발전기 기동정지계획을 수립하는 문제는 비선형적이며 많은 국부해가 존재하므로 최적해를 탐색하는데 많은 시간이 소요된다. 이러한 문제점을 해결하기 위해 16개 의 프로세서를 가진 병렬 시스템과 이를 이용한 병렬 유전 알고리즘을 제안하였으며, 제안한 방법의 효용성을 검토하기위해 10기 및 26기의 전력계통에 적용하여 시율레이션을 시행하였다. 시율레이션 결과 기존의 방법에 비해 탐색속도를 개선하였고 우수한 해를 구할 수 있었다.

독성물질의 세포사 기전 및 세포사 유발물질의 검색법 개발에 관한 연구(Ⅰ) : 독성물질로 인한 파킨슨병 모델에서의 세포사 기전 연구 Study on the cell-death mechanisms of toxin-induced parkinsonism

강태석,김종민,서경원,김영옥,김준규,오재호,이윤동,김규봉,오정자,송연정,임종준,전범석,문전옥,최광식 식품의약품안전청 2000 식품의약품안전청 연보 Vol.4 No.-

MPTP 독성물질이 도파민성 신경세포에 선택적으로 작용하여 산화성 손상에 의한 신경세포사를 일으키는 것을 이용하여 파킨슨병의 동물모델을 만들고, 이를 통해서 아폼토시스를 비롯한 포사의 기전에 대한 연구 및 너코틴의 신경세포 보호효과 여부를 판정하는 실험을 병행하고자 하였다. 파킨슨꾐의 동물모델을 MPTf 독성 물질을 이용하여 확립하였으며, MPTP(30mgag, i.p.)를 투여한 후 1, 2,3, 4, 5일째 흑질 조직을 채춰하여 tarm로 박걸하여 tyrosine hydroxylase 면역조직화학염색을 수행하여 cell countif우한 결과, control은 57.635ce11s, 1일째 친.OfDells,2일째 57.9±6cells,3일릴 없.3±죠ells, 4일째 49.0츠3cells, 5일째 39.4±Scells료 4, 3일째 뚜렷한 신경세포 수의 감소를 보였다. 신경세포사 기전 규명을 위한 아폼토시스 분걱에서는 벼PTP 투여 후 1, 2, 3, 4, 5일째 조직을 채취하여 Hoechst staining, TUNEL staining을 수곡하였는데 양성 반응을 보인 신경세포는 관찰되지 않아. 아폼토시스로 인한 세포사가 관찰되지 않았다. bIPTP 파킨슨병 동물모델에서 nicotine 보호효과 탐색에 관한 실험은 nicat푸e 0.2mgAg을 5일 퐁안 투여 후 리『fP(30mgag)를 CS7Bt/6 마은스에 복강 내주사로 nicotine과 병용 투여한 후 1, 2, 3, 4, 5일째 뇌를 적출하땄다. 신경세포사가 뚜렷이 관찰되기 시작하는 4, 5일째의 신경세포 수의 감소 정도를 20. 30% 정도 약화시키는 경향을 보였으나, nicotine 보호효과에 대한 추가 실헝이 현재 수행 중에 있다. The cause of Parkinson's disease (PD) is largely unknown. However, free radical toxicit? may plaf a role ip. the degeneration of substantia nigra, which is the Hajorfocus of pathological damages in PD. Recently, a neuroprotective effect of nicotine in PD has been suggested. Therefore, the mechanism of neurodegenerafion and protective potential o( nicotine in PD were investigated in the experimental modeB of Pll using a neurotoxin, C57BL/6mice were administered with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP, 30 mg/kg,j.p.). The degree of neurodegenerafion was determined by immunohistochemical stainiHB oftyrosine hydroxylase (TH). TH-positive cells on nigral sections were found 56.0 ±4, 57.9 ±6,52.315ce11s, 49.0±3cells, and 39,4±Scells at days 1, 2, 3, 4, 5, respectively (controls : 57.6±Scells). Hoechst and TUNEL staining showed no evidence of apoptosis. The exandnation on themice co-adrunistered with nicotine(0.2mgAg) and MPTP(30mgag) revealed a tendency ofnicotine protective effects. At days 4 and 5, the degree of TH-positive cells was decreased by20-30%, In corclusiffn, the role of apoptosis was not evidenced in this MPTP modeB of PB.The possible proteccon by nicotine should be elucidated with further studies.

PC Cluster based Parallel Adaptive Evolutionary Algorithm for Service Restoration of Distribution Systems

Mun, Kyeong-Jun,Lee, Hwa-Seok,Park, June-Ho,Kim, Hyung-Su,Hwang, Gi-Hyun The Korean Institute of Electrical Engineers 2006 Journal of Electrical Engineering & Technology Vol.1 No.4

This paper presents an application of the parallel Adaptive Evolutionary Algorithm (AEA) to search an optimal solution of the service restoration in electric power distribution systems, which is a discrete optimization problem. The main objective of service restoration is, when a fault or overload occurs, to restore as much load as possible by transferring the de-energized load in the out of service area via network reconfiguration to the appropriate adjacent feeders at minimum operational cost without violating operating constraints. This problem has many constraints and it is very difficult to find the optimal solution because of its numerous local minima. In this investigation, a parallel AEA was developed for the service restoration of the distribution systems. In parallel AEA, a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner are used in order to combine the merits of two different evolutionary algorithms: the global search capability of the GA and the local search capability of the ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. After AEA operations, the best solutions of AEA processors are transferred to the neighboring processors. For parallel computing, a PC cluster system consisting of 8 PCs was developed. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through switch based fast Ethernet. To show the validity of the proposed method, the developed algorithm has been tested with a practical distribution system in Korea. From the simulation results, the proposed method found the optimal service restoration strategy. The obtained results were the same as that of the explicit exhaustive search method. Also, it is found that the proposed algorithm is efficient and robust for service restoration of distribution systems in terms of solution quality, speedup, efficiency, and computation time.

Active Contour Model Based Object Contour Detection Using Genetic Algorithm with Wavelet Based Image Preprocessing

Kyeong-Jun Mun,Hyeon Tae Kang,Hwa-Seok Lee,Yoo-Sool Yoon,Chang-Moon Lee,June Ho Park 대한전기학회 2004 International Journal of Control, Automation, and Vol.2 No.1

PC Cluster based Parallel Adaptive Evolutionary Algorithm for Service Restoration of Distribution Systems

Kyeong-Jun Mun,Hwa-Seok Lee,June Ho Park,Hyung-Su Kim,Gi-Hyun Hwang 대한전기학회 2006 Journal of Electrical Engineering & Technology Vol.1 No.4

This paper presents an application of the parallel Adaptive Evolutionary Algorithm (AEA) to search an optimal solution of the service restoration in electric power distribution systems, which is a discrete optimization problem. The main objective of service restoration is, when a fault or overload occurs, to restore as much load as possible by transferring the de-energized load in the out of service area via network reconfiguration to the appropriate adjacent feeders at minimum operational cost without violating operating constraints. This problem has many constraints and it is very difficult to find the optimal solution because of its numerous local minima. In this investigation, a parallel ABA was developed for the service restoration of the distribution systems. In parallel AEA, a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner are used in order to combine the merits of two different evolutionary algorithms: the global search capability of the GA and the local search capability of the ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. After ABA operations, the best solutions of AEA processors are transferred to the neighboring processors. For parallel computing, a PC cluster system consisting of 8 PCs was developed. Each PC employs the 2 ㎓ Pentium Ⅳ CPU and is connected with others through switch based fast Ethernet. To show the validity of the proposed method, the developed algorithm has been tested with a practical distribution system in Korea. From the simulation results, the proposed method found the optimal service restoration strategy. The obtained results were the same as that of the explicit exhaustive search method. Also, it is found that the proposed algorithm is efficient and robust for service restoration of distribution systems in terms of solution quality, speedup, efficiency, and computation time.

Distribution System Reconfiguration Using the PC Cluster based Parallel Adaptive Evolutionary Algorithm

Mun Kyeong-Jun,Lee Hwa-Seok,Park June Ho,Hwang Gi-Hyun,Yoon Yoo-Soo The Korean Institute of Electrical Engineers 2005 KIEE International Transactions on Power Engineeri Vol.a5 No.3

This paper presents an application of the parallel Adaptive Evolutionary Algorithm (AEA) to search an optimal solution of a reconfiguration in distribution systems. The aim of the reconfiguration is to determine the appropriate switch position to be opened for loss minimization in radial distribution systems, which is a discrete optimization problem. This problem has many constraints and it is very difficult to find the optimal switch position because of its numerous local minima. In this investigation, a parallel AEA was developed for the reconfiguration of the distribution system. In parallel AEA, a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner are used in order to combine the merits of two different evolutionary algorithms: the global search capability of GA and the local search capability of ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. After AEA operations, the best solutions of AEA processors are transferred to the neighboring processors. For parallel computing, a PC-cluster system consisting of 8 PCs·was developed. Each PC employs the 2 GHz Pentium IV CPU, and is connected with others through switch based fast Ethernet. The new developed algorithm has been tested and is compared to distribution systems in the reference paper to verify the usefulness of the proposed method. From the simulation results, it is found that the proposed algorithm is efficient and robust for distribution system reconfiguration in terms of the solution quality, speedup, efficiency, and computation time.

Parallel Genetic Algorithm-Tabu Search Using PC Cluster System for Optimal Reconfiguration of Distribution Systems

Kyeong-Jun Mun,Hwa-Seok Lee,June-Ho Park 대한전기학회 2005 KIEE International Transactions on Power Engineeri Vol.a5 No.2

Parallel Genetic Algorithm-Tabu Search Using PC Cluster System for Optimal Reconfiguration of Distribution Systems

Mun Kyeong-Jun,Lee Hwa-Seok,Park June-Ho The Korean Institute of Electrical Engineers 2005 KIEE International Transactions on Power Engineeri Vol.a5 No.2

This paper presents an application of the parallel Genetic Algorithm-Tabu Search (GA- TS) algorithm, and that is to search for an optimal solution of a reconfiguration in distribution systems. The aim of the reconfiguration of distribution systems is to determine the appropriate switch position to be opened for loss minimization in radial distribution systems, which is a discrete optimization problem. This problem has many constraints and it is very difficult to solve the optimal switch position because of its numerous local minima. This paper develops a parallel GA- TS algorithm for the reconfiguration of distribution systems. In parallel GA-TS, GA operators are executed for each processor. To prevent solution of low fitness from appearing in the next generation, strings below the average fitness are saved in the tabu list. If best fitness of the GA is not changed for several generations, TS operators are executed for the upper 10$\%$ of the population to enhance the local searching capabilities. With migration operation, the best string of each node is transferred to the neighboring node after predetermined iterations are executed. For parallel computing, we developed a PC-cluster system consisting of 8 PCs. Each PC employs the 2 GHz Pentium IV CPU and is connected with others through switch based rapid Ethernet. To demonstrate the usefulness of the proposed method, the developed algorithm was tested and is compared to a distribution system in the reference paper From the simulation results, we can find that the proposed algorithm is efficient and robust for the reconfiguration of distribution system in terms of the solution quality, speedup, efficiency, and computation time.

PC 클러스터 기반 병렬 적응진화 알고리즘을 이용한 배전계통 고장복구

문경준(Kyeong-Jun Mun),이화석(Hwa-Seok Lee),박준호(June Ho Park) 대한전기학회 2005 대한전기학회 학술대회 논문집 Vol.2005 No.7