• Journal of Electrical Engineering and Technology
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• v.2 no.3
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• pp.353-357
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• 2007

The bearing diagnostics method is presented in this paper using fuzzy inference based on vibration data. Both time-domain and frequency-domain features are used as input data for bearing fault detection. The Adaptive Network based Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) have been proposed to select the fuzzy model input and output parameters. Training results give the optimized fuzzy inference system for bearing diagnosis based on measured vibration data. The result is also tested with other sets of bearing data to illustrate the reliability of the chosen model.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.40-42
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• 2003

본 논문에서는 유전자 알고리즘(Genetic Algorithm: GA)의 새로운 병렬화 방법을 제안 하고 있다. 기존의 병렬 유전자 알고리즘(Parallel Genetic Algorithm: PGA)은 전체 개체집단을 부개체집단 (Subpopulation)으로 나누어 해의 가능 영역을 동시에 탐색하는 것이 일반적인 방법인데 반해. 본 논문에서 제안하는 병렬화 방법은 전체 해의 영역을 나누어 각각의 영역에서 독립된 개체집단들이 서로 다른 영역을 탐색하게 하는 방법이다. 이 방법은 두 가지 단계의 병렬 유전자 알고리즘으로 구성된다. 먼저 적응교배 연산자(Adaptive Crossover Operator: ACO)를 이용한 PGA를 통해 지역해에 인접한 범위들로 해의 영역을 나누고, 이렇게 나누어진 각각의 영역들에서 다시 병렬로 GA를 적용시켜 자세하게 탐색하는 방법이다. 첫 번째 수행되는 PGA 단계에서는 탐색 시간을 줄이고 두 번째 PGA 단계에서는 보다 자세한 탐색을 하기 위해 정밀도(Precision)의 조정을 유전자 알고리즘의 병렬화에 적용하였으며. 이를 통해 빠르고 자세한 탐색이 가능한 유전자 알고리즘의 병렬화 방법을 제안하고 있다.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.289-291
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• 1998

This paper presents short-term hydro scheduling method for hydrothermal coordination by genetic algorithm. Hydro scheduling problem has many constraints with fixed final reservoir volume. In this paper, the difficult water balance constraints caused by hydraulic coupling satisfied throughout dynamic decoding method. Adaptive penalizing method was also proposed to handle the infeasible solutions that violate various constraints. The effectiveness of proposed method in this paper was examined through the case studies. Further studies for the validation of the hydro scheduling scheme obtained by genetic algorithm will be very appreciated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.1019-1028
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• 2007

In this paper, adaptive evolutionary algorithm(AEA) is proposed, which uses both genetic algorithm(GA) with good global search capability and evolution strategy(ES) with good local search capability in an adaptive manner, when population evolves to the next generation. In the reproduction procedure, proportion of the population for GA and ES is adaptively determined according to their fitness. The AEA is used to design membership functions and scaling factors of the fuzzy logic controller(FLC). To evaluate the performance of the proposed FLC design method, we make an experiment on the FLC for the speed control of an actual DC series motor system with nonlinear characteristics. Experimental results show that the proposed controller has better performance than PD controller.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2115-2123
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• 2006

The paper deals with the identification of Pareto optimal solutions using GA based coevolution in the context of multiobjective optimization. Coevolution is a genetic process by which several species work with different types of individuals in parallel. The concept of cooperative coevolution is adopted to compensate for each of single objective optimal solutions during genetic evolution. The present study explores the GA based coevolution, and develops prescribed and adaptive scheduling schemes to reflect design characteristics among single objective optimization. In the paper, non-dominated Pareto optimal solutions are obtained by controlling scheduling schemes and comparing each of single objective optimal solutions. The proposed strategies are subsequently applied to a three-bar planar truss design and an energy preserving flywheel design to support proposed strategies.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1345-1355
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• 2004

In a sender-initiated load redistribution algorithm, a sender(overloaded processor) continues to send unnecessary request messages for load transfer until a receiver(underloaded processor) is found while the system load is heavy. In a receiver-initiated load redistribution algorithm, a receiver continues to send unnecessary request messages for load acquisition until a sender is found while the system load is light. Therefore, it yields many problems such as low CPU utilization and system throughput because of inefficient inter-processor communications in this environment. This paper presents an approach based on genetic algorithm(GA) for adaptive load sharing in distributed systems. In this scheme, the processors to which the requests are sent off are determined by the proposed GA to decrease unnecessary request messages.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.552-557
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• 2000

In the paper, we proposed a face recognition method that uses GA-BP(Genetic Algorithm-Back propagation Network) that optimizes initial parameters such as bias values or weights. Each pixel in the picture is used for input of the neuralnetwork. The initial weights of neural network is consist of fixed-point real values and converted to bit string on purpose of using the individuals that arte expressed in the Genetic Algorithm. For the fitness value, we defined the value that shows the lowest error of neural network, which is evaluated using newly defined adaptive re-learning operator and built the optimized and most advanced neural network. Then we made experiments on the face recognition. In comparison with learning convergence speed, the proposed algorithm shows faster convergence speed than solo executed back propagation algorithm and provides better performance, about 2.9% in proposed method than solo executed back propagation algorithm.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.120-122
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• 2006

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. Thestructural optimization is realized via HFCGA and HCM 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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.4
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• pp.9-21
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• 2004

The current paper proposes a genetic algorithm (GA)-based segmentation method that can automatically extract and track moving objects. The proposed method mainly consists of spatial and temporal segmentation; the spatial segmentation divides each frame into regions with accurate boundaries, and the temporal segmentation divides each frame into background and foreground areas. The spatial segmentation is performed using chromosomes that evolve distributed genetic algorithms (DGAs). However, unlike standard DGAs, the chromosomes are initiated from the segmentation result of the previous frame, then only unstable chromosomes corresponding to actual moving object parts are evolved by mating operators. For the temporal segmentation, adaptive thresholding is performed based on the intensity difference between two consecutive frames. The spatial and temporal segmentation results are then combined for object extraction, and tracking is performed using the natural correspondence established by the proposed spatial segmentation method. The main advantages of the proposed method are twofold: First, proposed video segmentation method does not require any a priori information second, the proposed GA-based segmentation method enhances the search efficiency and incorporates a tracking algorithm within its own architecture. These advantages were confirmed by experiments where the proposed method was success fully applied to well-known and natural video sequences.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.1-8
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• 2009

There are a lot of influences, such as location of camera, luminosity, brightness, and direction of light, which affect the performance of 2-dimensional image recognition. This paper suggests an adaptive method for face-image recognition in noisy environments using evolvable filtering and feature extraction which uses one sample image from camera. This suggested method consists of two main parts. One is the environmental-adjustment module which determines optimum sets of filters, filter parameters, and dimensions of features by using "steady state genetic algorithm". The other another part is for face recognition module which performs recognition of face-image using the previous results. In the processing, we used Gabor wavelet for extracting features in the images and k-Nearest Neighbor method for the classification. For testing of the adaptive face recognition method, we tested the adaptive method in the brightness noise, in the impulse noise and in the composite noise and verified that the adaptive method protects face recognition-rate's rapidly decrease which can be occurred generally in the noisy environments.