• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.11-12
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• 2008

퍼지 시계열 예측은 전체 퍼지 구간에 따른 퍼지 소속 함수의 개수와 범위에 따라서 예측성능에 많은 영향을 미치고 있으며, 이러한 문제점을 개선하기 위한 방법으로 다수 객체들의 학습 및 군집 특성을 이용한 Particle Swarm Optimization기법을 도입하였다. 제안된 방법에서는 군집의 최적 객체를 전체 최적해와 각각의 퍼지 소속 함수들에 대한 최적해로 구분하여 탐색하는 기법을 제안한다. 실제 시계열 데이터를 이용한 실험을 통하여 기존의 연구 결과들과 비교함으로써 제안된 방법의 우수한 성능을 가짐을 검증하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1381-1384
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• 2004

대상 지점의 수질 예측은 단순한 모델로 설명하는데 쉽지 않을 뿐만 아니라 많은 오차를 내포하고 있다. 그러나 최근, 신경회로망, 퍼지 논리, 전문가 시스템 및 유전자 알고리즘과 같은 인공지능이 대두되면서 복잡한 비선형 과정들을 나타낼 수 있게 되었다. 나아가 진정한 인공 지능을 실현하기 위해서는 신경회로망, 퍼지 논리, 전문가 시스템 및 유전자 알고리즘을 보다 효과적으로 이용하고 통합해야 가능할 것으로 기대된다. 본 연구에서는 유전자 알고리즘(Genetic Algorithm)을 T-S 퍼지시스템(Takagj-Sugeno Fuzzy system)의 삼각형 멤버쉽 함수 형태와 규칙 베이스를 최적화하기 위한 도구로 사용하였으면, 예측은 T-S 퍼지 시스템을 이용하여 실시하였다. 대상지점은 영산강 유역의 나주지점을 선정하여 유량자료 및 수질자료를 이용하여 GA와 T-S 퍼지 시스템의 결합에 의해 수질 예측을 실시할 결과 돌연변이율$(P_m)$ $0.05\~0.1$에서 우수한 결과를 얻을 수 있었다.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1118-1120
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• 1998

In this study, an adaptive evolutionary computation (AEC), which uses adaptively a genetic algorithm having global searching capability and an evolution strategy having local searching capability with different methodologies, is suggested. We applied the AEC to design of fuzzy logic controllers for a PSS (power system stabilizer). FLCs for PSS controllers are designed for damping the low frequency oscillations caused by disturbances such as tile sudden changes of loads, outages in generators, transmission line faults, etc. The membership functions of FLCs is optimally determined by AEC.

• Steel and Composite Structures
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• v.47 no.4
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• pp.513-521
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• 2023

The goal of this work is to create a new and improved GWO (Grey Wolf Optimizer), the so-called Robot GWO (RGWO), for dynamic and static target tracking involving multiple robots in unknown environmental conditions. From applying ourselves with the Gray Wolf Optimization Algorithm (GWO) and how it works, as the name suggests, it is a nature-inspired metaheuristic based on the behavior of wolf packs. Like other nature-inspired metaheuristics such as genetic algorithms and firefly algorithms, we explore the search space to find the optimal solution. The results also show that the improved optimal control method can provide superior power characteristics even when operating conditions and design parameters are changed.

• Journal of Mechanical Science and Technology
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• v.17 no.2
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• pp.187-196
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• 2003

In this paper, a new methodology of evolutionary computations - An Adaptive Evolutionary Algorithm (AEA) is proposed. AEA uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary computations : global search capability of GA and local search capability of ES. In the reproduction procedure, the proportions of the population by GA and ES are adaptively modulated according to the fitness. AEA is used to. designing fuzzy logic controller (FLC) for a high-angle-of-attack flight system for a super-maneuverable version of F-18 aircraft. AEA is used to determine the membership functions and scaling factors of an FLC. The computer simulation results show that the FLC has met both robustness and performance requirements.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.282-287
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• 2006

This paper presents an original feature selection method for Emotion Recognition which includes many original elements. Feature selection has some merits regarding pattern recognition performance. Thus, we developed a method called thee 'Interactive Feature Selection' and the results (selected features) of the IFS were applied to an emotion recognition system (ERS), which was also implemented in this research. The innovative feature selection method was based on a Reinforcement Learning Algorithm and since it required responses from human users, it was denoted an 'Interactive Feature Selection'. By performing an IFS, we were able to obtain three top features and apply them to the ERS. Comparing those results from a random selection and Sequential Forward Selection (SFS) and Genetic Algorithm Feature Selection (GAFS), we verified that the top three features were better than the randomly selected feature set.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.138-145
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• 2000

A identification algorithm that finds optimal fuzzy membership functions and rule base to fuzzy model isproposed and a fuzzy controller is designed to get more accurate position and velocity control of wheeled mobile robot. This procedure that is composed of three steps has its own unique process at each step. The elements of output term set are increased at first step and then the rule base is varied according to increase of the elements. The adjusted system is in competition with system which doesn't include any increased elements. The adjusted system will be removed if the system lost. Otherwise, the control system is replaced with the adjusted system. After finished regulation of output term set and rule base, searching for input membership functions is processed with constraints and fine tuning of output membership functions is done.

• Proceedings of the KWS Conference
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• 2009.11a
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• pp.14-14
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• 2009

자동차 차체와 같은 박판을 접합하기 위해서 인버터 DC 저항 점 용접공정은 매우 널리 사용되어지고 있다. 이는 교류 용접에 비해 적은 전류로 용접이 가능하고, 더 넓은 적정 용접 영역을 가지며, 보다 적은 전극마모를 가지는 인버터 DC 저항 점용접의 특성에 기인한다. 아울러 최근에는 파워 소자와 같은 인버터 구성에 필요한 구성 요소의 가격이 낮아져, 전반적으로 용접기의 가격이 하락하였고, 구성 장치에 대한 신뢰성이 증가하였으며, 기존보다 전력의 사용량이 감소하여 인버터 DC 저항점 용접공정의 사용이 더욱 증가하고 있는 상황이다. 또한 차량의 경량화에 대한 요구가 증가함에 따라 고 장력 강판의 적용이 확대되고 있다. 이러한 재료의 우수한 용접을 위해 인버터 DC 저항 점 용접시스템의 개발이 더욱 활발하게 이루어지고 있다. 하지만 인버터 DC 저항 점용접 시스템을 구성하더라도 모재의 특성이 전류 파형에 영향을 주게 되어, 정 전류 제어가 적용되지 못하면 전류 파형이 불안정해지게 되고 원하는 전류가 발생되지 않게 되어 스패터가 발생하거나, 용접 품질에 영향을 줄 수 있게 된다. 본 연구에서는 인버터 DC 저항 점용접 시스템을 구성하고, 정 전류의 제어를 위한 퍼지 제어 알고리즘을 개발하여 적용하였다. 퍼지제어기의 환산 계수를 최적화하기 위해서 유전 알고리즘을 적용하였으며, 실험에는 고장력강을 대상으로 정 전류 용접 공정을 수행하였다.

• Industrial Engineering and Management Systems
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• v.9 no.2
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• pp.88-96
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• 2010

This paper presents a two-phase quantitative framework to aid the decision making process for effective selection of an efficient freight logistics hub from 8 alternatives in Thailand on the North-South economic corridor. Phase 1 employs both multiple regression and Pearson Feature selection to find the important criteria, as defined by logistics hub score, and to reduce number of criteria by eliminating the less important criteria. The result of Pearson Feature selection indicated that only 5 of 15 criteria affected the logistics hub score. Moreover, Genetic Algorithm (GA) was constructed from original 15 criteria data set to find the relationship between logistics criteria and freight logistics hub score. As a result, the statistical tools are provided the same 5 important criteria, affecting logistics hub score from GA, and data mining tool. Phase 2 performs the fuzzy stochastic AHP analysis with the five important criteria. This approach could help to gain insight into how the imprecision in judgment ratios may affect their alternatives toward the best solution and how the best alternative may be identified with certain confidence. The main objective of the paper is to find the best alternative for selecting freight logistics hub under proper criteria. The experimental results show that by using this approach, Chiang Mai province is the best place with the confidence interval 95%.

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.1-13
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• 2010

It is not easy to set RED(Random Early Detection) parameter according to environment in managing Network Device. Especially, it is more difficult to set parameter in the case of maintaining the constant service rate according to the change of environment. In this paper, we hypothesize the router that has Multi-core CPU in output queue and propose AI RED(Artificial Intelligence RED), which directly induces Genetic Algorithm of Artificial Intelligence in the output queue that is appropriate to the optimization of parameter according to RED environment, which is automatically adaptive to workload. As a result, AI RED Is simpler and finer than FuRED(Fuzzy-Logic-based RED), and RED parameter that AI RED searches through simulations is more adaptive to environment than standard RED parameter, providing the effective service. Consequently, the automation of management of RED parameter can provide a manager with the enhancement of efficiency in Network management.