• 한국농공학회지
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• 제44권2호
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• pp.127-135
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• 2002

Due to the fact that the design of a reinforced concrete structure changes in accordance with its shape and assigned load, total automation of the design system has not been achieved. For instance, since there is no general rule about setting up reinforcing steel quantity and arrangement location, it is simply not feasible to automatically decide the reinforcing arrangement location. In this study, the ESO(evolutionary structural optimization) technique and its related issues will be discussed. The ESO techniques is determined the reasonable load path which is traveling of load between in-flow and out-flow at a concrete structure using numerical analysis. And the results applied to the steel arrangement in reinforced concrete structures. The optimal algorithm, which determines the terminal criteria during ESO process, has been updated by using the obtained results. And the load path within the member has been determined automatically.

• World Technopolis Review
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• 제3권2호
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• pp.66-77
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• 2014

This paper aims at exploring a conceptual framework of analyzing the evolutionary processes of regional economies by reconciling the notion of regional resilience and the concepts prevailing in the disciplines of evolutionary economics and geography. The resilience framework appears to offer a promising outlet with which combination of the seemingly contradictory conceptions is made possible. It can address why some regions manage to adapt to external shocks, renew themselves, or lock out themselves, while others are more locked in decline. In addition, it can also explain how the spatial organization of economic production, distribution, and consumption is transformed over time. Then, regional economic resilience, together with its accompanying vehicle of panarchy, emerges as a workable framework of explaining regional differentiation in regional economic performance and trajectories. Despite the risk of being a fuzzy concept, the resilience conception can be properly operationalized to provide policy principles of regional economic innovation adjusted to region-specific contexts.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.354-360
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• 1998

In general, it is very difficult to find optimal fuzzy rules by experience when a system is dynamical and/or complex. Futhermore proper fuzzy partitioning is not deterministic and there is no unique solution. Therefore we propose a new design method of an optimal fuzzy logic controller, that is a co-evolutionary genetic algorithm finding optimal fuzzy rule and proper membership functions at the same time. We formalize the relation between fuzzy rules and membership functions in terms of fitness. We review the typical approaching methods to co-evolutionary genetic algorithms , and then classify them by fitness relation matrix. Applications of the proposed method to a path planning problem of autonomous mobile robots when moving objects exist are presented to demonstrate the performance and effectiveness of the method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
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• pp.123-126
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• 1998

The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. In the Meaning of these foundational concepts, simple genetic algorithm(SGA) allocate more trials to the schemata whose average fitness remains above average. Although SGA does well in many applications as an optimization method, still it does not guarantee the convergence of a global optimum. Therefore as an alternative scheme, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve in contrast with traditional single population evolutionary algorithms. In this paper, we propose a new design method of an optimal fuzzy logic controller using co-evolutionary concept. In general, it is very difficult to find optimal fuzzy rules by experience when the input and/or output variables are going to increase. So we propose a co-evolutionary method finding optimal fuzzy rules. Our algorithm is that after constructing two population groups m de up of rule vase and its schema, by co-evolving these two populations, we find optimal fuzzy logic controller. By applying the proposed method to a path planning problem of autonomous mobile robots when moving objects exist, we show the validity of the proposed method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.601-606
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• 1998

In this paper, we propose a new design method of an optimal fuzzy logic controller using co-evolutionary concept. In general, it is very difficult to find optimal fuzzy rules by experience when the input and/or output variables are going to increase. Futhermore proper fuzzy partitioning is not deterministic ad there is no unique solution. So we propose a co-evolutionary method finding optimal fuzzy rules and proper fuzzy membership functions at the same time. Predator-Prey co-evolution and symbiotic co-evolution algorithms, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. Our algorithm is that after constructing two population groups made up of rule base and membership function, by co-evolving these two populations, we find optimal fuzzy logic controller. By applying the propose method to a path planning problem of autonomous mobile robots when moving objects applying the proposed method to a pa h planning problem of autonomous mobile robots when moving objects exist, we show the validity of the proposed method.

• 한국경제지리학회지
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• 제11권4호
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• pp.509-526
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• 2008

본 연구의 목적은 진화경제학적 관점으로 한국 신발산업의 진화적 발전경로를 분석하는 것이다. 산업의 변화를 연구함에 있어 진화론적 관점은 기업의 선택과 모방, 진입과 퇴출, 기술적 특성과 혁신과정 등 산업의 발전경로에 관한 복잡한 요인들이 어떻게 생성되고 지속되며 공진화하는지를 설명할 수 있는 장점을 가지고 있다. 한국 신발산업에 관한 기존의 연구들은 한국 신발산업의 쇠퇴 요인을 임금 상승과 노동집약적 산업의 환경 변화의 측면에서 찾고 있다. 이에 반해, 본 연구에서는 한국 신발산업의 쇠퇴 요인을 국내 기업의 전략적 선택과 모방의 경로, 지배적 기술 패러다임, 기업을 둘러싸고 있는 제도적 규제의 문제, 그리고 글로벌생산체제로 변화하는 메조 궤적의 진화적 발전경로의 측면에서 찾고자 하였다. 이러한 분석 결과를 토대로 한국의 신발산업은 스포츠화, 특히 혁제운동화의 제조기술에 대한 학습과정과 OEM 주문생산에 기초하여 성장했으며, 한국 신발산업의 사양화는 대내외 환경변화에 따른 경쟁조건과 시장선택체제에 적응하기 위한 기업의 진화적 선택과정에 따른 결과임을 강조한다.

• 한국통신학회논문지
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• 제36권4B호
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• pp.412-421
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• 2011

선박경제운항이란 기상예측정보를 활용하여 연료소모량을 최소화하도록 선박을 운항하는 것으로서 최근 많은 시스템이 이를 위해 연구되고 있다. 기존의 시스템에서는 문제의 복잡성을 줄이기 위해 엔진의 출력을 고정하거나 속력을 일정하게 운항한다는 가정을 기반으로 접근하고 있다. 그러나 엔진출력을 잘 조절한다면 더 좋은 기상환경에서 선박이 운항할 수 있게 되어 연료소모량을 더욱 줄일 수 있다. 본 논문에서는 진화전략 알고리즘을 사용하여 항로의 세부구간별로 최적출력을 탐색할 수 있는 새로운 경제운항시스템을 제안하였다. 또한, 지리적 최단 경로를 찾을 수 있는 $A^*$ 알고리즘과 곡선 표현의 자유도를 높일 수 있는 방법을 사용함으로써 임의의 출발지와 목적지에 대해서 제안된 경제운항시스템을 적용할 수 있도록 하였다. 총 36가지의 운항 시나리오에 대해서 이 논문에서 제안된 시스템의 성능을 기존의 출력고정 운항방법과 비교한 결과, 운항소요시간은 거의 차이가 없으면서도 연료소모량을 평균적으로 1.3%, 최대로는 5.6% 개선시킬 수 있음을 관찰하였다.

• 한국경영과학회지
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• 제27권2호
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• pp.33-49
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• 2002

CBM (Condition-Based Maintenance) has increasingly drawn attention in industry because of its many benefits. CBM Problem Is characterized as a state-dependent scheduling model that demands simultaneous maintenance actions, each for an attribute that influences on machine condition. This problem is very hard to solve within conventional Markov decision process framework. In this paper, we present an intelligent machine maintenance scheduler, for which a new incremental decision tree learning method as evolutionary system identification model and shortest path problem as schedule generation model are developed. Although our approach does not guarantee an optimal scheduling policy in mathematical viewpoint, we verified through simulation based experiment that the intelligent scheduler is capable of providing good scheduling policy that can be used in practice.

• 산업경영시스템학회지
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• 제26권4호
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• pp.23-30
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• 2003

This study develops an efficient heuristic which is based on genetic approach for AGVs flow path layout problem. The suggested solution approach uses a algorithm to replace two 0-1 integer programming models and a branch-and-bound search algorithm. Genetic algorithms are a class of heuristic and optimization techniques that imitate the natural selection and evolutionary process. The solution is to determine the flow direction of line in network AGVs. The encoding of the solutions into binary strings is presented, as well as the genetic operators used by the algorithm. Genetic algorithm procedure is suggested, and a simple illustrative example is shown to explain the procedure.

• Steel and Composite Structures
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• 제15권4호
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• pp.451-466
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• 2013

This paper deals with the multi-objective optimization of tire reinforcement structures such as the tread belt and the carcass path. The multi-objective functions are defined in terms of the discrete-type design variables and approximated by artificial neutral network, and the sensitivity analyses of these functions are replaced with the iterative genetic evolution. The multi-objective optimization algorithm introduced in this paper is not only highly CPU-time-efficient but it can also be applicable to other multi-objective optimization problems in which the objective function, the design variables and the constraints are not continuous but discrete. Through the illustrative numerical experiments, the fiber-reinforced tire belt structure is optimally tailored. The proposed multi-objective optimization algorithm is not limited to the tire reinforcement structure, but it can be applicable to the generalized multi-objective structural optimization problems in various engineering applications.