• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.664-667
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• 2014

기존 온톨로지들을 공유 및 재사용하기 위하여 온톨로지 정렬이 연구되고 있다. 기존 정렬 시스템은 온톨로지 데이터 양에 따라 매트릭스를 생성하고 과도한 계산을 통해 처리하여 대용량 데이터 집합에 대하여 공간적 및 계산적으로 부하를 발생하여 효율적이지 않다. 이를 해결하기 위하여 온톨로지 정렬을 휴리스틱 알고리즘을 적용하여 연구 진행하였다. 기존 휴리스틱 알고리즘은 계산이 간단하지만 조율해야 하는 파라미터가 많기에 특정 도메인에 최적 조합이 필요하며 만족한 성능을 얻지 못하였다. 이 논문에서는 Discrete Cuckoo Search(DCS) 기반 온톨로지 정렬 알고리즘을 제안한다. 제안한 알고리즘은 조율해야 하는 파라미터의 개수가 적고 Levy Flight 분포에 따라 탐색하여 계산이 간단하다. 제안된 알고리즘의 성능을 평가하기 위해 OAEI(Ontology Alignment Evaluation Initiative)에서 제공하는 벤치마크 데이터를 사용하여 정확률(Precision)과 재현율(Recall)을 구하고 기존 휴리스틱 정렬 알고리즘과 비교 평가하였다.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.523-530
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• 2014

Ontology alignment is the way to share and reuse of ontology knowledge. Because of the ambiguity of concept, most ontology alignment systems combine a set of various measures and complete enumeration to provide the satisfactory result. However, calculating process becomes more complex and required time increases exponentially since the number of concept increases, more errors can appear at the same time. Lately the focus is on meta-matching using the heuristic algorithm. Existing meta-matching system tune extra parameter and it causes complex calculating, as a consequence, the results in the various data of specific domain are not good performed. In this paper, we propose a high performance algorithm by using DCS that can solve ontology alignment through simple process. It provides an efficient search strategy according to distribution of Levy Flight. In order to evaluate the approach, benchmark data from the OAEI 2012 is employed. Through the comparison of the quality of the alignments which uses DCS with state of the art ontology matching systems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.334-340
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• 2017

Reliability is considered a particularly important design factor for systems that have critical results once a failure occurs in a system, such as trains, airplanes, and passenger ships. The reliability of the system can be improved in several ways, but in a system that requires considerable reliability, the redundancy of parts is efficient in improving the system reliability. In the case of duplicating parts to improve reliability, the kind of parts and the number of duplicating parts should be determined under the system reliability, part costs, and resources. This study examined the redundancy allocation of multi-level systems with serial structures. This paper describes the definition of a multi-system and how to optimize the kind of parts and number of duplications to maximize the system reliability. To optimize the redundancy, the cuckoo search algorithm was applied. The search procedure, the solution representation and the development of the neighborhood solution were proposed to optimize the redundancy allocation of a multi-level system. The results of numerical experiments were compared with the genetic algorithm and cuckoo search algorithm.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.6
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• pp.57-65
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• 2015

Manufacturing optimization problem is to find the optimal solution under satisfying various and complicated constraints with the design variables of nonlinear types. To achieve the objective, this paper proposes a hybrid approach. The proposed hybrid approach is consist of genetic algorithm(GA), cuckoo search(CS) and hill climbing method(HCM). First, the GA is used for global search. Secondly, the CS is adapted to overcome the weakness of GA search. Lastly, the HCM is applied to search precisely the convergence space after the GA and CS search. In experimental comparison, various types of manufacturing optimization problems are used for comparing the efficiency between the proposed hybrid approach and other conventional competing approaches using various measures of performance. The experimental result shows that the proposed hybrid approach outperforms the other conventional competing approaches.