• 대한기계학회논문집A
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• 제29권10호
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• pp.1369-1376
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• 2005

In structural design, the design variables are frequently selected from certain discrete values. Various optimization algorithms have been developed fDr discrete design. It is well known that many function evaluations are needed in such optimization. Recently, sequential algorithm with orthogonal arrays (SOA), which is a search algorithm for a local minimum in a discrete space, has been developed. It considerably reduces the number of function evaluations. However, it only finds a local minimum and the final solution depends on the initial values of the design variables. A new algorithm is proposed to adopt a genetic algorithm (GA) in SOA. The GA can find a solution in a global sense. The solution from the GA is used as the initial design of SOA. A sequential usage of the GA and SOA is carried out in an iterative manner until the convergence criteria are satisfied. The performance of the algorithm is evaluated by various examples.

• Journal of information and communication convergence engineering
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• 제12권1호
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• pp.8-13
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• 2014

We consider the sensor selection problem in large sensor networks where the goal is to find the best set of sensors that maximizes application objectives. Since sensor selection typically involves a large number of sensors, a low complexity should be maintained for practical applications. We propose a geometry-based sensor selection algorithm that utilizes only the information of sensor locations. In particular, by observing that sensors clustered together tend to have redundant information, we theorize that the redundancy is inversely proportional to the distance between sensors and seek to minimize this redundancy by searching for a set of sensors with the maximum average distance. To further reduce the computational complexity, we perform an iterative sequential search without losing optimality. We apply the proposed algorithm to an acoustic sensor network for source localization, and demonstrate using simulations that the proposed algorithm yields significant improvements in the localization performance with respect to the randomly generated sets of sensors.

• International Journal of Contents
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• 제3권2호
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• pp.18-24
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• 2007

Biological sequences such as DNA and amino acid sequences typically contain a large number of items. They have contiguous sequences that ordinarily consist of more than hundreds of frequent items. In biological sequences analysis(BSA), a frequent contiguous sequence search is one of the most important operations. Many studies have been done for mining sequential patterns efficiently. Most of the existing methods for mining sequential patterns are based on the Apriori algorithm. In particular, the prefixSpan algorithm is one of the most efficient sequential pattern mining schemes based on the Apriori algorithm. However, since the algorithm expands the sequential patterns from frequent patterns with length-1, it is not suitable for biological datasets with long frequent contiguous sequences. In recent years, the MacosVSpan algorithm was proposed based on the idea of the prefixSpan algorithm to significantly reduce its recursive process. However, the algorithm is still inefficient for mining frequent contiguous sequences from long biological data sequences. In this paper, we propose an efficient method to mine maximal frequent contiguous sequences in large biological data sequences by constructing the spanning tree with a fixed length. To verify the superiority of the proposed method, we perform experiments in various environments. The experiments show that the proposed method is much more efficient than MacosVSpan in terms of retrieval performance.

• 한국정보통신학회논문지
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• 제18권1호
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• pp.217-222
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• 2014

본 논문은 마이크로소프트 엑셀에서 데이터 분할 알고리즘을 이용한 효율적인 데이터 검색 시스템을 제안한다. 엑셀에서 VBA 기능을 이용하여 해당 데이터를 빠르게 검색하는 알고리즘을 제안한다. 본 알고리즘은 찾고자 하는 데이터가 있는 시트를 지정하게 된다. 해당 시트가 지정이 되고나면 해당 시트 내의 데이터에 대한 시작과 끝을 찾게된다. 시작 셀의 위치에서부터 원하는 키워드와 중간 값을 비교하게 된다. 이런 식으로 데이터 끝까지 검색을 하게 된다. 본 논문에서 제안하는 알고리즘을 실제 VBA 프로그램을 이용하여 구현하고 실험을 하였다. 실험 결과 기존의 방식과 비교했을 때 일반적인 순차 검색 방식보다 성능이 좋음을 확인할 수 있었다.

• 대한기계학회논문집A
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• 제28권9호
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• pp.1399-1407
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• 2004

Structural optimization has been carried out in continuous design space or in discrete design space. Generally, available designs are discrete in design practice. However, the methods for discrete variables are extremely expensive in computational cost. An iterative optimization algorithm is proposed for design in a discrete space, which is called a sequential algorithm using orthogonal arrays (SOA). We demonstrate verifying the fact that a local optimum solution can be obtained from the process with this algorithm. The local optimum solution is defined in a discrete design space. Then the search space, which is a set of candidate values of each design variables formed by the neighborhood of a current design point, is defined. It is verified that a local optimum solution can be found by sequentially moving the search space. The SOA algorithm has been applied to problems such as truss type structures. Then it is confirmed that a local solution can be obtained by using the SOA algorithm

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 춘계학술대회
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• pp.1005-1010
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• 2004

The structural optimization has been carried out in the continuous design space or in the discrete design space. Generally, available designs are discrete in design practice. But methods for discrete variables are extremely expensive in computational cost. In order to overcome this weakness, an iterative optimization algorithm was proposed for design in the discrete space, which is called as a sequential algorithm using orthogonal arrays (SOA). We focus to verify the fact that the local solution can be obtained throughout the optimization with this algorithm. The local solution is defined in discrete design space. Then the search space, which is the set of candidate values of each design variables formed by the neighborhood of current design point, is defined. It is verified that a local solution can be founded by moving sequentially the search space. The SOA algorithm has been applied to problems such as truss type structures. Then it is confirmed that a local solution can be obtained using the SOA algorithm

• 산업기술연구
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• 제25권A호
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• pp.163-173
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• 2005

Recent sequential pattern mining algorithms mine all of the frequent sequences satisfying a minimum support threshold in a large database. However, when a frequent sequence becomes very long, such mining will generate an explosive number of frequent sequence, which is prohibitively expensive in time. In this paper, we proposed a novel sequential pattern algorithm using only closed frequent sequences which are small subset of very large frequent sequences. Our algorithm extends the sequence by depth-first search strategy with effective pruning. Using bitmap representation of underlying databases, we can obtain a closed frequent sequence considerably faster than the currently reported methods.

• 한국경영과학회지
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• 제41권1호
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• pp.41-53
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• 2016

Static repositioning is a well-known and commonly used strategy to maximize customer satisfaction in public bike-sharing systems. Repositioning is performed by trucks at night when no customers are in the system. In models that represent the static repositioning problem, the decision variables are truck routes and the number of bikes to pick up and deliver at each rental station. To simplify the problem, the decision on the number of bikes to pick up and deliver is implicitly included in the truck routes. Two relocation-based local search algorithms (1-relocate and 2-relocate) with the best-accept strategy are incorporated into a variable neighborhood search (VNS) to obtain high-quality solutions for the problem. The performances of the VNS algorithm with the effect of local search algorithms and shaking strength are evaluated with data on Tashu public bike-sharing system operating in Daejeon, Korea. Experiments show that VNS based on the sequential execution of two local search algorithms generates good, reliable solutions.

• 한국인터넷방송통신학회논문지
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• 제17권5호
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• pp.157-163
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• 2017

데이터 탐색법 중 가장 널리 알려진 이진법은 평균과 최악의 경우 $O(log_2n)$, 보간법은 평균 $O(log_2log_2n)$, 최악의 경우 O(n)의 수행 복잡도를 갖고 있다. 또한 기존의 보간탐색법은 사전정보없이 킷값이 확률적으로 위치한 정보에 근거하여 탐색을 한다. 본 논문에서는 데이터의 MSB 인덱스를 블록으로 하는 블록탐색법으로 해당 블록범위를 결정하고, 블록 내에서는 보간법을 적용하여 탐색하는 하이브리드 블록과 보간탐색 알고리즘을 제안한다. 제안된 알고리즘은 블록탐색법의 사전 정보를 활용하여 탐색범위를 축소시키고 축소된 탐색범위내에서 무정보 방법으로 탐색하는 방법으로 평균과 최악의 경우 모두 수행복잡도는 $O(log_2log_2n_i)$, $n_i{\simeq}0.1n$으로 보간탐색법의 평균 수행복잡도에 비해 10배 정도 시간을 단축시킬 수 있다.

• 정보처리학회논문지D
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• 제12D권6호
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• pp.807-816
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• 2005

순차 패턴 탐사에 대한 연구는 대용량의 데이터베이스에서 사용자에 의해 주어지는 최소 지지도를 만족하는 빈발 시퀀스를 찾는 문제를 다룬다. 하지만 현재까지 이루어진 순차 패턴 탐사 방법은 빈발 시퀀스들의 길이가 길어지거나 최소 지지도가 상대적으로 낮게 주어진 상황에서는 생성되는 시퀀스가 기하급수적으로 많아져서 성능이 급격히 저하되는 문제점을 가지고 있다. 본 논문에서는 이 문제를 해결하기 위해서 모든 빈발 시퀀스의 정보를 포함하며 그 수가 현저히 적은 닫힌 빈발 시퀀스를 찾는 방법을 제안한다. 제안하는 알고리즘은 효율적으로 가지치기를 수행하기 위해서 깊이우선 탐색 방법으로 후보 시퀀스를 생성하고 데이터베이스를 비트맵으로 표현하여 비트 연산으로 지지도를 효율적으로 계산한다. 또한, 비트맵으로 표현된 시퀀스 특성을 이용하여 가지치기할 시퀀스를 적은 연산 비용으로 찾을 수 있다. 이런 장점을 통하여 제안한 방법이 지금까지 제안된 알고리즘보다 훨씬 빨리 닫힌 빈발 시퀀스를 찾는 것을 성능 실험을 통하여 확인하였다.