• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.413-416
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• 2006

최근 위치 측위 기술의 발달 및 GPS 기술의 상용화로 인해 무선 통신 기기의 보급이 증가하면서 다양한 위치 기반 서비스 개발을 위한 노력이 활발히 진행되고 있다. 사용자들의 특성에 맞게 개인화되고 세분화된 위치 기반 서비스를 제공하기 위해서는 방대한 이동 객체의 위치 이동 데이터로부터 의미있는 지식인 유용한 패턴을 추출하기 위한 시간 패턴 탐사가 필요하다. 기존의 시간 패턴 탐사 기법들 중 일부는 이동 객체의 시간에 따른 공간 속성들의 변화를 충분히 고려하지 못하거나 또는 시공간 속성을 동시에 고려한 패턴 탐사는 가능하나 전체 이동 패턴들 중 추출하고자 하는 패턴에 반드시 포함되어야 하는 공간 정보에 대한 제약이 없어 특정 지점들 사이의 최적 이동 경로 탐색 문제나 단위기간 동안 이동 객체가 순회해야 지점들에 대한 스케줄링 경로 예측 문제 등에 적용하기 어렵다. 따라서 본 논문에서는 이동 객체의 위치 이력 데이터들에 대한 시공간 속성들을 고려하여 다양한 이동 패턴들 중 객체의 최적 이동 경로에 해당하는 패턴을 탐색하기 위한 새로운 시간 패턴 마이닝 알고리즘을 제안한다. 제안된 알고리즘은 특정한 지점들 사이를 이동한 객체의 위치 데이터들 중 객체가 가장 빈번하게 이동한 경로를 탐색하여 최적 경로를 결정하는 알고리즘으로, 공간 추상 계층의 각 계층별 영역 내 포함여부를 고려한 위치 일반화를 수행하여 보다 효과적으로 이동 패턴을 탐색할 수 있다.

• Journal of the Korea Society for Simulation
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• v.23 no.1
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• pp.33-42
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• 2014

In this paper we analyzed the search patterns for the anti-submarine warfare (ASW) surface ships cooperating with ASW helicopters. For this purpose, we modeled evasive motion of a submarine with a probabilistic method. And maneuvers and search actions of ships and helicopters participating in the anti-submarine search mission are designed. And for each simulation scenario, the case where a ship and a helicopter searches a submarine independently according to its optimized search pattern is compared with the case where the search platforms participate in the ASW mission cooperatively. Based on the simulation results, we proposed the reconfigured search patterns that help cooperative ASW surface ships increase the total cumulative detection probability (CDP).

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.113-122
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• 2009

Since the traditional pattern mining methods only probe unspecified moving patterns that seem to satisfy users' requests among diverse patterns within the limited scopes of time and space, they are not applicable to problems involving the mining of optimal moving patterns, which contain complex time and space constraints, such as 1) searching the optimal path between two specific points, and 2) scheduling a path within the specified time. Therefore, in this paper, we illustrate some problems on mining the optimal moving patterns with complex time and space constraints from a vast set of historical data of numerous moving objects, and suggest a new moving pattern mining method that can be used to search patterns of an optimal moving path as a location-based service. The proposed method, which determines the optimal path(most frequently used path) using pattern frequency retrieved from historical data of moving objects between two specific points, can efficiently carry out pattern mining tasks using by space generalization at the minimum level on the moving object's location attribute in consideration of topological relationship between the object's location and spatial scope. Testing the efficiency of this algorithm was done by comparing the operation processing time with Dijkstra algorithm and $A^*$ algorithm which are generally used for searching the optimal path. As a result, although there were some differences according to heuristic weight on $A^*$ algorithm, it showed that the proposed method is more efficient than the other methods mentioned.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.585-586
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• 2009

홉필드 네트웍은 패턴 매칭과 더불어 최적화 문제를 푸는 도구로 사용될 수 있다. Ali에 의해 제안된 최적 경로 탐색 알고리즘을 개선하여 계산량을 대폭 줄이고 효과적으로 최적 경로를 탐색할 수 있다. 노드의 제곱인 브랜치수의 뉴런이 필요한 Ali 알고리즘은 탐색 네트워크가 커지면 많은 시간이 소요되는 단점이 있다. 본 논문은 계산량은 줄이면서 효과적으로 최적 경로를 탐색하는 방법을 제안한다.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.367-372
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• 2008

최근 사용자들의 특성에 맞게 개인화되고 세분화된 위치 기반 서비스를 개발하기 위한 목적으로 이동 객체의 다양한 패턴들 중 의미있는 지식인 유용한 이동 패턴을 탐사하는 문제가 주요 이슈로 부각되고 있다. 이에 본 논문에서는 방대한 이동 객체의 이력 데이터 집합으로부터 특정 지점들 간의 최적 이동 경로나 정해진 시간내의 스케줄링 경로 탐색과 같이 복합적인 시간 및 공간 제약을 갖는 최적 이동 패턴을 탐사하는 문제에 대해 정의하고, 다양한 이동 패턴들 중 가장 빈발하게 발생하는 패턴이 최적의 비용을 소요할 것이라는 가정을 기반으로 최대 빈발 2-시퀀스를 추출하는 방법을 제안한다. 후보 시퀀스 집합으로부터 지지도 계산을 통해 추출되는 빈발 2-시퀀스들의 순차적인 조합은 패턴 탐사를 수행하는 각 패스 진행 시 후보 시퀀스 항목의 차수가 점차 감소하여 최적 이동 패턴 탐사 방법에 효과적으로 적용된다.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.362-368
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• 2005

This paper is related to the fast block matching algorithm, especially a method for expanding the search pattern using the second local matching point in the adaptive hexagonal search. To reduce the local minima problem in fast motion estimation, the proposed method expands the search pattern by adding new searching points selected by using the second local matching point to conventional search pattern formed by the first local matching point in the adaptive hexagonal search. According to estimating the motion vector by applying block matching algorithm based on hexagonal search to the expanded search pattern, the proposed method can effectively carry out fast motion estimation to improve the performance in terms of compensated image quality.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.79-93
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• 2009

For developing the location based service which is individualized and specialized according to the characteristic of the users, the spatio-temporal pattern mining for extracting the meaningful and useful patterns among the various patterns of the mobile object on the spatio-temporal area is needed. Thus, in this paper, as the practical application toward the development of the location based service in which it is able to apply to the real life through the pattern mining from the huge historical data of mobile object, we are proposed STOMP(using Frequency of sequence and Weight) that is the new mining method for extracting the patterns with spatial and temporal constraint based on the problems of mining the optimal moving pattern which are defined in STOMP(F)[25]. Proposed method is the pattern mining method compositively using weighted value(weights) (a distance, the time, a cost, and etc) for our previous research(STOMP(F)[25]) that it uses only the pattern frequent occurrence. As to, it is the method determining the moving pattern in which the pattern frequent occurrence is above special threshold and the weight is most a little bit required among moving patterns of the object as the optimal path. And also, it can search the optimal path more accurate and faster than existing methods($A^*$, Dijkstra algorithm) or with only using pattern frequent occurrence due to less accesses to nodes by using the heuristic moving history.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.5015-5022
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• 2010

Most of the existing pattern mining techniques are capable of searching patterns according to the continuous change of the spatial information of an object but there is no constraint on the spatial information that must be included in the extracted pattern. Thus, the existing techniques are not applicable to the optimal path search between specific nodes or path prediction considering the nodes that a moving object is required to round during a unit time. In this paper, the precision of the path search according to the spatial hierarchy is analyzed using the Spatial-Temporal Optimal Moving Pattern(with Frequency & Weight) (STOPM(FW)) algorithm which searches for the optimal moving path by considering the most frequent pattern and other weighted factors such as time and cost. The result of analysis shows that the database retrieval time is minimized through the reduction of retrieval range applying with the spatial constraints. Also, the optimal moving pattern is efficiently obtained by considering whether the moving pattern is included in each hierarchical spatial scope of the spatial hierarchy or not.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.58-64
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• 2007

In pattern recognition field, there are many optimization problems having exponential search spaces. To solve of sequential search algorithms seeking sub-optimal solutions have been used. The algorithms have limitations of stopping at local optimums. Recently lots of researches attempt to solve the problems using genetic algorithms. This paper explains the huge search spaces of typical problems such as feature selection, classifier ensemble selection, neural network pruning, and clustering, and it reviews the genetic algorithms for solving them. Additionally we present several subjects worthy of noting as future researches.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.3
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• pp.133-147
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• 2005

Various types of ray-tracing methods are used to predict the quantity measures of radiation illumination, the uniformity of illumination, radiation performance of LCD BLU(Hack-Light Unit). The uniformity of radiation illumination is one of the most important design factor of BLU and is usually controlled by the diffusive-ink pattern printed on the bottom of light-guide panel of BLU. Therefore it is desirable to produce an improved (ideally, the optimal) ink pattern to achieve the best uniformity of radiation illumination. In this paper, we applied the Welder-Mead simplex-search method among various direct search method to compute the optimal ink pattern. Direct search methods are widely used to optimize the functions which are often highly nonlinear, unpredictably discontinuous, and nondifferentiable, The ink-pattern controlling the uniformity of radiation illumination is one type of these functions. In this paper, we found that simplex search methods are well suited to computing the optimal diffusive-ink pattern. In extensive numerical testing, we have found the simplex search method to be reasonably efficient and reliable at computing the optimal diffusive-ink pattern. The result also suggests that optimization can improve the functionality of simulation tools which are used to design LCD BLU.