• ETRI Journal
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• 제38권4호
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• pp.714-723
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• 2016

Considerable attention has been given to processing graph data in recent years. An efficient method for computing the node proximity is one of the most challenging problems for many applications such as recommendation systems and social networks. Regarding large-scale, mutable datasets and user queries, top-k query processing has gained significant interest. This paper presents a novel method to find top-k answers in a node proximity search based on the well-known measure, Personalized PageRank (PPR). First, we introduce a distribution state transition graph (DSTG) to depict iterative steps for solving the PPR equation. Second, we propose a weight distribution model of a DSTG to capture the states of intermediate PPR scores and their distribution. Using a DSTG, we can selectively follow and compare multiple random paths with different lengths to find the most promising nodes. Moreover, we prove that the results of our method are equivalent to the PPR results. Comparative performance studies using two real datasets clearly show that our method is practical and accurate.

• 한국경영과학회지
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• 제32권4호
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• pp.1-18
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• 2007

In this paper, we develop an adaptive genetic algorithm (aGA). The aGA has an adaptive scheme which can automatically determine the use of local search technique and adaptively regulate the rates of crossover and mutation operations during its search process. For the adaptive scheme, the ratio of degree of dispersion resulting from the various fitness values of the populations at continuous two generations is considered. For the local search technique, an improved iterative hill climbing method is used and incorporated into genetic algorithm (GA) loop. In order to demonstrate the efficiency of the aGA, i) a canonical GA without any adaptive scheme and ii) several conventional aGAs with various adaptive schemes are also presented. These algorithms, including the aGA, are tested and analyzed each other using various test problems. Numerical results by various measures of performance show that the proposed aGA outperforms the conventional algorithms.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2018년도 춘계 학술논문 발표대회
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• pp.185-186
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• 2018

The slabfrom, which is commonly used in construction sites, has drawbacks in that the workability of the workers is reduced due to their heavy weight. This study investigates the possibility of design optimization of euro form between structural stability and weight using harmonic search algorithm. The harmonic search algorithm is a metaheuristic optimization technique that obtains multiple optimal solution candidates through iterative. As a result of multiple attempts of optimization through the algorithm, it was possible to design the formwork which is structurally stable and light in weight than the existing formwork.

• 한국통신학회논문지
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• 제24권7A호
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• pp.1080-1089
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• 1999

병렬 연쇄 컨볼루션 부호 및 직렬 연쇄 컨볼루션 부호의 ML 연판정 복호에 대한 비트오율확률의 상한치는 가중치 열거함수(Weight Enumerating Function; WEF)를 통해서 구할 수 있으며, 이 상한치는 반복 택 알고리듬과 양방향 탐색 알고리듬을 혼합한 새로운 오류사건 탐색 알고복호를 통해 얻을 수 있는 비트오류확률의 하한치가 된다. 본 논문에서는 스리듬을 제안하고, 얻어진 오류사건을 이용하여 WEF를 계산하는 알고리듬을 제안한다. 컴퓨터 시뮬레이션을 통해, 반복복호를 통해 얻을 수 있는 비트오류확률의 하한치가 됨을 확인하였다.

• Journal of the Korean Physical Society
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• 제73권11호
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• pp.1644-1649
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• 2018

Aiming at infrared thermal images with different buried depth defects, we study a variety of image segmentation algorithms based on the threshold to develop global search ability and the ability to find the defect area accurately. Firstly, the iterative thresholding method, the maximum entropy method, the minimum error method, the Ostu method and the minimum skewness method are applied to image segmentation of the same infrared thermal image. The study shows that the maximum entropy method and the minimum error method have strong global search capability and can simultaneously extract defects at different depths. However none of these five methods can accurately calculate the defect area at different depths. In order to solve this problem, we put forward a strategy of "divide and conquer". The infrared thermal image is divided into several local thermal maps, with each map containing only one defect, and the defect area is calculated after local image processing of the different buried defects one by one. The results show that, under the "divide and conquer" strategy, the iterative threshold method and the Ostu method have the advantage of high precision and can accurately extract the area of different defects at different depths, with an error of less than 5%.

• Journal of applied mathematics & informatics
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• 제8권1호
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• pp.181-197
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• 2001

A heuristic method TABU-CSP using Tabu Search (TS) is described for solving Constraint Satisfaction Problems (CSPs). The method is started with a complete but inconsistent solution of a binary CSP and obtained in prespecified number of iterations either a consistent solution or a near optimal solution with an acceptable number of conflicts. The repair in the solution at each iterative step is done by using two heuristics alternatively. The first heuristic is a min-conflicts heuristic that chooses a variable with the maximum number of conflicts and reassigns it the value which leads to the minimum number of conflicts. If the acceptable solution is not reached after the search continued for a certain number of iterations, the min-conflict heuristic is changed and the variable selected least number of times is chosen for repair. If an acceptable solution is not reached, the method switches back to the min-conflict heuristic and proceeds further. This allowed the method to explore a different region of search space space for the solution as well as to prevent cycling. The demonstration of the method is shown on a toy problem [9]which has no solution. The method is then tested on various randomly generated CSPs with different starting solutions. The performance of the proposed method in terms of the average number of consistency is checked and the average number of conflicts is conflicts is compared with that of the Branch and Bound(BB) method used to obtain the same solution. In almost all cases, the proposed method moves faster to the acceptable solution than BB.

• 센서학회지
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• 제24권3호
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• pp.188-193
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• 2015

This paper presents a new byte-wise BCH (4122, 4096, 2) decoder, which treats byte-wise parallel operations so as to enhance its throughput. In particular, we evaluate the parallel processing technique for the most time-consuming components such as syndrome generator and Chien search owing to the iterative operations. Even though a syndrome generator is based on the conventional LFSR architecture, it allows eight consecutive bit inputs in parallel and it treats them in a cycle. Thus, it can reduce the number of cycles that are needed. In addition, a Chien search eliminates the redundant operations to reduce the hardware complexity. The proposed BCH decoder is implemented with VHDL and it is verified using a Xilinx FPGA. From the simulation results, the proposed BCH decoder can enhance the throughput as 43% and it can reduce the hardware complexity as 67% compared to its counterpart employing parallel processing architecture.

• 한국비블리아학회지
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• 제25권1호
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• pp.19-37
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• 2014

본 연구는 이미지 기반 디지털 도서관의 이용자 검색 패턴을 효과적으로 분석하기 위해 이용자 검색 로그 데이터를 분석하였다. 기술 통계와 네트워크 분석 방법을 사용하여 한 달간 수집한 트랜잭션 로그 데이터를 분석하였다. 연구 결과는 이용자들이 특정 주제 내에서 검색 결과 보기와 이미지 아이템 평가를 반복적으로 수행하고 있음을 밝혀내었다. 본 연구는 이미지 자료 검색의 로그 분석을 위해 복합적 데이터 분석 방법을 이용하였다는 점에 의의가 있다.

• 한국정보처리학회논문지
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• 제2권3호
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• pp.354-363
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• 1995

본 논문은 목적 상태를 구성하는 부분목적들 사이의 선취관계를 이요하는 새로운 탐색방법을 제안한다.제안된 방법은 기존의 인공지능 탐색 방법에 부분 목적들간의 선취관계를 이용하여 분지인수(branching factor)와 탐색 공간의 깊이를 줄이고,직접 성취 가능한 극대 부분 목적과 필연적인 작업들을 즉시 실행하는 전략을 사용하여 효율성을 더육 제고 시킨다. 이러한 전략을 사용하는 제안된 알고리즘의 효율성을 이론적으로 보일 뿐 아니라,점증깊이 우선 탐색(DFID:Depth-First Iterative Deepening Search)방법과 IDA(Iteratice Deepening A)알고리즘에 제안도니 방법을 적용하여 얻은 변형된 탐색 알고리즘을 이용하는 로봇 계획 시스템을 구현하여 제안된 전략의 효율성을 실험적으로도 보인다.

• 한국컴퓨터정보학회논문지
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• 제9권4호
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• pp.117-125
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• 2004

컨테이너 터미널에서의 선석 및 크레인 일정계획은 일정 기간 동안 입항 예정인 선박들을 대상으로 선석을 배정하고 접안 시기와 기간을 결정하며, 또한 각 선박별로 컨테이너를 싣고 내릴 크레인을 배정하되 각 크레인의 서비스 시작과 완료시간을 지정하는 전 과정을 포함한다. 이 문제는 기본적으로 다양한 제약조건을 만족해야 하는 제약만족 탐색 문제인 동시에, 각 선박의 희망 입출항 시간을 최대한 존중하면서 터미널의 운영 비용을 최소화할 수 있는 계획을 도출해야 하는 최적화 문제이기도 하다. 본 논문에서는 이 문제를 효과적으로 해결하기 위해 반복적 개선 탐색의 틀 내에서 제약만족 탐색기법을 적용하는 방안을 제시하고 있다. 실제 컨테이너 터미널에 대한 실험 결과 기존 알고리즘을 적용했을 때보다 더 좋은 계획을 수립할 수 있음을 확인하였다.