• Journal of the Korean Wood Science and Technology
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• v.43 no.6
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• pp.804-809
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• 2015

Since Korea is home to Reticulitermes speratus, a kind of subterranean termites that prefer dark and humid conditions, there have been increasing damages to wooden structures by termites. One noticeable attribute of Korean subterranean termites is that they prefer than Pinus densiflora, the major construction material for Korean traditional houses. And because wide varieties of termites are distributed all over the world, it is not so easy to choose appropriate control methods depending on specific areas. This necessitates careful applications of the following control methods depending on the kinds of termites: fumigation treatment, soil termiticide, preservatives and insect treatment, termite colony elimination system, chemical treatment, and other physical and biological treatment methods. The purpose of this study is to investigate the control effects of environmentally-friendly Alkaline copper quaternary (ACQ), Copper Azole (CuAZ) and Micronized copper quarter (MCQ) on the termites contributing to the damage of wooden structures. It was found in this study that wood with preservative treatment produced a significantly higher termicidal efficacy than untreated wood.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1354-1356
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• 2012

최근 ITS(Intelligent Transportation Systems)의 개발과 함께 차량용 내비게이션의 사용이 급증하면서 경로탐색의 중요성이 더욱 가속화되고 있다. 현재 차량용 내비게이션은 멀티미디어 및 정보통신 기술의 결합과 함께 다양한 기능 및 정보를 사용자에게 제공하고 있으며 이러한 기능과 정보를 사용해서 목적지점까지의 최단경로를 탐색하는 것이 내비게이션 시스템의 핵심기능이다. 이러한 경로탐색 알고리즘은 교통시스템, 통신 네트워크, 운송 시스템은 물론 이동 로봇의 경로 설정 등 다양한 분야에 사용되고 있다. 개미 집단 최적화(Ant Colony Optimization, ACO) 알고리즘은 메타 휴리스틱 탐색 방법으로 그리디 탐색(Greedy Search)뿐만 아니라 긍정적 반응의 탐색을 사용한 모집단에 근거한 접근법으로 순환 판매원 문제(Traveling Salesman Problem, TSP)를 풀기 위해 처음으로 제안되었다. 본 논문에서는 개미 집단 최적화(ACO) 알고리즘이 기존의 경로 탐색 알고리즘으로 알려진 Dijkstra 보다 최단경로 탐색에 있어서 더 적합한 알고리즘이라는 것을 설명하고자 한다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.6
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• pp.87-94
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• 2003

One of the important fields for heuristic algorithm is how to balance between Intensification and Diversification. Ant Colony Optimization(ACO) is a new meta heuristic algorithm to solve hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as Breedy search It was first Proposed for tackling the well known Traveling Salesman Problem(TSP). In this paper, we deal with the performance improvement techniques through balance the Intensification and Diversification in Ant Colony System(ACS). First State Transition considering the number of times that agents visit about each edge makes agents search more variously and widen search area. After setting up criteria which divide elite tour that receive Positive Intensification about each tour, we propose a method to do addition Intensification by the criteria. Implemetation of the algorithm to solve TSP and the performance results under various conditions are conducted, and the comparision between the original An and the proposed method is shown. It turns out that our proposed method can compete with the original ACS in terms of solution quality and computation speed to these problem.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.9-12
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• 2000

컨테이너 터미널에서 효율적인 적하작업 계획을 자동으로 생성하는 알고리즘을 연구하였다. 실제 터미널에서 계획자들이 적하작업 계획시에 고려하는 제약소건 및 효율적인 계획을 위한 고려사항을 조사하였다. 이를 바탕으로 1단계에서는 개미시스템(ant system)이라는 인공지능기법을 적용하여 제약조건을 만족시키면서 원활한 적하작업이 진행될 수 있도록 컨테이너 크레인과 트랜스퍼 크레인의 이동순서와 위치를 결정하고, 2단계에서는 1단계에서의 결과를 바탕으로 빔탐색법(beam search)을 사용하여 컨테이너 개개의 작업순서를 결정하는 알고리즘을 개발하였다. 또한 개발된 시스템의 성능을 검증하기 위하여 최근의 대형선반에 대한 실제 현장자료를 바탕으로 실험을 수행하였다.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.4
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• pp.38-45
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• 2003

This paper presents a meta-heuristic solution technique, Ant System (AS)algerian to solve edge detection problem. We define the quality of edge in terms of dissimilarity, continuity, thickness and length. We cast edge detection as a problem in cost minimization. This is achieved by the formulation of a cost function that inversely evaluates the quality of edge configuration. Twelve windows for enhancing dissimilarity regions based on the valid edge structures are used. The AS algorithm finds the optimal set of edge pixels based on the cost function. The experimental results show that the properly reduced set of edge pixels could be found regardless how complicated the image is.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.203-210
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• 2011

Ant Colony System is a new meta heuristics algorithms to solve hard combinatorial optimization problems. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem. In this paper, we propose the searching method to consider the overlapping edge of the global best path of the previous and the current. This method is that we first determine the overlapping edge of the global best path of the previous and the current will be configured likely the optimal path. And, to enhance the pheromone for the overlapping edges increases the probability that the optimal path is configured. Finally, the performance of Best and Average-Best of proposed algorithm outperforms ACS-3-opt, ACS-Subpath and ACS-Iter algorithms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1309-1314
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• 2007

The assignment of cells to reporting or non-reporting cells is an NP-hard problem having an exponential complexity in the Reporting Cell Location Management (RCLM) system. Frequent location update may result in degradation of quality of service due to interference. Miss on the location of a mobile terminal will necessitate a search operation on the network when a call comes in. The number of reporting cells and which cell must be reporting cell should be determined to balance the registration (location update) and search (paging) operations to minimize the cost of RCLM system. T1is paper compares Max-Min ant system (MMAS), rank-based ant system (RAS) and hybrid method of MMAS and RAS that generally used to solve combinatorial optimization problems. Experimental results demonstrate that hybrid method of MMAS and RAS is an effective and competitive approach in fairly satisfactory results with respect to solution quality and execution time for the optimal design of location management system.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.7-13
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• 2008

This paper suggests a Path-planning algorithm for mobile agents. While there are a lot of studies on the path-planning for mobile agents, mathematical modeling of complex environment which constrained by spatio-temporally is very difficult and it is impossible to obtain the optimal solutions. In this paper, an optimal path-planning algorithm based on the graphic technique is presented. The working environment is divided into two areas, the one is free movable area and the other is not permissible area in which there exist obstacles and spatio-temporally constrained, and an optimal solution is obtained by using a new algorithm which is based on the well known ACO algorithm.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.862-866
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• 2003

Traveling Salesman Problem(TSP) is a combinational optimization problem, Genetic Algorithm(GA) and Lin-Kernighan(LK) Heuristic［1］that is Local Search Heuristic are one of the most commonly used methods to resolve TSP. In this paper, we introduce ACS(Ant Colony System) Algorithm as another approach to solve TSP and propose a new pheromone updating method. ACS uses pheromone information between cities in the Process where many ants make a tour, and is a method to find a optimal solution through recursive tour creation process. At the stage of Global Updating of ACS method, it updates pheromone of edges belonging to global best tour of created all edge. But we perform once more pheromone update about created all edges before global updating rule of original ACS is applied. At this process, we use the frequency of occurrence of each edges to update pheromone. We could offer stochastic value by pheromone about each edges, giving all edges' occurrence frequency as weight about Pheromone. This finds an optimal solution faster than existing ACS algorithm and prevent a local optima using more edges in next time search.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.221-226
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• 2004

Ant Colony System(ACS) Algorithm is new meta-heuristic for hard combinational optimization problem. It is a population-based approach that uses exploitation of positive feedback as well as greedy search. Recently, various methods and solutions are proposed to solve optimal solution of graph coloring problem that assign to color for adjacency node($v_i, v_j$) that they has not same color. In this paper introducing ANTCOL Algorithm that is method to solve solution by Ant Colony System algorithm that is not method that it is known well as solution of existent graph coloring problem. After introducing ACS algorithm and Assignment Type Problem, show the wav how to apply ACS to solve ATP And compare graph coloring result and execution time when use existent generating functions(ANT_Random, ANT_LF, ANT_SL, ANT_DSATUR, ANT_RLF method) with ANT_XRLF method that use XRLF that apply Randomize to RLF to solve ANTCOL. Also compare graph coloring result and execution time when use method to add re-search to ANT_XRLF(ANT_XRLF_R) with existent generating functions.