• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.363-365
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• 2009

대부분의 이동 로봇은 효율적인 경로계획을 위하여 최단거리 및 최소비용을 갖는 경로를 선택한다. 그러나 다수의 로봇이 존재하는 환경에서는 이웃하는 로봇 상호간에 동적 장애물로 인식되어 주행성능을 떨어뜨리게 된다. 또한 트래픽량이 거의 없는 환경에서는 무선 통신의 전송거리 제한으로 이동 로봇간 네트워킹이 원활하게 수행될 수 없는 문제도 있다. 따라서 적당한 거리의 이웃 로봇들과 협업을 위한 네트워킹을 하면서 동적인 경로계획 및 주행을 하는 것이 필수적이다. 본 논문에서는 기존의 A* 알고리즘을 수정하여 로봇의 동적인 트래픽을 고려한 경로계획 알고리즘을 제안한다. 제안된 기법을 이용하여 경로설정과정에서의 로봇 상호간 병목현상을 완화시키며, 일관된 협업 통신도 유지할 수 있다. 모의 실험을 통하여 제안된 알고리즘이 동적인 트래픽을 고려하여 경로를 선택함을 보인다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.265-267
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• 2021

본 논문에서는 코로나바이러스(COVID-19)와 같은 응급상황에서 필요한 환자의 이동 및 검사 경로 등을 MCMF(Min-cost Max-flow, 최소-비용 최대-유량) 알고리즘 기반의 에이전트 이동을 활용하여 효율적으로 푸는 방법에 대해 살펴본다. 환자의 수가 유동적으로 변화하기 때문에 고정된 경로가 아닌 매번 최적화시킬 수 있는 경로는 요즘 같은 COVID-19 시대에 필요한 기술이며, 이와 같은 응급상황에서는 이른 시일 내에 대처하고 조치하는 것이 피해를 최소화할 수 있다. 이러한 상황에서는 응급상황에 대처하기 위한 정보들을 어떻게 사용하는지에 따라 상황에 대한 처리 시간이 달라질 수 있다. 본 논문에서는 응급상황에 대한 처리 시간을 최소한으로 하기 위해 MCMF 알고리즘을 적용하고 지도 API와 실제 병원 위치 등을 이용하여 실제로 시간을 단축할 수 있는지 연구하고 분석한다.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.99-107
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• 2000

Path-based assignment(PBA) is valuable to dynamic traffic control and routing in integrated ITS framework. As one of widely studied PBA a1gorithms, Gradient Projection(GP) a1gorithm typically fields rapid convergence to a neighborhood of an optimal solution. But once it comes near a solution, it tends to slow down. To overcome this problem, we develop more efficient path-based assignment algorithm by combining Conjugate Gradient method with GP algorithm. It determines more accurate moving direction near a solution in order to gain a significant advantage in speed of convergence. Also this algorithm is applied to the Sioux-Falls network and verified its efficiency. Then we demonstrate that this type of method is very useful in improving speed of convergence in the case of user equilibrium problem.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.129-137
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• 2010

Currently used shortest path search algorithms involve graphs with vertices and weighted edges between each vertex. However, when finding the shortest path with a randomly shaped obstacle(an island, for instance) positioned in between the starting point and the destination, using such algorithms involves high memory inefficiency and is significantly time consuming - all positions in the map should be considered as vertices and every line connecting any of the two adjacent vertices should be considered an edge. Therefore, we propose a new method for finding the shortest path in such conditions without using weighted graphs. This algorithm will allow finding the shortest obstacle bypass given only the positions of the obstacle boundary, the starting point and the destination. When the row and column size of the minimum boundary rectangle to include an obstacle is m and n, respectively, the proposed algorithm has the maximum time complexity, O(mn). This performance shows the proposed algorithm is very efficient comparing with the currently used algorithms.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.132-133
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• 2023

The safe evacuation of people during disasters is of utmost importance. Various life safety evacuation simulation tools have been developed and implemented, with most relying on algorithms that analyze maps to extract the shortest path and guide agents along predetermined routes. While effective in predicting evacuation routes in stable disaster conditions and short timeframes, this approach falls short in dynamic situations where disaster scenarios constantly change. Existing algorithms struggle to respond to such scenarios, prompting the need for a more adaptive evacuation route algorithm that can respond to changing disasters. Artificial intelligence technology based on reinforcement learning holds the potential to develop such an algorithm. As a fundamental step in algorithm development, this study aims to evaluate whether an evacuation algorithm developed by reinforcement learning satisfies the performance conditions of the evacuation simulation tool required by IMO MSC.1/Circ1533.

• The Transactions of the Korea Information Processing Society
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• v.5 no.4
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• pp.1033-1043
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• 1998

In this paper, we present an improved multicasting path assignment algorithm based on the minimum cost spanning tree. In the method presented in this paper, a multicasting path is assigned preferentially when a node to be received is found among the next degree nodes of the searching node in the multicasting path assignment of the constrained steiner tree (CST). If nodes of the legacy group exist between nodes of the new group, a new path among the nodes of new group is assigned as long as the nodes may be excluded from the new multicasting path assignment taking into consideration characteristics of nodes in the legacy group. In assigning the multicasting path additionally, where the source and destination nodes which can be set for the new multicasting path exist in the domain of identical network (local area network) and conditions for degree constraint are satisfied, a method of producing and assigning a new multicasting path is used. The results of comparison of CST with MCSTOP, MCSTOp algorithm enhanced performance capabilities about the communication cost, the propagation delay, and the computation time for the multicasting assignment paths more than CST algorithm. Further to this, research activities need study for the application of the international standard protocol(multicasting path assignment technology in the multipoint communication service (MCS) of the ITU-T T.120).

• Proceedings of the IEEK Conference
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• 2003.07a
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• pp.382-385
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• 2003

Ad hoc 네트워크는 고정된 기반구조 없이 이동 노드들로만 구성된 네트워크를 가리킨다. 노드들의 이동으로 인해 네트워크 토폴로지는 예측할 수 없게 자주 변할 수 있다. 그 결과로 기 설정되어 있는 경로의 단절이 발생할 수 있으며, 새로운 경로가 설정될 때까지 데이터가 손실될 수 있다. 존재하는 경로의 단절로 인해 손실되는 패킷의 수를 줄이기 위해, 본 논문은 multiple-reply Ad hoc On-demand Distance Vector (mrAODV) 방식을 제안한다. 본 논문의 알고리즘은 발신지와 수신지 사이에 다중 경로를 설정한다. ns-2 시뮬레이션을 사용하여 제안된 방식에 대한 성능을 평가한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.157-161
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• 2003

본 논문에서는 차량의 주행경로 최적화를 위해 강화학습 개념을 적용하고자 한다. 강화학습의 특징은 관심 대상에 대한 구체적인 지배 규칙의 정보 없이도 최적화된 행동 방식을 학습시킬 수 있는 특징이 있어서, 실제 차량의 주행경로와 같이 여러 교통정보 및 시간에 따른 변화 등에 대한 복잡한 고려가 필요한 시스템에 적합하다. 또한 학습을 위한 강화(보상, 벌칙)의 정도 및 기준을 조절해 즘으로써 다양한 최적주행경로를 제공할 수 있다. 따라서, 본 논문에서는 강화학습 알고리즘을 이용하여 다양한 최적주행경로를 제공해 주는 시스템을 구현한다.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1266-1275
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• 2004

Bioinformatics is concerned with the creation and development of advanced information and computational technologies for problems in biology. It is divided into genomics, proteomics and metabolimics. In metabolimics, an organism is represented by metabolic pathway, i.e., well-displayed graph, and so the graph drawing tool to draw pathway well is necessary to understand it comprehensively. In this paper, we design an improved drawing algorithm. It enhances the readability by making use of the bipartite graph. Also it is possible to draw large graph properly by considering the facts that metabolic pathway graph is scale-free network and is composed of circular components, hierarchic components and linear components.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.6
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• pp.385-391
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• 2022

This paper presents to develop a path planning algorithm combining gradient descent-based path planning (GBPP) and particle swarm optimization (PSO) for considering prohibited flight areas, terrain information, and characteristics of fixed-wing unmmaned aerial vehicle (UAV) in 3D space. Path can be generated fast using GBPP, but it is often happened that an unsafe path can be generated by converging to a local minimum depending on the initial path. Bio-inspired swarm intelligence algorithms, such as Genetic algorithm (GA) and PSO, can avoid the local minima problem by sampling several paths. However, if the number of optimal variable increases due to an increase in the number of UAVs and waypoints, it requires heavy computation time and efforts due to increasing the number of particles accordingly. To solve the disadvantages of the two algorithms, hierarchical path planning algorithm associated with hierarchical particle swarm optimization (HPSO) is developed by defining the initial path, which is the input of GBPP, as two variables including particles variables. Feasibility of the proposed algorithm is verified by software-in-the-loop simulation (SILS) of flight control computer (FCC) for UAVs.