• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.1
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• pp.1-8
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• 2007

People tend to spend more time in indoor spaces than before such as shopping malls and subway stations. As buildings become larger and more complex, people feel difficulty in finding their ways to destinations. Consequently, a means to provide better paths can aid people in reducing time for finding ways. Routing information in large indoor spaces is especially required in emergency cases as fire, power failure and terror. This study suggests to compute optimal paths using $A^*$ algorithm based on raster GIS data structure. The suggested method can be used either in daily lives for path provision or in emergency cases for evacuation, and is illustrated on a campus building.

• The Transactions of the Korea Information Processing Society
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• v.3 no.1
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• pp.43-54
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• 1996

This paper presents new heuristic search algorithms for searching rectilinear r(L1), link metric, and combined shortest paths in the presence of orthogonal obstacles. The GMD(GuidedMinimum Detour) algorithm combines the best features of maze-running algorithms and line-search algorithms. The SGMD(Line-by-Line GuidedMinimum Detour)algorithm is a modiffication of the GMD algorithm that improves efficiency using line-by-line extensions. Our GMD and LGMD algorithms always find a rectilinear shortest path using the guided A search method without constructing a connection graph that contains a shortest path. The GMD and the LGMD algorithms can be implemented in O(m＋eloge＋NlogN) and O(eloge＋NlogN) time, respectively, and O(e＋N) space, where m is the total number of searched nodes, is the number of boundary sides of obstacles, and N is the total number of searched line segment. Based on the LGMD algorithm, we consider not only the problems of finding a link metric shortest path in terms of the number of bends, but also the combined L1 metric and Link Metric shortest path in terms of the length and the number of bands.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.724-726
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• 2004

본 논문에서는 L$_1$평면상에 도로망이 주어져 있어서 여행자들이 그 도로들을 이용하여 더욱 빠르게 이동할 수 있는 가정 하에서 가장 기초적인 기하문제 중에 하나인 두 점 사이의 최단 경로를 찾는 문제를 다룬다. 이 때, 두 점 사이의 거리는 L$_1$ 거리가 아닌 주어진 도로들을 이용하여 두 점 사이를 이동할 때 필요한 최소시간으로 측정한다. 단순한 평면상에서의 최단경로와는 달리 도로망이 설치되어 있는 경우는 그것을 해결하기가 일반적으로 쉽지 않다. 본 논문에서는 도로망이 있는 평면에 대한 깊은 관찰과 이해를 통해 도로망이 설치되어 있는 L$_1$ 평면상에서의 최단경로 문제를 해결하는 효율적인 알고리즘을 제시한다. 덧붙여, 본 논문에서 제시하는 문제 해결 방법은 L$_1$ 평면뿐만 아니라 유클리드 평면에도 어렵지 않게 적용할 수 있으며 보로노이 다이어그램으로의 일반화도 간단하다.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10b
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• pp.457-462
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• 2007

사용자가 가능한 센서 가까이에서 이동하는 문제를 최단거리유지문제라 하며, 무선 연결의 안정성을 높이기 위해서 두 개의 센서로부터 가까이에 위치하여 사용자가 움직이는 이동경로를 차선최단거리유지이동경로라고 한다. 이 논문에서는 주어진 센서들의 집합 U와 시작점 s, 끝점 t가 주어질 때, s에서 t까지 이르는 경로로서, 가장 가까운 두 개의 센서까지의 거리가 최소인 차선최단거리유지이동경로를 찾는 $O((c^2n+e)log(c^2n))$-시간 알고리즘을 제시한다. 여기서 c는 집합 U의 하나의 사이트가 갖는 최대 보로노이 선분의 개수이다.

• Journal of the Korean Society for Railway
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• v.10 no.5
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• pp.569-575
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• 2007

This paper presents a study on a Dijkstra algorithm for shortest paths to destinations and a modified algorithm of Multicommodity Network Flow Problem Model with a network transformation for evacuation planning from railroad accident in a directed network.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.6
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• pp.1-9
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• 2013

The studies on the shortest path algorithms based on Dijkstra algorithm has been done continuously to decrease the time for searching. $A^*$ algorithm is the most represented one. Although fast searching speed is the major point of $A^*$ algorithm, there are high rates of failing in search of the shortest path, because of complex and irregular networks. The failure of the search means that it either did not find the target node, or found the shortest path, witch is not true. This study proposed $A^*$ algorithm applying method that can reduce searching failure rates, preferentially organizing the relations between the starting node and the targeting node, and appling it in reverse according to the organized path. This proposed method may not build exactly the shortest path, but the entire failure in search of th path would not occur. Following the developed algorithm tested in a real complex networks, it revealed that this algorithm increases the amount of time than the usual $A^*$ algorithm, but the accuracy rates of the shortest paths built is very high.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1221-1228
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• 2022

With the development of modern technology, the use of unmanned automation equipment that can replace humans in logistics and industrial sites is increasing. The technology of one such automation facility, the Unmanned Carrier (AGV), includes Line Tracing, which allows you to recognize a line through infrared sensors and drive a predetermined route. In this paper, the shortest time algorithm using Arduino is configured in the line tracing technology to enable efficient driving. It is also designed to collect location and time information using RFID tags.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.575-582
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• 2000

Because FIFO thread scheduling used in the existing multithreaded models does not consider locality in programs, it may result in the decrease of the performance of execution, caused by the frequent context switching overhead and delay of execution of relatively short frames. Quantum unit scheduling enhances the performance a little, but it still has the problems such as the decrease in the processor utilization and the longer delay due to its heavy dependency on the priority of the quantum units. In this paper, we propose shortest-frame-first(SFF) thread scheduling algorithm. Our algorithm selects and schedules the frame that is expected to take the shortest execution time using thread size and synchronization information analyzed at compile-time. We can estimate the relative execution time of each frame at compile-time. Using SFF thread scheduling algorithm on the multithreaded models, we can expect the faster execution, better utilization of the processor, increased throughput and short waiting time compared to FIFO scheduling.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.2
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• pp.454-461
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• 2005

Car navigation system which is killer application fuses map management techniques into CPS techniques. Even if the existing navigation systems are designed for the shortest path, they are not able to cope efficiently with the change of the traffic flow and the bottleneck point of road. Therefore, it is necessary to find out shortest path algorithm based on time instead of distance which takes traffic information into consideration. In this paper, we propose a optimal path search algorithm based on the traffic information. More precisely. we introduce the system architecture for finding out optimal paths, and the limitations of the existing shortest path search algorithm are also analyzed. And then, we propose a new algorithm for finding out optimal path to make good use of the orientation of the collected traffic information.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.5
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• pp.425-433
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• 2012

Dijkstra's algorithm is one of well-known methods to find shortest paths over a network. However, more research on $A^*$ algorithm is necessary to discover the shortest route to a goal point with the heuristic information rather than Dijkstra's algorithm which aims to find a path considering only the shortest distance to any point for an optimal path search. Therefore, in this paper, we compared Dijkstra's algorithm and $A^*$ algorithm for bicycle route selection. For this purpose, the horizontal distance according to slope angle and average speed were calculated based on factors which influence bicycle route selection. And bicycle routes were selected considering the shortest distance or time-dependent shortest path using Dijkstra's or $A^*$ algorithm. The result indicated that the $A^*$ algorithm performs faster than Dijkstra's algorithm on processing time in large study areas. For the future, optimal path selection algorithm can be used for bicycle route plan or a real-time mobile services.