• The KIPS Transactions:PartC
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• v.15C no.6
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• pp.531-538
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• 2008

With the dynamic and mobile nature of ad hoc networks, links may fail due to topology changes. So, a major challenge in ad hoc network is dynamically to search paths from a source to destination with an efficient routing method, which is an important issue for delay-sensitive real-time application. The main concerns of graph theory in communications are finding connectivity and searching paths using given nodes. A topology of the nodes in ad hoc networks can be modeled as an adjacency matrix. In this paper, based on this adjacency matrix, we propose new path search algorithms using a sequence of matrix calculation. The proposed algorithms can search paths from a destination to a source using connectivity matrix. Two matrix-based algorithms for two different purposes are proposed. Matrix-Based Backward Path Search(MBBS) algorithm is designed for shortest path discovery and Matrix-Based Backward Multipath Search(MBBMS) algorithm is for multipath search.

• 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.

• Journal of Korean Society of Transportation
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• v.14 no.1
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• pp.51-67
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• 1996

The optimal path-finding problem becomes complicated when multiple variables are simultaneously considered such as physical route length, degree of congestion, traffic capacity of intersections, number of intersections and lanes, and existence of free ways. Therefore, many researchers in various fields (management science, computer science, applied mathematics, production planning, satellite launching) attempted to solve the problem by ignoring many variables for problem simplification, by developing intelligent algorithms, or by developing high-speed hardware. In this research, an integration of expert system technique and case-based reasoning in high level with a conventional algorithms in lower level was attempted to develop an optimal path-finding system. Early application of experienced driver's knowledge and case data accumulated in case base drastically reduces number of possible combinations of optimal paths by generating promising alternatives and by eliminating non-profitable alternatives. Then, employment of a conventional optimization algorithm provides faster search mechanisms than other methods such as bidirectional algorithm and $A^*$ algorithm. The conclusion obtained from repeated laboratory experiments with real traffic data in Seoul metropolitan area shows that the integrated approach to finding optimal paths with consideration of various real world constraints provides reasonable solution in a faster way than others.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.1-9
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• 2023

This paper presents an optimal path planning strategy for aerial searching and surveying of a user-designated area using multiple Unmanned Aerial Vehicles (UAVs). The method is designed to deal with a single unseparated polygonal area, regardless of polygonal convexity. By defining the search area into a set of grids, the algorithm enables UAVs to completely search without leaving unsearched space. The presented strategy consists of two main algorithmic steps: cellular decomposition and path planning stages. The cellular decomposition method divides the area to designate a conflict-free subsearch-space to an individual UAV, while accounting the assigned flight velocity, take-off and landing positions. Then, the path planning strategy forms paths based on every point located in end of each grid row. The first waypoint is chosen as the closest point from the vehicle-starting position, and it recursively updates the nearest endpoint set to generate the shortest path. The path planning policy produces four path candidates by alternating the starting point (left or right edge), and the travel direction (vertical or horizontal). The optimal-selection policy is enforced to maximize the search efficiency, which is time dependent; the policy imposes the total path-length and turning number criteria per candidate. The results demonstrate that the proposed cellular decomposition method improves the search-time efficiency. In addition, the candidate selection enhances the algorithmic efficacy toward further mission time-duration reduction. The method shows robustness against both convex and non-convex shaped search area.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1048-1055
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• 2002

The problem of generating torch paths for 2D laser cutting of a stock plate nested with a set of free-formed parts is investigated. The objective is to minimize the total length of the torch path starting from a blown depot, then visiting all the given Parts, and retuning back to the depot. A torch Path consists of the depot and Piercing Points each of which is to be specified for cutting a part. The torch path optimization problem is shown to be formulated as an extended version of the standard travelling salesman problem To solve the problem, a hybrid genetic algorithm is proposed. In order to improve the speed of evolution convergence, the algorithm employs a genetic algorithm for global search and a combination of an optimization technique and a genetic algorithm for local optimization. Traditional genetic operators developed for continuous optimization problems are used to effectively deal with the continuous nature of piercing point positions. Computational results are provided to illustrate the validity of the proposed algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.188-191
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• 2011

Path finding and path planning is crucial in today's world where time is an extremely valuable element. It is easy to plan the optimum path to a destination if provided a map but the same cannot be said for an unknown and unexplored environment. It will surely be exhaustive to search and explore for paths to reach the destination, not to mention planning for the optimum path. This is very much similar to finding for an exit of a maze. A very popular competition designed to tackle the maze solving ability of autonomous called Micromouse will be used as a guideline for us to design our maze. There are numerous ways one can think of to solve a maze such as Dijkstra's algorithm, flood fill algorithm, modified flood fill algorithm, partition-central algorithm [1], and potential maze solving algorithm [2]. We will analyze these algorithms from various aspects such as maze solving ability, computational complexity, and also feasibility to be implemented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2052-2067
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• 2011

Complementary trees are two spanning trees rooted at the sink node satisfying that any source node's two paths to the sink node on the two trees are node-disjoint. Complementary trees routing strategy is a special node-disjoint multi-path routing approach. Several complementary trees routing algorithms have been proposed, in which path discovery methods based on depth first search (DFS) or Dijkstra's algorithm are used to find a path for augmentation in each round of path augmentation step. In this paper, a novel path discovery method based on multi-tree-growing (MTG) is presented for the first time to our knowledge. Based on this path discovery method, a complementary trees routing algorithm is developed with objectives of low average path length on both spanning trees and low complexity. Measures are employed in our complementary trees routing algorithm to add a path with nodes near to the sink node in each round of path augmentation step. The simulation results demonstrate that our complementary trees routing algorithm can achieve low average path length on both spanning trees with low running time, suitable for wireless sensor networks in industrial scenarios.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.3 s.8
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• pp.9-22
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• 2005

Route travel cost in transportation networks consists of actual route travel cost and route perception cost. Since the route perception cost is differently perceived according to each origin and each destination, route search has limitation to reflect the note perception cost due to route enumeration problem. Thus, currently employed advanced traveller information systems (ATIS) have considered only actual route travel cost for providing route information. This study proposes an optimal and a K-route searching algorithm which are able to reflect the route perception cost but encompass route enumeration problem. For this purpose, this research defines the minimum nit of route as a link by adopting the link label technique in route searching, therefore the comparison of two adjacent links which can be finally expanded the comparison of two routes. In order to reflect the characteristics of route perception in real situation, an optimal shortest cost path algorithm that both the forward search from the origin and the backward search from the destination can be simultaneously processed is proposed. The proposed algorithm is applied for finding K number of shortest routes with an entire-path-deletion-type of K shortest route algorithm.

• 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.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.701-711
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• 2019

Purpose: The problem of optimizing redundancy allocation in multi-level systems is considered when each item in a multi-level system has alternative items with the same function. The number of redundancy of multi-level system is allocated to maximize the reliability of the system under path set and cost limitation constraints. Methods: Based on cost limitation and path set constraints, a mathematical model is established to maximize system reliability. Particle swarm optimization is employed for redundant allocation and verified by numerical experiments. Results: Comparing the particle swarm optimization method and the memetic algorithm for the 3 and 4 level systems, the particle swarm optimization method showed better performance for solution quality and search time. Particularly, the particle swarm optimization showed much less than the memetic algorithm for variation of results. Conclusion: The proposed particle swarm optimization considerably shortens the time to search for a feasible solution in MRAP with path set constraints. PS optimization is expected to reduce search time and propose the better solution for various problems related to MRAP.