• Journal of the Korea Society of Computer and Information
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• v.19 no.7
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• pp.103-111
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• 2014

This paper proposes a modified algorithm that improves on Dijkstra's algorithm by applying it to purely two-way traffic paths, given that a road where bi-directional traffic is made possible shall be considered as an undirected graph. Dijkstra's algorithm is the most generally utilized form of shortest-path search mechanism in GPS navigation system. However, it requires a large amount of memory for execution for it selects the shortest path by calculating distance between the starting node and every other node in a given directed graph. Dijkstra's algorithm, therefore, may occasionally fail to provide real-time information on the shortest path. To rectify the aforementioned shortcomings of Dijkstra's algorithm, the proposed algorithm creates conditions favorable to the undirected graph. It firstly selects the shortest path from all path vertices except for the starting and destination vertices. It later chooses all vertex-outgoing edges that coincide with the shortest path setting edges so as to simultaneously explore various vertices. When tested on 9 different undirected graphs, the proposed algorithm has not only successfully found the shortest path in all, but did so by reducing the time by 60% and requiring less memory.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.157-164
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• 2011

This paper presents a fast and scalable re-routing algorithm that adapts to dynamically changing networks. The proposed algorithm integrates Dijkstra's shortest path algorithm with the genetic algorithm. Dijkstra's algorithm is used to define the predecessor array that facilitates the initialization process of the genetic algorithm. After that, the genetic algorithm re-searches the optimal path through appropriate genetic operators under dynamic traffic situations. Experimental results demonstrate that the proposed algorithm produces routes with less traveling time and computational overhead than pure genetic algorithm-based approaches as well as the standard Dijkstra's algorithm for large-scale networks.

• 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 the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.109-117
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• 2014

In navigation route planning systems using A* algorithms, the cardinality of an Open list, which is a list of candidate nodes through which a terminal node can be accessed, increases as the path length increases. In this paper, a method of alternately utilizing the Dijkstra's algorithm and the A* algorithm to reduce the cardinality of the Open list is investigated. In particular, by employing a depth parameter, named Level, the two algorithms are alternately performed depending on the Level's value. Using the hybrid searching approach, the Open list constructed in the Dijkstra's algorithm is transferred into the Open list of the A* algorithm, and consequently, the unconstricted increase of the cardinality of the Open list of the former algorithm can be avoided and controlled appropriately. In addition, an optimal or nearly optimal path similar to the Dijkstra's route, but not available in the A* algorithm, can be found. The experimental results, obtained with synthetic and real-life benchmark data, demonstrate that the computational cost, measured with the number of nodes to be compared, was remarkably reduced compared to the traditional searching algorithms, while maintaining the similar distance to those of the latter algorithms. Here, the values of Level were empirically selected. Thus, a study on finding the optimal Level values, while taking into consideration the actual road conditions remains open.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.7
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• pp.12-18
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• 2017

This paper presents a Dijkstra's-search-based sphere decoding (SD) algorithm with limited complexity for the symbol detection in MIMO communication systems. The Dijkstra search-based SD is efficient to achieve a near-optimal error rate in the MIMO symbol detection, but has a critical problem in that its complexity is variable and can correspond to that of the exhaustive search in the worst case. The proposed algorithm limits the computations while achieving a near-optimal error rate. Simulation results show that the error rate is near optimal even with the limited complexity.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.5
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• pp.574-584
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• 2017

In this paper, the optimizing path finding control method is studied for a Legged-robot. It's named TITAN-VIII. It has a lot of advantages over the wheeled robot in the ability to walk freely on an irregular ground. However, the moving speed on the ground of the Legged-robot is slower than the Wheeled-robot's. Consequently, the purpose of the method is presented in this paper to minimize its time when it walks to a goal. It find the path, our approach is based on an algorithm which is called Dijkstra's algorithm. In the rest of paper, the various posture of the robot is discussed to keep the robot always in the statically stable. Based on above works, the math formulas are presented to determine the joint angles of the robot. After that an algorithm is designed to find and keep robot on the desired trajectory. Experimental results of the proposed method are demonstrated in the last of paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.893-900
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• 2007

This paper suggests an algorithm that improves the disadvantages of the Dijkstra algorithm that is commonly used in GPS navigation system, searching for the shortest path. Dijkstra algorithm, first of all, requires much memory for the performance of the algorithm. It has to carry out number of node minus 1, since it determines the shortest path from all the nodes in the graph, starting from the first node. Therefore, Dijkstra algorithm might not be able to provide the information on every second, searching for the shortest path between the roads of the congested city and the destination. In order to solve these problems, this paper chooses a method of searching a number of nodes at once by means of choosing the shortest path of all the path nodes (select of minimum weight arc in-degree and out-degree), excluding the departure and destination nodes, and of choosing all the arcs that coincide with the shortest path of the path nodes, from all the node outgoing arcs starting from the departure node. On applying the suggested algorithm to 14 various digraphs, we succeeded to search the shortest path. In addition, the result was obtained at the speed of 2 to 3 times faster than that of Dijkstra algorithm, and the memory required was less than that of Dijkstra algorithm.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.137-143
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• 2017

In this paper, we propose optimal route searching algorithm using ANN(Artificial Neural Network) and implement route searching system. Our proposed scheme shows that the route using artificial neural network is almost same as the route using Dijkstra's algorithm but the time in our propose algorithm is shorter than that of existing Dijkstra's algorithm. Proposed route searching method using artificial neural network has better performance than exiting route searching method because it use several weight value in making different routes. Through simulation, we show that our proposed routing system improves the performance and reduces time to make route irrespective of the number of hidden layers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.7
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• pp.3468-3473
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• 2013

Conditions that overlay multicast tree must satisfy for the real-time application system of a video-conference, an internet broadcasting is two things. First, the degree of nodes in a tree must be proper value. Second, the diameter of the multicast tree, distance between longest two users should be short. If the path between two users in the tree is long, the delay time in data transmission between two users great. So, it is not suitable to the application system such as video-conferences. In this paper, the cost of the dijkstra algorithm calculate with proposed score-function through checking the extra bandwidth, the delay and the requested bandwidth. It is composed the tree through the dijkstra algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.721-726
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• 2012

In this paper, we proposed an efficient algorithm based on SPFA(shortest path faster algorithm), which is an improved the Bellman-Ford algorithm. The Bellman-Ford algorithm can be used on graphs with negative edge weights unlike Dijkstra's algorithm. And SPFA algorithm used a queue to store the nodes, to avoid redundancy, though the Bellman-Ford algorithm takes a long time to update the nodes table. In this improved algorithm, an adjacency list is also used to store each vertex of the graph, applying dynamic optimal approach. And a queue is used to store the data. The improved algorithm can find the optimal path by continuous relaxation operation to the new node. Simulations to compare the efficiencies for Dijkstra's algorithm, SPFA algorithm and improved Bellman-Ford were taken. The result shows that Dijkstra's algorithm, SPFA algorithm have almost same efficiency on the random graphs, the improved algorithm, although the improved algorithm is not desirable, on grid maps the proposed algorithm is very efficient. The proposed algorithm has reduced two-third times processing time than SPFA algorithm.