• 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 Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2477-2484
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• 1997

Best-first heuristic search algorithm, such as $A^{\ast}$ algorithm, are one of the most important techniques used to solve many problems in artificial intelligence. A common feature of heuristic search is its high computational complexity, which prevents the search from being applied to problems is practical domains such as route-finding in road map with significantly many nodes. In this paper, several heuristic search algorithms are concerned. A new dynamic weighting heuristic method called the pat-sensitive heuristic is proposed. It is based on a dynamic weighting heuristic, which is used to improve search effort in practical domain such as admissible heuristic is not available or heuristic accuracy is poor. It's distinctive feature compared with other dynamic weighting heuristic algorithms is path-sensitive, which means that ${\omega}$(weight) is adjusted dynamically during search process in state-space search domain. For finding an optimal path, randomly scattered road-map is used as an application area.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.835-838
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• 2008

Many researches on hierarchical path search have been studied so far. Even though partitioning regions is essential part, the researches are not enough. This paper proposes two efficient methods to partition regions: 1)a method based on voronoi algorithm in which a major node is central point of a region, 2) a method based on fired grid that partitions regions into major and minor. The performances of the proposed methods are compared with the conventional hierarchical path search method in which a region is formed by the boundary line of nearest 4 points of a major node in terms of the path search time and the accuracy. The results obtained from the experiments show that the method based on voronoi achieves short execution time and the method based grid achieves high accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.12
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• pp.2221-2229
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• 2006

Optimal path search algorithm which is a killer application of mobile device to utilize location information should consider traffic flows of the roads as well as the distance between a departure and destination. The existing path search algorithms, however, are net able to cope efficiently with the change of the traffic flows. In this paper, we propose a new optimal path search algorithm. The algorithm takes the current flows into consideration in order to reduce the cost to get destination. It decomposes the road network into Fixed Grid to get variable heuristics. We also carry out the experiments with Dijkstra and Ar algorithm in terms of the execution time, the number of node accesses and the accuracy of path. The results obtained from the experimental tests show the proposed algorithm outperforms the others. The algorithm is highly expected to be useful in a advanced telematics systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.157-162
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• 2011

In this paper, we propose enhanced methods of path finding based on an abstract graph with extension of search space to improve the quality of path. The proposed methods that are called simple buffering method, velocity constrained method and distance constrained method are to extract buffering-cells for using search space with valid-cells. The simple buffering method is to extract adjacent cells of valid-cells as buffering-cells. velocity constrained method and distance constrained method are based on simple buffering method, these eliminate buffering-cells through each of threshold. In experiment, proposed methods can improve the quality of path. The proposed methods are applicable to develop various kinds of telematics application, such as path finding and logistics.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.137-141
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• 2005

Optimal path search algorithm should consider traffic flows of the roads as well as the distance between a departure and destination, The existing path search algorithms, however, usually don't apply the continuously changed traffic flows. In this paper, we propose a new optimal path search algorithm. the algorithm takes the current flows into consideration in order to reduce the cost to get destination. It decomposes the road networks into Fixed Grid to get variable heuristics. We also carry out the experiments with Dijkstra and $A^*$ algorithm in terms of the execution time, the number of node accesses and the accuracy of path. The results obtained from the experimental tests show the proposed algorithm outperforms the others. The algorithm is highly expected to be useful in a advanced telematics systems.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.91-98
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• 2010

Conventional path-finding has focused on finding short collision-free paths. However, as computer games become more sophisticated, it is required to take tactical information like ambush points or lines of enemy sight into account. One way to make this information have an effect on path-finding is to represent a heuristic function of a search algorithm as a weighted sum of tactics. In this paper we consider the problem of learning heuristic to optimize path-finding based on given tactical information. What is meant by learning is to produce a good weight vector for a heuristic function. Training examples for learning are given by a game level-designer and will be compared with search results in every search level to update weights. This paper proposes a learning algorithm integrated with search for tactical path-finding. The perceptron-like method for updating weights is described and a simulation tool for implementing these is presented. A level-designer can mark desired paths according to characters' properties in the heuristic learning tool and then it uses them as training examples to learn weights and shows traces of paths changing along with weight learning.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.91-97
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• 2002

This paper describes a heuristic search technique carrying out collision avoidance for Autonomous Underwater Vehicles(AUVs). Fuzzy relational products are used as the mathematical implement for the analysis and synthesis of relations between obstacles that are met in the navigation environment and available candidate nodes. In this paper, we propose a more effective evaluation function that reflects the heuristic information of domain experts on obstacle clearance, and an advanced heuristic search method performing collision avoidance for AUVs. The search technique adopts fuzzy relational products to conduct path-planning of intelligent navigation system. In order to verify the performance of proposed heuristic search, it is compared with $A^*$ search method through simulation in view of the CPU time, the optimization of path and the amount of memory usage.

• Journal of Korea Multimedia Society
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• v.24 no.11
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• pp.1538-1551
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• 2021

The current drone flight plan creation creates a flight path point of two-dimensional coordinates on the map and sets an arbitrary altitude value considering the altitude of the terrain and the possible flight altitude. If the created flight path is a simple terrain such as a mountain or field, or if the user is familiar with the terrain, setting the flight altitude will not be difficult. However, for drone flight in a city where buildings are dense, a safer and more precise flight path generation method is needed. In this study, using high-precision spatial information, we construct a drone safety flight map with a 3D grid map structure and propose a flight path search algorithm based on it. The safety of the flight path is checked through the virtual drone flight simulation extracted by searching for the flight path based on the 3D grid map created by setting weights on the properties of obstacles and terrain such as buildings.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.711-718
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• 2019

BDD is a well-known alternative to the conventional Boolean logic method in fault tree analysis. As the size of fault tree increases, the calculation time and computer resources for BDD dramatically increase. A new failure path search and path restructure method is proposed for efficient calculation of CS and MCS from BDD. Failure path grouping and bottom-up path search is proved to be efficient in failure path search in BDD and path restructure is also proved to be used in order to reduce the number of CS comparisons for MCS extraction. With these newly proposed methods, the top event probability can be calculated using the probability by ASDMP(Approximate Sum of Disjoint MCS Products), which is shown to be equivalent to the result by the conventional MCUB(Minimal Cut Upper Bound) probability.