• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.421-430
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• 2000

This paper presents a new approach which uses $A^*$ search and genetic algorithms for solving large scale multi-objective shortest path problem. The focus of this paper is motivated by the problem of finding Pareto optimal paths for an advanced traveler information system(ATIS) in the context of intelligent transportation system(ITS) application. The individual description, the decoding rule, the selection strategy and the operations of crossover and mutation are proposed for this problem. The keynote points of the algorithm are how to represent individuals and how to calculate the fitness of each individual. The high performance of the proposed algorithm is demonstrated by computer simulations.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.113-128
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• 2000

This paper presents a first year report of an ongoing multi-year project to develop a systolic parallel simulation system for dynamic traffic assignment. The fundamental approach to the simulation is systolic parallel processing based on autonomous agent modeling. Agents continuously act on their own initiatives and access to database to get the status of the simulation world. Various agents are defined in order to populate the simulation world. In particular existing modls and algorithm were incorporated in designing the behavior of relevant agents such as car-following model headway distribution Frank-Wolf algorithm and so on. Simulation is based on predetermined routes between centroids that are computed off-line by a conventional optimal path-finding algorithm. Iterating the cycles of optimization-then-simulation the proposed system will provide a realistic and valuable traffic assignment. Gangnum-Gu district in Seoul is selected for the target are for the modeling. It is expected that realtime traffic assignment services can be provided on the internet within 3 years.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.8
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• pp.864-871
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• 2014

In this paper, we propose an optimization method for obtaining beam curve which minimize the phase errors of Rotman lens. This method is based on idea that 3 path lengths from a beam port through equal phase points, which consist of the center point of array antenna and two points placed symmetrically or asymmetrically along array antenna, to the corresponding phase front are equal. According to this method, the optimal locations of beam ports can be obtained directly by finding each equal phase point set on array antenna to minimize the phase errors for each beam direction. Simulation results show that the proposed method is the most optimal and effective method for determining the beam curve of Rotman lens with low phase errors.

• 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 KIISE:Information Networking
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• v.32 no.3
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• pp.310-317
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• 2005

In opposition to conventional networks, mobile ad hoc networks usually do not offer trust about nodes or online access through certificate authorities or centralized servers. But, nodes in those systems need process that can search path as well as trust each other to exchange data in safety For these reasons, traditional security measures that require online trusted authorities or certificate storages are not well-suited for securing ad hoc networks. In this paper, I propose a secure and effective method to search the optimized path using profitable flooding techniques on certificate chains in MANETS(Mobile Ad hoc NETworks). This system includes not only using routing protocols that are generally broadcasting packets but also finding nodes securely and verifing the process through tiust relationships between nodes that are searched.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.31-44
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• 2004

The study of Estimation model for the short-term travel time prediction. There is a different solution which has predicted the link travel time to solve this problem. By using this solution, the link travel time is predicted based on link conditions from time to time. The predicated link travel time is used to search the shortest path. Before providing a dynamic shortest path finding, the prediction model should be verified. To verify the prediction model, three models such as Kalman filtering, Stochastic Process, ARIMA. The ARIMA model should adjust optimal parameters according to the traffic conditions. It requires a frequent adjustment process of finding optimal parameters. As a result of these characteristics, It is difficult to use the ARIMA model as a prediction. Kalman Filtering model has a distinguished prediction capability. It is due to the modification of travel time predictive errors in the gaining matrix. As a result of these characteristics, the Kalman Filtering model is likely to have a non-accumulative errors in prediction. Stochastic Process model uses the historical patterns of travel time conditions on links. It if favorably comparable with the other models in the sense of the recurrent travel time condition prediction. As a result, for the travel time estimation, Kalman filtering model is the better estimation model for the short-term estimation, stochastic process is the better for the long-term estimation.

• The Transactions of the Korea Information Processing Society
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• v.5 no.3
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• pp.671-680
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• 1998

Network services, such as teleconferencing, remote diagnostics and education, and CSCW require multicasting. Multicast routing methods can be divided into two categories. One is the shortest path tree method and the other is the minimal Steiner tree method. The latter has an advantage over the former in that only one Steiner tree is needed for a group. However, finding a minimal Steiner tree is an NP-complete problem and it is necessary to find an efficient heuristic algorithm. In this paper, we present an evolutionary optimization method for finding minimal Steiner trees without sacrificing too much computational efforts. In particular, we describe a tree-based genetic encoding scheme which is in sharp constast with binary string representations usually adopted in convetional genetic algorithms. Experiments have been performed to show that the presented method can find optimal Steiner trees for given vetwork configurations. Comparitivie studies have shown that the evolutionary method finds on average a better solution than other conventional heustric algorithms.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.86-95
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• 2009

Efficient DAG scheduling is critical for achieving high performance in heterogeneous computing environments. Finding an optimal solution to the problem of scheduling an application modeled by a directed acyclic graph(DAG) onto a set of heterogeneous machines is known to be an NP-complete problem. In this paper we propose a new list scheduling algorithm, called the Heterogeneous Rank-Path Scheduling(HRPS) algorithm, to exploit all of a program's available parallelism in distributed heterogeneous computing system. The primary goal of HRPS is to minimize the schedule length of applications. The performance of the algorithm has been observed by its application to some practical DAGs, and by comparing it with other existing scheduling algorithm such as CPOP, HCPT and FLB in term of the schedule length. The comparison studies show that HRPS significantly outperform CPOP, HCPT and FLB in schedule length.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.6
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• pp.943-950
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• 2002

Real time strategy game is a computer game genre of Playing with human or computer opponents in real time It differs from turn-type computer games in the game process method. Turn type games, such as chess, allow only one Player to move at a time. Real time strategy games allow two or more Players to move simultaneously. Therefore, in real time strategy computer games, the game components' movement plans must be calculated very quickly in order to not disturb other processes such as gathering resources, building structures, and combat activities. There are many approaches, which can reduce the amount of memory required for calculating path, search space, and reactive time of components. (or units). However, existing path finding algorithms tend to concentrate on achieving optimal Paths that are not as important or crucial in real time strategy game. This Paper introduces Dynamic Heuristic Af(DHA*) algorithm which is capable of reducing search space and reactive time of game units and compares with A* algorithm using static heuristic weighting.

• The Journal of the Korea Contents Association
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• v.19 no.12
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• pp.164-172
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• 2019

In this paper, we implement a wheeled mobile robot that accurately and autonomously finds the optimal route from the starting point to the destination point based on computer vision in a complex indoor environment. We get a number of waypoints from the starting point to get the best route to the target through deep reinforcement learning. However, in the case of autonomous driving, the majority of cases do not reach their destination accurately due to external factors such as surface curvature and foreign objects. Therefore, we propose an algorithm to deepen the waypoints and destinations included in the planned route and then correct the route through the waypoint recognition while driving to reach the planned destination. We built an autonomous wheeled mobile robot controlled by Arduino and equipped with Raspberry Pi and Pycamera and tested the planned route in the indoor environment using the proposed algorithm through real-time linkage with the server in the OSX environment.