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교통 네트워크에서 하나의 노드로부터 다른 노드로 가는 최단 경로 탐색은 탐색속도와 함께 정확성도 매우 중요시 되고 있다. 기존 A* 알고리즘은 빠른 탐색속도가 큰 장점이기는 하지만, 분석네트워크가 다소 복잡하고, 링크수가 많은 대규모 네트워크에서는 최단 통행경로를 가까운 노드의 순서대로 단계적으로 찾아내는 데 정확도가 다소 낮은 약점을 갖고 있다. 따라서 본 연구에서는 A* 알고리즘의 평가함수와 알고리즘을 수정하여 정확성을 높일 수 있도록 하였다. 구 체적으로는 평가함수를 선적인 개념에서 면적인 개념으로 전환하였고, 계산단계의 진행과정에서 실제 부하량이 적을수 록 무조건 좋은 것이 아니라, 부하량이 커도 목표노드에 가까운 것이라면 더욱 최단경로에 유리하다는 개념을 도입한 것이다. 마지막으로 평가함수 값은 반복계산을 수행할수록 적어야 하는데, 이렇지 못할 경우, 피드백 기능을 부가하여 탐색 정확도를 높이도록 알고리즘을 수정하였다. 이렇게 개선된 알고리즘을 실제 네트워크상에서 적용해 본 결과, 유용 성이 있는 것으로 밝혀졌다.

In transportation networks, searching speed and result accuracy are becoming more critical on searching minimum path algorithm. Current A* algorithm has a big advantage of high searching speed. However, it has disadvantage of complicated searching network and low accuracy rate of finding the minimum path algorithm. Therefore, this study developed A* algorithm’s heuristic function and focused on improving it’s disadvantages. Newly developed function in this study contains the area concept, not the line concept. During the progress, this study adopts the idea of a heavier node that remains lighter to the target node is better that the lighter node that becomes heavier when it is connected to the other. Lastly, newly developed algorithm has the feedback function, which allows the larger accuracy value of heuristic than before. This developed algorithm tested on real network, and proved that developed algorithm is useful.

Dijkstar 알고리즘에 기초하는 최단경로 탐색 알고리즘의 탐색속도 향상에 관한 많은 연구들이 지속되어 왔다. 그 대표적인 알고리즘이 A* 알고리즘이다. 빠른 탐색속도는 A* 알고리즘의 장점이지만, 복잡하고 불규칙한 가로 네트워크에서 실제의 최단경로 탐색이 실패할 확률이 높다. 탐색실패란 목적노드를 탐색하지 못한 경우와 최단경로가 아닌 경로를 구축하는 것을 의미한다. 본 연구는 A* 알고리즘의 최단경로 탐색 성공확률을 높이기 위한 방법으로 일차적으로 출발노드와 목적노드 간 연결 관계를 정리하고, 목적노드에서 출발노드까지 정리된 경로에 따라 A* 알고리즘을 역으로 적용한 것이다. 이 방법은 네트워크 및 경로 부하량 특성에 따라 실제의 최단경로가 아닌 경로를 최단경로로 구축하는 경우가 발생할 수는 있으나, 경로구축의 완전한 실패는 발생시키지 않는다. 이 방법을 실제 복잡한 네트워크에 적용하여 유효성을 검증한 결과, 통상적인 A* 알고리즘의 적용보다 탐색 소요시간은 약간 증가하나, 정확성은 상당히 높아지는 것으로 분석되었다.

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.

본 연구에서는 최단경로를 반드시 찾아내는 Dijkstra 알고리즘의 장점과 최단경로 탐색 소요시간을 단축시키는 A* 알고리즘의 장점을 결합시킨 새로운 최단경로 탐색 알고리즘을 개발하였다. 개발한 알고리즘은 탐색노드에서 목적노드까지의 최소 기대 부하량을 산출하고 이 값을 이용하여 계속 탐색 또는 이전 탐색노드로의 후퇴를 결정한다. 최소 기대 부하량은 목적노드까지의 직선거리에 최소 가로 부하량 원단위를 곱하여 산출하는데, 적용하는 네트워크에서는 그 값 이하의 부하량이 존재할 수 없는 값이다. 개발한 알고리즘을 실제 네트워크에 적용하여 최단경로를 탐색해 본 결과, 어느 정도의 탐색 소요시간은 필요로 하나, 완벽하게 최단경로를 구축하는 것으로 나타났다. 개발한 알고리즘은 광역의 네트워크를 이용하는 차량 경로 안내시스템 등에서 효과를 가질 것으로 판단한다.

This paper developed a new shortest path searching algorithm based on Dijkstra's algorithm and A* algorithm, so it guarantees to find a shortest path in efficient manner. In this developed algorithm, minimum expected weights implies the value that straight line distance from a visiting node to the target node multiplied by minimum link unit, and this value can be the lowest weights between the two nodes. In behalf of the minimum expected weights, at each traversal step, developed algorithm in this paper is able to decide visiting a new node or retreating to the previously visited node, and results are guaranteed. Newly developed algorithm was tested in a real traffic network and found that the searching time of the algorithm was not as fast as other A* algorithms, however, it perfectly found a minimum path in any case. Therefore, this developed algorithm will be effective for the domain of searching in a large network such as RGV which operates in wide area.

본 연구에서는 최단경로 탐색 소요시간을 줄이기 위한 목적으로 탐색영역을 축소하는 알고리즘을 개발하였다. 개발 된 알고리즘은 출발노드와 목적노드를 최소의 노드 수로 연결하면서 직선거리의 합이 최소인 임시경로를 구축하고, 구축된 임시경로의 부하량 보다 적은 부하량의 경로를 연결할 가능성이 있는 노드들을 찾는 것이다. 하나의 노드에서 출발노드까지의 직선거리와 목적노드까지의 직선거리 합이 임시경로의 부하량을 최소 가로 부하량 원단위로 나눈 값 보다 적을 경우, 그 노드는 임시경로 보다 더 적은 부하량을 가질 수 있는 경로를 구성할 가능성이 있는 노드가 된다. 이와 같은 노드들만을 탐색영역으로 하면 탐색영역이 축소됨에 따라 최단경로 탐색 소요시간을 줄일 수 있게 된다. 개발된 알고리즘은 큰 탐색영역에서 출발노드와 목적노드가 가까울 경우 더욱 효과적이다.

This study developed searching network reduction algorithm for reduce shortest path searching time. Developed algorithm is searching nodes that have the including possibility of less weights path than temporal path that consists minimum number of nodes and minimum sum of the straight line distances. The node that has the including possibility of shortest path is the node that the sum of straight line distance from start node and straight line distance to target node is less than the value that temporary path's weights divided by minimum weights units. If searching network reconstitutes only these nodes, the time of shortest path searching will be reduced. This developed algorithm has much effectiveness that start node and target node is close in large network.

Path planning algorithm is a key issue among robot control areas. In practical engineering applications, traditional methods have some limitations to a certain degrees in key aspects of cost, efficiency, security, flexibility, portability, etc. Through the analysis and comparison of A-star algorithm and Dijkstra algorithm, path planning problem supporting multiple cars run parallely (PPSMC for short) in a static and dynamic obstacles co-existing environment is studied. An A-STAR-Dijkstra-integrated algorithm is promoted to make multiple cars moving parallely without collision or deadlock. Both two algorithms are optimized too. The algorithm has applied in smart park.

Dynamic optimization scheduling strategy is carried out based on a comprehensive objective function which is constructed for reservoirs. Corresponding constraints and conditions are generally designed, and then the solution of the objective function is worked out based on those constraints and finally an optimal scheduling scheme suitable for reservoirs is acquired in combination with actual conditions. Focusing on the problems above, this study combines Dijkstra algorithm with genetic algorithm (GA) effectively and makes full use of their advantages. Consequently, an optimized Dijkstra - genetic algorithm (D – GA) is obtained and applied in the scheduling scheme. First of all, the thesis preliminarily introduces relevant algorithms on water conservancy project and sets up a data model consistent with the actual situation. Secondly, this work analyzes the two algorithms, improves the Dijkstra algorithm and applies D – GA to solution optimization. Thirdly, this study compares the results obtained by using D – GA and GA respectively and finally completes the simulation of water conservancy scheduling system.

This paper first represent the basic conceptions of anonymous communication, then introduce the fundamentals in Anonymous Communication. Afterwards, we went deep to the classification of anonymous network topological, therefore we could get a better understanding on P2P anonymous communication system. We also systematically analyzes the existing node-select algorithms. Furthermore, we propose and implement a new node selection algorithm based on Dijkstra algorithm, named NSDA algorithm. We give a belief description about the backgrounds and design of NSDA Algorithm. The algorithm can combine the node properties with link properties to select node, which can adjust the system performancing and anonymity. In order to evaluate the NSDA algorithm, we have done experiments in Network Simulator 3, and we also explain the reason why we choose Network Simulator 3 as the simulate tool. In the end, we analyze the characteristics of NSDA algorithm according to the results of experiments.

본 논문에서는 보행로봇의 일종인 TITAN-VIII라 불리는 로봇을 이용하여 가장 짧은 경로를 탐색하여 이동하는 방법에 관한 연구를 나타낸다. 보행로봇의 경우 바퀴구동 로봇에 비해 불규칙한 지면 위를 자유로이 이동 가능한 장점 등을 가지고 있는데 반해 이동속도는 바퀴구동 로봇에 비해 느린 편이다. 따라서 본 논문에서는 목적지에 도달하기까지 시간을 최소화하는 최적경로 탐색 제어방법을 제시하였다. 경로를 탐색하기 위해 Dijkstra's algorithm라 불리는 알고리즘을 기반으로 하여 적용하였다. 또한 로봇이 항상 정적인 자세를 유지하는 로봇의 다양한 자세에 대해서도 다루었다. 로봇의 자세제어와 알고리즘을 통하여 로봇의 관절각 결정에 필요한 여러 수학방정식을 제시하였다. 그 후 원하는 궤적으로 로봇이 이동하고 탐색하는 알고리즘을 고안하였고, 제안한 방법의 결과를 실험으로 확인하였다.

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.

In this paper, we develop the path planning algorithm using the improved Dijkstra algorithm and the particle swarm optimization. To get the optimal path, at first we construct the MAKLINK on the world environment and then make a graph associated with the MAKLINK. From the graph, we obtain the Dijkstra path between the starting point and the destination point. From the optimal path, we search the improved Dijkstra path using the graph. Finally, applying the particle swarm optimization to the improved Dijkstra path, we obtain the optimal path for the mobile robot. It turns out that the proposed method has better performance than the result in [1].

This paper presents a fast and effective methodology for service restoration in large-scale distribution systems. The service restoration problem is formulated as a constrained optimization problem and requires the fast computation time and superior solution because the more unfaulted out-of-service area should be restored as soon as possible. The proposed methodology is designed to consider the fast computation time and priority service restoration by dijkstra algorithm and fuzzy theory in large-scale distribution systems. Simulation results demonstrate the validity and effectiveness of the proposed on a 26-bus and 140-bus system.

본 논문에서는 비등방성 특징 기반의 다익스트라 알고리즘(Dijkstra algorithm)을 이용한 효율적인 감성 완화 프레임워크를 제안한다. 감성을 완화시키는 것은 감성 분석만큼이나 중요하며, 사람의 우울함이나 외로움을 자동으로 완화시켜줄 수 있는 프레임워크로써 인간과 컴퓨터의 상호작용(HCI, Human-Computer Interaction)측면에서도 매우 중요한 의미를 갖는다. 본 논문에서는 1) 마이크로소프트의 Emotion API를 이용하여 얼굴 표정으로부터 변화하는 감정값을 계산하고, 2) 이 감정값의 차이를 이용하여 우울이나 외로움 같은 이상 감정을 인지한다. 3) 마지막으로, 감성 히스토그램과 비등방성 특성을 고려한 감정 메시 기반의 매칭 과정을 거침으로써 사용자에게 완화된 감성이 내포된 이미지들을 제시해준다. 본 논문에서 제안하는 기법은 얼굴 영상을 이용하여 사용자가 쉽게 감성의 변화를 인지하고, 완화된 감성으로 감정을 트레이닝 할 수 있는 시스템이다.

In this paper, we propose an efficient emotional relaxation framework using Dijkstra algorithm based on anisotropic features. Emotional relaxation is as important as emotional analysis. This is a framework that can automatically alleviate the person's depression or loneliness. This is very important for HCI (Human-Computer Interaction). In this paper, 1) Emotion value changing from facial expression is calculated using Microsoft's Emotion API, 2) Using these differences in emotion values, we recognize abnormal feelings such as depression or loneliness. 3) Finally, emotional mesh based matching process considering the emotional histogram and anisotropic characteristics is proposed, which suggests emotional relaxation to the user. In this paper, we propose a system which can recognize the change of emotion easily by using face image and train personal emotion by emotion relaxation.

There are a variety of apps that are finding their way. And in car navigation, we launched a product that reflects Augmented Reality technology this year. However, existing apps have problems. It is implemented in 2D or 3D, has a large error range because it has been modified in most vehicles, is not updated in real time, and car augmented reality navigation is a vehicle, and a separate device is required, etc. In this study, we implemented a smartphone app for walking directions using augmented reality, and made it possible to intuitively use a route service from a user 's location to a destination. The Dijkstra algorithm is applied to calculate the shortest path to solve the problem of finding the route with the least cost. By using this application, it is possible to use the route search service even in a data-free environment, to solve the inconvenience of the language barrier, and to update in real time, so that the latest information can be always maintained. In the future, we want to develop an app that can be commercialized by using a character in the path to promote it.

The Distribution System consist of many tie-line switches and sectionalizing switches, operated a radial type. When an outage occurs in Distribution System, outage areas are isolated by system switches, has to restored as soon as possible. At this time, system operator have to get a information about network topology for service restoration of outage areas. Therefore, the searching result of restorative topology has to fast computation time and reliable result topology for to restore a electric service to outage areas, equal to optimal switching operation problem. So, the problem can be defined as combinatorial optimization problem. The service restoration problem is so important problem which have outage area minimization, outage loss minimization. Many researcher is applying to the service restoration problem with various techniques. In this paper, advanced Dijkstra algorithm is applied to searching a restoration topology, is so efficient to searching a shortest path in graph type network. Additionally, fuzzy rules and operator are applied to overcome a fuzziness of correlation with input data. The present technique has superior results which are fast computation time and searching results than previous researches, demonstrated by example distribution model system which has 3 feeders, 26 buses. For a application capability to real distribution system, additionally demonstrated by real distribution system of KEPCO(Korea Electric Power Corporation) which has 8 feeders and 140 buses.

This paper presents a restoration method by using Dijkstra algorithm for outage restoration problems in distribution system that considering load capacity constraints and operation constraints. Restoration problem in distribution system is difficult to solve problem in a short times, because of a distribution system that supplies power for customers combined with many tie-line switches and sectionalizing switches and have to satisfy plenty of operation conditions. Therefore, this paper applied Dijkstra algorithm which is satisfy radial operation conditions in distribution system. This proposed method used Kruskal algorithm include to Dijkstra algorithm Therefore, proposed method is restored to a outage sections in a short times and just then to satisfied with a operation conditions in distribution system. A 26-bus, 31-branch model system is used to demonstrate the effectiveness of the proposed method.

According to recent advances in technology, major robot technologies that have been developed and commercialized for industrial use are being applied to various fields in our everyday life such as guide robots and cleaning robots. Among them, the navigation based on the self localization has become an essential element technology of the robot. In the case of indoor environment, many high-priced sensors are used, which makes it difficult to activate the robot industry. In this paper, we propose a robotic platform and a moving algorithm that can travel by using Dijkstra algorithm. The proposed system can find a short route to the destination with its own position. Also, its performance is discussed through the experimentation of an actual robot.

본 연구는 산업현장에서 사용되는 이동로봇이 익숙하지 못한 환경에서 목적지에 도착할 수 있는 항법 알고리즘을 개발하고자 한다. 이를 위해 동적창 접근(DWA)과 Dijkstra 경로설정 알고리즘을 결합하여 항법 알고리즘을 제안한다. 이를 성능 비교하기 위해 로컬 동적창 접근(LDWA), 글로벌 동적창 접근(GDWA), 고속 탐색 랜덤 트리 (RRT) 알고리즘을 비교 분석한다. LDWA과 GDWA을 적용한 Dijkstra 알고리즘을 활용한 항법 알고리즘을 구현하여 제한된 정보를 이용하여 이동로봇이 목적지에 도달할 수 있도록 한다. 이 알고리즘들의 목적지에 도착할 때까지 걸리는 시간, 장애물 회피와 계산복잡도에 대한 비교 분석한다. 위 알고리즘의 한계를 극복하기 위한 새로운 항법 알고리즘을 제안하고 제시된 최적화된 항법 알고리즘의 산업현장에서의 활용 방안을 모색한다.

In this paper, we develop a new navigation algorithm for industrial mobile robots to arrive at the destination in unknown environment. To achieve this, we suggest a navigation algorithm that combines Dynamic Window Approach (DWA) and Dijkstra path planning algorithm. We compare Local Dynamic Window Approach (LDWA), Global Dynamic Window Approach(GDWA), Rapidly-exploring Random Tree (RRT) Algorithm. The navigation algorithm using Dijkstra algorithm combined with LDWA and GDWA makes mobile robots to reach the destination. and obstacles faced during the path planning process of LDWA and GDWA. Then, we compare on time taken to arrive at the destination, obstacle avoidance and computation complexity of each algorithm. To overcome the limitation, we seek ways to use the optimized navigation algorithm for industrial use.