• Journal of the Institute of Convergence Signal Processing
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• v.1 no.2
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• pp.160-168
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• 2000

In this paper, we study how the remote controllable mobile robot which could come to many via points with FLC(Fuzzy Logic Control) efficiently. The fabricated robot stop after the movement of single path method by four kinds of commands (forward, backward, turn left, turn right). To reduce disadvantages of this driving type, this paper reduce via points to goal position base on map which get from senor, let robot drive via point to via point on optimized path. An algorithm for the avoidance of unexpected obstacles by FLC is developed. And these algorithms are confirmed by computer simulations

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.331-340
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• 2021

The Internet of Things (IoT) is a rapidly growing physical network that depends on objects, vehicles, sensors, and smart devices. IoT has recently become an important research topic as it autonomously acquires, integrates, communicates, and shares data directly across each other. The centralized architecture of IoT makes it complex to concurrently access control them and presents a new set of technological limitations when trying to manage them globally. This paper proposes a new decentralized access control architecture to manage IoT devices using blockchain, that proposes a solution to concurrency management problems and enhances resource locking to reduce the transaction conflict and avoids deadlock problems. In addition, the proposed algorithm improves performance using a fully distributed access control system for IoT based on blockchain technology. Finally, a performance comparison is provided between the proposed solution and the existing access management solutions in IoT. Deadlock detection is evaluated with the latency of requesting in order to examine various configurations of our solution for increasing scalability. The main goal of the proposed solution is concurrency problem avoidance in decentralized access control management for IoT devices.

• Journal of KIISE
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• v.43 no.11
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• pp.1233-1244
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• 2016

In the IoT network environments for LLNs(Low power and Lossy networks), IPv6 Routing Protocol for Low Power and Lossy networks(RPL) has been proposed by IETF(Internet Engineering Task Force). The goal of RPL is to create a directed acyclic graph, without loops. As recommended by the IETF standard, RPL route recovery mechanisms in the event of a failure of a node should avoid loop, loop detection, DIO Poisoning. In this process, route recovery time and control message might be increased in the sub-tree because of the repeated route search. In this paper, we suggested RPL route recovery method to solve the routing overhead problem in the sub-tree during a loss of a link in the RPL routing protocol based on IoT wireless networks. The proposed method improved local repair process by utilizing a route that could not be selected as the preferred existing parents. This reduced the traffic control packet, especially in the disconnected node's sub tree. It also resulted in a quick recovery. Our simulation results showed that the proposed RPL local repair reduced the recovery time and the traffic of control packets of RPL. According to our experiment results, the proposed method improved the recovery performance of RPL.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.231-236
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• 2001

Virtual Reality (VR) is a new technology that enables humans to communicate with a computer, It allows the user to see, hear, fuel and interact in a three-dimensional virtual world created graphically. Virtual Reality Therapy (VRT), based on this sophisticated technology, has been recently used in the treatment of subjects diagnosed with acrophobia. Acrophobia is a disorder that is characterized by marked anxiety upon exposure to heights, avoidance of heights, and a resulting impairment in functioning. Conventional virtual reality systems for the treatment of acrophobia have limitations, such as overly expensive devices or somewhat unrealistic graphic scenes. The goal of this study is to develop an inexpensive and more realistic virtual environment in which to perform an exposure therapy fur acrophobia. It runs on a personal computer, and a virtual scene ova bunge-jump tower in the middle of a large city. The virtual scenario includes an open tin surrounded by props beside a tower, which allows the patient to feel a sense of heights. The effectiveness of the VR environment was evaluated through the clinical treatment of a subject who was suffering from the fear of heights. Based on pre- and post- questionnaires and subjective comments from the subject. This virtual reality environment proved to be an effective and realistic tool fur the treatment of acrophobia.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.506-512
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• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The FCS is based on the fuzzy controller system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. In this paper, the FCS modifies input message to fuzzified message and stores those in the message list. The FCS constructs rule-base through matching between messages of message list and classifiers of fuzzy classifier list. The FCS verifies the effectiveness of classifiers using Bucket Brigade algorithm. Also the FCS employs the Genetic Algorithms to generate new rules and modifY rules when performance of the system needs to be improved. Then the FCS finds the set of the effective rules. We will verifY the effectiveness of the poposed FCS by applying it to Autonomous Mobile Robot avoiding the obstacle and reaching the goal.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.701-704
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• 2010

In studying an autonomous navigation technique of UUV(Unmaned Underwater Vehicle), one of the many fundamental techniques is to plan a 3D path to complete the mission via realtime information received by sonar showing landscapes and obstacles. The simulation system is necessary to verify the algorithm in researching and developing 3D path planning of UUV. It is because 3D path planning of UUV should consider guide control, the dynamics, ocean environment, and search sonar models on the basis of obstacle avoidance technique. The simulation system developed in this paper visualizes the UUV's movement of avoiding obstacles, arriving at the goal position via waypoints by using C++ and OpenGL. Plus, it enables the user to setup the various underwater environment and obstacles by a user interface. It also provides a generalization that can verify path planning algorithm of UUV studied in any developing environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.51-56
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• 2003

The main goal of existing mobile robot system was a complete autonomous navigation and the vision information was just used as an assistant way such as monitoring For this reason, the researches have been going towards sophistication of autonomousness gradually and the production costs also has been risen. However, it is also important to control remotely an inexpensive mobile robot system which has no intelligence at all. Such systems may be much more effective than fully autonomous systems in practice. Visual information from a simple camera and distance information from ultrasonic sensors are used for this system. Collision avoidance becomes the most important problem for this system. In this paper, we developed a force feedback joystick to control the robot system remotely with collision avoiding capability. Fuzzy logic is used for the algorithm in order to implement the expert s knowledge intelligently. Some experimental results show the force feedback joystick werks very well.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.8
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• pp.971-980
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• 2010

In this paper, we present a navigation control algorithm for mobile robots that move in environments having static and moving obstacles. The algorithm includes a global and a local path-planning algorithm that uses $D^*$ search algorithm, a fuzzy logic for determining the immediate level of danger due to collision, and a fuzzy logic for evaluating the required wheel velocities of the mobile robot. To apply the $D^*$ search algorithm, the two-dimensional space that the robot moves in is decomposed into small rectangular cells. The algorithm is verified by performing simulations using the Python programming language as well as by using the dynamic equations for a two-wheeled mobile robot. The simulation results show that the algorithm can be used to move the robot successfully to reach the goal position, while avoiding moving and unknown static obstacles.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.268-275
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• 2011

This paper proposes two optimal path planning methods of a mobile robot using uDEAS (univariate Dynamic Encoding Algorithm for Searches). Before start of autonomous traveling, a self-controlled mobile robot must generate an optimal global path as soon as possible. To this end, numerical optimization method is applied to real time path generation of a mobile robot with an obstacle avoidance scheme and the basic path generation method based on the concept of knot and node points between start and goal points. The first improvement in the present work is to generate diagonal paths using three node points in the basic path. The second innovation is to make a smooth path plotted with the blending polynomial using uDEAS. Effectiveness of the proposed schemes are validated for several environments through simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.5
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• pp.489-496
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• 2006

The random-based cleaning algorithm is a simple algorithm widely used in commercial vacuum cleaning robots. This algorithm has two limitations, that is, cleaning takes a long time and there is no guarantee that the cleaning will cover the whole cleaning area. This has lead to customer dissatisfaction. Thus, in recent years, many intelligent cleaning algorithms that takes into consideration information gathered from the cleaning area environment have been proposed. The plowing-based algorithm, which is the most efficient algorithm known to date when there are no obstacles in the cleaning area, has a deficiency that when obstacle prevail, its performance is not guaranteed. In this paper, we propose the Group-k algorithm that is efficient for that situation, that is, when obstacle prevail. The goal is not to complete the cleaning as soon as possible, but to clean the majority of the cleaning area as fast as possible. The motivation behind this is that areas close to obstacles are usually difficult for robots to handle, and hence, many require human assistance anyway In our approach, obstacles are grouped by the complexity of the obstacles, which we refer to as 'complex rank', and then decide the cleaning route based on this complex rank. Results from our simulation-based experiments show that although the cleaning completion time takes longer than the plowing-based algorithm, the Group-k algorithm cleans the majority of the cleaning area faster than the plowing algorithm.