• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.05a
• /
• pp.179-182
• /
• 2000

A Classifier System processes a discrete coded information from the environment. When the system codes the information to discontinuous data, it loses excessively the information of the environment. The Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. It is the FCS that applies this ability of the machine learning to the concept of fuzzy controller. It is that the antecedent and consequent of classifier is same as a fuzzy rule of the rule base. In this paper, the FCS is the Michigan style and fuzzifies the input values to create the messages. The system stores those messages in the message list and uses the implicit Bucket Brigade Algorithms. Also the FCS employs the Genetic Algorithms(GAs) to make new rules and modify rules when performance of the system needs to be improved. We will verify the effectiveness of the proposed FCS by applying it to AMR avoiding the obstacle.

• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.8
• /
• pp.907-913
• /
• 1999

For an automated transportation system like PRT(Personal Rapid Transit) system or IVHS, an efficient vehicle-merging algorithm is required for smooth operation of the network. For management of merging, collision avoidance between vehicles, ride comfort, and the effect on traffic should be considered. This paper proposes an unmanned vehicle-merging algorithm that consists of two procedures. First, a longitudinal control algorithm is designed to keep a safe headway between vehicles in a single lane. Secondly, 'vacant slot and ghost vehicle' concept is introduced and a decision algorithm is designed to determine the sequence of vehicles entering a converging section considering energy consumption, ride comfort, and total traffic flow. The sequencing algorithm is based on fuzzy rules and the membership functions are determined first by an intuitive method and then trained by a learning method using a neural network. The vehicle-merging algorithm is shown to be effective through simulations based on a PRT model.

• Journal of Communications and Networks
• /
• v.8 no.4
• /
• pp.432-441
• /
• 2006

As the community antenna television (CATV) networks becomes ubiquitous, instead of constructing an entirely new broadband network infrastructure, it has emerged as one of the rapid and economic technologies to interconnecting heterogeneous network to provide broadband access to subscribers. How to support ubiquitous real-time multimedia applications, especially in a heavy traffic environment, becomes a critical issue in modern CATV networks. In this paper, we propose a time guaranteed and efficient upstream minislots allocation algorithm for supporting quality-of-service (QoS) traffic over data over cable service interface specification (DOCSIS) CATV networks to fulfill the needs of realtime interactive services, such as video telephony, video on demand (VOD), distance learning, and so on. The proposed adaptive fast expansion algorithm and the loading statistics with dynamic swapping algorithm have been shown to perform better than that of the multimedia cable network system (MCNS) DOCSIS.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2003.10a
• /
• pp.584-588
• /
• 2003

In this paper, we propose a method to avoid obstacles that have unstable limit cycles in a Hyperchaos trajectory surface. We assume all obstacles in the chaos trajectory surface have a Van der Pol equation with an unstable limit cycle. When a chaos robot meets an obstacle in a hyper-chaos equation trajectory, the obstacle reflects the robot. We also show computer simulation result of hyperchaos equation trajectories with one or more Van der Pol obstacles.

• Journal of Information Processing Systems
• /
• v.16 no.5
• /
• pp.1048-1063
• /
• 2020

In this study, we propose a reinforcement learning agent to control the data transmission rates of nodes in carrier sensing multiple access with collision avoidance (CSMA/CA)-based wireless networks. We design a reinforcement learning (RL) agent, based on Q-learning. The agent learns the environment using the timeout events of packets, which are locally available in data sending nodes. The agent selects actions to control the data transmission rates of nodes that adjust the modulation and coding scheme (MCS) levels of the data packets to utilize the available bandwidth in dynamically changing channel conditions effectively. We use the ns3-gym framework to simulate RL and investigate the effects of the parameters of Q-learning on the performance of the RL agent. The simulation results indicate that the proposed RL agent adequately adjusts the MCS levels according to the changes in the network, and achieves a high throughput comparable to those of the existing data transmission rate adaptation schemes such as Minstrel.

• Journal of the Korean Mathematical Society
• /
• v.61 no.3
• /
• pp.565-602
• /
• 2024

We study the collective behaviors of two second-order nonlinear consensus models with a bonding force, namely the Kuramoto model and the Cucker-Smale model with inter-particle bonding force. The proposed models contain feedback control terms which induce collision avoidance and emergent consensus dynamics in a suitable framework. Through the cooperative interplays between feedback controls, initial state configuration tends to an ordered configuration asymptotically under suitable frameworks which are formulated in terms of system parameters and initial configurations. For a two-particle system on the real line, we show that the relative state tends to the preassigned value asymptotically, and we also provide several numerical examples to analyze the possible nonlinear dynamics of the proposed models, and compare them with analytical results.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.2
• /
• pp.918-925
• /
• 2017

This paper presents a path planning problem for multi-robot system in the environment with dynamic obstacles. In order to guide the robots move along a collision-free path efficiently and reach the goal position quickly, a navigation method based on fuzzy logic controllers has been developed by using proximity sensors. There are two kinds of fuzzy controllers developed in this work, one is used for obstacle avoidance and the other is used for orientation to the target. Both static and dynamic obstacles are included in the environment and the dynamic obstacles are defined with no type of restriction of direction and velocity. Here, the environment is unknown for all the robots and the robots should detect the surrounding information only by the sensors installed on their bodies. The simulation results show that the proposed method has a positive effectiveness for the path planning problem.

• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.8
• /
• pp.990-994
• /
• 1999

This paper proposes a new fuzzy-neural algorithm for navigation of a mobile robot with stationary and moving obstacles environment. The proposed algorithm uses fuzzy algorithm for its speed control and neuralnetwork for effective collision avoidance. Some computer simulation results for a mobile robot equipped with ultrasonic range sensors show that the suggested navigation algorithm is very effective to escape in stationary and moving obstacles environment.

• IEIE Transactions on Smart Processing and Computing
• /
• v.3 no.4
• /
• pp.173-180
• /
• 2014

Random access protocols feature inherent sensing functionality and distributed coordination, making them suitable for cognitive radio communication environments, where secondary users must detect the white space of the primary spectrum and utilize the idle primary spectrum efficiently without centralized control. These characteristics have led to the adoption of carrier-sensing-multiple-access/collision-avoidance (CSMA/CA) in cognitive radio. This paper proposes a new analytical framework for evaluating the performance of a CSMA/CA protocol that considers the characteristics of idle periods based on the primary traffic behavior in cognitive radio systems. In particular, the CSMA/CA-based secondary network was analyzed in the terms of idle period utilization, which is the average effective data transmission time portion in an idle period. The use of the idle period was maximized by taking its statistical features into consideration.

• Journal of Institute of Control, Robotics and Systems
• /
• v.4 no.6
• /
• pp.722-728
• /
• 1998

In this paper real-time estimation methods for identifying the tire-road friction coefficient are presented. Taking advantage of the Magic Formula Tire Model, the similarity technique and the specific model for the vehicle dynamics, a reduced order observer/filtered-regressor-based method is proposed. The Proposed method is evaluated on simulations of a full-vehicle model with an eight state nonlinear vehicle/transmission model and nonlinear suspension model. It has been shown through simulations that it is possible to estimate the tire-road friction from measurements of engine rpm, transmission output speed and wheel speeds using the proposed identification method. The proposed method can be used as a useful option as a part of vehicle collision warning/avoidance systems and will be useful in the implementation of a warning algorithm since the tire-road friction can be estimated only using RPM sensors.