• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.11
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• pp.2152-2156
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• 2011

In this paper, we propose the novel behavior control algorithm by using the efficient searching method based on the characteristic of the swarm robots in unknown space. The proposed method consists of identifying the position and moving state of a robot by the dynamic modelling of a wheel drive vehicle, and planing behavior control rules of the swarm robots based on the sensor range zone. The cooperative search for unknown space is carried out by the proposed behavior control. Finally, some experiments show the effectiveness and the feasibility of the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4553-4562
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• 2010

With developing networks, information security is going to be important and therefore lots of intrusion detection system has been developed. Intrusion detection system has abilities to detect abnormal behavior and unknown intrusions also it can detect intrusions by using patterns studied from various penetration methods. Various algorithms are studying now such as the statistical method for detecting abnormal behavior, extracting abnormal behavior, and developing patterns that can be expected. Etc. This study using clustering of data mining and association rule analyzes detecting areas based on two models and helps design detection system which detecting abnormal behavior, unknown attack, misuse attack in a large network.

• Structural Engineering and Mechanics
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• v.55 no.3
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• pp.535-554
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• 2015

Dolphin echolocation (DE) optimization algorithm is a recently developed meta-heuristic in which echolocation behavior of Dolphins is utilized for seeking a design space. The computational performance of meta-heuristic algorithms is highly dependent to its internal parameters. But the computational time of adjusting these parameters is usually extensive. The DE is an efficient optimization algorithm as it includes few internal parameters compared with other meta-heuristics. In the present paper a modified Dolphin echolocation (MDE) algorithm is proposed for optimization of steel frame structures. In the MDE the step locations are determined using one-dimensional chaotic maps and this improves the convergence behavior of the algorithm. The effectiveness of the proposed MDE algorithm is illustrated in three benchmark steel frame optimization test examples. Results demonstrate the efficiency of the proposed MDE algorithm in finding better solutions compared to standard DE and other existing algorithms.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.915-918
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• 2001

In this paper we study a new convergence behavior of the least mean fourth (LMF) algorithm where the error raised to the power of four is minimized for a multiple sinusoidal input and Gaussian measurement noise. Here we newly obtain the convergence equation for the sum of the mean of the squared weight errors, which indicates that the transient behavior can differ depending on the relative sizes of the Gaussian noise and the convergence constant. It should be noted that no similar results can be expected from the previous analysis by Walach and Widrow.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12A
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• pp.2043-2049
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• 2001

In this paper we study the convergence behavior of the least mean fourth(LMF) algorithm where the error raised to the power of four is minimized for a multiple sinusoidal input and Gaussian measurement noise. Here we newly obtain the convergence equation for the sum of the mean of the squared weight errors, which indicates that the transient behavior can differ depending on the relative sizes of the Gaussian noise and the convergence constant. It should be noted that no similar results can be expected from the previous analysis by Walach add Widrow.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2186-2188
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• 1998

In this paper, Fundamental rules governing group intelligence "arrangement" behavior of multiple number of autonomous mobile robots are represented by a small number of fuzzy rules. Complex lifelike behavior is considered as local interactions between simple individuals under small number of fundamental rules. The fuzzy rules for arrangement are generated from clustering the input-output data obtained from the arrangement algorithm. Simulation shows the fuzzy rules successfully realizes fundamental rules of the flocking group behavior.

• The Journal of the Acoustical Society of Korea
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• v.18 no.2E
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• pp.3-9
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• 1999

In this paper we study the convergence behavior of the least mean fourth(LMF) algorithm where the error raised to the power of four is minimized for a multiple sinusoidal input and Gaussian measurement noise. Here we newly obtain the convergence equation for the sum of the mean of the squared weight errors, which indicates that the transient behavior can differ depending on the relative sizes of the Gaussian noise and the convergence constant. It should be noted that no similar results can be expected from the previous analysis by Walach and Widrow/sup [1]/.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.504-510
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• 1998

In this paper, Fundamental rules governing group intelligence "obstacle avoidance" behavior of multiple autonomous mobile robots are represented by a small number of fuzzy rules. Complex lifelike behavior is considered as local interactions between simple individuals under small number of fundamental rules. The fuzzy rules for obstacle avoidance are generated from clustering the input-output data obtained from the obstacle avoidance algorithm. Simulation shows the fuzzy rules successfully realizes fundamental rules of the obstacle avoidance behavior.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.249-254
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• 2001

Abstract In this paper, we propose a method of cooperative control based on artifical intelligent system in distributed autonomous robotic system. In general, multi-agent behavior algorithm is simple and effective for small number of robots. And multi-robot behavior control is a simple reactive navigation strategy by combining repulsion from obstacles with attraction to a goal. However when the number of robot goes on increasing, this becomes difficult to be realized because multi-robot behavior algorithm provide on multiple constraints and goals in mobile robot navigation problems. As the solution of above problem, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for obstacle avoidance. Here, we propose an architecture of fuzzy system for each multi-robot speed control and fuzzy-neural network for their direction to avoid obstacle. Our focus is on system of cooperative autonomous robots in environment with obstacle. For simulation, we divide experiment into two method. One method is motor schema-based formation control in previous and the other method is proposed by this paper. Simulation results are given in an obstacle environment and in an dynamic environment.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.763-770
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• 2010

This paper proposes an algorithm to enhance the performance of the spatial partitioning method for a flocking behavior. One of the characteristics in a flocking behavior is that two agents may share many common neighbors if they are spatially close to each other. This paper improves the spatial partitioning method by applying this characteristic. While the conventional spatial partitioning method computes the k-nearest neighbors of an agent one by one, the proposed method computes simultaneously the k-nearest neighbors of agents if they are spatially close to each other. The proposed algorithm was implemented and its performance was experimentally compared with the original spatial partitioning method. The results of the comparison showed that the proposed algorithm outperformed the original method by about 33% in average.