• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.7
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• pp.1146-1151
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• 2003

In this paper, results of an analysis of driving behavior characteristics and a driver-adaptive control algorithm for adaptive cruise control systems have been described. The analysis has been performed based on real-world driving data. The vehicle longitudinal control algorithm developed in our previous research has been extended based on the analysis to incorporate the driving characteristics of the human drivers into the control algorithm and to achieve natural vehicle behavior of the adaptive cruise controlled vehicle that would feel comfortable to the human driver. A driving characteristic parameters estimation algorithm has been developed. The driving characteristics parameters of a human driver have been estimated during manual driving using the recursive least-square algorithm and then the estimated ones have been used in the controller adaptation. The vehicle following characteristics of the adaptive cruise control vehicles with and without the driving behavior parameter estimation algorithm have been compared to those of the manual driving. It has been shown that the vehicle following behavior of the controlled vehicle with the adaptive control algorithm is quite close to that of the human controlled vehicles. Therefore, it can be expected that the more natural and more comfortable vehicle behavior would be achieved by the use of the driver adaptive cruise control algorithm.

• The KIPS Transactions:PartA
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• v.16A no.1
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• pp.35-42
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• 2009

This paper proposes a new coverage algorithm for intelligent robot. Many algorithms for improving the performance of coverage have been focused on minimizing the total coverage completion time. However, if one does not have enough time to finish the whole coverage, the optimal path could be different. To tackle this problem, we propose a new coverage algorithm, which we call MaxCoverage algorithm, for covering maximal area within the deadline. The MaxCoverage algorithm decides the navigation flow by greedy algorithm for Set Covering Problem. The experimental results show that the MaxCoverage algorithm performs better than other algorithms for random deadlines.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.436-443
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• 2007

In this paper, an intelligent method called BELBIC (Brain Emotional Learning Based Intelligent Controller) is used to control of Locally Linear Neuro-Fuzzy Model (LOLIMOT) of Washing Machine. The Locally Linear Neuro-Fuzzy Model of Washing Machine is obtained based on previously extracted data. One of the important issues in using BELBIC is its parameters setting. On the other hand, the controller design for Washing Machine is a multi objective problem. Indeed, the two objectives, energy consumption and effectiveness of washing process, are main issues in this problem, and these two objectives are in contrast. Due to these challenges, a Multi Objective Genetic Algorithm is used for tuning the BELBIC parameters. The algorithm provides a set of non-dominated set points rather than a single point, so the designer has the advantage of selecting the desired set point. With considering the proper parameters after using additional assumptions, the simulation results show that this controller with optimal parameters has very good performance and considerable saving in energy consumption.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.3 no.1
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• pp.115-125
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• 1999

Simple Genetic Algorithm(SGA) proposed by J. H. Holland is a population-based optimization method based on the principle of the Darwinian natural selection. The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. Although GA does well in many applications as an optimization method, still it does not guarantee the convergence to a global optimum in some problems. In designing intelligent systems, specially, since there is no deterministic solution, a heuristic trial-and error procedure is usually used to determine the systems' parameters. As an alternative scheme, therefore, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve. In this paper we review the existing co-evolutionary algorithms and propose co-evolutionary schemes designing intelligent systems according to the relation between the system's components.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.27-34
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• 1996

GENIE is a learning-based engine for building intelligent systems. Learning in GENIE proceeds by incrementally modeling its human or technical environment using a neural network and a genetic algorithm. The neural network is used to represent the knowledge for solving a given task and has the ability to grow its structure. The genetic algorithm provides the neural network with training examples by actively exploring the example space of the problem. Integrated into the training examples by actively exploring the example space of the problem. Integrated into the GENIE system architecture, the genetic algorithm and the neural network build a virtually self-teaching autonomous learning system. This paper describes the structure of GENIE and its learning components. The performance is demonstrated on a robot learning problem. We also discuss the lessons learned from experiments with GENIE and point out further possibilities of effectively hybridizing genetic algorithms with neural networks and other softcomputing techniques.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.240-246
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• 2006

This paper proposes the intelligent immigration control system that authorizes the traveler through immigration and detects forged passports by using automatic recognition of passport codes, the passport photo and face verification. The proposed system extracts and deskewes the areas of passport codes from the passport image. This paper proposes the novel ART algorithm creating the adaptive clusters to the variations of input patterns and it is applied to the extracted code areas for the code recognition. After compensating heuristically the recognition result, the detection of forged passports is achieved by using the picture and face verification between the passport photo extracted from the passport image and the picture retrieved from the database based on the recognized codes. Due to the proposed ART algorithm and the heuristic refinement, the proposed system relatively shows better performance.

• Proceedings of the KSPS conference
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• 2005.11a
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• pp.117-120
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• 2005

This paper suggests an algorithm that can estimate the direction of the sound source in realtime. Our intelligent service robot, WEVER, is used to implement the proposed method at the home environment. The algorithm uses the time difference and sound intensity information among the recorded sound source by four microphones. Also, to deal with noise of robot itself, the kalman filter is implemented. The proposed method takes shorter execution time than that of an existing algorithm to fit the real-time service robot. The result shows relatively small error within the range of ${\pm}$ 7 degree.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.619-624
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• 1998

This paper presents a new clustering algorithm called FCM algorithm for the design of fuzzy controller. FCM is an extended version of FCM(Fuzzy c-Means) algorithm and can estimate the number of clusters automatically and give membership grades $u_{ik}$ suitable for making fuzzy control rules. This paper also shows an example of its application to the line pursuit control of a car.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.218-224
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• 2014

The logic-based scheduling language RCPSV may be used to model resource-constrained project scheduling problems with variants for minimizing the project completion time. A diagram-based, nonredundant enumeration algorithm for the RCPSV-problem is proposed and the correctness of the algorithm is proved.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.279-282
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• 1996

Collision avoidance is a method to direct a mobile robot without collision when traversing the environment. This kind of navigation is to reach a destination without getting lost. In this paper, we use a genetic algorithm for the path planning and collision avoidance. Genetic algorithm searches for path in the entire, continuous free space and unifies global path planning and local path planning. It is a efficient and effective method when compared with traditional collision avoidance algorithm.