• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2572-2574
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• 2000

In this paper, cell-mediated immune algorithm(CMIA) controller was proposed and applied for the autonomous guided vehicle(AGV) driving. It was based on specific immune response of the biological immune system which is the cell-mediated immunity. To verify the performance of the designed CMIA controller, some experiments were performed for the control of steering and speed of AGV. And then the displacement and speed tracking error of the AGV was mainly investigated. As results, the capability of realization and reliableness were proved by comparing the response characteristics of the classical controller with the proposed CMIA controller.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.10 no.1
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• pp.39-52
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• 2000

컴퓨터망의 확대 및 컴퓨터 이용의 급격한 증가에 따른 부작용으로 컴퓨터 보안 문제가 중요하게 대두되고 있다. 이에 따라 침입자들로부터 침입을 줄이기 위한 침입탐지시스템에 관한 연구가 활발하다. 본 논문에서는 컴퓨터 면역 시스템을 바탕으로 한 새로운 IDS 모델을 제안하고, 이를 설계하고 프로토타입을 구현하는 그 타당성을 보인다. 제안한 모델에서 IDS들은 여러 컴퓨터에 분산되고, 분산된 IDS들 중 어느 하나가 특권 프로세스(Privilege process)에 의해 발생된 시스템 호출 순서 중 비정상적인 시스템 호출을 탐지한 경우 이를 다른 IDS들과 서로 동적으로 공유하여 새로운 침입에 대한 면역력을 향상시킨다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.257-260
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• 2007

In this paper addresses the low-level behavior of fuzzy control and the high-level behavior selector for Autonomous Mobile Robots (AMRs) based on a Fuzzy Artificial Immune Network. The sensing information that comes from ultrasonic sensors is the antigen it, and stimulates antibodies. There are many possible combinations of actions between action-patterns and external situations. The question is how to handle the situations to decide the proper action. We propose a fuzzy artificial immune network to solve the above problem. and the computer simulation for an AMR action selector shows the usefulness of the proposed action selector.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2083-2089
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• 2007

In this paper addresses the low-level behavior of fuzzy control and the high-level behavior selector for Autonomous Mobile Robots(AMRs) based on a Fuzzy Artificial Immune Network. The sensing information that comes from ultrasonic sensors is the antigen it, and stimulates antibodies. There are many possible combinations of actions between action-patterns and external situations. The question is how to handle the situations to decide the proper action. We propose a fuzzy artificial immune network to solve the above problem. and the computer simulation for an AMR action selector shows the usefulness of the proposed action selector.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.161-163
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• 2001

In this paper, a method for optimal design of PID controller using the immune algorithm(IA) has been proposed to improve the stability of A.C.-D.C. power system. To design optimal PID controller, formulation of AC-DC system equation, selection of stability analysis model, formulation immune algorithm and application model of optimal PID controller are proposed in order of the paper. In case of various disturbance, computer simulations have been performed and the proposed IA-PID controller has been compared with base controller which is conventional control technique for dynamics. From simulation results, it is demonstrated that proposed IA-PID controller has good dynamic responses about the disturbance of power system and reliability as compared with the base control.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1286-1302
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• 2012

In this paper, the resource allocation problem with proportional fairness rate in cognitive OFDM-based wireless network is studied. It aims to maximize the total system throughput subject to constraints that include total transmit power for secondary users, maximum tolerable interferences of primary users, bit error rate, and proportional fairness rate among secondary users. It is a nonlinear optimization problem, for which obtaining the optimal solution is known to be NP-hard. An efficient bio-inspired suboptimal algorithm called immune clonal optimization is proposed to solve the resource allocation problem in two steps. That is, subcarriers are firstly allocated to secondary users assuming equal power assignment and then the power allocation is performed with an improved immune clonal algorithm. Suitable immune operators such as matrix encoding and adaptive mutation are designed for resource allocation problem. Simulation results show that the proposed algorithm achieves near-optimal throughput and more satisfying proportional fairness rate among secondary users with lower computational complexity.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.11
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• pp.1103-1109
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• 2009

In this paper, the control of a membrane used in reverse osmosis desalination plant by using immune algorithm(IA) is addressed. The proposed algorithm IA of auto tuning method can find optimal gains and compared with conventional Ziegler-Nichols tuning method. The results of computer simulation represent that the proposed IA shows a good control performances better than Ziegler-Nichols tuning method.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.561-567
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• 2006

This paper deals with a fuzzy controller using IA(Immune Algorithm) for Trajectory Tracking of 2-DOF WMR(Wheeled Mobile Robot). The global inputs to the WMR are reference position and reference velocity, which are time variables. The global output of WMR is a current position. The tracking controller makes position error to be converged 0. In order to reduce position error, a compensation velocities on the track of trajectory is necessary. Therefore, a FIAC(Fuzzy-IA controller) is proposed to give velocity compensation in this system. Input variables of fuzzy part are position errors in every sampling time. The output values of fuzzy part are compensation velocities. IA are implemented to adjust the scaling factor of fuzzy part. The computer simulation is performed to get the result of trajectory tracking and to prove efficiency of proposed controller.

• Journal of information and communication convergence engineering
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• v.14 no.4
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• pp.233-239
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• 2016

A remote control system (RCS) can monitor a user's confidential information by using the broadcast receivers in Android OS. However, the current RCS detection methods are based only on a virus vaccine. Therefore, if the user's smartphone is infected by a brand new RCS, these methods cannot detect this new RCS immediately. In this paper, we present a secure message transmission medium. This medium is completely isolated from networks and can communicate securely through a QR code channel by using symmetric key cryptography such as the AES block cipher and public key cryptography such as elliptic curve cryptography for providing security. Therefore, the RCS cannot detect any confidential information. This approach is completely immune to any RCS attacks. Furthermore, we present a secure QR code-based key exchange protocol by using the elliptic curve Diffie-Hellman method and message transmission protocols; the proposed protocol has high usability and is very secure.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.419-422
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• 2003

To improve the recognition accuracy of a developed artificial odor discrimination system for three mixture fragrance recognition, Fuzzy Similarity based Self-Organized Network inspired by Immune Algorithm (F-SONIA) is proposed. Minimum, average, and maximum values of fragrance data acquisitions are used to form triangular fuzzy numbers. Then the fuzzy similarity treasure is used to define the relationship between fragrance inputs and connection strengths of hidden units. The fuzzy similarity is defined as the maximum value of the intersection region between triangular fuzzy set of input vectors and the connection strengths of hidden units. In experiments, performances of the proposed method is compared with the conventional Self-Organized Network inspired by Immune Algorithm (SONIA), and the Fuzzy Learning Vector Quantization (FLVQ). Experiments show that F-SONIA improves recognition accuracy of SONIA by 3-9％. Comparing to the previously developed artificial odor discrimination system that used FLVQ as pattern classifier, the recognition accuracy is increased by 14-25％.