• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.330-337
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• 2000

This paper proposes a new concept of coordinating green this which controls 10 traffic intersection systems. For instance, if we have a baseballs game at 8 pm today, traffic volume toward the baseball game at 8 pm today, traffic volume toward the baseball game will be incr eased 1 hour or 1 hour 30 minutes before the baseball game. at that time we can not pred ict optimal green time Even though there have smart elctrosensitive traffic light system. Therefore, in this paper to improve average vehicle speed and reduce average vehicle waiting time, we created optimal green time using fuzzy rules and neural network. Computer simulation results proved reducing average vehicle waiting time proposed coordinating green time better than electro-sensitive traffic light system. Therefore, in this paper to improvevehicle speed and reduce average vehicle waiting time, we created optiual green time fuzzy rules and neural network. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive traffic light system dosen't consider coordinating green time.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.292-297
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• 2000

This paper proposes a new concept of coordinating green time which controls 10 traffic intersection systems. For instance, if we have a baseball game at 8 pm today, traffic volume toward the baseball game at 8 pm today, traffic volume toward the baseball game will be increased 1 hour or 1 hour and 30 minutes before the baseball game. At that time we can not predict optimal green time Even though there have smart elctro-sensitive traffic light system. Therefore, in this paper to improve average vehicle speed and reduce average vehicle waiting time, we created optimal green time using fuzzy rules and neural network. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive traffic light system dosen't consider coordinating green time.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.2
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• pp.65-75
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• 2004

This paper presents a generalized predictive control method based on a fuzzy neural network(FNN) model, which uses the on-line multi-step prediction, fur the intelligent control of chaotic nonlinear systems whose mathematical models are unknown. In our design method, the parameters of both predictor and controller are tuned by a simple gradient descent scheme, and the weight parameters of FNN are determined adaptively during the operation of the system. In order to design a generalized predictive controller effectively, this paper describes computing procedure for each of the two important parameters. Also, we introduce a projection matrix to determine the control input, which deceases the control performance function very rapidly. Finally, in order to evaluate the performance of our controller, the proposed method is applied to the Doffing and Henon systems, which are two representative continuous-time and discrete-time chaotic nonlinear systems, res reactively.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.10a
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• pp.144-148
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• 2001

자동차의 발전과 함께 유지 보수를 위한 사용자의 요구는 급증하고 있으나 정비사의 부족으로 인해 경제성 및 신속성 등 이 문제가 되고 있고 이를 해결하기 위해 현재 개발되고 있는 장치들은 대부분 전자 제어 유닛에서 발생시키는 신호를 분석하거나 운전자와의 대화를 통하여 진단하는 방식으로 고장으로 인한 소음이나 진동등 운전자들의 주관적인 평가대상에 대해서는 적절한 해결책으로 제시해 주지 못하고 있다. 그러므로 계측에 의한 소음과 진동 데이터를 이용하여 전문가의 판단을 가지고 고장의 원인을 규명하며 운전자를 위한 오디오적인 표현을 해 줄 수 있는 진단 전문가 시스템이 필요하게 되었다. 본 논문에서는 자동차의 여러 단품중 쇼크 옵서버와 에어컨에 대하여 소음 진동 현상의 정상 및 이상 증상과 신호 계측 방법을 연구하였고 계측된 신호에 대해 패턴 화하여 인공 신경 회로망과 퍼지 추론을 통한 진단을 할 수 있는 알고리즘을 개발하였으며 차후 계속되는 연구에 사용될 정상 및 이상신호에 대한 기본적인 데이터 베이스를 구축하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.239-242
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• 2002

점차 많은 형태의 가정용 로봇이 개발되고 있으며 일부 제품화되어 출시되고 있다. 이러한 가정용 로봇을 원격지에서 제어하고자 할 때 대부분이 인터넷이나 무선을 이용한 원격제어 또는 실시간 모니터링을 통해 제어하여 그 상황을 시뮬레이션으로 구현하고 있다. 그러나 제어대상이 극히 제한적인 수의 이동로봇인데 반해 다수의 가족 구성원인 제어조작자가 중복적으로 청소로봇과 같은 이동로봇을 제어하고자 접근을 시도했을 때 이동로봇에 다중적인 업무가 주어지게 될 수 도 있다. 이에 Database 를 이용하여 수행 업무의 우선순위와 제어조작자의 권한의 우선순위를 지정한 명령어를 Database 내에 구성하여 간접적으로 전송하여 제어하고자 한다. 그리고 제어를 통한 동작 시 실제 이동로봇의 속도와 방향 제어를 위해 퍼지 및 신경망을 이용한 지능제어를 구성하여 제어의 효율성을 극대화 하고자 한다.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3033-3044
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• 1997

This paper presents a system which recognizes dynamic hand gesture for virtual reality (VR). A dynamic hand gesture is a method of communication for human and computer who uses gestures, especially both hands and fingers. Since the human hands and fingers are not the same in physical dimension, the produced by two persons with their hands may not have the same numerical values where obtained through electronic sensors. To recognize meaningful gesture from continuous gestures which have no token of beginning and end, this system segments current motion states using the state automata. In this paper, we apply a fuzzy min-max neural network and feature analysis method using fuzzy logic for on-line pattern recognition.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.730-736
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• 2011

In this paper, we designed the models for pattern classification which can reflect the latest trend in time series. It has been shown that fusion models based on statistical and AI methods are superior to traditional ones for the pattern classification model supporting decision making. Especially, the hit rates of pattern classification models combined with fuzzy theory are relatively increased. The statistical SVM models combined with fuzzy membership function, or the models combining neural network and FCM has shown good performance. BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, and Regression Analysis were used for pattern classification models in the experiments of this paper. The economical indices DB with time series properties of the financial market(Korea, KOSPI200 DB) and the electrocardiogram DB of arrhythmia patients in hospital emergencies(USA, MIT-BIH DB) were used for data base.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.49-54
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• 2011

In this paper, we represent a mobile robot which can recognize chemical odor, measure concentration, and track its source indoors. The mobile robot has the function of smell that can sort several gases in experiment such as ammonia, ethanol, and their mixture with neural network algorithm and measure each gas concentration with fuzzy rules. In addition, it can not only navigate to the desired position with vision system by avoiding obstacles but also transmit odor information and warning messages earned from its own operations to other nodes by multi-hop communication in wireless sensor network. We suggest the way of odor sorting, concentration measurement, and source tracking for a mobile robot in wireless sensor network using a hybrid algorithm with vision system and gas sensors. The experimental studies prove that the efficiency of the proposed algorithm for odor recognition, concentration measurement, and source tracking.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.43-58
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• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.

• Journal of IKEEE
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• v.23 no.2
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• pp.716-721
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• 2019

A novel switching differentiator-based backstepping controller for uncertain pure-feedback nonlinear systems is proposed. Using asymptotically convergent switching differentiator, time-derivatives of the virtual controls are directly estimated in every backstepping design steps. As a result, the control law has an extremely simple form and asymptotical stability of the tracking error is guaranteed regardless of parametric or unstructured uncertainties and unmatched disturbances in the considered system. It is required no universal approximators such as neural networks or fuzzy logic systems that are adaptively tuned online to cope with system uncertainties. Simulation results show the simplicity and performance of the proposed controller.