• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.673-682
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• 2004

The adaptive beam-forming is promising approach for noise reduction in hearing aids. This approach has come in the focus of interest only recently, because of the availability of new and powerful digital signal processors. The adaptation U using usually a Least Mean Squares algorithm, updates the weight vector. In this Paper, we propose a fast wavelet based adaptive algorithm using variable step size algorithm which varies adaptive constant by the change of signal environment. We compared the performance of the proposed algorithm with the known adaptive algorithm using computer simulation of multi channel adaptive bemformer in hearing aids. As the result the proposed algorithm is suitable for adaptive signal processing area using hearing aids and has advantages reducing computational complexity. And we show the beam-forming system using proposed algorithm converges stably in a sudden change of system environment.

• The Journal of the Acoustical Society of Korea
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• v.34 no.1
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• pp.46-51
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• 2015

In fields of controlling acoustical noises, the overall adaptive control system is nonlinear due to the loudspeaker, amplifiers, converters, and microphones, etc. and the performance of noise control is decreased by the extent of nonlinearities, so an adaptive control system compensating nonlinear distortions is needed. In this paper, a new multi-channel adaptive noise controller was proposed, which was combined with the adaptive compensator to effectively linearize nonlinear distortions in the overall adaptive control system. Through computer simulations, the proposed adaptive compensator could linearize the nonlinear distortions and the proposed noise controller had better capability of controlling the noises than the conventional LMS controller.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.479-493
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• 2010

Along with the transformation of the knowledge-based environment, e-learning has become a main teaching and learning method, prompting various research efforts to be conducted in this field. One major research area in e-learning involves adaptive learning systems that provide personalized learning content according to each learner's characteristics by taking into consideration a variety of learning circumstances. Active research on ontology-based adaptive learning systems has recently been conducted to provide more efficient and adaptive learning content. In this paper, we design and propose an adaptive learning system based on the concept lattice of Formal Concept Analysis (FCA) with the same objectives as those of ontology approaches. However, we are in pursuit of a system that is suitable for learning of specific domains and one that allows users to more freely and easily build their own adaptive learning systems. The proposed system automatically classifies the learning objects and concepts of an evolved domain in the structure of a concept lattice based on the relationships between the objects and concepts. In addition, the system adaptively constructs and presents the learning structure of the concept lattice according to each student's level of knowledge, learning style, learning preference and the learning state of each concept.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.931-934
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• 2005

This paper describes an adaptive recommendation system that provides real-time personalized trading advice to the investors based on their profiles and trading information environment. A proposed system integrates Stochastic technical analysis and artificial neural network that incorporates an adaptive user modeling. The user model is constructed and updated based on initial user profile and recorded user interactions with the system. The information presented to each individual user is also tailor-made to fit the user's behavior and preference. A system prototype was implemented in JAVA. Experiments used to evaluate the system's performance were done on both human subjects and synthetic users. The results show our proposed system is able to rapidly learn to provide appropriate advice to different types of users.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.11
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• pp.497-505
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• 2001

The power system stabilizer(PSS) is important for the suppression of low-frequency oscillation and the improvement of system stability. In this paper, An Adaptive Neurofuzzy-based Power System Stabilizer(ANF PSS) is proposed as the new PSS type. The proposed PSS employs a multi-layer adaptive network. The network is trained directly from the input and the output of the generating unit. The algorithm combines the advantages of the Artificial Neural Network(ANN) and Fuzzy Logic Control(FLC) schemes. Studies show that the proposed ANF PSS can provide good damping of the power system over the wide range of operating conditions and improve the dynamic performance of the system.

• ETRI Journal
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• v.37 no.3
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• pp.626-636
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• 2015

This paper proposes an adaptive multimodal in-vehicle information system for safe driving. The proposed system filters input information based on both the priority assigned to the information and the given driving situation, to effectively manage input information and intelligently provide information to the driver. It then interacts with the driver using an adaptive multimodal interface by considering both the driving workload and the driver's cognitive reaction to the information it provides. It is shown experimentally that the proposed system can promote driver safety and enhance a driver's understanding of the information it provides by filtering the input information. In addition, the system can reduce a driver's workload by selecting an appropriate modality and corresponding level with which to communicate. An analysis of subjective questionnaires regarding the proposed system reveals that more than 85% of the respondents are satisfied with it. The proposed system is expected to provide prioritized information through an easily understood modality.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.993-995
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• 1996

This paper proposes an adaptive Filtered-X LMS algorithm with robustness. The proposed system updates filter parameters to keep the performance even if the conventional Active Noise Control (ANC) system well does not work. As a result of the computer simulation, the proposed system has all the advantage of the system using the conventional Filtered-X LMS algorithm. Also some results from the proposed algorithm present that the system is stable when the Adaptive filter does not work. Therefore, the proposed system using the Adaptive Filtered-X LMS algorithm must have much advantage than the system using the conventional algorithm in the side of the stability and performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.9
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• pp.1676-1679
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• 2007

In control of the time delay systems, if the disturbance information can be estimated exactly, the ideal response for the disturbance can be achieved. Therefore, in this paper, a new adaptive disturbance observer for the time delay system control is proposed. The disturbance observer is designed and adaptive laws for adjusting the observer are proposed. The new Smith predictor controller to eliminate the effects of the disturbance can be achieved by adding an estimated disturbance to the Smith model. The improved performances of the proposed system and the adaptive disturbance observer are verified by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.401-405
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• 1993

This paper presents a neuro adaptive control method for nonlinear dynamical systems based on artificial neural network systems. The proposed neuro adaptive controller consists of 3 layers artificial neural network system and parallel PD controller. At the early stage in learning or identification process of the system characteristics the PD controller works mainly in order to compensate for the inadequacy of the learning process and then gradually the neuro contrller begins to work instead of the PD controller after the learning process has proceeded. From the simulation studies the neuro adaptive controller is seen to be robust and works effectively for nonlinear dynamical systems from a practical applicational points of view.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.5
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• pp.609-616
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• 2003

The purpose of this study is to design the adaptive speed control system of a marine diesel engine by combining the Model Matching Method and the Nominal Model Tracking Method. The authors proposed already a new method to determine efficiently the PID control Parameters by the Model Matching Method. typically taking a marine diesel engine as a non-oscillatory second-order system. But. actually it is very difficult to find out the exact model of a diesel engine. Therefore, when diesel engine model and actual diesel engine are unmatched as an another approach to promote the speed control characteristics of a marine diesel engine, this paper Proposes a Model Reference Adaptive Speed Control system of a diesel engine, in which PID control system for the model of a diesel engine is adopted as the nominal model and Fuzzy controller and derivative operator are adopted as the adaptive controller.