• Journal of Information Processing Systems
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• v.13 no.1
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• pp.8-25
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• 2017

This survey paper explores the application of multimodal feedback in automated systems for motor learning. In this paper, we review the findings shown in recent studies in this field using rehabilitation and various motor training scenarios as context. We discuss popular feedback delivery and sensing mechanisms for motion capture and processing in terms of requirements, benefits, and limitations. The selection of modalities is presented via our having reviewed the best-practice approaches for each modality relative to motor task complexity with example implementations in recent work. We summarize the advantages and disadvantages of several approaches for integrating modalities in terms of fusion and frequency of feedback during motor tasks. Finally, we review the limitations of perceptual bandwidth and provide an evaluation of the information transfer for each modality.

• Journal of The Korean Association of Information Education
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• v.8 no.3
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• pp.307-317
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• 2004

In web-based education environment, it is necessary to provide individually adjusting feedback according to learner's characteristic. Despite this necessity, it is a current state that there are difficulties in deriving the variables of learners' characteristics and lack in developing the systematic strategies and practical tools for providing individually adjusting feedback. This study analyzed the learners' learning patterns, one of learner's characteristic variables regarded as important in web-based teaching and learning environment by employing Apriori algorithm, and also grouped the learners by learning pattern. Under this framework, the e-SRM feedback system was designed and developed to provide learning content, learning channel, and learning situation, etc. for individual learners. The proposed system in this study is expected to provide an optimal learning environment complying with learner's characteristic.

• Journal of Industrial Technology
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• v.18
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• pp.303-308
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• 1998

In this paper a neural network controller called "Feedback-State Learning" for control of the attitude of a wheeled inverted pendulum is presented. For the controller the design of a stable feedback controller is necessary, so the LQR is used for the feedback controller because the LQR has good performance on controlling nonlinear systems. And the neural networks are used for a feed forward controller. The designed controller is applied to the stabilization of a wheeled inverted pendulum. Because of its nonlinear characteristics such as friction and parameter variations in the linearization, the wheeled inverted pendulum is used for demonstration of the effectiveness of the proposed controller.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.1
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• pp.39-42
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• 2002

For the purpose of modeling EEG signal which has nonstationary and nonlinear dynamic characteristics, this paper propose a state feedback real time recurrent neural network model. The state feedback real time recurrent neural network is structured to have memory structure in the state of hidden layers so that it has arbitrary dynamics and ability to deal with time-varying input through its own temporal operation. For the model test, Mackey-Glass time series is used as a nonlinear dynamic system and the model is applied to the prediction of three types of EEG, alpha wave, beta wave and epileptic EEG. Experimental results show that the performance of the proposed model is better than that of other neural network models which are compared in this paper in some view points of the converging speed in learning stage and normalized mean square error for the test data set.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1009-1012
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• 1993

An association memory, solving an optimization problem, a Boltzmann machine scheme learning and a back propagation learning in our chaos neuron models are reviewed and some new results are presented. In each model its microscopicrule (a parameter of a chaos system in a neuron) is subject to its macroscopic state. This feedback and chaos dynamics are essential mechanisms of our model and their roles are briefly discussed.

• The Journal of Korean Association of Computer Education
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• v.10 no.4
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• pp.125-133
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• 2007

Many researches have tried to introduce affective computing for Human-Computer Interaction (HCI). In the affective aspect, emotional memories significantly affect on people's cognitive processing activities. In this paper, to observe the effect of affective feedback for emotional state of learners in an e-learning environment, selected emotional feedback messages and delivery method are integrated into an e-learning system. Self reporting button for recognizing learner's emotional state are used for detecting learner's emotional states and the test results show that providing affective feedback to learner has positive effects in e-learning environment in terms of learner's academic achievements.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.10
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• pp.2567-2576
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• 1994

In this paper we propose a new method to determine the learning rates of fuzzy learning algorithm(FLA) in nonlinear MIMO system. The state feedback gains are used from the linearized system of the nonlinear MIMO system. Through this method, it is easy to determine the learing rates. And it is quarauteed the good convergence and confirmed the performance of FLA is better than that of linear controller(LC) through the simulation. Acrobatic robot system is selected as an example(one-input two-output system), and FLA is implemented through the experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3867-3886
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• 2015

This article investigates the problem of opportunistic spectrum access in dynamic environment, in which the signal-to-noise ratio (SNR) is time-varying. Different from existing work on continuous feedback, we consider more practical scenarios in which the transmitter receives an Acknowledgment (ACK) if the received SNR is larger than the required threshold, and otherwise a Non-Acknowledgment (NACK). That is, the feedback is discrete. Several applications with different threshold values are also considered in this work. The channel selection problem is formulated as a non-cooperative game, and subsequently it is proved to be a potential game, which has at least one pure strategy Nash equilibrium. Following this, a multi-agent Q-learning algorithm is proposed to converge to Nash equilibria of the game. Furthermore, opportunistic spectrum access with multiple discrete feedbacks is also investigated. Finally, the simulation results verify that the proposed multi-agent Q-learning algorithm is applicable to both situations with binary feedback and multiple discrete feedbacks.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1140-1145
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• 1989

A general dynamic iterative learning control scheme is proposed for a class of nonlinear systems. Relying on stabilizing high-gain feedback loop, it is possible to show the existence of Cauchy sequence of feedforward control input error with iteration numbers, which results in a uniform convergance of system state trajectory to the desired one.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1520-1526
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• 1999

This paper presents a current feedback learning controller for dynamic control of DC motors. The proposed controller uses the full third-order dynamics model of DC motor system to drive stable learning rules for virtual current learning input, voltage learning input, and the coefficient of electromotive force. It is shown that the proposed learning controller drives the state of uncertain DC motor system with unknown system parameters and external load torque to the desired one globally asymptotically. Computer simulation and experimental results are given to demonstrate the effectiveness of the proposed adaptive learning controller.