• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5A
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• pp.500-508
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• 2008

Direct-sequence ultra-wideband(DS-UWB) system is being considered as one of promising transmission technologies for wireless personal area networks(WPANs). Due to relatively low spreading factors and huge bandwidth of transmit signal, a DS-UWB receiver needs to be equipped not only with a rake receiver but also with an equalizer, of which the equalizer is not required for traditional direct-sequence code division multiple access(DS-CDMA) systems. The number of rake fingers is limited in practice, influencing the performance of the subsequent equalizer. In this paper, we derive a decision feedback equalizer(DFE) for DS-UWB systems based on the minimum mean square error(MMSE) criterion, and investigate the impact of various parameters on the DFE performance in realistic scenarios. In particular, we propose an approach to improving the performance of the DFE using additional channel estimates for multipaths not combined in the rake receiver, and discuss how the accuracy of channel estimation affects desirable DFE configuration. Moreover, we present simulation results that show the impact of turbo equalization on the DFE performance.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.407-410
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• 1996

In this Paper, we propose the feedback method having neural network to control the chaotic signals to periodic signals. This controller has very simple structure, it is immune to small parameter variations, the precise access to system parameters is not required and it is possible to follow ones of its inherent periodic orbits or the desired orbits without error, The controller consist of linear feedback gain and neural network. The learning of neural network is achieved by error-backpropagation algorithm. To prove and analyze the proposed method, we construct a software tool using c-language.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1943-1945
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• 2001

In this paper, we present an adaptive output feedback control scheme for a class of uncertain nonlinear output-feedback form with time-varying parameters to which adaptive observer backstepping technique may not be applicable directly. In observer design, with the introduction of design function, we can deal with time-varying parameters in a very effective way. By the presented scheme, estimation error can be tuned to a desired small region around the origin via the design constants. Consequently, the observer with the presented design functions and the backstepping methodology achieve a robust regulation of the output tracking error while maintaining boundedness of all the signals and states.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.217-221
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• 2007

This paper deals with the robust nonlinear controller design using output feedback for a Chua circuit which is one of the well-known nonlinear models. First, an exosystem for reference signal tracking is defined, and error dynamic equations are derived from the differentiation of the output tracking error equation. The normal sliding surface is modified using the integral type servo compensator. The parameters in the equations of the modified sliding surface and servo compensator are determined by using the Hurwitz condition of stability. Especially the error signals can't be obtained directly from the output because all parameters are assumed unknown. So instead, a high gain observer is designed. From this estimated error signals, a stabilizing controller is designed. Simulation is done for demonstrating the effectiveness of the suggested algorithm.

• Journal of Communications and Networks
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• v.18 no.2
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• pp.210-217
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• 2016

In this paper, we propose a low complexity multiple-input multiple-output (MIMO) detection algorithm with adaptive interference mitigation in downlink multiuser MIMO (DL MU-MIMO) systems with quantization error of the channel state information (CSI) feedback. In DL MU-MIMO systems using the imperfect precoding matrix caused by quantization error of the CSI feedback, the station receives the desired signal as well as the residual interference signal. Therefore, a complexMIMO detection algorithm with interference mitigation is required for mitigating the residual interference. To reduce the computational complexity, we propose a MIMO detection algorithm with adaptive interference mitigation. The proposed algorithm adaptively mitigates the residual interference by using the maximum likelihood detection (MLD) error criterion (MEC). We derive a theoretical MEC by using the MLD error condition and a practical MEC by approximating the theoretical MEC. In conclusion, the proposed algorithm adaptively performs interference mitigation when satisfying the practical MEC. Simulation results show that the proposed algorithm reduces the computational complexity and has the same performance, compared to the generalized sphere decoder, which always performs interference mitigation.

• Korean Journal of Cognitive Science
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• v.28 no.1
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• pp.65-90
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• 2017

Rewards or penalties become informative only when contingent on an immediately preceding response. Our goal was to determine if the brain responds differently to motivational events depending on whether they provide feedback with the contingencies effective for learning. Event-related fMRI data were obtained from 22 volunteers performing a visuomotor categorical task. In learning-condition trials, participants learned by trial and error to make left or right responses to letter cues (16 consonants). Monetary rewards (+500) or penalties (-500) were given as feedback (learning feedback). In random-condition trials, cues (4 vowels) appeared right or left of the display center, and participants were instructed to respond with the appropriate hand. However, rewards or penalties (random feedback) were given randomly (50/50%) regardless of the correctness of response. Feedback-associated BOLD responses were analyzed with ANOVA [trial type (learning vs. random) x feedback type (reward vs. penalty)] using SPM8 (voxel-wise FWE p < .001). The right caudate nucleus and right cerebellum showed activation, whereas the left parahippocampus and other regions as the default mode network showed deactivation, both greater for learning trials than random trials. Activations associated with reward feedback did not differ between the two trial types for any brain region. For penalty, both learning-penalty and random-penalty enhanced activity in the left insular cortex, but not the right. The left insula, however, as well as the left dorsolateral prefrontal cortex and dorsomedial prefrontal cortex/dorsal anterior cingulate cortex, showed much greater responses for learning-penalty than for random-penalty. These findings suggest that learning-penalty plays a critical role in learning, unlike rewards or random-penalty, probably not only due to its evoking of aversive emotional responses, but also because of error-detection processing, either of which might lead to changes in planning or strategy.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.176-184
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• 2000

TDC(Time Delay Control) deals with the time-varying system parameters, unknown dynamics and unexpected disturbances using time delay. TDC can be divided into two separate parts: an auxiliary controller and a servo controller. The two controllers can be designed independently. The auxiliary controller is used to reduce sensitivity to parameter variations, nonlinear effects, and other disturbances. The servo controller is to reduce the error between the desired command and output. We propose the model-following time delay controller with modified error feedback controller. This was applied to follow the desired reference model for the uncertain time-varying overhead crane. The model generates the damped-out swinging motion trajectory to suppress the swinging motion caused by the acceleration and the deceleration of crane transportation. The control performance was evaluated through simulations. The theoretical results indicate that this control method shows excellent performance to an overhead crane with the uncertain time-varying parameters.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.247-252
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• 2015

Adaptive algorithms based on the criterion of zero-error probability (ZEP) have robustness to impulsive noise and their decision feedback (DF) versions are known to compensate effectively for severe multipath channel distortions. However the ZEP-DF algorithm computes several summation operations at each iteration time for each filter section and this plays an obstacle role in practical implementation. In this paper, the ZEP-DF with recursive gradient estimation (RGE) method is proposed and shown to reduce the computational burden of O(N) to a constant which is independent of the sample size N. Also the weight update of the initial state and the steady state is a continuous process without bringing about any propagation of gradient estimation error in DF structure.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.8 no.3
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• pp.88-95
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• 1994

A scheme of IESFVSC(Integral Error State Feedback Variable Structure Controller) is proposed for a DC servo position control system with the disturbances which do not satisfy the matching condition. The proposed control system is composed of servo compensator and state feedback VSC. The servo compensator enhances the robustness of the control system against various types of disturbance, and makes effective tracking possible without using error dynamics. The IESFVSC is applied to the practical design of a robust DC servo control system and the control performances are verified through theoretical analyses and simulations.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.2
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• pp.74-80
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• 2003

In this paper, a robust two-degree-of-freedom(TDF) the speed control system using $H_{\infty}$ optimization method and real genetic algorithm is proposed for the robust stability and the robust performance in dc servo motor system. This control system composed of feedback and feedforward controller. The feedback(FB) controller with $H_{\infty}$ optimization method is designed for real genetic algorithm that is model matching problem using mixed sensitivity function. The feedforward(FF) controller with $H_{\infty}$optimization method is minimized the error between transfer function of the optimal model and the overall transfer function. The proposed robust two-degree-of-freedom speed control system is simulated to the dc servo motor. By the simulation, feedback controller can obtain the robust stability property and feedforward controller can obtain the robust performance property under modelling error. The performance of the dc servo motor is analyzed by the experiment setting. The validity of the proposed method is verified through being compared with pid(proportional integrated differential)control system design method for the dc servo motor.