• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.103-108
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• 1998

The increase in the capacity of the digital magnetic recording systems inevitably causes severe intersymbol interference (ISI) and nonlinear distortions in the digital magnetic recording channel. In this paper, to cope with severe ISI and nonlinear distortions a neural decision feedback equalizer (NDFE) is applied to the digital magnetic recording channel - partial erasure channel model. In the performance comparison of bit error probability (or bit error ratio : BER) between the NDFE and the conventional decision feedback equalizer (DFE) via computer simulations. It has been found that as nonlinear distortions increase the NDFE has more SNR (SIgnal-to-Noise Ratio) advantage over the conventional DFE. In addition, in spite of the same recording density, as nonlinear distortions are increased, NDFE has the better performance of BER and the greater stability over conventional DFE.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3434-3439
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• 2015

As a criterion of information theoretic learning, the Euclidean distance (ED) of two error probability distribution functions (minimum ED of error, MEDE) has been adopted in nonlinear (decision feedback, DF) supervised equalizer algorithms and has shown significantly improved performance in severe channel distortion and impulsive noise environments. However, the MEDE-DF algorithm has the problem of heavy computational complexity. In this paper, the recursive ED for MEDE-DF algorithm is derived first, and then the feed-forward and feedback section gradients for weight update are estimated recursively. To prove the effectiveness of the recursive gradient estimation for the MEDE-DF algorithm, the number of multiplications are compared and MSE performance in impulsive noise and underwater communication environments is compared through computer simulation. The ratio of the number of multiplications between the proposed DF and the conventional MEDE-DF algorithm is revealed to be $2(9N+4):2(3N^2+3N)$ for the sample size N with the same MSE learning performance in the impulsive noise and underwater channel environment.

• Journal of the Korea Computer Graphics Society
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• v.26 no.3
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• pp.69-77
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• 2020

This paper proposes an effective method of giving users feedback on 3-dimensional drawing and measures its performance to ensure that feedback can help users enter the correct position in 3D. In the experiment of drawing a given line shape using a hand-held controller, the user is provided with three levels of visual, auditory, and haptic feedback for the position input error. As a result of analyzing the position input accuracy according to the type of feedback, all types of feedback are able to significantly reduce errors, and visual feedback and haptic feedback are more effective than auditory feedback.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.1
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• pp.38-46
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• 2012

In this paper, we propose a decision feedback equalizer based on LDPC(Low Density Parity Check) code for the fast processing and performance improvement in OFDM system. LDPC code has good error correcting capability and its performance approaches the Shannon capacity limit. However, it has longer parity check matrix and needs more iteration numbers. In our proposed system, MSE(Mean Square Error) of signal between decision device and decoder is fed back to equalizer. This proposed system can improve BER performance because it corrects estimated channel response more accurately. In addition, the proposed system can reduce complexity because it has a lower number of iterations than system without feedback at the same performance. Simulation results evaluate and show the performance of OFDM system with the CFO and phase noise in multipath channel.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.852-857
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• 2008

A new adaptive neuro-control algorithm for a SISO strict-feedback nonlinear system is proposed. All the previous adaptive neural control algorithms for strict-feedback nonlinear systems are based on the backstepping scheme, which makes the control law and stability analysis very complicated. The main contribution of the proposed method is that it demonstrates that the state-feedback control of the strict-feedback system can be viewed as the output-feedback control problem of the system in the normal form. As a result, the proposed control algorithm is considerably simpler than the previous ones based on backstepping. Depending heavily on the universal approximation property of the neural network (NN), only one NN is employed to approximate the lumped uncertain system nonlinearity. The Lyapunov stability of the NN weights and filtered tracking error is guaranteed in the semi-global sense.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.108-122
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• 2014

Massive antenna array is an attractive candidate technique for future broadband wireless communications to acquire high spectrum and energy efficiency. However, such benefits can be realized only when proper channel information is available at the transmitter. Since the amount of the channel information required by the transmitter is large for massive antennas, the feedback is burdensome in practice, especially for frequency division duplex (FDD) systems, and needs normally to be reduced. In this paper a novel channel feedback reduction scheme based on the theory of distributed compressive sensing (DCS) is proposed to apply to massive antenna arrays with spatial correlation, which brings substantially reduced feedback load. Simulation results prove that the novel scheme is better than the channel feedback technique based on traditional compressive sensing (CS) in the aspects of mean square error (MSE), cumulative distributed function (CDF) performance and feedback resources saving.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.294-299
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• 2006

This paper studies a visual feedback control scheme for robot manipulators with camera-in-hand configurations. We design a robust controller that compensates for bounded parametric uncertainties of robot mechanical dynamics. In order to reduce steady state tracking error of the robot arms due to uncertain dynamics, integral action is included in the control input. Using the Lyapunov stability criterion, the uniform ultimate boundedness of the tracking error is proved. Simulation and experimental results with a 2-1ink robot manipulator illustrate the robustness and effectiveness of the proposed control algorithm.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.9
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• pp.23-30
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• 1997

In this paper, a decision feedback recurrent neural equalization (DFRNE) scheme is proposed for adaptive equalization problems. The proposed equalizer models a nonlinear infinite impulse response (IIR) filter. The modified Real-Time recurrent Learning Algorithm (RTRL) is used to train the DFRNE. The DFRNE is applied to both linear channels with only intersymbol interference and nonlinear channels for digital video cassette recording (DVCR) system. And the performance of the DFRNE is compared to those of the conventional equalizaion schemes, such as a linear equalizer, a decision feedback equalizer, and neural equalizers based on multi-layer perceptron (MLP), in view of both bit error rate performance and mean squared error (MSE) convergence. It is shown that the DFRNE with a reasonable size not only gives improvement of compensating for the channel introduced distortions, but also makes the MSE converge fast and stable.

• Journal of the Ergonomics Society of Korea
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• v.19 no.3
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• pp.51-60
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• 2000

Non-verbal sound feedback, called earcon, has been used for portable digital electronic products to give appropriate information for the selected function. This study evaluated usability based on user cognition time, error rate, and subjective satisfaction using 20 male and female subjects. The study compared five major user functions from a portable digital electronic product with currently available earcons and the same functions from the product with the new earcons (suggested by this study) which considered user cognitive characteristics, such as loudness, pitch, melody, and length. For subjective evaluation, the study assessed various earcons by subjective impression of sounds using the seven-point rating scales. Major statistical results indicated that the new earcons significantly reduced user error rates and generally improved user performance functions, such as 'play, off, stop, fast forward, and rewind.'

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.474-478
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• 1998

A new approach for the decision feedback equalizer(DFE) based on the back-propagation neural networks is described. We propose the method of optimal structure for back-propagation neural networks model. In order to construct an the optimal structure, we first prescribe the bounds of learning procedure, and the, we employ the method of incrementing the number of input neuron by utilizing the derivative of the error with respect to an hidden neuron weights. The structure is applied to the problem of adaptive equalization in the presence of inter symbol interference(ISI), additive white Gaussian noise. From the simulation results, it is observed that the performance of the propose neural networks based decision feedback equalizer outperforms the other two in terms of bit-error rate(BER) and attainable MSE level over a signal ratio and channel nonlinearities.