• The Journal of the Acoustical Society of Korea
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• v.16 no.2
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• pp.71-79
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• 1997

In this paper, a codeword-dependent distance normalization(CDDN) and an instar-formed fuzzy smoothing of output distribution are proposed for robust estimation of output probabilities in the FVQ(fuzzy vector quantization)-DHMM(discrete hidden Markov model). The FVQ-DHMM is a variant of DHMM in which the state output probability is estimated by the sum oft he product of the output probability and its weighting factor for each codeword on an input vector. As the performance of the FVQ-DHMM is influenced by weighting factor and output distribution from a state, it is required to get a method to get robust estimation of weighting factors and output distribution for each state. From experimental results, the proposed CDDN method has reduced 24% of error rate over the conventional FVQ-DHMM, and also reduced 79% of error rate when the smoothing of output distribution is also applied to the computation of an output probability. These results indicate that the use of CDDN and the fuzzy smoothing of output distribution to the FVQ-DHMM lead to improved recognition, and therefore it may be used as an alternative to the robust estimation of output probabilities for HMMs.

• Journal of Broadcast Engineering
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• v.10 no.4 s.29
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• pp.540-547
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• 2005

In the DFE(Decision Feedback Equalizer) for ATSC DTV receivers, there are decision errors in the slicer or. the simplified trellis decoder, and these decided false data comes to the feedback filter to make the error propagation phenomenon. The error propagation degrades the equalizer performance by increasing residual errors as well as slowing down the convergence rate. In this paper we propose a dual-feedback equalization structure. There are two feedback filters. One is the decision feedback filter which uses the simplified trellis decoder output data, the other is non-decision feedback filter which uses the equalizer output data. The additional non-decision feedback filter doesn't introduce the error propagation, so it can compensate the error propagation. The proposed structure accelerates the convergence rate as well as reduces output men-square error(MSE). We analyzed the performance enhancement of DTV receiver using dual-feedback equalization structure.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6254-6259
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• 2015

On the optical system and the system itself of the noise of the noise from the outside always present. This noise is to function as reducing performance of the optical system. Therefore, the probability of error, thereby changing the system. In this paper, the error probability of the optical system due to changes in various values by introducing the characteristic variable the value of the optical system was calculated using the ${\kappa}$-square method. Was confirmed also in accordance with the calculation result is an error probability of the optical system changes, it was confirmed that when the value of the holding case for holding the standard about 400 Lux on the probability of the optical system. This case was found to be an optical system using a light source with a low output, so that means the smaller output is no problem to maintain the error probability value of the optical system is large. This means that more effective and less expensive to implement because it means that the optical system does not require the use of pre-amplifier for amplifying the signal at the receiving end of an optical system using a light source with a low output when the normal case.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.11
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• pp.68-76
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• 2014

This paper presents an offset error compensation algorithm for the accurate phase angle of the grid voltage in single-phase grid-connected inverters. The offset error generated from the grid voltage measurement process cause the fundamental harmonic component with grid frequency in the synchronous reference frame phase lock loop (PLL). As a result, the grid angle is distorted and the power quality in power systems is degraded. In addition, the dq-axis currents in the synchronous reference frame and phase current have the dc component, first and second order ripples compared with the grid frequency under the distorted grid angle. In this paper, the effects of the offset and scaling errors are analyzed based on the synchronous reference frame PLL. Particularly, the offset error can be estimated from the integrator output of the synchronous reference frame PLL and compensated by using proportional-integral controller. Moreover, the RMS (Root Mean Square) function is proposed to detect the offset error component. The effectiveness of the proposed algorithm is verified through simulation and experiment results.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.177-177
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• 2000

In this paper, we propose a two-input two-output fuzzy controller to improve the performance of transient response and to eliminate the steady state error. The outputs of this controller are the control input calculated by position-type fuzzy controller and the accumulated error scaling factor. Here, the accumulated error scaling factor is adjusted on-line by fuzzy rules according to the current trend of the controlled process. To show the usefulness of the proposed controller, it is applied to several systems that are difficult to get satisfactory response by conventional PD controllers or PI controllers.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.215-218
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• 2000

In this paper, a new algorithm for adaptive IIR filters based on equation error methods using the RLS algorithm is proposed. In the proposed algorithm, the concept of feedback of the scaled output error proposed by tin and Unbehauen is employed and the forgetting factor is varied in adaptation process for avoiding the accumulation of the estimation error for additive noise . The proposed algorithm has the good convergence property without the parameter estimation error under the existence of mea-surement noise.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1073-1076
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• 1991

This paper presents a robust model reference adaptive controller for continuous-time single-input single-output linear time-invariant systems which are subjected to output-dependent disturbances as well as bounded external disturbances. In the derived controller form, an additional output error feedback term is included to over-ride the destabilizing effects by the output-dependent disturbances.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.769-772
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• 1999

In this paper we propose an FDI(fault detection and isolation) algorithm to detect and isolate single faults in linear systems. When a change in the system occurs the errors between the system output and the estimated output cross a threshold, and once a fault in the system is detected, the FCFM statistically isolates the fault by using the error between each neural network based fault model output and the system output.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.115-118
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• 2001

In this paper we propose an FDI(fault detection and isolation) algorithm using neural network-based multi-fault models to detect and isolate single faults in nonlinear systems. When a change in the system occurs, the errors between the system output and the neural network nominal system output cross a threshold, and once a fault in the system is detected, the fault classifier statistically isolates the fault by using the error between each neural network-based fault model output and the system output.