• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.4
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• pp.430-439
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• 2011

In this paper, we design a digital predistortion(DPD) for linearity enhancement of RF power amplifier operating in 900 MHz band. We verify improvement of linearity by comparing the proposed DPD using tanh as a nonlinear indexing function and the DPD using linear indexing function based on signal amplitude. The digital predistortion is realized by look-up table(LUT) method, and the Saleh model is employed for power amplifier modeling, then a commercial power amplifier module is used for measurement. The LUT has 256 tables, and the NLMS(Normalized Least Mean Square) algorithm was utilized for an adaptive algorithm for estimation. As a result, we improve the ACLR(Adjacent Channel Leakage Ratio) by around 15 dB.

• The Journal of the Acoustical Society of Korea
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• v.33 no.6
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• pp.375-382
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• 2014

In many practical active noise control applications, the primary noise contains multiple closely-spaced harmonics. A narrowband ANC system consists of adaptive filters excited by a composite reference signal, which is the set or sum of sinusoids. This paper analyzes and shows that the convergence speeds of the direct form, parallel form, and simplified parallel form narrowband ANC systems are affected by the fundamental frequency and frequency separation between two adjacent sinusoids in the reference signal. This paper also proposes the new simplified parallel form narrowband ANC system whose convergence speed is independent on the frequency of the reference signal. Computer simulations are conducted to verify the analysis presented in the paper and to compare the proposed narrowband ANC system with the conventional narrowband ANC system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.2
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• pp.114-123
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• 2005

A nonlinear speed control of a PMSM using a sequential parameter auto-tuning algorithm for servo equipments is presented. The nonlinear control scheme gives an undesirable output performance under the mismatch of the system parameters and load conditions. Recently, to improve the performance, an adaptive linearization scheme, a sliding mode control and an observer-based technique have been reported. Although a good performance can be obtained, the performance is not satisfactory any more under specific conditions such as a large inertia variation, a fast speed transient or an increased sampling time. The simultaneous estimation of principal parameters giving a direct influence on speed dynamics is generally not simple. To overcome this problem, a a sequential parameter auto-tuning algorithm at start-up is proposed, where dominant parameters are estimated in a prescribed regular sequence based on the method that one parameter is estimated during each interval. The proposed scheme is implemented on a PMSM using DSP TMS320C31 and the effectiveness is verified through simulations and experiments.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.4
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• pp.395-404
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• 2000

A damage detection and assessment algorithm is developed by measuring accelerations at limited locations of a structure under forced vibrations. The developed algorithm applies a time-domain system identification (SI) method that identifies a structure by solving a linearly constrained nonlinear optimization problem for optimal structural parameters. An equation error of the dynamic equilibrium of motion is minimized to estimate optimal parameters. An adaptive parameter grouping scheme is applied to localize damaged members with sparse measured accelerations. Damage is assessed in a statistical manner by applying a time-windowing technique to the measured time history of acceleration. Displacements and velocities at the measured degrees of freedom (DOF) are computed by integrating the measured accelerations. The displacements at the unmeasured DOF are estimated as additional unknowns to the unknown structural parameters, and the corresponding velocities and accelerations we computed by a numerical differentiation. A numerical simulation study with a truss structure is carried out to examine the efficiency of the algorithm. A data perturbation scheme is applied to determine the thresholds lot damage indices and to compute the damage possibility of each member.

• Journal of Power Electronics
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• v.16 no.6
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• pp.1991-2004
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• 2016

An LC filter is required to reduce the output current ripple in the auxiliary resonant snubber inverter (ARSI) for high-performance applications. However, if the traditional control method is used in the ARSI with LC filter, then unnecessary current flows in the auxiliary circuit. In addressing this problem, a novel load-adaptive control that fully uses the filter inductor current ripple to realize the soft-switching of the main switches is proposed. Compared with the traditional control implemented in the ARSI with LC filter, the proposed control can reduce the required auxiliary current, contributing to higher efficiency and DC-link voltage utilization. In this study, the detailed circuit operation in the light load mode (LLM) and the heavy load mode (HLM) considering the inductor current ripple is described. The characteristics of the improved ARSI are expressed mathematically. A prototype with 200 kHz switching frequency, 80 V DC voltage, and 8 A maximum output current was developed to verify the effectiveness of the improved ARSI. The proposed ARSI was found to successfully operate in the LLM and HLM, achieving zero-voltage switching (ZVS) of the main switches and zero-current switching (ZCS) of the auxiliary switches from zero load to full load. The DC-link voltage utilization of the proposed control is 0.758, which is 0.022 higher than that of the traditional control. The peak efficiency is 91.75% at 8 A output current for the proposed control, higher than 89.73% for the traditional control. Meanwhile, the carrier harmonics is reduced from -44 dB to -66 dB through the addition of the LC filter.

• The Transactions of the Korea Information Processing Society
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• v.7 no.11
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• pp.3408-3416
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• 2000

This paper proposes an efficient method for improving the performance of a general regression neural network by using the feature to the independent variables as the center for partern-layer neurons. The adaptive principal component analysis is applied for extracting, efficiently the fcarures by reducing the dimension of given independent variables. In can acluevc a supertor property of the principal component analysis that converts input data into set of statistically independent features and the general regression neuralnetwork, espedtively. The proposed general regression neural network has been applied to regress the Solow's economy(2-independent variable set) and the wie elephone(1-independent vanable set). The simulation results show that the proposed meural networks have better performances of the regressionfor the lest data, in comparison with those using the means or the weighted means of independent variables. Also,it is affected less by the number of neurons and the scope of the smoothing factor.

• Journal of Biomedical Engineering Research
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• v.20 no.3
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• pp.291-297
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• 1999

Source consislency filtering (SCF) is very effective at removing nOIse when only one or a few leads among multi-lead ECG signals are contaminated. When the noise at one or only a few leads are statistically uncorrelated with signals at other leads, SCF seleclIvely removes the noise with a neglIgIble amount of distortion in the original signal waveform. In order to enhance the understanding of this new method, we describe the lheory and implementational details of SCF in this paper. Numerical implementation and test results of SCF on a multi-lead ECG dalabase show that SCF is a new kind of adaptive filters utilizmg spatial as well as temporal information in multi-c.hannel signals origmatmg from a single source. We also describe the limitations and future improvements in using SCF.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.2 s.42
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• pp.7-15
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• 2005

Numerical analysis model is proposed to predict the dynamic behavior of a single-degree-of-freedom structure that is equipped with hybrid base isolation system. Hybrid base isolation system is composed of friction pendulum systems (FPS) and a magnetorheological (MR) damper. A neuro-fuzzy model is used to represent dynamic behavior of the MR damper. Fuzzy model of the MR damper is trained by ANFIS (Adaptive Neuro-Fuzzy Inference System) using various displacement, velocity, and voltage combinations that are obtained from a series of performance tests. Modelling of the FPS is carried out with a nonlinear analytical equation that is derived in this study and neuro-fuzzy training. Fuzzy logic controller is employed to control the command voltage that is sent to MR damper. The dynamic responses of experimental structure subjected to various earthquake excitations are compared with numerically simulated results using neuro-fuzzy modeling method. Numerical simulation using neuro-fuzzy models of the MR damper and FPS predict response of the hybrid base isolation system very well.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.10 s.340
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• pp.1-8
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• 2005

This Paper Proposes the algorithm for improving the Performance or symbol timing synchronization in hoc terrestrial DTV system. The Gardner algerian is used for symbol timing synchronization has good performance in multipath fading environment but degradation of performance is caused by jitter. Though the amount of jitter becomes more little as narrow bandwidth of loop Inter, convergence speed becomes slower. This paper propose the algorithm that averages out values of loop filter every certain time and gradually reduces the bandwidth of loop filter after estimating offset using this average for the high speed of convergence and reducing the met of jitter. The proposed algorithm has better performance with high speed of convergence and the amount of jitter than conventional method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.6
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• pp.77-82
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• 2005

This paper is proposed fuzzy neural network(FNN) and artificial neural network(ANN) based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed control and estimation of speed of induction motor using fuzzy and neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the experimental results to verify the effectiveness of the new method.