• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.1
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• pp.81-90
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• 2002

This paper presents an Implementation of digital high-performance position sensorless control system of Reluctance Synchronous Motor(RSM) drives with Direct Torque Control(DTC). The system consists of stator flux observer, speed and torque estimator, two digital hysteresis controllers, an optimal switching look-up table, Insulated Gate Bipolar Transistor(IGBT) voltage source inverter, and TMS320C31 DSP board. The stator flux observer Is based on the combined voltage and current model with stator flux feedback adaptive control of which inputs are current and voltage sensed on motor terminal for wide speed range. In order to prove the suggested sensorless control algorithm for industrial field application, we have some simulation and actual experiment at low and high speed range. The developed high-performance speed control by fully digital system are shown a good response characteristic of control results and high performance features using 1.0[kW] RSM having 2.57 reluctance ratio of $L_d/L_q$.

• Journal of Power Electronics
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• v.13 no.3
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• pp.369-380
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• 2013

Direct torque control based on space vector modulation (SVM-DTC) protects the DTC transient merits. Furthermore, it creates better quality steady-state performance in a wide speed range. The modified method of DTC using SVM improves the electrical magnitudes of asynchronous machines, such as minimizing the stator current distortions, the stator flux with electromagnetic torque without ripple, the fast response of the rotor speed, and the constant switching frequency. In this paper, the proposed method is based on two new control strategies for direct torque control with space vector modulation. First, fuzzy logic control is used instead of the PI torque and a PI flux controller to minimizing the torque error and to achieve a constant switching frequency. The voltages in the direct and quadratic reference frame ($V_d$, $V_q$) are achieved by fuzzy logic control. In this scheme, the switching capability of the inverter is fully utilized, which improves the system performance. Second, the close loop of stator flux estimation based on the voltage model and a low pass filter is used to counteract the drawbacks in the open loop of the stator flux such as the problems saturation and dc drift. The response of this new control strategy is compared with DTC-SVM. The experimental and simulation results demonstrate that the proposed control topology outperforms the conventional DTC-SVM in terms of system robustness and eliminating the bad outcome of dc-offset.

• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.3
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• pp.275-284
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• 1999

Unbalanced source voltage in the 3-phase power system is decomposed into positive, negative and zero sequence c components. Also, assuming there is no neutral path in the system, the zero sequence component is not shown on the l load side. Therefore, in the unbalanced power system without neutral path. it is possible to provide balanced voltage to t the load side by compensating negative sequence component and also to regulate the voltage amplitude by controlling t the positive sequence component. In addition, the symmetrical components due to voltage unbalance can be effectively d detected on the synchronous reference frame by using dlongleftarrowq transformation. In this paper, an algorithm not only c compensating unbalanced source voltage by canceling the negative sequence component on the synchronous reference f frame but also maintaining load voltages constantly is proposed. Also a novel method for phase angle detection s synchronized by positive sequence component under unbalanced source voltage is suggested and this detected phase a angle is used for d-q transformation. The performances and characteristics of the proposed compensating system are a analyzed by simulation and verified through experimental results.

• The Transactions of the Korean Institute of Power Electronics
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• v.12 no.1
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• pp.65-73
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• 2007

This paper presents an on-line correction method of current measurement errors for a pure-integration-based flux estimation down to 1-Hz stator frequency. An observer-based approach is taken as one possible solution of eliminating the dc offset and the negative sequence component of unbalanced gains in the synchronous coordinate. At the same time, the positive sequence component estimation is performed by creating an error signal between a motor model reference and an estimated q-axis rotor flux established by a permanent magnet (PM) in the synchronous coordinate. The compensator utilizes a PI controller that controls the error signal to zero. The proposed technique further contains a residual error compensator to completely eliminate miscellaneous disturbances in the estimated flux. The developed algorithm has been implemented on a 1.1-kW permanent magnet synchronous motor (PMSM) drive to confirm the effectiveness of the proposed scheme.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.2
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• pp.73-78
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• 2020

In this study, a novel reactive power control scheme is proposed to supply stable reactive power to the distribution line by compensating a ripple voltage of DC link. In a single-phase system, a magnitude of second harmonic is inevitably generated in the DC link voltage, and this phenomenon is further increased when the capacity of DC link capacitor decreases. Reactive power control was performed by controlling the d-axis current in the virtual synchronous reference frame, and the voltage control for maintaining the DC link voltage was implemented through the q-axis current control. The proposed method for compensating the ripple voltage was classified into three parts, which consist of the extraction unit of DC link voltage, high pass filter (HPF), and time delay unit. HPF removes an offset component of DC link voltage extracted from integral, and a time delay unit compensates the phase leading effect due to the HPF. The compensated DC voltage is used as feedback component of voltage control loop to supply stable reactive power. The performance of the proposed algorithm was verified through simulation and experiments. At DC link capacitance of 375 uF, the magnitude of ripple voltage decreased to 8 Vpp from 74 Vpp in the voltage control loop, and the total harmonic distortion of the current was improved.

• Journal of the korean society of oceanography
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• v.32 no.4
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• pp.163-170
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• 1997

Some CMP gathers acquired from shallow marine seismic reflection survey in offshore Korea do not show the hyperbolic trend of moveout. It originated from so-called swell effect of source and streamer, which are towed under rough sea surface during the data acquisition. The observed time deviations of NMO-corrected traces can be entirely ascribed to the swell effect. To correct these time deviations, a residual statics is introduced using Genetic Algorithms (GA) into the swell correction. A new class of global optimization methods known as GA has recently been developed in the field of Artificial Intelligence and has a resemblance with the genetic evolution of biological systems. The basic idea in using GA as an optimization method is to represent a population of possible solutions or models in a chromosome-type encoding and manipulate these encoded models through simulated reproduction, crossover and mutation. GA parameters used in this paper are as follows: population size Q=40, probability of multiple-point crossover P$_c$=0.6, linear relationship of mutation probability P$_m$ from 0.002 to 0.004, and gray code representation are adopted. The number of the model participating in tournament selection (nt) is 3, and the number of expected copies desired for the best population member in the scaling of fitness is 1.5. With above parameters, an optimization run was iterated for 101 generations. The combination of above parameters are found to be optimal for the convergence of the algorithm. The resulting reflection events in every NMO-corrected CMP gather show good alignment and enhanced quality stack section.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.9
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• pp.1654-1665
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• 2008

This paper presents bit splitting methods to apply multilevel modulation to iterative codes such as turbo code, low density parity check code and turbo product code. Log-likelihood ratio method splits multilevel symbols to soft decision symbols using the received in-phase and quadrature component based on Gaussian approximation. However it is too complicate to calculate and to implement hardware due to exponential and logarithm calculation. Therefore this paper presents Euclidean, MAX, sector and center focusing method to reduce the high complexity of LLR method. Also, this paper proposes optimal soft symbol split method for three kind of iterative codes. Futhermore, 16-APSK modulator method with double ring structure for applying DVB-S2 system and 16-QAM modulator method with lattice structure for T-DMB system are also analyzed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.76-86
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• 2023

Radars used by air-crafts have two important characteristics; First, they should have a real-time signal processing system finishing signal processing before deadline while getting and processing successive in-phase and quadrature data. Second, they can cover a lot of modes including A2A(Air to Air), A2G(Air to Gound), A2S(Air to Sea), and Ground Map(GM). So the structure of radar signal processing SWs in modern airborne radars are becoming more complicate. Also, the implementation of radar signal processing SW needs to reuse common code blocks between other modes for efficiency or change some of the code blocks into alternative algorithm blocks. These are the reason why the radar signal processing SW framework suggested in this paper is taking advantage of modular programming. This paper proposes an modular framework applicable on the airborne radar signal processing SW maintaining the real-time characteristic using the signal processing procedures for A2G/A2S as examples.

• Journal of Biomedical Engineering Research
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• v.43 no.4
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• pp.185-192
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• 2022

Early detection of mild cognitive impairment can help prevent the progression of dementia. The purpose of this study was to design and validate a machine learning model that automatically differential diagnosed patients with mild cognitive impairment and identified cognitive decline characteristics compared to a control group with normal cognition using resting-state quantitative electroencephalogram (qEEG) with eyes closed. In the first step, a rectified signal was obtained through a preprocessing process that receives a quantitative EEG signal as an input and removes noise through a filter and independent component analysis (ICA). Frequency analysis and non-linear features were extracted from the rectified signal, and the 3067 extracted features were used as input of a linear support vector machine (SVM), a representative algorithm among machine learning algorithms, and classified into mild cognitive impairment patients and normal cognitive adults. As a result of classification analysis of 58 normal cognitive group and 80 patients in mild cognitive impairment, the accuracy of SVM was 86.2%. In patients with mild cognitive impairment, alpha band power was decreased in the frontal lobe, and high beta band power was increased in the frontal lobe compared to the normal cognitive group. Also, the gamma band power of the occipital-parietal lobe was decreased in mild cognitive impairment. These results represented that quantitative EEG can be used as a meaningful biomarker to discriminate cognitive decline.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.518-523
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• 2017

This paper proposes a novel denoising method based on non-local(NL) means. The NL-means algorithm is effective for removing an additive Gaussian noise, but the denoising parameter should be controlled depending on the noise level for proper noise elimination. Therefore, the proposed method optimizes the denoising parameter according to the noise levels. The proposed method consists of two processes: off-line and on-line. In the off-line process, the relations between the noise level and the denoising parameter of the NL-means filter are analyzed. For a given noise level, the various denoising parameters are applied to the NL-means algorithm, and then the qualities of resulting images are quantified using a structural similarity index(SSIM). The parameter with the highest SSIM is chosen as the optimal denoising parameter for the given noise level. In the on-line process, we estimate the noise level for a given noisy image and select the optimal denoising parameter according to the estimated noise level. Finally, NL-means filtering is performed using the selected denoising parameter. As shown in the experimental results, the proposed method accurately estimated the noise level and effectively eliminated noise for various noise levels. The accuracy of noise estimation is 90.0% and the highest Peak Signal-to-noise ratio(PSNR), SSIM value.