• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.11a
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• pp.179-181
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• 2009

In this paper, we propose an adaptive noise detection and modified Gaussian removal algorithm using local statistics for impulse noise. In order to determine constraints for noise detection, the local mean, variance, and maximum values are used. In addition, a modified Gaussian filter that integrates the tuning parameter to remove the detected noises. Experimental results show that our method is significantly better than a number of existing techniques in terms of image restoration and noise detection.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11a
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• pp.275-281
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• 2001

While basic input parameters for the performance and noise of axial fan are flow rate, pressure rise, rotating speed, and fan diameter, the geometric parameters of blade are sweep angle, solidity, and camber angle. The sweep angle does not affect fan performance much, but on fan noise significantly. Solidity and camber angle are very critical design parameters acting on the fan performance directly. The solidity and camber angle are closely related, therefore they have to be carefully determined for the low-noise and high-performance fan. In This paper, different design points are selceted and also geometric parameters are deliberately changed for the comparison of fan noise. As a result, at the same performance, the input rotational speed affects radiated noise more significantly than others. When solidity and camber angle are increased more than those by iDesignFan／sup TM／ program, more noise is experienced. The blade sweep method and blade numbers at same solidity are observed to results in different levels of performance and noise.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1149-1154
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• 2017

To obtain good recognition performance of speech recognition system under background noise, it is very important to select appropriate feature parameters of speech. The feature parameter used in this paper is Mel frequency cepstral coefficient (MFCC) with the human auditory characteristics applied to Wiener filter method. That is, the feature parameter proposed in this paper is a new method to extract the parameter of clean speech signal after removing background noise. The proposed method implements the speaker recognition by inputting the proposed modified MFCC feature parameter into a multi-layer perceptron network. In this experiments, the speaker independent recognition experiments were performed using the MFCC feature parameter of the 14th order. The average recognition rates of the speaker independent in the case of the noisy speech added white noise are 94.48%, which is an effective result. Comparing the proposed method with the existing methods, the performance of the proposed speaker recognition is improved by using the modified MFCC feature parameter.

• Journal of Integrative Natural Science
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• v.13 no.3
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• pp.92-96
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• 2020

Taguchi's robust parameter design is an approach to reduce the performance variation of quality characteristics in products and processes. In robust design, the signal-to-noise ratio (SN ratio) was used to find the optimum condition to minimize the variation of quality characteristics as much as possible and bring the average of quality characteristics closer to the target value. In this paper, we propose a simultaneous optimization method based on a linear model of the SN ratio as a method to find the optimal condition of the control factor in case of multi-characteristics. In addition, the proposed method and the existing method were compared and studied by taking actual cases.

• ETRI Journal
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• v.34 no.4
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• pp.641-644
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• 2012

The problem of sinusoidal parameter estimation at two channels with common frequency in white Gaussian noise is addressed. By making use of the linear prediction property, an iterative linear least squares (LLS) algorithm for accurate frequency estimation is devised. The remaining parameters are then determined according to the LLS fit with the use of the frequency estimate. It is proven that the variance of the frequency estimate achieves Cram$\acute{e}$r-Rao lower bound at sufficiently small noise conditions.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.6
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• pp.58-66
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• 1997

This paper proposes a multiresolution surface parameter estimation method for range images. Based on robust estimation of surface parameters, it approximates a patch to a planar surface in the locally adaptive window. Selection of resolution is made pixelwise by comparing a locally computed homogeneity measure with th eglobal threshold determined by te distribution of the approximation error. The proposed multiresolution surface parameter estimation method is applied to range image reconstruction. Computer simulation results with noisy rnag eimages contaminated by additive gaussian noise and impulse noise show that the proposed multiresolution reconstruction method well preserves step and roof edges compared with the conventional methods. Also the segmentation method based on the estimated surface parameters is shown to be robust to noise.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.475-480
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• 2023

When modeling a sensor system mathematically, we assume that the sensor noise is Gaussian and white to simplify the model. If this assumption fails, the performance of the sensor model-based controller or estimator degrades due to incorrect modeling. In practice, non-Gaussian or non-white noise sources often arise in many digital sensor systems. Additionally, the noise parameters of the sensor model are not known in advance without additional noise statistical information. Moreover, disturbances or high nonlinearities often cause unknown sensor modeling errors. To estimate the uncertain noise and model parameters of a sensor system, this paper proposes an iterative batch calibration method using data-driven machine learning. Our simulation results validate the calibration performance of the proposed approach.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.342-348
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• 2009

This paper introduces a novel denoising algorithm for the partial-discharge(PD) signals from power apparatuses. The developed algorithm includes three kinds of specific denoising sub-algorithms. The first sub-algorithm uses the fuzzy logic which classifies the noise types in the magnitude versus phase PD pattern. This sub-algorithm is especially effective in the rejection of the noise with high and constant magnitude. The second one is the method simply removing the pulses in the phase sections below the threshold count in the count versus phase pattern. This method is effective in removing the occasional high level noise pulses. The last denoising sub-algorithm uses the grouping characteristics of PD pulses in the 3D plot of the magnitude versus phase versus cycle. This special technique can remove the periodical noise pulses with varying magnitudes, which are very difficult to be removed by other denoising methods. Each of the sub-algorithm has different characteristic and shows different quality of the noise rejection. On that account, a parameter which numerically expresses the noise possessing degree of signal, is defined and evaluated. Using the parameter and above three sub-algorithms, an adaptive complex noise rejection algorithm for the on-line PD diagnosis system is developed. Proposed algorithm shows good performances in the various real PD signals measured from the power apparatuses in the Korean plants.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.2
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• pp.111-123
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• 2019

In this paper, we propose a method for defining the input termination for on-wafer-type device under test (DUT) measurement. Using the newly defined input termination and noise wave correlation matrix (NWCM) measurement method based on noise power ratio, the NWCM of the on-wafer-type DUT was measured. We demonstrate a noise measurement configuration that includes wafer probes and bias tees to measure the on-wafer DUT. The S-parameter of the adapter that combines the bias tee, probe, and a line terminated by open is required to define the input termination for on-wafer DUT measurement. To measure the S-parameter of the adapter, a 2-port S-parameter measurement method using 1-port measurement is introduced. Using the measured S-parameters, a method for defining the new input termination for on-wafer-type DUT measurement is applied. The proposed method involves the measurement of the NWCM of the chip with a 1.5 dB noise figure. The noise parameters of the chip were obtained using the measured NWCM. The results indicate that the obtained values of the noise parameters are similar to those mentioned on a datasheet for the chip. In addition, repeated measurements yielded similar results, thereby confirming the reliability of the measurements.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.11
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• pp.1633-1640
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• 2013

In this paper, noise parameters for high frequency modeling of dual-gate FET are extracted and analyzed. To extract thermal noise parameter of dual gate, noise characteristics are measured by changing input impedance of noise source using Tuner, and the influence of pad parasitic elements are subtracted using open and short dummy structure. Measured results indicated that the dual-gate FET is improved the noise figure by 0.2dB compared with conventional cascode structure FET at 5GHz, and it confirmed that the noise figure has dropped due to reduction of capacitances between the drain and source, gate and drain by simulation and analysis of small-signal parameters.