• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.881-885
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• 2001

The study about a drainpipe of water closet was performed as reduction of drainage noise for water closet. The Drainage noise is composed two characteristics. One is Low frequency noise and the other is high frequency noise. Low frequency noise is dominant in the first stage and high frequency noise is dominant in the last stage. This is due to water splashing and that is caused by formation of chamber pot. In this experiment, for the purpose for reducing the noise, we choose the hollow rubber pipe element. As a result, we reduced drainage noise about 11㏈A.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.479-481
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• 2006

This paper describes a 2.5V, 320MHz low-noise and low-power Phase Locked Loop(PLL) using a noise-rejected Voltage Controlled ring Oscillator(VCO) fabricated in a TSMC 0.25um CMOS technology. In order to improve the power consumption and oscillation frequency of the PLL, The VCO consist of three-stage fully differential delay cells that can obtain the characteristic of high speed, low power and low phase noise. The VCO operates at 7MHz -670MHz. The oscillator consumes l.58mA from a 320MHz frequency and 2.5V supply. When the PLL with fully-differential ring VCO is locked 320MHz, the jitter and phase noise measured 26ps (rms), 157ps (p-p) and -97.09dB at 100kHz offset. We introduce and analysis the conditions in which ring VCO can oscillate for low-power operation.

• Journal of Environmental Impact Assessment
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• v.20 no.4
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• pp.425-434
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• 2011

The energy produced by wind power generation is a clean energy product because it is acquired by using renewable resource. Wind power plants("wind farms), in Korea, have been built and operated as 345.6MW facilities from 2001 until now 2009. Nevertheless, environmental issues regarding construction of wind power plants have arisen. accordingly it is time to consider the environmental and social issues of wind power in accordance with the government's policy objectives of increased wind power production. In this study, we investigated the influence that noise and low frequency noise caused by Wind power plants have on neighborhood and residents. We also sought solutions to these issues. In order to analyze the issues of wind power facilities, we compared and examined precedents and the solutions for noise and low frequency noise in Europe, the United states and Japan. We intended to examine the influences of wind power facilities and propose alternative in dealing with these issues.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.861-864
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• 2005

Performing accurate and repeatable low frequency noise measurement is critical for modeling and simulation of flicker noise. Through the accurate and repeatable on-wafer measurement, low frequency noise characteristics of the 0.18 ${\mu}m$ n-MOSFETs are discussed. And on-wafer flicker noise measurement system is presented. The on-wafer measurement system consists of cascade probe station, low noise current amplifier (SR570), and dynamic signal analyzer (HP35670A).

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.90-95
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• 2014

In order to investigate low frequency swishing noise of wind turbines, singularity in circular motion with large radius is introduced as a noise source model. By employing Lowson's acoustic analogy, simple exact solution is obtained. The solution shows that time histories of acoustic pressure at receiver points varied significantly according to receiver's directional location, even when the retarded time distributions are similar. However, the corresponding spectra of sound pressure for the receiver locations where the retarded time distributions are almost the same are not significantly different. It can be inferred from these results that the time-averaged sound pressure spectra which cannot take into account the detailed difference in the time-variation of wind turbine noise may not represent the sound quality of wind turbines due to its swishing.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.163-166
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• 1999

In this paper, frequency synthesizer that can be used in IMT-2000 was designed and fabricated using dual loop PLL(Phase Locked Loop). For improving phase noise characteristic Voltage Controlled Oscillator was fabricated using coaxial resonator and eliminated frequency divider using SPD as phase detector and increased open loop gain. Fabricated frequency synthesizer had 1.82㎓ center frequency, 160MHz tuning range and -119.73㏈c/Hz low phase noise characteristic.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.1004-1019
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• 2021

To solve the problems of the low image contrast, fuzzy edge details and edge details missing in noisy image fusion, this study proposes a noisy infrared and visible light image fusion algorithm based on non-subsample contourlet transform (NSCT) and an improved bilateral filter, which uses NSCT to decompose an image into a low-frequency component and high-frequency component. High-frequency noise and edge information are mainly distributed in the high-frequency component, and the improved bilateral filtering method is used to process the high-frequency component of two images, filtering the noise of the images and calculating the image detail of the infrared image's high-frequency component. It can extract the edge details of the infrared image and visible image as much as possible by superimposing the high-frequency component of infrared image and visible image. At the same time, edge information is enhanced and the visual effect is clearer. For the fusion rule of low-frequency coefficient, the local area standard variance coefficient method is adopted. At last, we decompose the high- and low-frequency coefficient to obtain the fusion image according to the inverse transformation of NSCT. The fusion results show that the edge, contour, texture and other details are maintained and enhanced while the noise is filtered, and the fusion image with a clear edge is obtained. The algorithm could better filter noise and obtain clear fused images in noisy infrared and visible light image fusion.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.82-87
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• 2020

The comprehensive and physics-based compact noise models for advanced CMOS devices were presented. The models incorporate important physical effects in nanoscale MOSFETs, such as the low frequency correlation effect between the drain and the gate, the trap-related phenomena, and QM (quantum mechanical) effects in the inversion layer. The drain current noise model was improved by including the tunneling assisted-thermally activated process, the realistic trap distribution, the parasitic resistance, and mobility degradation. The expression of correlation coefficient was analytically described, enabling the overall noise performance to be evaluated. With the consideration of QM effects, the comprehensive low frequency noise performance was simulated over the entire bias range.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.30-35
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• 2007

Various acoustic tests were carried out to investigate the acoustic performance of diesel engine exhaust silencers. In order to consider flow effects, test equipment composed of fan, duct and silencer was set up. Using the test equipment, insertion loss tests were carried out to improve the performance in the low-frequency ranges. Through a series of tests, the fact that array resonators may be effective in the low-frequency noise has been verified. Consequently, the hybrid-type silencer which is the combination of reflective silencer with array resonators and conventional absorptive silencer were proposed and its high acoustic performance in the low-frequency range has also been verified.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.88-93
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• 2020

Based on the capture-emission energy (CEE) maps of CMOS devices, a physics-informed machine learning model for the bias temperature instability (BTI)-induced threshold voltage shifts and low frequency noise is presented. In order to incorporate physics theories into the machine learning model, the integration of artificial neural network (IANN) is employed for the computation of the threshold voltage shifts and low frequency noise. The model combines the computational efficiency of IANN with the optimal estimation of Gaussian mixture model (GMM) with soft clustering. It enables full lifetime prediction of BTI under various stress and recovery conditions and provides accurate prediction of the dynamic behavior of the original measured data.