• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.474-477
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• 2002

Hearing loss caused by exposure to industrial machine noise results in devastating disability that is mostly preventable. And recent researches indicate that the noise may also induce hypertension and cardiovascular disease. In addition the sleep polygraph provides many indicators of sleep disturbance by the noise. In this paper we make an analysis on ball bearing machine noise, a kind of the industrial noise. The analysis of Its power spectrum is based on FFT(Fast Fourier Transform). And then the spectral results of the noise are compared with that of the spectrum for an auditory signal. The signal is measured from the pronunciation of two Koreans. Finally we suggest the most important stratagem to prevent the noise for worker's health and efficiency.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.774-779
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• 2004

This paper addresses the way in which we can find where impact noise sources are. Specifically, we have an interest in the case that the signal is embedded in noise. We propose a signal processing method that can identify impulsive sources’location. The method is robust with respect to noise; spatially distributed noise. This has been achieved by a beamforming method with regard to cepstrum domain is used. It is noteworthy that the cepstrum has the ability to detect periodic pulse signal in noise. Numerical simulation and experiments are performed to verify the method. Results show that the proposed technique is quite powerful for localizing the faults in noisy environments. The method also required less microphones than conventional beamforming method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.663-668
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• 2008

In this paper, we analyzed the error rate Performance of convolution coded 16 QAM signal in impulsive noise Environments. We used convolution rode and maximum ratio combining diversity for performance improvement. We analyzed the error rate performance of 16 QAM signal in implusive noise environments compared with gaussian noise environments. As a result of analysis, there is a BER segment where the efficiency of system does not improve until which limit to raise a signal power potential from impulsive noise environment when the signal power potential which goes over this limit is supplied, BER efficiency improve much more.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.4
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• pp.469-474
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• 2016

Abnormal sound was heard from a heat exchanger of condensate water system in a nuclear power plant, which was identified as impact sound of a loose part later. Nuclear power plants are normally equipped with loose part monitoring system for primary water system, but not for secondary water system. The abnormal sound was analyzed by using the impact signal-processing methodology based on the Hertz theory. The predicted results for impact location and size of the loose part showed good agreement with those of the actual loose part found during the overhaul period in the plant. So, this analysis methodology for the impact signal will be widely utilized for the primary and secondary side of the nuclear power plant.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.1306-1311
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• 2006

The root cause of abnormal sound from a heat exchanger of condensate water system in a nuclear power plant is investigated by using the impact signal-processing methodology based on the Hertz theory. The predicted results for the location of impact force and the loose part size meet good agreement with the identified materials during the overhaul period in the plant. Nuclear power plants have experienced several loose parts and the frequency of the loose part will be increased along the aging of the plants. So, this analysis methodology for the impact signal will be widely utilized for the primary and secondary side of the nuclear power plant.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.9
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• pp.1293-1298
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• 2013

This paper proposes a noise reduction algorithm using Wiener filter to remove the noise components from the noisy speech in order to improve the speech signal. The proposed algorithm first removes the noise spectrums of white noise from the noisy signal based on the noise reshaping and reduction method at each frame. And this algorithm enhances the speech signal using Wiener filter based on linear predictive coding analysis. In this experiment, experimental results of the proposed algorithm demonstrate using the speech and noise data by Japanese male speaker. Based on measuring the spectral distortion (SD) measure, experiments confirm that the proposed algorithm is effective for the speech by contaminated white noise. From the experiments, the maximum improvement in the output SD values was 4.94 dB better for white noise compared with former Wiener filter.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.22.1-22
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• 2001

In this paper we present Multiple-Channel Active Noise Control[ANC] system by employing Independent Component Analysis[ICA] and Adaptive Network Fuzzy Inference System[ANFIS]. ICA is widely used in signal processing and communication and it use prewhiting and appropriate choice of non-linearities, ICA can separate mixed signal. ANFIS controller is trained with the hybrid learning algorithm to optimize its parameters for adaptively canceling noise. This new method which minimizes a statistical dependency of mutual information(MI) in mixed low frequency noise signal and there is no need to secondary path modeling. The proposed implementations achieve more powerful and stable noise reduction than Filtered-X LMS algorithms which is needed for LTI assumption and precise secondary error

• Phonetics and Speech Sciences
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• v.4 no.4
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• pp.87-92
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• 2012

Speech enhancement aims to improve speech quality by removing background noise from noisy speech. Independent vector analysis is a type of frequency-domain independent component analysis method that is known to be free from the frequency bin permutation problem in the process of blind source separation from multi-channel inputs. This paper proposed a new method of microphone array based speech enhancement that combines independent vector analysis and beamforming techniques. Independent vector analysis is used to separate speech and noise components from multi-channel noisy speech, and delay-sum beamforming is used to determine the enhanced speech among the separated signals. To verify the effectiveness of the proposed method, experiments for computer simulated multi-channel noisy speech with various signal-to-noise ratios were carried out, and both PESQ and output signal-to-noise ratio were obtained as objective speech quality measures. Experimental results have shown that the proposed method is superior to the conventional microphone array based noise removal approach like GSC beamforming in the speech enhancement.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.1
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• pp.150-156
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• 2007

Noise is generated by several causes, when signal is processed. Hence, it generates error in the process of data transmission and decreases recognition ratio of image and speech data. Therefore, after eliminating those noises, a variety of methods for reconstructing the signal have been researched. Recently, wavelet transform which has time-frequency localization and is possible for multiresolution analysis is applied to many fields of technology. Then threshold-and correlation-based methods are proposed for removing noise. But, conventional methods accept a lot of noise as an edge and are impossible to remove the additive white Gaussian noise (AWGN) and the impulse noise at the same time. Therefore, in this paper we proposed new wavelet-based algorithm for reconstructing degraded signal by noise and compared it with conventional methods.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.648-653
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• 2018

Analysis of a specific wavelength band of a video signal is an old research field. However, studies on the analysis of signals having the characteristics of a specific wavelength band or the generation of samples have been conducted actively in recent years. In this paper, we analyze the signal of digital halftoned image. The interpretation of halftoning in terms of signals was first made by Ulichney. We have come to the view that the signal characteristics between pixels used for half toning are good for blue noise. Lau has developed a halftoning method that uses green-noise characteristics by using output-dependent feedback as an error diffusion method. Jho has developed a method for producing halftoned images using a reaction diffusion model. In this paper, we analyze reaction diffusion halftoning in terms of signal to show green noise and compare it with existing green noise half tone.