• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.4
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• pp.62-68
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• 2001

In this paper, As the wavelet transform has the properties of multi-resolution analysis and time-frequency domain localization, application of wavelet transform is used at partial discharge(PD) signal detected by electrical detection method to extract PD signal's various frequency component and its time domain. therefore we can analyzed PD signal's time-frequency domain simultaneously. On the other hand, using wavelet transform denoising process, included noise signal in detected PD signal is well eliminated. we can propose the true shine of PD signal.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1483-1484
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• 2015

부분방전 진단 기술 분야에서는 노이즈의 영향을 최소화할 수 있는 기법이 요구되고 있는 실정이다. 본 논문에서는 부분방전과 노이즈 신호 추출을 위한 로그앰프의 반응 속도차와 부분방전 신호특성을 비교하여 노이즈를 제거하는 SRPDA(Speed Related Partial Discharge Analysis)를 적용한 부분방전 진단 시스템의 개발 내용을 나타내었다. 또한 본 시스템은 광대역 방식의 장점인 빠른 검출 속도와 협대역 방식의 장점인 적은 노이즈의 영향의 장점을 취하는 동시에, 광대역 방식의 단점인 큰 노이즈 영향과 협대역 방식의 단점인 느린 진단 속도를 개선할 수 있는 하이브리드 방식을 채택하여, 기존 부분방전 진단 시스템의 신뢰성을 개선할 수 있을 것으로 사료된다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.125-126
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• 2010

채널의 다중경로를 통과한 신호들은 지연확산 영향으로 심하게 왜곡이 되거나 Inter-Symbol Interference(ISI)가 발생하므로 왜곡된 채널을 추정하여 보상해야 한다. 기존 iterative 채널 추정 방식에서는 채널 시간 지연 길이 밖으로 zero padding함으로 노이즈 성분을 제거하는 알고리즘이다. 반면에 본 논문은 채널 시간 지연 길이 안으로 있는 노이즈 성분까지 노이즈 제거 문턱 값 추정(noise elimination threshold estimation: NETE) 알고리즘을 사용하여 노이즈를 효과적으로 제거한다. 시뮬레이션 결과는 채널의 mean square error(MSE)를 통하여 제안된 기법을 적용할 경우 채널 추정 성능 개선이 나타남을 확인할 수 있었다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.17-23
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• 2010

In this paper, we present an adaptive noise removal method using local statistics and generalized Gaussian filter. we propose a generalized Gaussian filter for removing noise effectively and detecting noise adaptively using local statistics based human visual system. The simulation results show the objective and subjective capabilities of the proposed algorithm.

• Journal of IKEEE
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• v.18 no.4
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• pp.523-531
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• 2014

In this paper, a fast single image blind denoising algorithm is presented, where noise parameters are estimated by local statistics of an observed degraded image without a prior information about the additive noise. The estimated noise parameters are used to define the constraints on the noise detection which is coupled with the 1st-order Markov Random Field. In addition, an adaptive modified weighted Gaussian filter is introduced, where variable window sizes and weighting coefficients defined by the constraints are used to control the degree of the smoothness of the reconstructed image. The experimental results demonstrate the capability of the proposed algorithm. Please put the abstract of paper here.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.491-501
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• 2019

In side-channel analysis, which exploit physical leakage from a cryptographic device, deep learning based attack has been significantly interested in recent years. However, most of the state-of-the-art methods have been focused on classifying side-channel information in a profiled scenario where attackers can obtain label of training data. In this paper, we propose a new method based on deep learning to improve non-profiling side-channel attack such as Differential Power Analysis and Correlation Power Analysis. The proposed method is a signal preprocessing technique that reduces the noise in a trace by modifying Auto-Encoder framework to the context of side-channel analysis. Previous work on Denoising Auto-Encoder was trained through randomly added noise by an attacker. In this paper, the proposed model trains Auto-Encoder through the noise from real data using the noise-reduced-label. Also, the proposed method permits to perform non-profiled attack by training only a single neural network. We validate the performance of the noise reduction of the proposed method on real traces collected from ChipWhisperer board. We demonstrate that the proposed method outperforms classic preprocessing methods such as Principal Component Analysis and Linear Discriminant Analysis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.255-259
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• 2003

The noise canceling on gray image morphing with median filter is presented. The processing is that interpolate the image with B-spline, specify the distinctive points, cancel the noise by median filtering and perform the morphing. The experiment results denoise the blocking degradation as 20%, correct and present a soft morphing image processing.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.447-454
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• 2015

This paper proposes geographical name denoising by machine learning of event detection based on twitter. Recently, the increasing number of smart phone users are leading the growing user of SNS. Especially, the functions of short message (less than 140 words) and follow service make twitter has the power of conveying and diffusing the information more quickly. These characteristics and mobile optimised feature make twitter has fast information conveying speed, which can play a role of conveying disasters or events. Related research used the individuals of twitter user as the sensor of event detection to detect events that occur in reality. This research employed geographical name as the keyword by using the characteristic that an event occurs in a specific place. However, it ignored the denoising of relationship between geographical name and homograph, it became an important factor to lower the accuracy of event detection. In this paper, we used removing and forecasting, these two method to applied denoising technique. First after processing the filtering step by using noise related database building, we have determined the existence of geographical name by using the Naive Bayesian classification. Finally by using the experimental data, we earned the probability value of machine learning. On the basis of forecast technique which is proposed in this paper, the reliability of the need for denoising technique has turned out to be 89.6%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.306-309
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• 2006

SVDD(support vector data description)는 one-class 서포트 벡터 학습 방법론 중 하나로 비정상 물체에서 정상 데이터를 구분하기 위해서 특징 공간에서 정의된 구를 이용하는 전략을 쓰는 방법론이다. 본 논문에서는 SVDD를 이용해서 노이즈가 섞인 비정상 데이터를 노이즈가 제거된 정상 데이터로 복원하는 방법에 대해서 논한다. 그리고 저해상도의 이미지를 고해상도의 이미지로 복원함으로써 본 논문의 방법론이 어떻게 실용적으로 적용되는지에 대해서 다룬다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1088-1097
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• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.