• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.4
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• pp.328-335
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• 2017

In this paper, we propose an efficient method to remove noise component contained in high resolution range profile(HRRP) to improve target identification performance. The proposed method can effectively eliminate the noise component using both the statistical characteristics of the noise component and EMD algorithm. Experimental results show that the proposed method can substantially improve the identification capability, removing the noise component effectively.

• Journal of the Korea society of information convergence
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• v.6 no.2
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• pp.45-49
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• 2013

Electrocardiogram(ECG) signal noise as well as conducting other bio-signal measurement were generated. It was intened to enhance the accuracy of cadiac disease diagnosis with removing signal white-noise. Sampling signal was made with generating white-noise. The noise were removed using wavelet transforms and thresholding. Removed noise were compared numerical using SNR(signal to noise ratio). The results compared SNR showed that SURE method was 5.931, 4.9301 in 3, 5dB noise, uninversal was 3.6590, 1.9698 in 7, 9dB noise. De-noising by Thresholding removed noise effectively. ECG signal is expected to improve the accuracy of cadiac desease dianosis.

• Bulletin of the Korean Chemical Society
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• v.32 no.10
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• pp.3821-3823
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• 2011

• Journal of Satellite, Information and Communications
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• v.10 no.4
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• pp.58-63
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• 2015

Due to GPS signal's vulnerability of jamming attack, various enhancement techniques are needed. Among variety of techniques, we focused on GPS receiver's anti-jamming techniques. There are many anti-jamming methods at GPS receivers which include filtering methods in time domain, frequency domain and space domain. However, these methods are ineffective to signals, which include both jamming and noise. To solve the problem, this paper proposes a jamming separation scheme by using a BSS method in a jamming environment. As separated GPS signals include noise after the jamming separation method, it is difficult to receive accurate GPS signals. For this reason, this paper also proposes a wavelet de-noising method to effectively eliminate noise. Experimental results of this paper are based on a real field test data of an integrated GPS/QZSS/Wi-Fi positioning system. At the end, the simulation result demonstrates its superiority by showing improved positioning accuracy.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.404-406
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• 2001

This paper is detecting a partial discharge(PD) using antenna. The wavelet transform is applied for the analysis of PD pulse signal. It is difficult to identify PD signal using electromagnetic waves detected by antenna. And so we can removed noise of PD signal using wavelet de-noising method.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05b
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• pp.705-710
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• 1998

We describe a multigrid wavelet-based natural pixel (WNP) method for image reconstruction in emission computed tomography (ECT). The ECT is used to identify the tagged radioactive material's position in the body for detection of abnormal tissue such as tumor or cancer, as in SPECT and PET. With ECT methodology in parallel beam mode, we formulate a matrix-based reconstruction method for radionuclide sources in the human body. The resulting matrix for a practical problem is very large and nearly singular. To overcome this ill-conditioning, wavelet transform is considered in this study. Wavelets have inherent de-noising and multiscale resolution properties. Therefore, the multigrid wavelet-based natural pixel (WNP) method is very efficient to reconstruct image from projection data that is noisy and incomplete. We test this multigrid wavelet natural pixel (WNP) reconstruction method with the MCNP generated projection data for diagnosis of the simulated cancerous tumor.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.7
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• pp.328-336
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• 2005

Power quality has become concern both utilities and their customers with wide spread use of electronic and power electronic equipment. The poor quality of electric power causes malfunctions, instabilities and shorter lifetime of the load. In power system operation, power system disturbances such as faults, overvoltage, capacitor switching transients, harmonic distortion and impulses affects power quality. For diagnosing power quality problem, the causes of the disturbances should be understood before appropriate actions can be taken. In this paper we present a new approach to detect, localize, and investigate the feasibility of classifying various types of power quality disturbances. This paper deals with the use of a multi-resolution analysis by a discrete wavelet transform to detect power system disturbances such as interruption, sag, swell, transients, etc. We also proposed do-noising and threshold technique to detect power system disturbances in a noisy environment. To find the better mother wavelet for detecting disturbances, we compared the performance of the disturbance detection with the several mother wavelets such as Daubechies, Symlets, Coiflets and Biorthogonals wavelets. In our analysis, we adopt db4 wavelet as mother wavelet because it shows better results for detecting several disturbances than other mother wavelets. To show the effectiveness of the proposed method, a various case studies are simulated for the example system which is constructed by using PSCAD/EMTDC. From the simulation results. proposed method detects time Points of the start and end time of the disturbances.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.888-893
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• 2021

The International Atomic Energy Agency has developed a tomographic imaging system for accomplishing the total fuel rod-by-rod verification time of fuel assemblies within the order of 1-2 h, however, there are still limitations for some fuel types. The aim of this study is to develop a deep learning-based denoising process resulting in increasing the tomographic image acquisition speed of fuel assembly compared to the conventional techniques. Convolutional AutoEncoder (CAE) was employed for denoising the low-quality images reconstructed by filtered back-projection (FBP) algorithm. The image data set was constructed by the Monte Carlo method with the FBP and ground truth (GT) images for 511 patterns of missing fuel rods. The de-noising performance of the CAE model was evaluated by comparing the pixel-by-pixel subtracted images between the GT and FBP images and the GT and CAE images; the average differences of the pixel values for the sample image 1, 2, and 3 were 7.7%, 28.0% and 44.7% for the FBP images, and 0.5%, 1.4% and 1.9% for the predicted image, respectively. Even for the FBP images not discriminable the source patterns, the CAE model could successfully estimate the patterns similarly with the GT image.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.309-314
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• 2003

Impact Echo method has been successful in detecting a variety of defects in concrete structure. This study has the objectives to show important aspects of applying the Discrete Wavelet Transform(DWT) to signal processing of Modified Impact Echo(ModIE) Measurement systems and to the understanding of the seismic wave propagation. The data of ModIE were processed by DWT and compared with the results of conventional ModIE Analysis. Although it is inconsistent in the evaluated thickness of concrete lining, the DWT provides the features of separation, synthesis and de-noising in the original signal. The application of technique by wavelet was explained numerically with ABAQUS and performed experimentally with a real scale model in this work. Further works on the possible ways for creating new mother wavelet are specially needed for the enhancement of seismic signal analysis.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.221-229
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• 2004

The Continuous Wavelets Transform project signal f(t) to "Time-scale"plan utilizing the time varied function which called "wavelets". This Transformation permit to analyze scale time dependence of signal f(t) thus the local or global scale properties can be extracted. Moreover, the signal f(t) can be reconstructed stably by utilizing the Inverse Continuous Wavelets Transform. In this paper, the EEG signal is analyzed by wavelets coherence method and the De-noising procedure is represented.