• Journal of computational fluids engineering
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• v.22 no.1
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• pp.103-109
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• 2017

The aerodynamic noise reduction of railway traction motor is required to satisfy new enhanced Korean noise regulations for a train. This paper is the study result on a noise reduction of a railway traction motor using Lattice Boltzmann Method(LBM). To verify the reliability of numerical analysis, the noise performance of the base model evaluated using LBM, and calculated result was compared with the experimental data. In addition, main noise sources were selected to design parameters through analyzing the flow field of the base model. Based on the noise sources analysis result, a design improvement model of traction motor for this study was derived to reduce the noise. The performance of a design improvement model was evaluated by applying a validated numerical scheme. As a result, it was confirmed that the noise was reduced due to the suppression of the internal turbulent flow components.

• Journal of Korea Multimedia Society
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• v.5 no.2
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• pp.147-157
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• 2002

Noise in image sequences is visually offensive and may mask important image detail. In addition to degradation of visual quality, the noise pattern increases the entropy of the image, and thus hinders effective compression. This paper proposes a spatial and a temporal joint filters to reduce the noise by jointly connecting two adaptive noise reducers with different characteristics, and we also propose an IIR-type 3D noise reduction litter scheme connecting the spatial and the temporal joint filters. The proposed 3D IIR filter not only strongly removes noise in uniform image regions while preserving edges and details but also effectively suppresses temporal flicker caused by noise. Experimental results show that the proposed scheme improves subjective quality as well as objective quality as compared with the various noise filtering techniques.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.917-917
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• 2003

The auditory experiments based on the subjective annoyance responses were undertaken for the establishment of the adverb modifiers of the heavy-weight impact noises. The standard heavy weight impact noise, impact ball noise and adult walking noise were recorded by dummy head at a newly-built apartment and were presented to the subjects by headphones. The levels of the three impact noises were varied from 30 to 60㏈(A) and the subjects matched one of the adverb modifiers to each level of the noise sources. As a result, seven scale modifiers were established and the intervals between the modifiers were found as equal. In addition, it was found that the lower annoyance noise limits for the heavyweight impact, impact ball and walking were 40-45㏈ (L$\sub$I, Fmax. AW), which is 6㏈ lower than in the previous study. The background noise level was as low as 21㏈(A) in the test booth, therefore, the testing conditions need to be concerned for evaluation of floor impact noise.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.189-195
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• 2003

This paper represents an identification procedure for leading mechanism of a car A/V system which is composed of a DC motor and a set of plastic gears. In addition, we studied dominant noise source of rattle noise generated by external forced vibration as a car drives. we made a dynamometer to produce stationary operation on loading mechanism of A/V system because noise generated by actual loading mechanism is non-stationary signal. operating the dynamometer setup at various motor speeds, sound pressure spectra are measured and the results are analyzed. its dominant noise source is also identified by using a sound Intensity technique. we made use of multi-dimensional spectral analysis to rind a dominant rattle noise. this method is so useful to eliminate coherence between vibration sources and helps us obtain coherent output spectrum of individual vibration source which make a rattle noise.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.397-400
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• 2016

In digital images, the addition due to noise occurs in the process of obtaining, saving, and transmitting. For examples of noise, there are salt and pepper noise, Gaussian noise, and composition noise where various noises are mixed. Existing filters have insufficient noise removal characteristics because it uses single filters in composite noise environment. Therefore the study suggested a switching filter that processes with special weighted value and median filter according to local mask salt and pepper noise density when central pixel is damaged by salt and pepper noise, and processes by applying weighted values differently according to standard deviation of local mask when damaged by Gaussian noise.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.21-34
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• 2024

In medical images such as MRI(Magnetic Resonance Imaging) and CT(Computed Tomography) images, noise removal has a significant impact on the performance of medical imaging systems. Recently, the introduction of deep learning in image processing technology has improved the performance of noise removal methods. However, there is a limit to removing only noise while preserving details in the image domain. In this paper, we propose a wavelet transform-based CNN(Convolutional Neural Network) model, namely the WT-DnCNN(Wavelet Transform-Denoising Convolutional Neural Network) model, to improve noise removal performance. This model first removes noise by dividing the noisy image into frequency bands using wavelet transform, and then applies the existing DnCNN model to the corresponding frequency bands to finally remove noise. In order to evaluate the performance of the WT-DnCNN model proposed in this paper, experiments were conducted on MRI and CT images damaged by various noises, namely Gaussian noise, Poisson noise, and speckle noise. The performance experiment results show that the WT-DnCNN model is superior to the traditional filter, i.e., the BM3D(Block-Matching and 3D Filtering) filter, as well as the existing deep learning models, DnCNN and CDAE(Convolution Denoising AutoEncoder) model in qualitative comparison, and in quantitative comparison, the PSNR(Peak Signal-to-Noise Ratio) and SSIM(Structural Similarity Index Measure) values were 36~43 and 0.93~0.98 for MRI images and 38~43 and 0.95~0.98 for CT images, respectively. In addition, in the comparison of the execution speed of the models, the DnCNN model was much less than the BM3D model, but it took a long time due to the addition of the wavelet transform in the comparison with the DnCNN model.

• Corrosion Science and Technology
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• v.12 no.5
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• pp.220-226
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• 2013

The uncertainty in statistical noise resistance measurement was evaluated for a type 316 stainless steel in NaCl solutions at room temperature. Sensitivity coefficients were determined for measurands or variables such as NaCl concentration, pH, solution temperature, surface roughness, inert gas flow rate and bias potential amplitude. The coefficients were larger for the variables such as NaCl concentration, pH, inert gas flow rate and solution temperature, and they were the major factors increasing the combined standard uncertainty of noise resistance. However, the contribution to the uncertainty in noise resistance measurement from the above variables was remarkably low compared to that from repeated measurements of noise resistance, and thus, it is difficult to lower the uncertainty in noise resistance measurement significantly by lowering the uncertainties related with NaCl concentration, pH, inert gas flow rate and solution temperature. In addition, the uncertainty in noise resistance measurement was high amounting to 17.3 % of the mean, indicating that the reliability in measurement of noise resistance is low.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.3
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• pp.395-403
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• 2014

This paper presents a new approach to improve the signal to noise ratio (SNR) for local seismic disaster preventing system in densely populated area. The seismic data measured in the local site includes various sensing noises (offset or measurement noise) and man-made/natural noises (road and rail traffic noise, rotating or hammering machinery noise, human activity noise such as walking and running, wind/atmospheric pressure-generated noise, etc.). These additive noises are different in time and frequency characters. The proposed method uses 3-stages processing to reduce these different additive noises. In the first stage, misalignment offset noise are diminished by time average processing, and then the second and third stages, coherent/incoherent noises such as man-made/natural noises are suppressed by array stacking. In addition, we derived the theoretical equation of the SNR gain improved by the proposed method. To evaluate the performance of the proposed method, computer simulations were performed with real seismic data and test equipment generated data as the input.

• 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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• 1990.10a
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• pp.53-58
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• 1990

The use of spreadsheet packages for solving noise control problems has been cited by several authors, eg Saha[1] and Thornton[2]. The effectiveness of using spreadsheet packages compared with the traditional computer programs written in high level languages was demonstrated when applied to relatively simple problems, such as the selection of hearing protectors or the prediction of noise equation which includes logarithmic additions at most represents the physics of the problem. The simplicity of the governing equation together with the requirement to handle a vast amount of data are considered to be the major reasons for noise control engineers to use spreadsheet packages. Although shipboard noise prediction seems to be very complicated, the calculation procedure itself is, in essence, identical especially true for prediction methods based on empirical formulae[3,4], ie the procedure that consists of the three basic elements, ie source, path and receiver. This paper discusses the application of spreadsheet package LOTUS 1-2-3 to shipboard noise prediction problems. A utility program of the package is written using macro functions and is shown to be especially useful for noise control engineers who are unfamiliar with spreadsheet packages. In addition, a new type of empirical formula, to estimate structureborne noise transmission loss, is proposed.