• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.8 s.173
• /
• pp.143-150
• /
• 2005

To verify applicability of multi-dimensional spectral analysis (MDSA) fur noise source identification two different approaches which are frequency response and coherent function have been investigated. The coherence function approach appears able to separate the correlated system when the noise sources were coherent. In this study, we identify contribution of structure-borne-noise of vehicle HVAC system using MDSA method. Firstly, to identify the applicability of MDSA method, 4-inputs of vehicle HVAC system were the signals measured by accelerometers attached on the selected noise sources which were composed of blower, evaporator, heater and duct. While 1-output which was driver's position sound was the SPL signals measured by a remote microphone, when the blower motor was operating. We identify efficiency of systems modeled with four Inputs/single output through ordinary coherence function (OCF) and partial coherence function (PCF). As a result of experiment, the blower accounted for $62-88\%$ of the overall level of sound energy density. Also, according to the analysis of acoustic signal and vibration signals measurement, an investigation of the noise source identification in the vehicle HVAC is presented. With the sound intensity method, the major sources of the vehicle HVAC radiation are verified. Also the method of improving the noise reduction is proposed by attaching damping patch access to blower motor and noise reduction is verified.

• The Journal of the Acoustical Society of Korea
• /
• v.26 no.3E
• /
• pp.87-96
• /
• 2007

In this paper we propose a new noise reduction algorithm for stationary and nonstationary noisy environments. Our algorithm classifies the speech and noise signal contributions in time-frequency bins, and is not based on a spectral algorithm or a minimum statistics approach. It relies on calculating the ratio of the standard deviation of the noisy power spectrum in time-frequency bins to its normalized time-frequency average. We show that good quality can be achieved for enhancement speech signal by choosing appropriate values for ${\delta}_t\;and\;{\delta}_f$. The proposed method greatly reduces the noise while providing enhanced speech with lower residual noise and somewhat higher mean opinion score (MOS), background intrusiveness (BAK) and signal distortion (SIG) scores than conventional methods.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2009.10a
• /
• pp.240-241
• /
• 2009

The method of the reduction of the duct noise can be classified by the method of passive control and the method of active control. However, the passive control method has a demerit to reduce the effect of noise reduction at low frequency (below 500Hz) range and to be limited by a space. Whereas, the active control method can overcome the demerit of passive control method. The algorithm of active control is mostly used the Least-Mean-Square (LMS) algorithm because the LMS algorithm can easily obtain the complex transfer function in real-time. Especially, When the Filtered-X LMS (FXLMS) algorithm is applied to an ANC system.

• Journal of the Korean Society of Radiology
• /
• v.17 no.5
• /
• pp.709-715
• /
• 2023

In this study, the performance evaluation of image quality for noise reduction was implemented using the U-net deep learning architecture in computed tomography (CT) images. In order to generate input data, the Gaussian noise was applied to ground truth (GT) data, and datasets were consisted of 8:1:1 ratio of train, validation, and test sets among 1300 CT images. The Adagrad, Adam, and AdamW were used as optimizer function, and 10, 50 and 100 times for number of epochs were applied. In addition, learning rates of 0.01, 0.001, and 0.0001 were applied using the U-net deep learning model to compare the output image quality. To analyze the quantitative values, the peak signal to noise ratio (PSNR) and coefficient of variation (COV) were calculated. Based on the results, deep learning model was useful for noise reduction. We suggested that optimized hyper parameters for noise reduction in CT images were AdamW optimizer function, 100 times number of epochs and 0.0001 learning rates.

• Journal of Radiation Protection and Research
• /
• v.46 no.4
• /
• pp.160-169
• /
• 2021

Background: Dual-energy X-ray images (DEI) can distinguish or improve materials of interest in a two-dimensional radiographic image, by combining two images obtained from separate low and high energies. The concepts of DEI performance describing the performance of double-exposure DEI systems in the Fourier domain been previously introduced, however, the performance of double-exposure DEI itself in terms of various parameters, has not been reported. Materials and Methods: To investigate the DEI performance, signal-difference-to-noise ratio, modulation transfer function, noise power spectrum, and noise equivalent quanta were used. Low- and high-energy were 60 and 130 kVp with 0.01-0.09 mGy, respectively. The energy-separation filter material and its thicknesses were tin (Sn) and 0.0-1.0 mm, respectively. Noise-reduction (NR) filtering used the Gaussian-filter NR, median-filter NR, and anti-correlated NR. Results and Discussion: DEI performance was affected by Sn-filter thickness, weighting factor, and dose allocation. All NR filtering successfully reduced noise, when compared with the dual-energy (DE) images without any NR filtering. Conclusion: The results indicated the significance of investigating, and evaluating suitable DEI performance, for DE images in chest radiography applications. Additionally, all the NR filtering methods were effective at reducing noise in the resultant DE images.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2004.11a
• /
• pp.1016-1019
• /
• 2004

The study of the noise reduction of an automobile has been concentrated on the reduction of the automotive engine noise because the engine noise is the major cause of automotive noise. However, many studies of automotive engine noise led to the interest of the noise reduction of the exhaust and intake system. The method of the reduction of the induction noise can be classified by the method of passive control and the method of active control. However, the passive control method has a demerit to reduce the effect of noise reduction at low frequency (below 500Hz) range and to be limited by a space of the engine room. Whereas, the active control method can overcome the demerit of passive control method. The algorithm of active control is mostly used the LMS (Least-Mean-Square) algorithm because the LMS algorithm can easily obtain the complex transfer function in real-time. Especially, Filtered-X LMS (FXLMS) algorithm is applied to an ANC system. However, the convergence performance of LMS algorithm goes bad when the FXLMS algorithm is applied to an active control of the induction noise under rapidly accelerated driving conditions. So, in order to this problem, the modified FXLMS algorithm using Moving Bandpass Filter was proposed. In this study, MBPF was implemented and use ANC for automotive intake under revived rapidly accelerated driving conditions and it was verified its performance.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.48 no.3
• /
• pp.97-103
• /
• 1999

This paper investigates reduction of acoustic noise and cogging torque in a BLDC motor with larger stator slot open width. Using energy method, cogging torque is analytically determined with airgap MMF function and airgap permeance function and confirmed by FEM analysis. It shows that the cogging torque is firstly governed by NL GNL BNL with the fundamental period of NL, where NL is the least common multiple of the number of slots and the number of poles, GNL, airgap permeance function and BNL, airgap MMF function. It also shows that there exist several tooth width which minimizes the cogging torque, for the motors that smaller slot open width or stator teeth notching is not available. And it proposes a teeth pairing with two different tooth width which can effectively eliminate the cogging torque and thus the acoustic noise. Experimental results show that the proposed teeth pairing reduces the cogging torque by 85% and the acoustic noise by 3.1dB.

• Journal of the Society of Naval Architects of Korea
• /
• v.45 no.6
• /
• pp.688-694
• /
• 2008

The reduction of the structure-borne noise is very important in order to reduce the noise of a ship. The noise at the high frequency range usually comes from the fluid flowing. The noise from the sea-water conveying pipe is one of the main source on these high frequency range. Therefore, the transmissibility variations are evaluated according to the shape of the rubber mount. The evaluations are performed with the frequency response function numerically and experimentally.

• Journal of KSNVE
• /
• v.10 no.5
• /
• pp.806-810
• /
• 2000

This paper presents a practical method for reduction of interior noise and improvement of sound quality in compartment of passenger car. The tested vehicle has a booming noise problem at rear passenger seats. In order to identify the transfer path of interior noise, the running modal analysis, the vibro-acoustic frequency transfer response and the noise path analysis are systematically employed. Using these various methods, it has been founded that the rear part of the roof of the test car was a noise source for the booming noise. Through the modification of the roof, the booming noise has been reduced and sound quality inside car also has been improved.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.11a
• /
• pp.806-811
• /
• 2007

In this study, we propose a more systematic design process for the structure-borne noise. The proposed way consists of 4 steps: Problem definition, Cause analysis, Development of counter-measure and Validation. Especially, we improved the second step: Cause analysis. According to the PCA(Panel Contribution Analysis), a reduction in vibration of the panels of which panel contribution is positive and larger, results in a reduction in structure-borne noise. We have, however, met the case in which the concept of PCA is no valid in a few vehicle tests. In order to understand this phenomenon, we compared the major panels selected by PCA with the one chosen by DSA(Design Sensitivity Analysis). After investigating the difference between the two results, a more improved process is suggested. The proposed one for the second step in the design process consists of not only the previous way: PCA with deformation analysis results but also DSA. It is finally validated that the proposed design process decreases the sound pressure of the concerned noise transfer function more than 3.5 dB.