• Journal of the Korean Society of Radiology
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• v.12 no.4
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• pp.491-496
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• 2018

The purpose of this study is to analyze the noise spectra in DWI (diffusion-weighted imaging) pulse sequences of 1.5 Tesla and 3.0 Tesla MRI, The ACR (American College of Radiology) phantom and noise spectrum were analyzed by FFT (fast Fourier transform) and TFFT (temporal frequency analysis) using WavePad sound editor version 8.13 (NCH software, Greenwood Village, CO, USA). Noise spectra, FFT and TFFT were analyzed for laboratory 1.5Tesla and 3.0Tesla DWI MR pulse sequences. The noise threshold of the frequency amplitude in the FFT and TFFT at 3.0Tesla compared to 1.5Tesla was between 1.5Tesla and -6 dB, and between 3.0Tesla and 0 dB, the DWI pulse sequence for the patient's noise reduction was appropriately MR examination needs to be applied.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1610-1614
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• 1987

The harmonies of the fundamental frequency in speech signal make a minute line spectrum in frequency domain. In this paper, we propose a new algorithm to detect a pitch interval in voiced sound based on the fact that the number of harmonies can represent the period of the pitch in the time domain.

• Journal of computational fluids engineering
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• v.15 no.4
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• pp.60-66
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• 2010

Unsteady Reynolds Averaged Navier-Stokes(URANS) and Large Eddy Simulation(LES) simulation of an axial flow fan are calculated upon same conditions and computational grids in order to study aeroacoustic noise of an axial flow fan numerically. Results of computed performance and predicted noise are compared with those of measurement. Both performances show accurate results with a significant difference of less than 5%. However, noise of LES result is more close to measured noise qualitatively than URANS. Levels of tonal noises of both LES and URANS are quite similar with those of measured at BPF(Blade Passing Frequency) in sound spectrum. However, as leading edge separation and tip vortex shedding phenomena of LES are showed more clearly than those of URANS, sound level of broadband noise of LES corresponds better than that of URANS, especially.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.222-228
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• 2009

This study compared the sound spectrums of pyeonjong remains and pyeonjong replica to investigate tuning ways of bells. pyeonjong remains, exhibited at King Sejong Memorial Museum and pyeonjong replica, played at the National Center for Korean Traditional Performance Arts were analyzed. To get mode frequencies and mode shapes, pyeonjong replicas' sound spectrums were analyzed and modal analyses by TV holography were performed. Also pyeonjong remains' sound spectrum were analyzed. Nominal frequencies on the pyeonjong replica and remains showed differences in a range between 9.8 cent and 203 cent. Two facts were inferred as causes of the differences, the tuning conditions of pyeonjong remains were not good and C4 in western tempered scale was preferred as the sound standard of Kukak, whangjong. Relative ratio of higher mode frequencies to the nominal frequencies were calculated to figure out tonal differences between two pyeonjongs. The differences in relative ratio of higher mode frequencies except (3,0)a and (3,0)b modes were significants as well as beyond the just noticeable difference. These results implied that the tonal differences between two pyeonjongs could exist. More pyeonjong remains are needed to be investigated to confirm this result in addition to the analyses of alloy components and bell structure of pyeonjong remains and replica.

• 대한공업교육학회지
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• v.31 no.1
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• pp.185-199
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• 2006

In this paper, to detect lateral direction sound pressure fiber optic sensor using Fabry-Perot interferometeric sensor array was fabricated and experimented. This parallel sensor array composed of one light source and the light split into each sensor using directional coupler and to see the output signal the array system do not need any digital signal processor. As a lateral direction sound source arbitrary sound frequency of 100Hz, 200Hz, and 655Hz using by nondirectional speaker were applied to the array sensor which installed on $60cm{\times}60cm{\times}60cm$ latticed structure. The detected signals from the two sensors were analyzed in the time and frequency domains. It was confirmed that the suggested sensor array detected applied sound source well but there were a little amplitude differences in between the sensors. Because the sensor supported simply at both ends theoretical analysis was performed and its solution was suggested. To compare the theoretical and experimental results arbitrary sound frequency of 2kHz was applied to the sensor array. It shows that experimental results was good agreement with theoretical results.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.429-438
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• 1997

Mechanical valve is one of the most widely used implantable artificial organs of which the reliability is so important that its failure means the death of patient. Therefore early noninvasive detection is essentially required, though mechanical valve failure with thrombosis is the most common. The objective of this paper is to detect the thrombosis formation by spectral analysis and neural network. Using microphone and amplifier, we measured the sound from the mechanical valve which is attached to the pneumatic ventricular assist device. The sound was sampled by A/D converter(DaqBook 100) and the periodogram is the main algorithm for obtaining spectrum. We made the thrombosis models using pellethane and silicon and they are thrombosis model on the valvular disk, around the sewing ring and fibrous tissue growth across the orifice of valve. The performance of the measurment system was tested firstly using 1 KHz sinusoidal wave. The measurement system detected well 1KHz spectrum as expected. The spectrum of normal and 5 kinds of thrombotic valve were obtained and primary and secondary peak appeared in each spectrum waveform. We find that the secondary peak changes according to the thrombosis model. So to distinguish the secondary peak of normal and thrombotic valve quantatively, 3 layer back propagation neural network, which contains 7, 000 input node, 20 hidden layer and 1 output was employed The trained neural network can distinguish normal and valve with more than 90% probability. As a conclusion, the noninvasive monitoring of implanted mechanical valve is possible by analysing the acoustical spectrum using neural network algorithm and this method will be applied to the performance evaluation of other implantable artificial organs.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.10
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• pp.1062-1068
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• 2009

In this study, the impact force levels of bang machine and impact ball were measured, then the heavy-weight impact sounds generated by the bang machine and impact ball were investigated. It was found that the heavy-weight impact sources generated through modal excitation, and the impact force of the impact ball was similar to that of real impact source. The heavy-weight impact sounds were also measured in the real apartments with different slab thickness and floor structures. The results showed that the floor impact sound levels in terms of $L_{iFmax,AW}$, generated by impact ball sounds were reduced by using the resilient isolators. The frequency characteristics of heavy-weight impact sounds at 125 and 250 Hz were consistent with the characteristics of impact force spectrum. However, the difference between the impact sounds and the impact forces were found at 63 and 500 Hz due to the resonance of the floor structure and flanking noise, respectively.

• Journal of Korea Multimedia Society
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• v.22 no.11
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• pp.1233-1241
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• 2019

The goal of the sound-based mechanical fault diagnosis technique is to automatically find abnormal signals in the machine using acoustic emission. Conventional methods of using mathematical models have been found to be inaccurate due to the complexity of industrial mechanical systems and the existence of nonlinear factors such as noise. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose an automatic fault diagnosis method using discrete wavelet transform and power spectrum density using multi pattern recognition. First, we perform DWT-based filtering analysis for noise cancelling and effective feature extraction. Next, the power spectral density(PSD) is performed on each subband of the DWT in order to effectively extract feature vectors of sound. Finally, each PSD data is extracted with the features of the classifier using multi pattern recognition. The results show that the proposed method can not only be used effectively to detect faults as well as apply to various automatic diagnosis system based on sound.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.7
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• pp.895-906
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• 2000

A three dimensional linear frequency-domain lifting surface panel method was used for the aerodynamic analysis of axial flow type micro-fans. As proven by the duct modeling, the tip clearance of the micro-fans tested is large enough to ignore the calculated effect of the duct system. As the numerical results and experimental data agreed well in the operating point region, the method was applicable in the parametric studies to determine the design parameters of axial flow fans. Experiments on micro-fans were carried out based on KS B 6311. The newly designed micro-fan showed improvements in both static pressure rise and volumetric flow rate compared to the existing fans at a given operating condition. No detection of surging and the smooth characteristic curve proved the improvement in performance. To reduce the fan noise in the fan design, it was necessary to make use of the frequency spectrum analysis data. Measurement of sound pressure level for micro-fans was conducted based on KS B 6361 and KS A 0705. The peak - which occurs at blade passage frequency and its higher harmonics due to the fan noise - was not detected. This justifies the design methodology of the blade.

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