• The Journal of the Acoustical Society of Korea
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• v.29 no.7
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• pp.411-421
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• 2010

A time-reversal mirror (TRM) is useful in diverse areas, such as reverberation ing, target echo enhancement and underwater communication. In underwater communication, the bit error rate has been improved significantly due to the increased signal-to-noise ratio by spatio-temporal focusing. This paper deals with two issues. First, the optimal number of array elements for a given environment was investigated based on the exploitation of spatial diversity. Second, an algorithm was developed to determine the optimal location of the given number of array elements. The formulation is based on a genetic algorithm maximizing the contrast between the foci and area of interest as an objective function. In addition, the developed algorithm was applied to the matched field processing with ocean experimental data for verification. The sea-going data and simulation showed almost 3 dB improvement in the output power at the foci when the array elements were optimally distributed.

• Electronics and Telecommunications Trends
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• v.25 no.5
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• pp.1-10
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• 2010

스마트폰, 태블릿 PC의 등장과 더불어 모바일 IT 제품들은 빠르게 소형화, 슬림화, 저전력화 되고 있으며, 이런 시스템의 요구에 대응하기 위하여 음향부품은 기술 전환기를 맞이하고 있다. 대표적 음향소자로는 소리를 전기적 신호로 변환하는 센서인 마이크로폰과 전기 에너지를 소리 에너지로 변환하는 액추에이터인 스피커가 있는데, 최근초소형 MEMS 마이크로폰과 초박형 압전 스피커와 같은 새로운 제품이 시장에서 주목받고 있다. 본 고에서는 음향소자의 기술 개요, 새로운 제품의 장단점 및 개발 동향, 그리고 시장 및 산업 동향을 살펴봄으로써 관련 국내 업계가 기술 전환기를 대비하는 데 필요한 기초 정보를 제공하고자 한다.

• Journal of Korean Association for Spatial Structures
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• v.8 no.3
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• pp.65-73
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• 2008

The present work deals with subjective assessments to propose the objective parameter related to the subjective attribute of reverberation in the coupled room varied the aperture opening size. A 1/10 scale model was built and used for the measurements and subjective assessments. For the subjective tests, binaural impulse responses(BIRs) were measured using a dummy head and the measured BIRs were convolved with anechoic recorded music for the presentation over the headphones. The results showed that the perceived reverberation was the largest in the aperture opening size 12.5%(1.97% of mail) room surface area)and decreased with increasing the aperture opening size over 25% (3.94% of main room surface area) in the main room coupled with the secondary reverberant room. In the main room coupled with the secondary absorptive room, there was no changes in the perceived reverberation up to the aperture opening size 6.25%(0.99% of main room surface area) and it gradually decreased with increasing the aperture opening site over 12.5%. The objective parameter, T30/T15, showed a very low correlation with the perceived reverberation in the coupled room varied the aperture opening size but showed a high correlation with a new objective parameter, T30/Tbp, proposed by the authors. Because the late part decay energy more rapidly increases with increasing the aperture opening site than the early and middle part decay energy, the LDT/EDT or T3/T1 is a better quantifier to measure double slopes in the coupled room than the T30/T15.

• Composites Research
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• v.17 no.3
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• pp.15-22
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• 2004

Acoustic load generated by rocket propulsion system is one of major dynamic loads during lift-off phase so that it causes the structural failure and electronic malfunction of payloads. Acoustic loads can be greatly reduced by an appropriate acoustical design of nose faring structures. This paper deals with the acoustical design of the nose fairing structure for launch vehicle. It is well known that a honeycomb sandwich structure is a poor sound insulator because of its high specific stiffness. In this paper, the sound transmission characteristics of four kinds of honeycomb structures for noise fairing were investigated by means of numerical and experimental ways. In order to estimate transmission loss, infinite plate theory by Moore and Lyon and statistical energy analysis (SEA) method were used. The predicted results showed a good agreement with measured ones. These enabled us to determine a proper core material for nose fairing, which shows good sound insulation performance per weight.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.978-983
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• 2010

Acoustic target classification in wireless sensor network is important research at environmental surveillance, invasion surveillance, multiple target separation. General sensor node signal processing methods concentrated on received signal energy based target detection and received raw signal compression. The former is not suited to target classification because of almost every target information are lost except target energy. The latter bring down life-time of sensor node owing to high computational complexity and transmission energy. In this paper, we introduce an feature extraction algorithm for acoustic target classification in wireless sensor network which has time and frequency information. The proposed method extracts time information and de-noised target classification information using wavelet decomposition step. This method reduces communication energy by 28% of original signal and computational complexity.

• Geophysics and Geophysical Exploration
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• v.21 no.4
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• pp.213-219
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• 2018

In this study, we simulated 3D wave propagation modeling using a Xeon Phi x200 processor and compared the parallel computation performance with that using a Xeon CPU. Unlike the 1st generation Xeon Phi coprocessor codenamed Knights Corner, the 2nd generation x200 Xeon Phi processor requires no additional communication between the internal memory and the main memory since it can run an operating system directly. The Xeon Phi x200 processor can run large-scale computation independently, with the large main memory and the high-bandwidth memory. For comparison of parallel computation, we performed the modeling using the MPI (Message Passing Interface) and OpenMP (Open Multi-Processing) libraries. Numerical examples using the SEG/EAGE salt model demonstrated that we can achieve 2.69 to 3.24 times faster modeling performance using the Xeon Phi with a large number of computational cores and high-bandwidth memory compared to that using the 12-core CPU.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.691-698
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• 2017

In this study, the injector transfer function (ITF) of a gas-gas coaxial jet-swirl injector is measured by applying excitation to jet or swirl flow using a loudspeaker. As a result of measuring the ITF according to the variation of feed system length, the ITF peak occurs at the resonance frequency of the space where the perturbed flow passes. When applying the excitation to the jet flow, as the jet flow increases up to 56 slpm, the magnitude of ITF decreases, and ITF increases thereafter. Therefore the larger the velocity difference between the jet and the swirl flow, the larger the ITF. In the case of the swirl excitation, the ITF decreases as the jet flow increases because of the decrease of the energy with respect to the constant flow at the downstream. This difference is caused by the location of the hot wire anemometer on the downstream of the injector center axis.

• Journal of the Korean Society of Propulsion Engineers
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• v.22 no.4
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• pp.99-107
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• 2018

In this study, the injector transfer function (ITF) of a gas-gas coaxial jet-swirl injector is measured by perturbing jet or swirl flow using a speaker as jet flow increases. As a result of measuring the ITF varying feed system length, a peak occurs at a resonance frequency of space where the perturbed flow passes. With jet excitation, the ITF magnitude decreases, but increases thereafter as increasing the jet flow. Therefore the larger the velocity difference between jet and swirl flow, the larger the ITF. With swirl excitation, ITF decreases as increasing the jet flow because of the energy decrease with respect to the constant downstream flow.

• Geophysics and Geophysical Exploration
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• v.2 no.1
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• pp.33-42
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• 1999

AVO and complex trace analyses mainly used to characterize natural gas reservoir were tested in this paper for a possible application to detection of major geological discontinuities such as fracture zones. The test data used in this study were calculated by utilizing a viscoelastic numerical program which was based on the generalized Maxwell body for a horizontal fracture model. In AVO analysis of a horizontal fracture zone, p-wave reflection appears to be variant depending upon the acoustic-impedence contrast and the offset distance. The fracture zone is also effectively clarified both in gradient stack and range-limited stack in which fracture zone reflection is attenuated with the increasing offset distance. In complex attribute plots (instantaneous amplitude, frequency, and phase), the top and bottom of the fracture Tone are characterized by a zone of strong amplitudes and an event of the same phase. Low frequency characteristics appear at the fracture zone and the underneath. Amplitude attenuation and waveform dispersion are dependent on Q-contrast between the fracture zone and the surrounding media. They were properly compensated by optimum inverse Q-filtering.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.603-616
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• 2023

In this study, machine learning-based clustering and classification of residential noise in apartment buildings was conducted using frequency and temporal characteristics. First, a residential noise source dataset was constructed . The residential noise source dataset was consisted of floor impact, airborne, plumbing and equipment noise, environmental, and construction noise. The clustering of residential noise was performed by K-Means clustering method. For frequency characteristics, L_eq and L_max values were derived for 1/1 and 1/3 octave band for each sound source. For temporal characteristics, Leq values were derived at every 6 ms through sound pressure level analysis for 5 s. The number of k in K-Means clustering method was determined through the silhouette coefficient and elbow method. The clustering of residential noise source by frequency characteristic resulted in three clusters for both L_eq and L_max analysis. Temporal characteristic clustered residential noise source into 9 clusters for L_eq and 11 clusters for L_max. Clustering by frequency characteristic clustered according to the proportion of low frequency band. Then, to utilize the clustering results, the residential noise source was classified using three kinds of machine learning. The results of the residential noise classification showed the highest accuracy and f1-score for data labeled with L_eq values in 1/3 octave bands, and the highest accuracy and f1-score for classifying residential noise sources with an Artificial Neural Network (ANN) model using both frequency and temporal features, with 93 % accuracy and 92 % f1-score.