• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 춘계학술대회논문집
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• pp.749-754
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• 2003

In this paper, we introduce the measurement of the underwater noise with 32channel hydrophone array of Samsung CAvitation Tunnel (SCAT) and the detection technique of noise sources by using the beam-forming method. Measurement and way signal Processing under fluid flow are essential works for the underwater acoustics, especially for the detection of noise sources. As the acoustic impedance of the water is relatively high and the tunnel is an enclosed system, we have to consider the interaction between tunnel and water together with the reflection of noise in the beam-forming technique. Also, for a hydrophone array system that is fixed on one side of tunnel wall as done in SCAT is liable to suffer from some limitations in the detection of the noise sources with the array, we discuss these limitations particularly on the frequency range and spacing of noise sources.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.455-462
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• 2020

Noise which is unwanted sound is a serious pollutant that can affect human health, as well as the working and living environment if exposed to humans. However, current noise management on the construction project is generally conducted after the noise exceeds the regulation standard, which increases the conflicts with inhabitants near the construction site and threats to the safety and productivity of construction workers. To overcome the limitations of the current noise management methods, the activities of construction equipment which is the main source of construction noise need to be managed throughout the construction period in real-time. Therefore, this paper proposed a framework for automatically detecting noise sources in construction sites in real-time based on convolutional neural networks (CNNs) according to the following four steps: (i) Step 1: Definition of the noise sources; (ii) Step 2: Data preparation; (iii) Step 3: Noise source classification using the audio CNN; and (iv) Step 4: Noise source detection using the visual CNN. The short-time Fourier transform (STFT) and temporal image processing are used to contain temporal features of the audio and visual data. In addition, the AlexNet and You Only Look Once v3 (YOLOv3) algorithms have been adopted to classify and detect the noise sources in real-time. As a result, the proposed framework is expected to immediately find construction activities as current noise sources on the video of the construction site. The proposed framework could be helpful for environmental construction managers to efficiently identify and control the noise by automatically detecting the noise sources among many activities carried out by various types of construction equipment. Thereby, not only conflicts between inhabitants and construction companies caused by construction noise can be prevented, but also the noise-related health risks and productivity degradation for construction workers and inhabitants near the construction site can be minimized.

• 한국해양공학회지
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• 제22권1호
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• pp.30-36
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• 2008

Underwater ambient noise originating from geophysical, biological, and man-made acoustic sources contains information on the source and the ocean environment. Such noise affectsthe performance of sonar equipment. In this paper, three steps are used to identify the ambient noise source, detection, feature extraction, and similarity measurement. First, we use the zero-crossing rate to detect the ambient noisesource from background noise. Then, a set of feature vectors is proposed forthe ambient noise source using the Hilbert-Huang transform and the Karhunen-Loeve transform. Finally, the Euclidean distance is used to measure the similarity between the standard feature vector and the feature vector of the unknown ambient noise source. The developed algorithm is applied to the observed ocean data, and the results are presented and discussed.

• Smart Structures and Systems
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• 제18권5호
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• pp.1063-1085
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• 2016

Taking advantage of the high sensitivity and long-distance detection capability of acoustic emission (AE) technique, this paper focuses on the crack detection in rail head, which is one of the most vulnerable parts of rail track. The AE source location and noise cancellation were studied on the basis of practical rail profile, material and operational noise. In order to simulate the actual AE events of rail head cracks, field tests were carried out to acquire the AE waves induced by pencil lead break (PLB) and operational noise of the railway system. Wavelet transform (WT) was first utilized to investigate the time-frequency characteristics and dispersion phenomena of AE waves. Here, the optimal mother wavelet was selected by minimizing the Shannon entropy of wavelet coefficients. Regarding the obvious dispersion of AE waves propagating along the rail head and the high operational noise, the wavelet transform-based modal analysis location (WTMAL) method was then proposed to locate the AE sources (i.e. simulated cracks) respectively for the PLB-induced AE signals with and without operational noise. For those AE signals inundated with operational noise, the Hilbert transform (HT)-based noise cancellation method was employed to improve the signal-to-noise ratio (SNR). Finally, the experimental results demonstrated that the proposed crack detection strategy could locate PLB-simulated AE sources effectively in the rail head even at high operational noise level, highlighting its potential for field application.

• 한국음향학회지
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• 제35권2호
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• pp.110-117
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• 2016

수동소나 시스템에서는 함정의 소음원에서 발생하는 방사 소음을 분석하여 표적을 탐지 및 식별한다. 소나의 탐지 범위 안에 다수의 소음원이 존재하면 신호를 분석할 때 각 소음원에서 나오는 성분들이 혼합되어 각각의 소음원을 규명하기가 어렵다. 이를 해결하기 위해 일반적으로는 배열 센서를 이용한 빔을 형성하여 소음원의 신호를 공간적으로 분리하는 기법이 사용되지만 환경에 따라 여전히 어려운 점이 있다. 본 연구에서는 수동소나 표적신호를 분리하기 위한 새로운 방법으로 주파수영역 독립성분분석(FDICA: Frequency Domain Independent Component Analysis)을 적용하고, 혼합된 표적신호를 분리하는 모의실험을 수행하여 그 타당성을 검증하였다. 표적신호 합성을 위한 특징 정보로는 기계류 토널 성분 및 프로펠러 성분을 사용하였고, 분리 전 후의 결과를 LOFAR(Low Frequency Analysis and Recording), DEMON(Detection Envelope Modulation On Noise) 분석을 통해 비교하였다.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2006년 창립20주년기념 정기학술대회 및 국제워크샵
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• pp.307-310
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• 2006

Underwater ambient noise originating from the geophysical, biological, and man-made acoustic sources contains much information on the sources and the ocean environment affecting the performance of the sonar equipments. In this paper, a set of feature vectors of the ambient noises using MFCC is proposed and extracted to form a data base for the purpose of identifying the noise sources. The developed algorithm for the pattern recognition is applied to the observed ocean data, and the initial results are presented and discussed.

• 한국정밀공학회지
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• 제31권4호
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• pp.327-334
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• 2014

This paper presents a method of fault detection and fault localization from acoustic noise measurements. In order to detect the presence of noise sources wavelet transform is applied to acoustic signal. In addition, a cross correlation based method is proposed to calculate the exact location of the noise allowing the user to quickly diagnose and resolve the source of the noise. The fault detection system is implemented using two microphones and a computer system. Experimental results show that the system can detect faults due to artifacts accidentally inserted during the manufacturing process and estimate the location of the fault with approximately 1 cm precision.

• IEIE Transactions on Smart Processing and Computing
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• 제3권4호
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• pp.246-250
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• 2014

Large mutual capacitance touch screen panels (TSP) are susceptible to display and ambient noise. This paper presents a multi-touch detection algorithm using an efficient noise compensation technique for large mutual capacitance TSPs. The sources of noise are presented and analyzed. The algorithm includes the steps to overcome each source of noise. The algorithm begins with a calibration technique to overcome the TSP mutual capacitance variation. The algorithm also overcomes the shadow effect of a hand close to TSP and mutual capacitance variation by dynamic threshold calculations. Time and space filters are also used to filter out ambient noise. The experimental results were used to determine the system parameters to achieve the best performance.

• 한국음향학회지
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• 제20권2호
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• pp.86-92
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• 2001

소음원 위치와 방위를 규명하기 위해 다양한 배열처리기술이 발전되어 왔다. 배열처리기술의 기본은 두 개의 수신센서에 수신된 신호의 시간차를 이용하여 소음원의 위치와 방위를 구하는 것으로 응용분야나 신호처리방법에 따라 고유의 특성을 갖는 빔형성기법, 상관함수기법 및 NAH (Near-Field Acoustic Holography) 등이 있다 본 연구에서는 이러한 기법들 중 광대역 소음원 탐지에 적용되는 상관함수기법을 채택하여 소음원의 대역폭과 측정 잡음원 간의 상관 관계가 위치나 방위 탐지 정확도에 미치는 영향을 분석하여 효과적인 소음원 탐지기법을 제안한다. 본 연구에서 채택한 배열의 기하학적 형상은 위치나 방위의 3차원적 모호성을 없애기 위한 3차원 비선형이며 제안된 기법의 타당성은 수치모의 실험 및 실제 실험으로 검증되었다.

• 한국산업보건학회지
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• 제28권1호
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• pp.100-107
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• 2018

Objectives: This study examines exposure to hazardous substances in the working environment caused by exposure to toxic substances produced in the aluminum die casting process in the automobile manufacturing industry. Materials and Methods: The exposure concentration levels, detection rates and time-trend of 15 hazardous factors in the aluminum die casting process over 10 years(from 2006 to 2016) were used as a database. Results: The study found that hazardous factors in the aluminum die casting process were mostly metals. The rate for detected samples was 70.6%(405 samples), and that for not detected samples was 29.4%. The noise for an eight-hour work shift showed a 49.7% exceedance rate for TLV-TWA. Average noise exposure was 89.0 dB. The maximum exposure level was 105.1 dB. Conclusion: The high numbers of no-detection rates for hazardous substance exposure shows that there is no need to do a work environment measurement. Therefore, alternatives are necessary for improving the efficiency and reliability of the work environment measurement. Moreover, to prevent noise damage, reducing noise sources from automation, shielding, or sound absorbents are necessary.