• KSBB Journal
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• 제18권2호
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• pp.79-89
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• 2003

This article contains an overview of acoustic wave devices, the theory and application of piezoelectric quartz crystal microbalances(PZ QCM), clinical analysis, gas phase detection, DNA biosensors, drug analysis, food microbial analysis and environmental analysis.

• 한국정밀공학회지
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• 제7권3호
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• pp.75-82
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• 1990

Automatic monitoring of the cutting conditions is one of the most improtant technologies in machining. In this study, the feasibility in applying acoustic emission(AE) signals for the in-process detection of endmill wear and fracture has been investigated by performing experimental test on the NC vertical milling machine with SM45C for specimen. As the results of detecting and analyzing AE signals on various cutting conditions, the followings have confirmed. (1) The RMS value of acoustic emission is related sensitively to the cutting velocity, but is not affected largely by feed rate. (2) The burst type AE signals of high level have been observed when removing chips distorderly and discontinuously. (3) When the RMS value grows up rapidly due to the increase of wear the endmill are generally broken or fractured, but when the endmills fracture at the conditions of smooth chip-flow or built-up-edge(BUE) occurred frequently, the rapid change of the RMS arenot found. And it is expected that this technigue will be quite useful for in-process sensing of tool wear and fracture.

• Structural Monitoring and Maintenance
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• 제7권1호
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• pp.13-25
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• 2020

One of the most important aspects in structural health monitoring is the detection of fatigue damage. Structural components such as heavy-duty bolts work under high dynamic loads, and thus are prone to accumulate fatigue damage and cracks may originate. Those heavy-duty bolts are used, for example, in wind power generation and mining equipment. Therefore, the investigation of new and more effective monitoring technologies attracts a great interest. In this study the acoustic emission (AE) technology was employed to detect incipient damage during fatigue testing of a M36 bolt. Initial results showed that the AE signals have a high level of background noise due to how the load is applied by the fatigue testing machine. Thus, an advanced signal processing method in the time-frequency domain, the Hilbert-Huang Spectrum (HHS), was applied to reveal AE components buried in background noise in form of high-frequency peaks that can be associated with damage progression. Accordingly, the main contribution of the present study is providing insights regarding the detection of incipient damage during fatigue testing using AE signals and providing recommendations for further research.

• 한국광학회:학술대회논문집
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• 한국광학회 2002년도 하계학술발표회
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• pp.252-253
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• 2002

원적외선 영역에서 혈중성분들의 분광 특성이 존재함이 확인되면서 이 영역의 빛을 이용한 혈중 성분의 정량 분석을 통한 진단기술 개발이 활발히 진행되고 있다1). 특히 원적외선 영역에서 특정 파장에서의 반응성을 이용한 여러 방법들 중 photo-Acoustic을 이용한 방법이 여러 가지로 연구되어지고 있다. (2) 현재 수용액 상태의 혈중 성분들의 원적외선 분광 특성을 연구하였으며, 이를 이용한 혼합 성분들의 흡수 spectrum 정량-정성 분석이 가능하며, 이러한 특성을 이용하여 non-invasive로 이용 가능한 분광법을 연구 진행 중이다. (중략)

• 한국공작기계학회논문집
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• 제10권5호
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• pp.9-16
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• 2001

Turning experiments are conducted to investigate characteristics of acoustic emission due to wear of the coated tool. The AE signals are obtained with a sensor attached to tool holder side. Tool states are identified with scanning electron microscopy and optical microscopy. It is demonstrated that the AE signals provide reliable informations about the cutting processes and tool states. Moreover, tool wear can be detected successfully using the AE-RMS.

• 대한방사선기술학회지:방사선기술과학
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• 제31권1호
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• pp.61-69
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• 2008

유방초음파 검사는 유방암 검사에 있어 유방촬영술에 비하여 많은 장점이 있으나, 미세석회화 발견에는 적합하지 않은 단점이 있다. 이에 유방초음파 검사에서 기존의 7.5 MHz 선형 탐촉자를 사용하여 매질의 공명현상을 이용한 유방조직 석회화 병변을 관측할 수 있는 방법을 연구하였다. 먼저 gelatin과 돼지 젖가슴살을 이용하여 유방조직 팬텀을 제작하였으며, 외부 진동을 변화시켜 가며 석회화 병변을 관측하였다. 유방조직 팬텀안에 주입된 석회화는 주변 조직과 다른 공명을 일으키면서 외부진동에 따라 음향공명의 정도가 파워도플러의 ROI 영역 내의 색상의 밝기와 영역의 차이로 나타내었다. 낮은 주파수 영역에는 음향공명이 거의 나타나지 않았으며, 약 $300{\sim}400\;Hz$ 사이에서 일정한 플래토우 영역을 나타내었으며, 이후 주파수가 증가함에 따라 색상이 사라짐을 확인하였다.

• 한국해양공학회지
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• 제34권4호
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• pp.277-284
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• 2020

Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.

• The Journal of the Acoustical Society of Korea
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• 제24권2E호
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• pp.46-53
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• 2005

Agency for Defense Development (ADD) developed the Submarine Acoustic Information Management System (SAIMS Version 1.0) capable of interfacing some submarine sensors in operation and predicting detection environments for sonars. The major design concepts are as follows: 1) A proper acoustic model is examined and optimized to cover wide spectra of frequency ranges for both active and passive sonars. 2) Interfacing the submarine sensors to an electric navigation chart, the system attempts to maximize the applicability of the information produced. 3) The state-of-the-art database in large area is built and managed on the system. 4) An algorithm, which is able to estimate a full sound speed profile from the limited oceanographic data, is developed and employed on the system. This paper briefly describes design concepts and algorithms embedded in the SAIMS. The applicability of the SAIMS was verified through three sea experiments in October 2003-February 2004.

• Journal of Computing Science and Engineering
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• 제6권1호
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• pp.40-50
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• 2012

Increase in the number of older people due to demographic changes poses great challenges to the social healthcare systems both in the Western and as well as in the Eastern countries. Support for older people by formal care givers leads to enormous temporal and personal efforts. Therefore, one of the most important goals is to increase the efficiency and effectiveness of today's care. This can be achieved by the use of assistive technologies. These technologies are able to increase the safety of patients or to reduce the time needed for tasks that do not relate to direct interaction between the care giver and the patient. Motivated by this goal, this contribution focuses on applications of acoustic technologies to support users and care givers in ambient assisted living (AAL) scenarios. Acoustic sensors are small, unobtrusive and can be added to already existing care or living environments easily. The information gathered by the acoustic sensors can be analyzed to calculate the position of the user by localization and the context by detection and classification of acoustic events in the captured acoustic signal. By doing this, possibly dangerous situations like falls, screams or an increased amount of coughs can be detected and appropriate actions can be initialized by an intelligent autonomous system for the acoustic monitoring of older persons. The proposed system is able to reduce the false alarm rate compared to other existing and commercially available approaches that basically rely only on the acoustic level. This is due to the fact that it explicitly distinguishes between the various acoustic events and provides information on the type of emergency that has taken place. Furthermore, the position of the acoustic event can be determined as contextual information by the system that uses only the acoustic signal. By this, the position of the user is known even if she or he does not wear a localization device such as a radio-frequency identification (RFID) tag.

• 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.