• 터널과지하공간
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• 제18권1호
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• pp.1-9
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• 2008

미소파괴음(Acoustic Emission, AE)과 미소지진음(Microseismic event, MS event)은 응력의 재분배에 의한 균열이 생성될 때 나오는, 순간적인 에너지 방출에 의한 탄성파이다. AE/MS 이벤트는 일반적으로 대규모의 파괴에 앞서 그 발생이 현저해지는 경향이 있다. 이들은 계측영역의 주파수 대역에 따라 구분되며, MS이벤트에 비해 상대적으로 고주파의 AE 신호는 보다 미세한 파괴를 검출할 수 있다. 일반적으로 암반구조물은 파괴되기까지 작은 변형이 발생하여 종래에 사용되고 있는 변위계측으로는 그 전조현상을 포착하기 어렵기 때문에 국부적인 파괴나 갑작스러운 파괴에 대한 사전예측이 어려운 현실이다. 그러나 AE/MS 이벤트의 파형을 측정할 수 있는 경우 암반구조물의 파괴를 사전에 예측할 수 있으며, 초동이 명확한 경우 미세한 파괴위치지점과 함께 파괴메커니즘의 규명도 가능하다. 본 보고에서는 AE/MS 이벤트에 대한 기본이론과 함께 이들 활용한 계측기술 개발현황과 적용사례 등을 소개한다.

• 한국안전학회지
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• 제18권2호
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• pp.6-15
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• 2003

A method is presented for diagnosing the converter bearings by using acoustic emission. The flaking mechanism causing the large-scale bearing for furnace to flaw is investigated and a possibility of defect is verified by Finite Element method. he diagnosis logic is proposed fir detecting the flaw of a non-continuous rotating machine. It is proved that the acoustic emission energy can be used as a representative parameter for an acoustic event. Applying the method to the tilting bearings for steel mill in operation, the effectiveness of this logic is evaluated. It is shown that AE signal is generated only when the bearing is tilting, and the trend analysis can be focused upon this process.

• 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.

• 한국음향학회:학술대회논문집
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• 한국음향학회 2004년도 춘계학술발표대회 논문집 제23권 1호
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• pp.341-344
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• 2004

발전소에서 운전 중인 발전 설비의 장비 및 기계의 동작, 감시, 진단은 매우 중요한 일이다. 발전소의 이상 감지를 위해 상태 모니터링이 사용되며, 이상이 발생되었을 때 고장의 원인을 분석하고 적절한 조치를 계획하기 위한 이상 진단 과정을 따르게 된다. 본 논문에서는 산업 현장에서 기기들의 운전시에 발생하는 기기 발생 음을 획득하여 정상/비정상을 판정하기 위한 알고리듬에 대하여 연구하였다. 사운드 감시(Sound Monitoring) 기술은 관측된 신호를 acoustic event로 분류하는 것과 분류된 이벤트를 정상 또는 비정상으로 구분하는 두 가지 과정으로 진행할 수 있다. 기존의 기술들은 주파수 분석과 패턴 인식의 방법으로 간단하게 적용되어 왔으며, 본 논문에서는 K-means clustering 알고리듬을 이용하여 사운드를 acoustic event로 분류하고 분류된 사운드를 정상 또는 비정상으로 구분하는 알고리듬을 개발하였다.

• 대한임베디드공학회논문지
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• 제10권4호
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• pp.189-198
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• 2015

Most IoT-related approaches have tried to establish the relation by connecting the network between things. The proposed research will present how the pervasive interaction of eco-system formed by touching the objects between humans and things can be recognized on purpose. By collecting and sharing the detected patterns among all kinds of things, we can construct the environment which enables individualized interactions of different objects. To perform the aforementioned, we are going to utilize technical procedures such as event-driven signal processing, pattern matching for signal recognition, and hardware in the loop simulation. We will also aim to implement the prototype of sensor processor based on Arduino MCU, which can be integrated with system using Arduino-Matlab/Simulink hybrid-interoperation environment. In the experiment, we use piezo transducer to detect the vibration or vibrates the surface using acoustic wave, which has specific frequency spectrum and individualized signal shape in terms of time axis. The signal distortion in time and frequency domain is recorded into memory tracer within sensor processor to extract the meaningful pattern by comparing the stored with lookup table(LUT). In this paper, we will contribute the initial prototypes for the acoustic touch processor by using off-the-shelf MCU and the integrated framework based on Matlab/Simulink model to provide the individualization of the touch-sensing for the user on purpose.

• 한국음향학회지
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• 제34권3호
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• pp.234-239
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• 2015

비음수 행렬 분해(Nonnegative Matrix Factorization, NMF) 기법은 사전행렬과 크기성분을 번갈아 가며 업데이트 하면서 구하는 방법이며 직관적 해석 및 구현의 용이성으로 인해 중첩음향이벤트 분리 및 검출방법으로 널리 활용되었다. 하지만 비음수 행렬 분해의 고유한 특성인 부분기반표현(part-based representation)으로 인해 하나의 음향 이벤트를 구성 하는 사전(dictionary)의 파편화 현상이 발생하고, 다른 음향이벤트와 중복되는 사전이 생성되어 결과적으로 분리, 검출 성능의 저하 문제가 발생한다. 본 논문에서는 사전 획득 단계의 부분기반표현에 의한 문제를 해소하기 위해 K-Singular Value Decomposition(K-SVD)을 사용하여 사전을 획득하고, 음향이벤트 검출 단계 에서는 기존 비음수 행렬 분해 기법을 이용하여 크기를 획득 한다. 제안하는 방식을 통해 비음수 행렬 분해 기반의 사전을 사용하는 경우보다 중첩음향이벤트 검출 성능이 개선되는 것을 확인하였다.

• 한국음향학회지
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• 제40권5호
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• pp.479-487
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• 2021

본 논문에서는 잡음 학생 모델 기반의 자가 학습을 활용한 음향 사건 검지 기법을 제안한다. 제안된 음향 사건 검지 모델은 두 단계로 구성된다. 첫 번째 단계에서는 잔차 합성곱 순환 신경망(Residual Convolutional Recurrent Neural Network, RCRNN)을 훈련하여 레이블이 지정되지 않은 비표기 데이터셋의 레이블 예측에 활용한다. 두 번째 단계에서는 세 가지 잡음 종류를 적용한 잡음 학생 모델을 자가학습 기법으로 반복하여 학습한다. 여기서 잡음 학생 모델은 SpecAugment, Mixup, 시간-주파수 이동을 활용한 특징 잡음, 드롭아웃을 활용한 모델 잡음, 그리고 semi-supervised loss function을 적용한 레이블 잡음을 활용하여 학습된다. 제안된 음향 사건 검지 모델의 성능은 Detection and Classification of Acoustic Scenes and Events(DCASE) 2020 Challenge Task 4의 validation set으로 평가하였다. DCASE 2020 챌린지 데이터셋의 baseline 및 최상위 랭크된 모델과 이벤트 단위 F1 점수 성능을 비교한 결과, 제안된 음향 사건 검지 모델이 단일 모델과 앙상블 모델에서 최상위 모델 대비 F1 점수를 각각 4.6 %와 3.4 % 향상시켰다.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.175-182
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• 2022

In our daily life, we come across different types of information, for example in the format of multimedia and text. We all need different types of information for our common routines as watching/reading the news, listening to the radio, and watching different types of videos. However, sometimes we could run into problems when a certain type of information is required. For example, someone is listening to the radio and wants to listen to jazz, and unfortunately, all the radio channels play pop music mixed with advertisements. The listener gets stuck with pop music and gives up searching for jazz. So, the above example can be solved with an automatic audio classification system. Deep Learning (DL) models could make human life easy by using audio classifications, but it is expensive and difficult to deploy such models at edge devices like nano BLE sense raspberry pi, because these models require huge computational power like graphics processing unit (G.P.U), to solve the problem, we proposed DL model. In our proposed work, we had gone for a low complexity model for Audio Event Detection (AED), we extracted Mel-spectrograms of dimension 128×431×1 from audio signals and applied normalization. A total of 3 data augmentation methods were applied as follows: frequency masking, time masking, and mixup. In addition, we designed Convolutional Neural Network (CNN) with spatial dropout, batch normalization, and separable 2D inspired by VGGnet [1]. In addition, we reduced the model size by using model quantization of float16 to the trained model. Experiments were conducted on the updated dataset provided by the Detection and Classification of Acoustic Events and Scenes (DCASE) 2020 challenge. We confirm that our model achieved a val_loss of 0.33 and an accuracy of 90.34% within the 132.50KB model size.

• 산업기술연구
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• 제19권
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• pp.197-208
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• 1999

The process of localization of cracks and movement of the fracture process zone(FPZ) was studied using the acoustic-emission(AE) techniques. The rate of AE events and sources of AE activity were studied for mortar and rock specimens loaded in uniaxial compression. A series of transducers could be used to detect and AE activity. Based on the time differences between detection of the event at different transducers, source of AE activity could be detected. The rate of AE events increased sharply before peak load. The highest rate occurred just after peak load was attained. The effective crack length estimated from the modified linear-elastic fracture mechanics seemed consistent with the optical and AE measurements.

• 로봇학회논문지
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• 제17권3호
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• pp.273-281
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• 2022

In this paper, we propose a robot sound recognition system that detects various sound events. The proposed system is designed to detect various sound events in real-time by using a microphone on a robot. To get real-time performance, we use a VGG11 model which includes several convolutional neural networks with real-time normalization scheme. The VGG11 model is trained on augmented DB through 24 kinds of various environments (12 reverberation times and 2 signal to noise ratios). Additionally, based on random forest algorithm, a decision logic is also designed to generate event signals for robot applications. This logic can be used for specific classes of acoustic events with better performance than just using outputs of network model. With some experimental results, the performance of proposed sound recognition system is shown on real-time device for robots.