• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.361-364
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• 2003

The safe operation and reliable maintenance of nuclear power plants is one of the most fundamental and important tasks. It is known that a loose part such as a disengaged and drifting metal inside of reactor coolant systems might lead to a serious damage because of their impact on the components of the coolant system. In order to estimate the impact position of a loose par, three accelerometers attached to the wall of the coolant system have been used. These accelerometers measure the vibration of the coolant system induced by loose part impact. In the conventional analysis system, the low pass filtered version of the vibration data was used for the estimation of the position of a loose part. It is often difficult to identify the initial point of the impact signal by using just a low passed time signal because the impact wave is dispersed during propagation into the sensor. In this paper, the impact signal is analysed by use of various time frequency methods including the short time Fourier transform(STFT), the wavelet transform, and the Wigner-Vill distribution for finding a convenient way to identify the starting point of a impact signal and their advantages and limits are discussed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.513-517
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• 2005

The condition monitoring of rotating machinery such as turbines, pumps and compressors, determine what repairs are needed to avoid shutdown and disassembly of the machine in an industrial plant Many diagnosis methods have been developed for use when the machine is running at steady state, the stationary condition. But much information can be gained about a rotor's condition during non-stationary conditions such as run-up and run-down. Order tracking analysis is a powerful tool for analyzing the condition of a rotating machine when its speed changes over time. Powerful OTA using digital signal processing has some advantages(cheap hardware, the powerful methods, the accurate post processing) and also some disadvantages(calculation time, high speed sampling). New OTA tool based on the chirplet transform is similar to the short time Fourier transform. But, it has good resolution at high speed like other OTA methods based STFT and more resolution for constant frequency components than re-sampling OTA.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1108-1109
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• 2023

본 논문은 선박 내부 소음을 효과적으로 감소시키기 위한 ANC(Active Noise Cancellation)및 인공 지능 (AI) 결합 시스템의 개발과 적용에 관한 연구를 다룬다. 선박 환경에서의 소음은 승원의 스트레스 증가와 불편을 초래하므로, 이를 해결하기 위한 방법을 제안하고자 한다. 외부 소음과 내부 소음 데이터를 수집하고, STFT(Short-Time Fourier Transform)알고리즘을 통해 소음 데이터를 분석 가능한 형태로 전처리한다. 그 후, LSTM(Long Short-Term Memory)알고리즘을 사용하여 선박 외부에서 발생한 소음을 입력으로 받아 내부에서 들리는 외부 소음을 예측하고 제어하는 모델을 훈련시킨다. 이후 최적화 과정을 거쳐 예측 소음의 반대 파형을 생성 및 출력을 통해 ANC 를 구현한다.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1397-1402
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• 2008

In this paper, damage detection method by using the time reversal is developed to detect damage on composit structures. The time reversal was investigated for direct root between PZT and PZT, but in case of a circular PZT, lamb wave moves not only along the direct root but also another roots. The center frequency of lamb wave is kept when the lamb waves are reflected from damage. This paper presents experimental and theoretical results for the new structural health monitoring method by above features of lamb wave, and we can increase accuracy of the new structural health monitoring method by using STFT(Short Time Fourier Transform).

• Journal of IKEEE
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• v.23 no.3
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• pp.805-810
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• 2019

We present a time-frequency analysis algorithm based on the multitaper method and the state-space frameworks. In general, time-frequency representations have a trade-off between the time duration and the spectral bandwidth by the uncertainty principle. To optimize the trade-off problems, the short-time Fourier transform and wavelet based algorithms have been developed. Alternatively, the authors proposed the state-space frameworks based on the multitaper method in the previous work. In this paper, we develop a real-time algorithm to estimate variances and spectrum using the state-space framework. We test our algorithm in spectral analysis of simulated data.

• International conference on construction engineering and project management
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• 2020.12a
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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.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.121-126
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• 2020

Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2010.11a
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• pp.469-473
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• 2010

In this study, combustion tests using liquefying fuels with fast regression rate were performed. The chamber pressure oscillation was analyzed and hazards of combustion instabilities were examined. In case of Liquefying fuel with fast regression rate, the amplitude of chamber pressure oscillation was increased compared to the polymeric fuels. However, the critical combustion instability can hardly occur in liquefying fuel. This is because the rapid change of inner chamber diameter limits the amplification of chamber pressure oscillation. The chamber pressure oscillation due to the large increase of fuel production and the vortex shedding in pre-chamber violently occurs during combustion using single-port axial injector.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.3 s.108
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• pp.255-262
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• 2006

Frequency spectrum using the conventional Fourier analysis gives adequate information about the dynamic characteristics of the slider air bearing for the linear and stationary cases. The intermittent contacts for the extremely low flying height, however, generate nonlinear and nonstationary vibration at the instant of contact. Nonlinear dynamic model should be developed to simulate the impulse response of the air bearing during slider-disk contact. Time-frequency analysis is widely used to investigate the nonstationary signal. Several time-frequency analysis methods are employed and compared for the slider vibration signal caused by the impact against an artificially induced scratch on the disk. The representative Wigner-Ville distribution leads to the severe interference problem by cross terms even though it gives good resolution both in time and frequency. The smoothing process improves the interference problem at the expense of resolution. In order to get the results with good resolution and little interference, the reassignment method is proposed. Among others the reassigned Gabor spectrogram shows the best resolution and readability with negligible interference.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.429-434
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• 2012

This paper presents a technology for manipulating external devices by Brain Computer Interface (BCI) system. Recently, BCI based rehabilitation and assistance system for disabled people, such as patient of Spinal Cord Injury (SCI), general paralysis, and so on, is attracting tremendous interest. Especially, electroencephalogram (EEG) signal is used to organize the BCI system by analyzing the signals, such as evoked potential. The general findings of neurophysiology support an availability of the EEG-based BCI system. We concentrate on the event-related synchronization of motor imagery EEG signal, which have an affinity with an intention for moving control of external device. To analyze the brain activity, short-time Fourier transform and particle swarm optimization are used to optimal feature selection from the preprocessed EEG signals. In our experiment, we can verify that the power spectral density correspond to range mu-rhythm(${\mu}8$~12Hz) have maximum amplitude among the raw signals and most of particles are concentrated in the corresponding region. Result shows accuracy of subject left hand 40% and right hand 38%.