• Proceedings of the KSR Conference
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• 2008.06a
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• pp.2066-2072
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• 2008

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).

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.263-268
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• 2008

The measurement of magnetic field is performed AC magnetic field emission density in driver cab and saloon's compartment of rolling stock. In order to measure magnetic-field emission, a three-axial magnetic-field sensor is used and connected to data process system. The AC magnetic field is checked and analysis through BNC output, DAQ cad and notebook PC. The spectral analysis is performed by short time Fourier transform(STFT) for time-domain emission signal.

• Information and Communications Magazine
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• v.11 no.8
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• pp.60-77
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• 1994

This paper presents a survey of video coding schemes which use wavelet transform for the videophone on very low bit rate commun ication chan nel( ego 10 Kbps Public Service Telephone Network). Firstly, we introduce the standardization efforts to make the low bit rate videophone architecture and the typical application of low bit rate video coding scheme. Secondly, we summarize the several requirements on videophone, delay, encoder/decoder complexity, low bitrate, and progressive transmission capability. Third, we review the basic theory of wavelet transform without much mathematics. We compare the wavelet transform with short-time fourier transform and subband filters. Fourth, we summarize the video coding schemes proposed so far, and evaluate them with Ule requirements. Lastly, we conclude with fu¬ture research directions.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.58-63
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• 1998

B.Widrow established fundamental relations between the least-mean-square (LMS) algorithm and the digital Fourier transform［1］. By extending these relations, we proposed the short time spectra analysis system using the LMS algorithm［2]. In that paper, we used the normal LMS algorithm on the thought of dealing with only real analytical signal. This algorithm minimizes the real mean-square by recursively altering the complex weight vector at each sampling instant. But, the short time spectra analysis sometimes deals with the complex signal that is outputted from complex analog filter. So, in order to optimize and develop this methods, furthermore it is necessary to derive an algorithm for the complex analytical signal. In this paper, we first discuss the new adaptive system for the spectra analysis using the complex LMS algorithm and then derive convergence condition, time constant of coefficient adjustment and frequency resolution by extending the discussion. Finally, the effectiveness of the proposed method is experimentally demonstrated by applying it to the measurement of transfer performance on complex analog filter.

• Bulletin of the Korean Chemical Society
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• v.28 no.9
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• pp.1523-1530
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• 2007

The first reduction peak of the cyclic voltammogram (CV) for sulfur reduction in dimethyl sulfoxide has been studied using time resolved Fourier transform electrochemical impedance spectroscopic (FTEIS) analysis of small potential step chronoamperometric currents. The FTEIS analysis results reveal that the impedance signals obtained during short potential steps can be resolved into electron transfer reactions of two different time constants in a high frequency region. The FTEIS method provides snap shots of impedance profiles during an earlier phase of the reaction, leading to time resolved EIS measurements. Our results obtained by the FTEIS analysis are consistent with a series of electron transfer and chemical equilibrium steps of a complex reaction, making up an ECE (electrochemical-chemical-electrochemical) mechanism postulated from the results of computer simulation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.107-111
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• 2005

It has been reviewed whether it would be suitable that the application of the time-frequency signal analysis techniques to estimate the location of the impact source in plate structure. The STFT(Short Time Fourier Transform), WVD(Wigner-Ville distribution) and CWT(Continuous Wavelet Transform) methods are introduced and the advantages and disadvantages of those methods are described by using a simulated signal component. The essential of the above proposed techniques is to separate the traveling waves in both time and frequency domains using the dispersion characteristics of the structural waves. These time-frequency methods are expected to be more useful than the conventional time domain analyses fer the impact localization problem on a plate type structure. Also it has been concluded that the smoothed WVD can give more reliable means than the other methodologies for the location estimation in a noisy environment.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.357-363
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• 2023

In underwater signal processing, separating individual signals from mixed signals has long been a challenge due to low signal quality. The common method using Short-time Fourier transform for spectrogram analysis has faced criticism for its complex parameter optimization and loss of phase data. We propose a Triple-path Recurrent Neural Network, based on the Dual-path Recurrent Neural Network's success in long time series signal processing, to handle three-dimensional tensors from multi-channel sensor input signals. By dividing input signals into short chunks and creating a 3D tensor, the method accounts for relationships within and between chunks and channels, enabling local and global feature learning. The proposed technique demonstrates improved Root Mean Square Error and Scale Invariant Signal to Noise Ratio compared to the existing method.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2000.11a
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• pp.128-133
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• 2000

The objective of this research is to develop real-time failure detection techniques for damage assessment of composite materials using optical fiber sensors. Signals from matrix cracking or fiber fracture in composite laminates are treated by signal processing unit in real-time. This paper describes the implementation of time-frequency analysis such as the Short Time Fourier Transform(STFT) to determine the time of occurrence of failure. In order to verify the performance of the optical fiber sensor for stress wave detection, we performed pencil break test with EFPI sensor and compared it with that of PZT. The EFPI sensor was embedded in composite beam to sense the failure signals and a tensile test was performed. The signals of the fiber optic sensor when damage occurred were characterized using STFT and wavelet transform. Failure detection system detected the moment of failure accurately and showed good sensitivity with the infinitesimal failure signal.

• 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.