• International Journal of Precision Engineering and Manufacturing
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• v.6 no.4
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• pp.26-30
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• 2005

A rotor run-up or run-down process provide more useful information for modal analysis than normal operation conditions. A traditional difficulty associated with rotor run-up or run-down analysis is the non-stationary nature of vibration data. This paper compares Short-Time Fourier Transform (STFT) and the wavelets analysis in these non-stationary signal analyses. An Adaptive Wavelet Analysis (AWT) is proposed to analyze these signals. Although simulations and experiments in a simple rotor-bearing system show that both STFT and AWT can be used to analyze non-stationary vibration signals in rotor dynamics, proposed AWT provides better results than STFT analysis. From the amplitude-frequency curve obtained by AWT, the modal frequency and damping ratio are calculated. This paper also analyzes the characteristics of signals when the shaft touches the outer hoop in a run-up process. The AWT can give a good result in this complex dynamic analysis of the touching process.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.2 s.21
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• pp.38-48
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• 2005

Instantaneous frequency of doppler signals is used to get the information of the relative velocity and the miss distance between a missile and a corresponding target. In this paper, we have performed time-frequency analysis and instantaneous frequency estimation with Short Time Fourier Transform(STFT), Wigner Ville Distribution(WVD) and Continuous Wavelet Transform(CWT) about the doppler signals generated by moving targets. Performance evaluation was performed using simulated doppler signals generated by a single moving target and two moving targets. From the results of the time-frequency analysis, we found that WVD method was the most efficient instantaneous frequency estimator among the three methods. But in case of two moving targets, WVD method got cross talks and CWT method got oscillation when two doppler frequencies were close to each other.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1103-1108
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• 2023

In this paper, we propose a new algorithm for attenuating the background noises in acoustic signal. This algorithm improves the noise attenuation performance by using the FNN(: Full-connected Neural Network) deep learning algorithm instead of the existing adaptive filter after wavelet transform. After wavelet transforming the input signal for each short-time period, noise is removed from a single input audio signal containing noise by using a 1024-1024-512-neuron FNN deep learning model. This transforms the time-domain voice signal into the time-frequency domain so that the noise characteristics are well expressed, and effectively predicts voice in a noisy environment through supervised learning using the conversion parameter of the pure voice signal for the conversion parameter. In order to verify the performance of the noise reduction system proposed in this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed. As a result of the experiment, the proposed deep learning algorithm improved Mean Square Error (MSE) by 30% compared to the case of using the existing adaptive filter and by 20% compared to the case of using the STFT(: Short-Time Fourier Transform) transform effect was obtained.

• Geomechanics and Engineering
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• v.13 no.1
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• pp.161-171
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• 2017

The underlying ground state of a railway plays a significant role in maintaining the integrity of the overlying concrete slab and ultimately supporting the train load. While effective nondestructive tests have been used to evaluate the rail track system, they can only be performed during non-operating time due to the stress wave generated by active sources. In this study, finite element numerical simulations are conducted to investigate the feasibility of detecting unfavorable substructure conditions by using a moving train load. First, a train load module is developed by converting the train load into time-variant equivalent forces. The moving forces based on the shape functions are applied at the nodes. A parametric study that takes into account the bonding state and the train class is then performed. All the synthetic signals obtained from numerical simulations are analyzed at the frequency domain using a Fast Fourier transform (FFT) and at the time-frequency domain using a Short-Time Fourier transform (STFT). The presence of a void condition amplifies the acceleration amplitude and the vibration response. This study confirms the feasibility of using a moving train load to systematically evaluate a rail track system.

• Journal of Korea Multimedia Society
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• v.16 no.11
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• pp.1325-1337
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• 2013

Music affects sensitivity and emotion of human, emotional power of the music has been applied to various fields. Especially, to visualize as well as listen to music is able to create various atmosphere. In this paper, we proposed sensitivity control system for interaction with people to merge music and lighting. Because existing FT(Fourier Transform) has not information about the time, to analyze information of changed signal according to the time is difficult. In order to solve such a problem, we use STFT(Short Time Fourier Transform) method to analyze music signal. and also, we classified music for three genre and compared the frequency characteristics according to genre, and control the color, brightness of LED light based on the frequency components within analysis range. Unlike existing LED lighting control study using music, we had color control of emotional lighting and brightness control using variation amount of music signal in this paper. Proposed lighting control system will be able to utilize various industry fields as well as emotional lighting.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2005.11a
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• pp.27-30
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• 2005

This paper studied the PVDF(polyvinylidene fluoride) and Acoustic Emission sensors characteristics of the laminated composite panels under the low velocity impact. The various impact test by changing impact height is performed on the instrumented drop weight impact tester. The STFT(short time Fourier transform) and WT(wavelet transform) are used to decompose the each sensor signals. A ultrasonic C-scan and digital scope are used to define damaged area in each case. The test result indicated that the individual sensor signals involve the damage initiation and development.

• Composites Research
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• v.20 no.6
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• pp.21-27
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• 2007

Low-velocity impact damage in a composite stiffened panel was detected using built-in piezoelectric active sensor arrays. Using these piezoelectric active sensors, various diagnostic signals were generated to propagate Lamb waves through the structure and the responses were picked up to detect changes in the structure's vibration signature due to the damage. Three algorithms - ADI(Active Damage Interrogation), TD RMS (Time Domain Root Mean Square) and STFT (Short Time Fourier Transform) - were examined to express the features of the signal changes as one damage index. From damage detecting tests, two impact induced delaminations were detected and the location was estimated with the algorithms and diagnostic signals.

• Journal of Sensor Science and Technology
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• v.32 no.4
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• pp.246-251
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• 2023

Internet of Things (IoT) systems process signals from various sensors using signal processing algorithms suitable for the signal characteristics. To analyze complex signals, these systems usually use signal processing algorithms in the frequency domain, such as fast Fourier transform (FFT), filtering, and short-time Fourier transform (STFT). In this study, we propose a multi-mode sensor signal processor (SSP) accelerator with an FFT-based hardware design. The FFT processor in the proposed SSP is designed with a radix-2 single-path delay feedback (R2SDF) pipeline architecture for high-speed operation. Moreover, based on this FFT processor, the proposed SSP can perform filtering and STFT operation. The proposed SSP is implemented on a field-programmable gate array (FPGA). By sharing the FFT processor for each algorithm, the required hardware resources are significantly reduced. The proposed SSP is implemented and verified on Xilinxh's Zynq Ultrascale+ MPSoC ZCU104 with 53,591 look-up tables (LUTs), 71,451 flip-flops (FFs), and 44 digital signal processors (DSPs). The FFT, filtering, and STFT algorithm implementations on the proposed SSP achieve 185x average acceleration.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.7
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• pp.1575-1581
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• 2003

As a society has progressed rapidly toward a highly advanced digital information age, a multimedia communication service for acquisition, transmission and storage of image data as well as voice has being commercialized externally and internally. However, in the process of digitalization or transmission of data, noise is generated by several causes, and researches for eliminating those noises have been continued until now. There were the existing FFT(fast fourier transform) and STFT(short time fourier transform) for removing noise but it's impossible to know information about time and time-frequency localization capabilities has conflictive relationship. Therefore, for overcoming these limits, wavelet transform which is presented as a new technique of signal processing field is being applied in many fields recently. Because it has time-frequency localization capabilities it's Possible for multiresolution analysis as well as easy to analyze various signal. And when two wavelet base were designed to form Hilbert transform pair, wavelet pair provide superior performance than the existing DWT(discrete wavelet transform) in data characteristic detection. Therefore in this parer, we removed impulse noise by using adaptive-length median filter and two dyadic wavelet base which is designed by truncated coefficient vector.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.151-157
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• 2019

Recently, monitoring respiration of people has been of interest using non-invasive method. Among the vital signals usually used for indicating health status, non-invasive and portable device based monitoring respiratory status is practically useful and enable one to promptly deal with abnormal physical status. This paper proposes the approach to real-time detection of apnea signal based on Short-Time-Fourier-Transform(STFT). Contrary to the analysis of a signal in frequency domain using Fast-Fourier Transform, this paper employs Short-time-Fourier-Transform so that frequency response can be analyzed in short time interval. The respiratory signal is acquired using UWB radar sensor that enables one to obtain respiration signal in contactless way. Detection of respiratory status is carried out by analyzing frequency response, and classification of respiratory status can be provided. In particular, STFT is employed to analyze respiratory signal in real-time, leading to effective analysis of the respiratory status in practice. In the case of existence of noise in the signal, appropriate filtering process is employed as well. The proposed method is straightforward and is workable in practice to analyze the respiratory status of people. To evaluate the proposed method, experimental results are provided.