• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.115-126
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• 2023

Due to the fluctuating random and periodical nature of renewable energy generation power quality disturbances occurred more frequently in power generation transformation transmission and distribution. Various power quality disturbances may lead to equipment damage or even power outages. Therefore it is essential to detect and classify different power quality disturbances in real time automatically. The traditional PQD identification method consists of three steps: feature extraction feature selection and classification. However, the handcrafted features are imprecise in the feature selection stage, resulting in low classification accuracy. This paper proposes a deep neural architecture based on Convolution Neural Network and Long Short Term Memory combining the time and frequency domain features to recognize 16 types of Power Quality signals. The frequency-domain data were obtained from the Fast Fourier Transform which could efficiently extract the frequency-domain features. The performance in synthetic data and real 6kV power system data indicate that our proposed method generalizes well compared with other deep learning methods.

• Journal of Mechanical Science and Technology
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• v.17 no.3
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• pp.391-399
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• 2003

The thermo-acoustic emission (AE) technique has been applied for nondestructive characterization of composite laminates subjected to cryogenic cooling. Thermo-AE events during heating and cooling cycles showed a Kaiser effect. An analysis of the thermo-AE behavior obtained during the 1st heating period suggested a method for determining the stress-free temperature of the composite laminates. Three different thermo-AE types classified by a short-time Fourier transform of AE signals enabled to offer a nondestructive estimation of the cryogenic damages of the composites, in that the different thermo-AE types corresponded to secondary microfracturing in the matrix contacting between crack surfaces and some abrasive contact between broken fiber ends during thermal load cycles.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1092-1097
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• 2014

In this paper, we propose the grid-based output control method for wind farm. To do this, we propose the output control method using the PSO(Particle Swarm Optimization) algorithm. Secondly, we propose the method for detecting the harmonics using STFT(Short-Time Fourier Transform) algorithm. And last, we propose the method for compensating the harmonics using neural network. Finally, we show the effectiveness and feasibility of the proposed method though some simulations.

• Proceedings of the Korean Fiber Society Conference
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• 1997.10a
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• pp.477-480
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• 1997

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.10 s.113
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• pp.913-919
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• 2006

This paper presents a technique for motion compensation of ISAR(Inverse SAR) images for a moving target. If a simple fourier transform is employed to obtain ISAR image for a moving target, the image is usually blurred. These images blurring problem can be solved with the time-frequency transform. In this paper, motion compensation algorithms of ISAR image such as STFT(Short Time Fourier Transform), GWT(Gabor Wavelet Transform) are described. In order to show the performances of each algorithm, we use scattering wave of the ideal point scatterers and simulated MIG-25 to obtain motion compensated ISAR image, and display the resolution of STFT and GWT ISAR image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2548-2554
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• 2010

This paper describes a design of fast Fourier transform(FFT) processor for EEG(electroencephalogram) signal analysis for health care services. Hamming window function with 1/2 overlapping is adopted to perform short-time FFT(ST-FFT) of a long period EEG signal occurred in real-time. In order to analyze efficiently EEG signals which have frequency characteristics in the range of 0 Hz to 100 Hz, a 256-point FFT processor is designed, which is based on a single-memory bank architecture and the radix-4 algorithm. The designed FFT processor has been verified by FPGA implementation, and has high accuracy with arithmetic error less than 2%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.88-91
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• 2010

This paper describes a design of fast Fourier transform(FFT) processor for EEG(electroencephalogram) signal analysis for health care services. Hamming window function with 1/2 overlapping is adopted to perform short-time FFT(ST-FFT) of a long period EEG signal occurred in real-time. In order to analyze efficiently EEG signals which have frequency characteristics in the range of 0 Hz to 100 Hz, a 256-point FFT processor based on single-memory bank architecture and radix-4 algorithm is designed. The designed FFT processor has high accuracy with arithmetic error less than 3%.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.9
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• pp.425-432
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• 2000

Wavelet transform is a new method fro electric power quality analysis. Several types of mother wavelets are compared using voltage sag data. Investigations on the use of some mother wavelets, namely Daubechies, Symlets, Coiflets, Biorthogonal, are carried out. On the basis of extensive investigations, optimal mother wavelets for the detection of voltage sag are chosen. The recommended mother wavelet is 'Daubechies 4(db4)' wavelet. 'db4', the most commonly applied mother wavelet in the power quality analysis, can be used most properly in disturbance phenomena which occurs rapidly for a short time. This paper presents a discrete wavelet transform approach for determining the beginning time and end time of voltage sags. The technique is based on utilising the maximum value of d1(at scale 1) coefficients in multiresolution analysis(MRA) based on the discrete wavelet transform. The procedure is fully described, and the results are compared with other methods for determining voltage sag duration, such as the RMS voltage and STFT(Short-Time Fourier Transform) methods. As a result, the voltage sag detection using wavelet transform appears to be a reliable method for detecting and measuring voltage sags in power quality disturbance analysis.

• The Journal of the Acoustical Society of Korea
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• v.23 no.5
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• pp.353-361
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• 2004

Propagation of the guided waves in a steam generator (SG) tube was investigated. Dispersion curves and the incident angles corresponding to the specific modes were calculated for the SG tube. The modes of guided wave were identified by time-frequency diagrams obtained by short time Fourier transform. Group velocities were also determined from the time-frequency diagrams obtained at the different separations of transducers. In experiment. distinct mode conversion was not observed when the guided ultrasound passed curved region of the S/G tube. The optimum mode of guided wave for the inspection of SG tube was suggested and verified by experiments.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.12
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• pp.1371-1379
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• 2010

This paper presents an analysis of impulse dispersion for impulse radio ultra-wide band(IR-UWB) antenna. A set of antenna structure configurations are highlighted with verification based on the STFT(Short Time Fourier Transform) in 3.1~5.1 GHz: first, a taper-slotted antenna allowing the optimal impulse transmission, and second, 4 types of the omni-directional IR-UWB antenna using different feed structures(microstrip line, and CPW(Coplanar Waveguide)). The proposed STFT allows the analysis of the IR-UWB antenna's dispersion characteristic.