• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.102-108
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• 2008

AC generator plays an important role of power system. The large AC generator fault may lead to large impacts or perturbations in power system. And then the protection of a generator has very important role in maintaining stability in a power system. In present, the DFT(discrete Fourier transform) based RDR(ratio differential relay) had been widely applied to a internal fault of a generator stator winding. But DFT has a serious drawback. In the course of transforming a target signal to frequency domain, time information is lost. DWT uses a time-scale region. This paper proposes an advanced fault detection algorithm using DWT(discrete Wavelet transform) to enhance the drawback of conventional DFT based relaying. To evaluate the performance of the proposed relaying, we used the test data which were sampled with 720 [Hz] per cycle and obtained from ATP(alternative transient program) simulation. And we made a comparative study of conventional DFT based RDR and the proposed relaying.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4A
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• pp.336-344
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• 2005

Discrete Wavelet Transform(DWT) is the oncoming generation of compression technique that has been selected for MPEG4 and JEPG2000, because it has no blocking effects and efficiently determines frequency property of temporary time. In this paper, we propose an efficient bit-serial architecture for the low-power and low-complexity DWT filter, employing two-channel QMF(Qudracture Mirror Filter) PR(Perfect Reconstruction) lattice filter. The filter consists of four lattices(filter length=8) and we determine the quantization bit for the coefficients by the fixed-length PSNR(peak-signal-to-noise ratio) analysis and propose the architecture of the bit-serial multiplier with the fixed coefficient. The CSD encoding for the coefficients is adopted to minimize the number of non-zero bits, thus reduces the hardware complexity. The proposed folded 1D DWT architecture processes the other resolution levels during idle periods by decimations and its efficient scheduling is proposed. The proposed architecture requires only flip-flops and full-adders. The proposed architecture has been designed and verified by VerilogHDL and synthesized by Synopsys Design Compiler with a Hynix 0.35$\mu$m STD cell library. The maximum operating frequency is 200MHz and the throughput is 175Mbps with 16 clock latencies.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.5
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• pp.247-254
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• 2002

This paper describes a fault location algorithm in real combined transmission systems with underground power cable. The algorithm to calculate the fault location was developed using DWT wavelet transform and travelling wave occurred at fault point. And the proposed algorithm is also used the transient signal of one end in stead of the signal information of two ends. On the other hand, in this papers, the method to discriminate fault point between overhead line and cable section is also Proposed. Variety simulations were carried out to verify the accuracy and effectiveness of the proposed algorithm using EMTP/ATFDraw and Matlab. Simulation results show that the proposed method has the excellent ability for discrimination of fault section and fault location in combined transmission systems with power cables.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.3
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• pp.45-50
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• 2010

In this study, Discrete Wavelet Transform(DWT), which can detect more correct QRS complex, approximated through impulse response for reducing complexity to suit real-time system during exercise. Also, rhythm information, which is Arrythmia, Bradycardia and Tachycardia, is provided through continuously monitoring QRS complex. Proposed algorithm is evaluated by computer simulation of ECG signal that is measured during exercise.

• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.113-123
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• 2014

Electrophysiological recordings are considered a reliable method of assessing a person's alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in recognition of alertness level. In this paper, EEG signals have been analyzed using wavelet transform as well as discrete wavelet transform and classification using statistical classifiers such as euclidean and mahalanobis distance classifiers and a promising method SVM (Support Vector Machine). As a result of simulation, the average values of accuracies for the Linear Discriminant Analysis (LDA)-Quadratic, k-Nearest Neighbors (k-NN)-Euclidean, and Linear SVM were 48%, 34.2%, and 86%, respectively. The experimental results show that SVM classification method offer the better performance for reliable classification of the EEG signal in comparison with the other classification methods.

• Structural Engineering and Mechanics
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• v.40 no.6
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• pp.745-762
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• 2011

Damage detection methodologies based on the direct examination of the nonlinear-nonstationary characteristics of the structure dynamic response may play an important role in online structural health monitoring applications. Different signal processing based damage detection methodologies have been proposed based on the uncovering of spikes in the high frequency component of the structural response obtained via Discrete Wavelet transforms, Hilbert-Huang transforms or high pass filtering. The performance of these approaches in systems subjected to different types of excitation is evaluated in this paper. It is found that in the case of random excitations, like earthquake accelerations, the effectiveness of such methodologies is limited. An alternative damage detection approach using the Continuous Wavelet Transform (CWT) is also evaluated to overcome this limitation. Using the CWT has the advantage that the central frequencies at which it operates can be defined by the user while the frequency bands of the detail functions obtained via DWT are predetermined by the sampling period of the signal.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.4
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• pp.271-276
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• 2021

Arc fault detector based on multilevel DWT with analysis of high-frequency noise components over 100 kHz is proposed in this study to improve the performance in detecting serial arcs and distinguishing them from inverter noise in PV systems. PV inverters generally operate at a frequency range of 20-50 kHz for switching operation and maximum power tracking control, and the effect of these frequency components on the signal for arc detection leads to negative arc detection. High-speed ADC and multilevel DWT are used in this study to analyze frequency components above 100 kHz. Such high frequency components are less influenced by inverter noise and utilized to detect as well as separate DC series arc from inverter noise. Arc detectors identify the input current of PV inverters using a Rogowski coil. The sensed signal is filtered, amplified, and used in 800kSPS ADC and DWT analysis and arc occurrence determination in DSP. An arc detection simulation facility in UL1699B was constructed and AFD tests the proposed detector were conducted to verify the performance of arc detection and performance of distinction of the negative arc. The satisfactory performance of the arc detector meets the standard of arc detection and extinguishing time of UL1699B with an arc detection time of approximately 0.11 seconds.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.6
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• pp.33-43
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• 2003

In this paper, we proposed a real-time watermarking algorithm to be combined and to work with a DWT(Discrete Wavelet Transform)-based image compressor. To reduce the amount of computation in selecting the watermarking positions, the proposed algorithm uses a pre-established look-up table for critical values, which was established statistically by computing the correlation according to the energy values of the corresponding wavelet coefficients. That is, watermark is embedded into the coefficients whose values are greater than the critical value in the look-up table which is searched on the basis of the energy values of the corresponding level-1 subband coefficients. Therefore, the proposed algorithm can operate in a real-time because the watermarking process operates in parallel with the compression procession without affecting the operation of the image compression. Also it improved the property of losing the watermark and the efficiency of image compression by watermark inserting, which results from the quantization and Huffman-Coding during the image compression. Visual recognizable patterns such as binary image were used as a watermark The experimental results showed that the proposed algorithm satisfied the properties of robustness and imperceptibility that are the major conditions of watermarking.

• Journal of Advanced Navigation Technology
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• v.10 no.3
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• pp.219-225
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• 2006

This paper presents a method of object classification based on discrete wavelet transform (DWT) and fuzzy inference(FI). It concentrated not only on the design of fuzzy inference algorithm which is suitable for low speed uninhabited transportation such as, conveyor but also on the minimize the number of fuzzy rule. In the preprocess of feature extracting, feature parameters are extracted by using characteristics of the coefficients matrix of DWT. Such feature parameters as area, perimeter and a/p ratio are used obtained from DWT coefficients blocks. Secondly, fuzzy if - then rules that can be able to adapt the variety of surroundings are developed. In order to verify the performance of proposed scheme, In the middle of fuzzy inference, the Mamdani's and the Larsen 's implication operators are utilized. Experimental results showed that proposed scheme can be applied to the variety of surroundings.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.1
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• pp.116-124
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• 2009

This paper proposes a new Artificial-Intelligent(AI) Power Quality(PQ) diagnosis algorithm using Discrete Wavelet Transform(DWT), Fast Fourier Transform(FFT), Root-Mean-Square(RMS) value. The developed algorithm is able to detect and classify the PQ problems such as the transient, the voltage sag, the voltage swell, the voltage interruption and the total harmonics distortion. The 15.36[kHz] sampling frequency is used to measure the voltages in a power system. The measured signals are used for DWT, FFT, RMS calculation. For AI diagnosis of the PQ problems, a simple multi-layered Artificial Neural Network(ANN) with the back-propagation algorithm is adopted, programmed in C++ and tested in PSIM simulation studies. Finally, the algorithm, which is installed in MP PQ+256 with TI DSP320C6713, is proved to diagnose the PQ problems efficiently.