• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.832-837
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• 2015

The group method of data handling (GMDH) algorithm has proven to be a powerful and effective way to extract rules or polynomials from an electric load pattern. However, because it is nonstationary, the load pattern needs to be decomposed using a discrete wavelet transform. In addition, if a load pattern has a complicated curve pattern, GMDH should use a higher polynomial, which requires complex computing and consumes a lot of time. This paper suggests a method for short-term electric load forecasting that uses a wavelet transform and a GMDH algorithm. Case studies with the proposed algorithm were carried out for one-day-ahead forecasting of hourly electric loads using data during the years 2008-2011. To prove the effectiveness of our proposed approach, the results were evaluated and compared with those obtained by Holt-Winters method and artificial neural network. Our suggested method resulted in better performance than either comparison group.

• Journal of Korea Multimedia Society
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• v.21 no.1
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• pp.61-68
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• 2018

In this paper, we present an effective encryption algorithm for 3D printing models in the frequency domain of discrete cosine transform to prevent illegal copying, access in the secured storage and transmission. Facet data of 3D printing model is extracted to construct a three by three matrix that is then transformed to the frequency domain of discrete cosine transform. The proposed algorithm is based on encrypting the DC coefficients of matrixes of facets in the frequency domain of discrete cosine transform in order to generate the encrypted 3D printing model. Experimental results verified that the proposed algorithm is very effective for 3D printing models. The entire 3D printing model is altered after the encryption process. The proposed algorithm is provide a better method and more security than previous methods.

• Journal of the Korea institute for structural maintenance and inspection
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• v.9 no.2
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• pp.129-136
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• 2005

The vehicle loading test under the strict traffic control is generally carried out as a present practice in an evaluation process of the bearing capacity of a bridge. The quasi-static load test is recently proposed to mitigate the traffic condition of test, and analyze the disturbed acceleration time-history data of free vibration due to the ambient traffic on the bridge by Fourier transform to calculate only the natural frequencies of the bridge. The calculated frequencies have some errors due to the analysis technique as well as the influence of ambient traffic loads, and in addition to it is cumbersome to obtain the free vibration data during a quasi-static load test. In this study, the wavelet transform technique using Morlet wavelet is used to analyze the acceleration data recorded during a quasi-static load test on a bridge and calculate the natural frequencies and the modal damping ratios of the bridge. The study results show that the wavelet transform technique is a reliable and reasonable method to analyze test data and obtain the natural frequencies and the modal damping ratios of a bridge regardless of the data types i.e. free or forced vibrations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4C
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• pp.533-540
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• 2004

This paper proposes a new multispectral image data compression algorithm that can efficiently reduce spatial and spectral redundancies by applying classified prediction, a Karhunen-Loeve transform (KLT), and the three-dimensional set partitioning in hierarchical trees (3-D SPIHT) algorithm in the wavelet transform (WT) domain. The classification is performed in the WT domain to exploit the interband classified dependency, while the resulting class information is used for the interband prediction. The residual image data on the prediction errors between the original image data and the predicted image data is decorrelated by a KLT. Finally, the 3-D SPIHT algorithm is used to encode the transformed coefficients listed in a descending order spatially and spectrally as a result of the WT and KLT. Simulation results showed that the reconstructed images after using the proposed algorithm exhibited a better quality and higher compression ratio than those using conventional algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.1
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• pp.119-126
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• 2012

H.264/AVC can use three transforms depending on types of residual data which are to be coded. H.264/AVC always executes $4{\times}4$ DCT transform. In $16{\times}16$ intra mode only, $4{\times}4$ Hadamard transform for luma DC coefficients and $2{\times}2$ Hadamard transform for chroma DC coefficients are performed additionally. Quantization is carried out to achieve further data compression after transform coding is completed. In this paper, the hardware implementation for DCT transform, Hadamard transform and quantization is studied. Especially, the proposed architecture adopting the pipeline technique can output a quantized result per clock cycle after 33-clock cycle latency. The proposed architecture is coded in Verilog-HDL and synthesized using Xilinx 7.1i ISE tool. The operating frequency is 106MHz at SPARTAN3S-1000. The designed IP can process maximum 33-frame at $1920{\times}1080$ HD resolution.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.569-579
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• 2013

In this study, land-cover classification was implemented using features extracted from multi-temporal SAR data through 3D wavelet transform and the applicability of the 3D wavelet transform as a feature extraction approach was evaluated. The feature extraction stage based on 3D wavelet transform was first carried out before the classification and the extracted features were used as input for land-cover classification. For a comparison purpose, original image data without the feature extraction stage and Principal Component Analysis (PCA) based features were also classified. Multi-temporal Radarsat-1 data acquired at Dangjin, Korea was used for this experiment and five land-cover classes including paddy fields, dry fields, forest, water, and built up areas were considered for classification. According to the discrimination capability analysis, the characteristics of dry field and forest were similar, so it was very difficult to distinguish these two classes. When using wavelet-based features, classification accuracy was generally improved except built-up class. Especially the improvement of accuracy for dry field and forest classes was achieved. This improvement may be attributed to the wavelet transform procedure decomposing multi-temporal data not only temporally but also spatially. This experiment result shows that 3D wavelet transform would be an effective tool for feature extraction from multi-temporal data although this procedure should be tested to other sensors or other areas through extensive experiments.

• Journal of the Korean earth science society
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• v.34 no.1
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• pp.69-80
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• 2013

Terrain data is one of important basic data in various areas of Earth science. Recently, finer DEM data is available, which necessary to develop a method that deals with such huge data efficiently. This study was conducted on the lossless compression of DEM data and efficient partial reconstruction of terrain information from compressed data. In this study, we compressed the wavelet coefficients of DEM, obtained from integer wavelet transform (IWT) by entropy encoding. CDF (Cohen-Daubechies-Feauveau) 3.5 wavelet showed the best compression ratio of about 45.4% and the optimum decomposition level was 3. Results also showed that a small region of terrain could be restored from the inverse wavelet transform with a part of the wavelet coefficients that are related to such region instead of whole reconstruction. We discussed the potential applications of the terrain data compression for precise gravity terrain correction.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2002.05a
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• pp.219-223
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• 2002

Voltage and current signals during impulse tests on transformer are treated as non-stationary signals. A new method incorporating signal-processing method such as Wavelets and courier transform is proposed for failure identification. It is now possible to distinguish failure during impulse tests. The method is experimentally validated on a transformer winding. The wavelet transforms enables the detection of the time of occurrence of switching or failure events. After establishing the time of occurrence, the original waveform is split into two or more sections. The wavelet transform has ability to analysis the failure signal on time domain as well as frequency domain. Therefore, the wavelet transform is superior than courier transform to analysis the failure signal. In this paper, the fact was proved by real data which was achieved.

• Communications for Statistical Applications and Methods
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• v.22 no.2
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• pp.159-172
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• 2015

This paper considers the problem of reconstructing an underlying signal from noisy data. This paper presents a reconstruction method based on synchrosqueezed wavelet transform recently developed for multiscale representation. Synchrosqueezed wavelet transform based on continuous wavelet transform is efficient to estimate the instantaneous frequency of each component that consist of a signal and to reconstruct components. However, an objective selection method for the optimal number of intrinsic mode type functions is required. The proposed method is obtained by coupling the synchrosqueezed wavelet transform with cross-validation scheme. Simulation studies and musical instrument sounds are used to compare the empirical performance of the proposed method with existing methods.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.2
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• pp.87-92
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• 2012

Voltage and current phasor estimation has been executed by GPS-based synchronized PMU, which has become an important way of wide-area blackout protection for the prevention of expending faults in a power system. The PMU technique can not easily get the field data and it is impossible to share information, so that there has been used a FNET(Frequency Monitoring Network) method for the wide-area intelligent protection in USA. It consists of FDR(Fault Disturbance Recorder) and IMS(Information Management System). Therefore, FDR must provide an optimal frequency estimation method that is robust to noise and failure. In this paper, we present comparative studies for the frequency estimation method using IRDWT(Improved Recursive Discrete Wavelet Transform), FRDWT(Fast Recursive Discrete Wavelet Transform), and DFT(Discrete Fourier Transform). The Republic of Korea345[kV] power system modeling data by EMTP-RV are used to evaluate the performance of the proposed two kinds of RDWT(Recursive Discrete Wavelet Transform) and DFT. The simulation results show that the proposed frequency estimation technique using FRDWT could be the optimal frequency measurement method, and thus be applied to FDR.