• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2002.05a
• /
• pp.585-589
• /
• 2002

In this paper, hierarchical retrieval system based on efficient feature extraction is proposed. In order to retrieval the image with robustness for geometrical transformation such as translation, scaling, and rotation. After performing the 2-level wavelet transform on image, We extract moment in low-level subband which was subdivided into subimages and texture feature, contrast of GLCM(Gray Level Co-occurrence Matrix). At first we retrieve the candidate images in database by the ones of image. To perform a more accurate image retrieval, the edge information on the high-level subband was subdivided horizontally, vertically and diagonally. And then, the energy rate of edge per direction was determined and used to compare the energy rate of edge between images for higher accuracy.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.57 no.2
• /
• pp.91-95
• /
• 2008

The large AC generator fault may lead to large impacts or perturbations in power system. The generator protection control systems in Korea have been imported and operated through a turn-key from overseas entirely. Therefore a study of the generator protection field has in urgent need for a stable operation of the imported goods. In present, the algorithm using the current ratio differential relaying based DFT for stator winding protection or a fault detection had been applied that of internal fault protection of a generator. the DFT used for the analysis of transient state signal conventionally had defects losing a time information in the course of transforming a target signal to frequency domain. In this paper, the discrete wavelet transform (DWT) was applied a fault detection of the generator being superior to a transient state signal analysis and being easy to real time realization. The fault signals after executing a terminal fault modeling collect using a MATLAB package, and calculate the wavelet coefficients through the process of a muiti-level decomposition (MLD). The proposed algorithm for a fault detection using the Daubechies WT (wavelet transform) was executed with a C language and the commend line function for the real time realization after analyzing MATLAB's graphical interface. The advanced technique had improved faster a speed of fault discrimination than a conventional DFR based on DFT.

• Economic and Environmental Geology
• /
• v.28 no.5
• /
• pp.527-533
• /
• 1995

A disadvantage of Fourier analysis is that frequency information can only be extracted for the complete duration of a signal f(t). Since the Fourier transform integral extends over all time, from $-{\infty}$ to $+{\infty}$), the information it provides arises from an average over the whole length of the signal. If there is a local oscillation representing a particular feature, this will contribute to the calculated Fourier transform $F({\omega})$, but its location on the time axis will be lost There is no way of knowing whether the value of $F({\omega})$ at a particular ${\omega}$ derives from frequencies present throughout the life of f(t) or during just one or a few selected periods. This disadvantage is overcome in wavelet analysis which provides an alternative way of breaking a signal down into its constituent parts. The main advantage of the wavelet transform over the conventional Fourier transform is that it can not only provide the combined temporal and spectral features of the signal, but can also localize the target information in the time-frequency domain simultaneously. The wavelet transform distinguishes itself from Short Time Fourier Transform for time-frequency analysis in that it has a zoom-in and zoom-out capability.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2000.11a
• /
• pp.796-801
• /
• 2000

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a signal classification method for diagnosing the rotating machinery using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them are compared with each other. This paper is focused on the development of an advanced signal classifier to automatise the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• Journal of Sensor Science and Technology
• /
• v.27 no.6
• /
• pp.407-413
• /
• 2018

Most sleep apnea patients exhibit severe snoring, and long-lasting sleep apnea may cause insomnia, hypertension, cardiovascular diseases, stroke, and other diseases. Although polysomnography is the typical sleep diagnostic method to accurately diagnose sleep apnea by measuring a variety of bio-signals that occur during sleep, it is inconvenient as the patient has to sleep with attached electrodes at the hospital for the diagnosis. In this study, a diagnostic pillow is designed to measure respiration, heart rate, and snoring during sleep, using only one polyvinylidene fluoride (PVDF) sensor. A PVDF sensor with piezoelectric properties was inserted into a specially made instrument to extract accurate signals regardless of the posture during sleep. Wavelet analysis was used to identify the extractability and frequency domain signals of respiration, heart rate, and snoring from the signals generated by the PVDF sensor. In particular, to separate the respiratory signal in the 0.2~0.5 Hz frequency region, wavelet analysis was performed after removing 1~2 Hz frequency components. In addition, signals for respiration, heart rate, and snoring were separated from the PVDF sensor signal through a Butterworth filter and median filter based on the information obtained from the wavelet analysis. Moreover, the possibility of measuring sleep apnea from these separated signals was confirmed. To verify the usefulness of this study, data obtained during sleeping was used.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.5
• /
• pp.2434-2453
• /
• 2019

To improve the separation performance of time-frequency overlapped radar and communication signals from a single channel, this paper proposes an effective separation method based on an improved empirical wavelet transform (EWT) that introduces a fast boundary detection mechanism. The fast boundary detection mechanism can be regarded as a process of searching, difference optimization, and continuity detection of the important local minima in the Fourier spectrum that enables determination of the sub-band boundary and thus allows multiple signal components to be distinguished. An orthogonal empirical wavelet filter bank that was designed for signal adaptive reconstruction is then used to separate the input time-frequency overlapped signals. The experimental results show that if two source components are completely overlapped within the time domain and the spectrum overlap ratio is less than 60%, the average separation performance is improved by approximately 32.3% when compared with the classic EWT; the proposed method also improves the suitability for multiple frequency shift keying (MFSK) and reduces the algorithm complexity.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.37 no.1
• /
• pp.1-10
• /
• 2000

In this paper, a new digital watermarking algorithm using wavelet transform is proposed. Wavelet transform is widely used for image processing, because of its multiresolution characteristic which conforms to the principles of the human visual system(HVS). It is also very efficient for localizing images in the spatial and frequency domain. Since wavelet coefficients can be characterized by the gaussian distribution, the proposed algorithm uses a gaussian distributed random vector as the watermark in order to achieve invisibility and robustness. After the original image is transformed using DWT(Discrete Wavelet Transform), the coefficients of all subbands including LL subband are utilized to equally embed the watermark to the whole image. To select perceptually significant coefficients for each subband, we use level-adaptive thresholding. The watermark is embedded to the selected coeffocoents, using different scale factors according to the wavelet characteristics. In the process of watermark detection, the similarity between the original watermark and the extracted watermark is calculated by using vector projection method. We analyze the performance of the proposed algorithm, compared with other transform-domain watermarking methods. The experimental results tested on various images show that the proposed watermark is less visible to human eyes and more robust to image compressions, image processings, geometric transformations and various noises, than the existing methods.

• Proceedings of the IEEK Conference
• /
• 1999.06a
• /
• pp.69-72
• /
• 1999

In this paper, an efficient time domain equalization algorithm for discrete wavelet multitone(DWMT) data transmission is developed. In this algorithm, the time domain equalizer(TEQ) consists of two stages, i.e., the channel impulse response shortening equalizer(TEQ-S) in the first stage and the channel frequency flattening equalizer(TEQ-F) in the second stage. TEQ-S reduces the length of transmission channel impulse response to decrease intersymbol interference(ISI) followed by TEQ-F that enhances the channel frequency response characteristics to the level of an ideal channel, hence diminishes the bit error rate. TEQ-S is implemented using the least-squares(LS) method, while TEQ-F is designed by using the least mean-square(LMS) algorithm. Since DWMT system also requires of the frequency domain equalizer in order to further reduce ICI and ISI the hardware complexity is an another concern. However, by adopting an well designed and trained TEQ, the hardware complexity of the whole DWMT system can be greatly reduced.

• Journal of Intelligence and Information Systems
• /
• v.5 no.1
• /
• pp.103-123
• /
• 1999

Input filtering as a preprocessing method is so much crucial to get good performance in time series forecasting. There are a few preprocessing methods (i.e. ARMA outputs as time domain filters, and Fourier transform or wavelet transform as time-frequency domain filters) for handling time series. Specially, the time-frequency domain filters describe the fractal structure of financial markets better than the time domain filters due to theoretically additional frequency information. Therefore, we, first of all, try to describe and analyze specially some issues on the effectiveness of different filtering methods from viewpoint of the performance of a neural network based forecasting. And then we discuss about neural network model architecture issues, for example, what type of neural network learning architecture is selected for our time series forecasting, and what input size should be applied to a model. In this study an input selection problem is limited to a size selection of the lagged input variables. To solve this problem, we simulate on analyzing and comparing a few neural networks having different model architecture and also use an embedding dimension measure as chaotic time series analysis or nonlinear dynamic analysis to reduce the dimensionality (i.e. the size of time delayed input variables) of the models. Throughout our study, experiments for integration methods of joint time-frequency analysis and neural network techniques are applied to a case study of daily Korean won / U. S dollar exchange returns and finally we suggest an integration framework for future research from our experimental results.

• Proceedings of the Optical Society of Korea Conference
• /
• 2009.02a
• /
• pp.359-360
• /
• 2009