• Journal of Biomedical Engineering Research
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• v.24 no.2
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• pp.99-106
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• 2003

In this paper, a method of discriminating EEG for diagnoses of mental activity is proposed. The proposed method for classification of schizophrenia and normal EEG is based on the wavelet transform and the artificial neural network. The wavelet coefficients of $\alpha$ band, $\beta$ band, $\theta$ band, and $\delta$ band are obtained using the wavelet transform. The magnitude, mean, and variance of wavelet coefficients for each EEG band are applied to the input data of the system's ANN. The architecture of the ANN s a four layered feedforward network with two hidden layer which implements the error back propagation learning algorithm. Through the classification of schizophrenia composed of 19 ANNs corresponding to 19 channels, the classifying system show that it can classify the 100% of the normal EEG group and the 86.67% of the schizophrenia EEG group.

• The Journal of the Acoustical Society of Korea
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• v.13 no.4
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• pp.60-68
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• 1994

In this paper we propose a new Hybrid learning rule applied to multilayer feedforward neural networks, which is constructed by combining Hebbian learning rule that is a good feature extractor and Back-Propagation(BP) learning rule that is an excellent classifier. Unlike the BP rule used in multi-layer perceptron(MLP), the proposed Hybrid learning rule is used for uptate of all connection weights except for output connection weigths becase the Hebbian learning in output layer does not guarantee learning convergence. To evaluate the performance, the proposed hybrid rule is applied to classifier problems in two dimensional space and shows better performance than the one applied only by the BP rule. In terms of learning speed the proposed rule converges faster than the conventional BP. For example, the learning of the proposed Hybrid can be done in 2/10 of the iterations that are required for BP, while the recognition rate of the proposed Hybrid is improved by about $0.778\%$ at the peak.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.7
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• pp.726-735
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• 2009

In this paper, we propose an approach to signal model-based fault detection and diagnosis system for induction motors. The current fault detection techniques used in the industry are limit checking techniques, which are simple but cannot predict the types of faults and the initiation of the faults. The system consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, the system extracts the significant features from sound signals using combination of variance, cross-correlation and wavelet. Consequently, the pattern classification technique is applied to the fault diagnosis process to recognize the system faults based on faulty symptoms. The sounds generated from different kinds of typical motor's faults such as motor unbalance, bearing misalignment and bearing loose are examined. We propose two approaches for fault detection and diagnosis system that are waveletand-variance-based and wavelet-and-crosscorrelation-based approaches. The results of our experiment show more than 95 and 78 percent accuracy for fault classification, respectively.

• Journal of Yeungnam Medical Science
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• v.9 no.2
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• pp.321-326
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• 1992

The study consisted of all patients over 35years old undergoing surgical repair of atrial septal defect for the period from June 1985, to August 1992. The following results were observed. 1. ASD was closed with patch in 11(73%) patients. 2. The relationship of pulmonary artery systolic pressure to Qp/Qs ratio was not significant. 3. Before operation 6 patients were in NYHA functional class II, 8 were in class III, After operation 8 patients were in class I, 6 were in class II. 4. Atrial fibrillation has persisted in 3 patients and returned regular rhythm in 1 patient after surgery. 5. There was no operative mortality and we had good surgical results regardless of patient's age.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.436-443
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• 1999

Most of the 3D object reconstruction techniques divide the object into multiplane and approximate the surfaces of the object. The Marching Cubes Algorithm which initializes the mesh structure using a given isovalue. and Delaunay Tetrahedrisation are widely used. Deformable models are well-suited for general object reconstruction because they make little assumptions about the shape to recover and they can reconstruct objects *om various types of datasets. Now, many researchers are studying the reconstruction systems based on a deformable model. In this paper, we propose a novel method for reconstruction of 3D objects. This method, for a 3D object composed of curved planes, compresses the 3D object based on the adaptive simplexmesh technique. It changes the pre-defined mesh structure, so that it may approach to the original object. Also, we redefine the geometric characteristics such as curvatures. As results of simulations, we show reconstruction of the original object with high compression and concentration of vertices towards parts of high curvature in order to optimize the shape description.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.3
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• pp.21-30
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• 1990

This paper describes a hardware implementation of the interframe monochrome video CODEC using a MC-CVQ(Motion Compensated and Classified Vector Quantization) algorithm. The specifications of this CODEC are (1) the resolution of image is $128{\times}128$ pixels, and (2) the transmission rates are about 10frames/sec at the 64Kbps channel. In order to design the CODEC under these conditions, it is implemented by a multiprocessor system composed of MC unit, CVQ nuit and decoder unit, which are controlled by microprogramming technique. And the 3~stage pipelined ALU(Arithmetic and Logic Unit) is adopted to calculate the minimum error distance in the MC unit and CVQ nuit. The realized system shows that the transmission rates are 6-15 frames/sec according to the relative motion of the video signal.

• The Journal of the Acoustical Society of Korea
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• v.32 no.2
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• pp.157-166
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• 2013

This paper proposes a non-intrusive speech quality estimation method considering the effects of packet loss to perceptual quality. Packet loss is a major reason of quality degradation in a packet based speech communications network, whose effects are different according to the input speech characteristics or the performance of the embedded packet loss concealment (PLC) algorithm. For the quality estimation system that involves packet loss effects, we first observe the packet loss of G.729 codec which is one of narrowband codec in VoIP system. In order to quantify the lost packet affects, we design a classification algorithm only using speech parameters of G.729 decoder. Then, the degradation values of each class are iteratively selected that maximizes the correlation with the degradation PESQ-LQ scores, and total quality degradation is modeled by the weighted sum. From analyzing the correlation measures, we obtained correlation values of 0.8950 for the intrusive model and 0.8911 for the non-intrusive method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.3
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• pp.1-10
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• 2000

This paper proposes a novel video coding system with rate control based on fractal algorithm To overcome the demerits of excessive amounts of coded bit generated by previous fractal coding methodology. the proposed system classifies the Image into three classes such as background, motion compensation, and fractal coding area. The motion vector for motion compensation, and the fractal offset value that is difference value between the predicted offset and the least-square approximated value are coded with variable length code The decision method which determines threshold value of partitioning quadtree is applied to the bit-rate control algorithm considering the quantity of currently generated bits and fixed channel bandwidth Experimental result shows that the proposed system enhances compression ratio 1.8 times higher than previous method for the same image quality, and performs efficient rate control for fixed channel bandwidth.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.90-95
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• 2009

This paper presents a new feature extraction algorithm which can deal with the problems of linear discriminant analysis, widely used for linear dimensionality reduction. The scatter matrices included in linear discriminant analysis are defined by the distances between each datum and its class mean, and those between class means and mean of whole data. Use of these scatter matrices can cause computational problems and the limitation on the number of features. In addition, these definition assumes that the data distribution is unimodal and normal, for the cases not satisfying this assumption the appropriate features are not achieved. In this paper we define a new scatter matrix which is based on the differently weighted distances between individual data, and presents a feature extraction algorithm using this scatter matrix. With this new method. the mentioned problems of linear discriminant analysis can be avoided, and the features appropriate for discriminating data can be achieved. The performance of this new method is shown by experiments.

• Geophysics and Geophysical Exploration
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• v.14 no.3
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• pp.214-219
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• 2011

We perform the frequency-domain waveform inversion based on the residual-selection strategy. In the residual-selection strategy, we classify time-domain residual wavefields into several groups according to the order of absolute amplitudes. Because the residual wavefields are normalized after regularization of the gradient directions within each group, the residual-selection strategy plays a role in enhancing the small-amplitude wavefields, which contributes to improving the deep parts of inverted subsurface images. After classifying residuals in the time domain, they are transformed to the frequency domain. Waveform inversion is performed in the frequency domain using the back-propagation technique which has been popularly used in reverse-time migration. The residual-selection strategy is applied to the SEG/EAGE salt and IFP Marmousi models. Numerical results show that the residual-selection strategy yields better results than the conventional frequency-domain waveform inversion.