• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.51.1-51.1
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• 2010

본 논문에서는 wavelet 변환을 이용하여 태양광 발전 시스템의 계통 전원 고조파를 측정하는 방법을 연구하였다. PCS(Power Conditioning System)는 태양전지의 전력을 교류로 변환하여 계통에 연계시키는 장치이다. 직류에서 교류로 변환할 때 스위칭 노이즈가 발생하고, 전력품질이 약화되게 된다. Wavelet 이론은 시간 파형을 주파수 성분으로 분해할 수 있는 기술이다. 이중에서 MLD(Multi-evel Decomposition)기법은, 계산량이 적으면서도 빠른 시간 내에 고조파 성분들을 알아낼 수 있다. 시스템 모델링과 wavelet 이론 소개, 그리고 컴퓨터 모의실험과 DSP 제어기를 이용한 실험 결과로서 본 연구의 타당성을 입증하였다.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.619-622
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• 1998

본 논문은 웨이브렛 패킷에 기초한 직교 다중화 변복조 방식을 소개하고 특히 시스템 설계자 입장에서 전송 신호의 특성을 시간-주파수 공간에서 신호 파형을 설계하고 채널 특성에 맞게 설계할 수 있는 궂를 제공하는 WPM(wavelet packet modulation) 방식을 기술하였다. 직교 기저 함수 집합을 만들어 시간주파수 공간을 임의적으로 partitioning하고 간섭 잡음 재철에 더잘 적응할 수 있는 구조를 찾는 방법을 소개하였고 튜닝 알고리듬의 실험적인 결과를 가지고 WPM변조 방식의 간섭 잡음에 대한 우수한 성능을 갖음을 보였다.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.12 no.1
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• pp.25-50
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• 2001

Using Fourier transformation and wavelet analysis, we compared the auditory event-related potentials of the patients with attention deficit-hyperactivity disorders(abbr. ADHD, 13 boys) and normal control children(8 boys). Amplitudes of the event-related potentials which were calculated via Fourier transformation were compared between the groups and between conditions(non-target versus target) in each group. To the non-target stimuli, the patients with ADHD showed significantly greater amplitudes across almost all of the electrode sites and frequencies. To the target stimuli, the incidents which ADHD patients showed much higher amplitudes than normal controls significantly decreased, while those of the reverse results increased significantly. These results were consistent with the comparison results about negative difference wave(abbr. Nd wave) using Fourier transformation. In summary, it was proved that non-target stimulus which should be ignored elicited more robust electrical response from the patients with ADHD than normal children, but the target stimulus which reguired active processing did much less electrical activity in the patients. For the patients, they showed much inhibited electrical response to the target stimuli in some electrodes and frequency ranges. Normal children were more strongly stimulated by the target stimuli in almost all electrodes and frequency ranges than the patients, but less in prefrontal leads and frontal leads. Wavelet analysis results proved that early responses(0-300msec) to the nontarget stimuli of the patients were significantly greater than the normal controls in prefrontal, anterior frontal, some parts of temporal, and occipital lobes and that late response(300-370msec) were significantly lesser than normal children in parietal and central electrodes. Target stimuli elicited significantly higher electrical activity in both group than non-target stimuli did. Prefrontal and frontal lobes showed stronger responses in the patients than normal children irrespective of stimulus condition, but parietal and temporal lobes did higher activities in normal children than the patients only to the target stimuli. In conclusion, the patients with ADHD showed much greater responses to the stimuli which should be ignored, but failed to activated the necessary processes to the target stimuli. Also, we found that the frequency-dimension analysis and wavelet analysis were useful for the signal processing such as event related potentials.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.178-184
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• 2007

The EEG signal in general can be categorized as the Alpha wave, the Beta wave, the Theta wave, and the Delta wave. The alpha wave, showed in stable state, is the dominant wave for a human EEG and the beta wave displays the excited state. The subject of this paper was to recognize the stable state of EEG quantitatively using wavelet transform and power spectrum analysis. We decomposed EEG signal into the alpha wave and the beta wave in the process of wavelet transform, and calculated each power spectrum of EEG signal, using Fast Fourier Transform. And then we calculated the stable state quantitatively by stable state ratio, defined as the power spectrum of the alpha wave over that of the beta wave. The study showed that it took more than 10 minutes to reach the stable state from the normal activity in 69 % of the subjects, 5 -10 minutes in 9%, and less than 5 minutes in 16 %.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.195-196
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• 2008

ECG 기반의 부정맥 자동 분류에 관한 연구는 지난 수십 년간 다양한 방법으로 연구되어 왔다. 많은 연구들이 부정맥을 구별해 낼 수 있는 특징 벡터를 찾아내기 위해 연구하였으나, 피험자의 ECG 특징이 각기 다르기 때문에 부정맥으로 인한 차이와 개인 간 차이를 구별하기 어려웠다. 생체데이터는 그 특성상 서로 다른 특징을 갖고 있으며, 다양한 특징을 가진 사람들에게 적용하기 위한 범용성과 부정맥 검출의 정확성 사이에 교환적 관계를 갖게 된다. 특히 ECG 데이터의 경우 사람 식별 데이터로 사용하고자 하는 연구가 있을 정도로 개인 간 편차가 분명하다. wavelet 분석방법은 다양한 mother wavelet을 사용할 수 있다는 점을 큰 장점으로 가지고 있으며, wavelet parameterization 기법을 사용하여 임의의 직교 wavelet basis를 발생시킬 수 있다. 본 논문은 wavelet parameterization을 사용하여 개인 간의 ECG 파형의 차이를 상쇄시키고, 부정맥의 차이만을 부각시킴으로써 ECG 기반의 부정맥 자동 분류 성능을 높이고자 하는데 목적이 있다.

• Geophysics and Geophysical Exploration
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• v.10 no.2
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• pp.131-137
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• 2007

With consideration of the first arrival picking methodology and inherent errors in picking process, I propose, from the computerization point of view, a practical algorithm for picking and error computation. The proposed picking procedure consists of 2-step; 1) picking the first coherent peak or trough events, 2) derive a line which approximates the record in the interval prior to the pick, and set the intercept time of the line as the first break. The length of fitting interval used in experiment, is few samples less than 1/4 width of the arriving wavelet. A quantitative measure of the error involved in first arrival picking is defined as the time length that needed to determine if an event is the first arrival or not. The time length is expressed as a function of frequency bandwidth of the signal and the S/N ratio. For 3 sets of cross-well seismic data, first breaks are picked twice, by manually, and by the proposed method. And at the same time, the error bound for each trace is computed. Experiment results show that good performance of the proposed picking method, and the usefulness of the quantitative error measure in pick-quality evaluation.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.2
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• pp.70-78
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• 1998

Spike detection in long-term EEG monitoring forepilepsy by wavelet transform(WT), artificial neural network(ANN) and the expert system is presented. First, a small set of wavelet coefficients is used to represent the characteristics of a singlechannel epileptic spikes and normal activities. In this stage, two parameters are also extracted from the relation between EEG activities before the spike event and EEG activities with the spike. then, three-layer feed-forward network employing the error back propagation algorithm is trained and tested using parameters obtained from the first stage. Spikes are identified in individual EEG channels by 16 identical neural networks. Finally, 16-channel expert system based on the context information of adjacent channels is introducedto yield more reliable results and reject artifacts. In this study, epileptic spikes and normal activities are selected from 32 patient's EEG in consensus among experts. The result showed that the WT reduced data input size and the preprocessed ANN had more accuracy than that of ANN with the same input size of raw data. Ina clinical test, our expert rule system was capable of rejecting artifacts commonly found in EEG recodings.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.358-361
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• 1997

This paper describes a multichannel epileptic seizure detection algorithm based on wavelet transform(WT), artificial neural network(ANN) and expert system. First, through the WT, a small number of wavelet coefficients is used to represent the single channel epileptic spike. Next, 3-layer feed-forward network employing the error back propagation algorithm is trained and tested using parameters obtained above. Finally, 16 channel expert system which is based on clinical experience is introduced as a artifact rejection and reliable detection. The suggested algorithm was implemented on personal computer(PC). Two main events i.e., epileptiform and normal activities, were selected from 32 person's EEGs(normal: 20, seizure disorder: 12) in consensus among experts. The result was that WT reduced data input size and ANN detected 97 of the 100 EEGs containing definite spike - sensitivity of 97%. Expert rule system was capable of rejecting a wide variety of artifacts commonly found in EEG recordings. It also reduced false positive detections of ANN.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.312-321
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• 2003

In this paper, we implement robot which are ability to recognize obstacles and moving automatically to destination. we present two results in this paper; hardware implementation of image processing board and software implementation of visual feedback algorithm for a self-controlled robot. In the first part, the mobile robot depends on commands from a control board which is doing image processing part. We have studied the self controlled mobile robot system equipped with a CCD camera for a long time. This robot system consists of a image processing board implemented with DSPs, a stepping motor, a CCD camera. We will propose an algorithm in which commands are delivered for the robot to move in the planned path. The distance that the robot is supposed to move is calculated on the basis of the absolute coordinate and the coordinate of the target spot. And the image signal acquired by the CCD camera mounted on the robot is captured at every sampling time in order for the robot to automatically avoid the obstacle and finally to reach the destination. The image processing board consists of DSP (TMS320VC33), ADV611, SAA7111, ADV7l76A, CPLD(EPM7256ATC144), and SRAM memories. In the second part, the visual feedback control has two types of vision algorithms: obstacle avoidance and path planning. The first algorithm is cell, part of the image divided by blob analysis. We will do image preprocessing to improve the input image. This image preprocessing consists of filtering, edge detection, NOR converting, and threshold-ing. This major image processing includes labeling, segmentation, and pixel density calculation. In the second algorithm, after an image frame went through preprocessing (edge detection, converting, thresholding), the histogram is measured vertically (the y-axis direction). Then, the binary histogram of the image shows waveforms with only black and white variations. Here we use the fact that since obstacles appear as sectional diagrams as if they were walls, there is no variation in the histogram. The intensities of the line histogram are measured as vertically at intervals of 20 pixels. So, we can find uniform and nonuniform regions of the waveforms and define the period of uniform waveforms as an obstacle region. We can see that the algorithm is very useful for the robot to move avoiding obstacles.