• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.791-792
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• 2006

본 논문은 심전도의 리드III 파형을 이용하여 신원확인이 가능한 생체인식 기술을 제안한다. 인식을 위한 심전도의 리드III파형을 특징추출하기 위해 $4{\sim}30Hz$의 대역통과 필터를 사용하여 피크(peak)점만 남겨놓고 모든 잡음을 제거한 후, AAV(absolute amplitude value)를 이용하여 피크점의 값을 추출한다. 추출된 피크 점은 원신호의 피크점과 같으므로 이를 기준으로 전체파형을 특징추출을 위한 단위 파형으로 분리한다. 분리된 신호는 정의된 4가지 형태(type)의 파형 중 가장 유사한 파형타입으로 분류되며, 분류된 형태를 기준으로 꼭지점, 최대 피크점, 최소 피크점, 최대.최소 피크점 비, 파형 간격(interval) 및 파형의 세부 모양 등 총22가지의 특징들을 추출한다. 추출된 특징들은 오류역전파 신경회로망(back-propagation neural network)의 입력으로 사용되었으며, 성인남녀 31명을 대상으로 제한된 파형 내에서 실험한 결과 100%의 인식률을 보였다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.18-26
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• 2009

In this paper, we proposed multipurpose signal detection methods for ECG (electrocardiogram) based u-healthcare systems. For ECG based u-healthcare system, QRS signal extraction for cardiovascular disease diagnosis is essential. Also, for security and convenience reasons, it is desirable if u-healthcare system support biometric identification directly from user's bio-signal such as ECG for this case. For this, from Lead II signal, we developed QRS signal detection method and also, we developed signal extraction method for biometric identification using Lead II signal which is relatively robust from signal alteration by aging and diseases. For QRS signal detection capability from Lead II signal, ECG signals from MIT-BIH database are used and it showed 99.36% of accuracy and 99.68% of sensitivity. Also, to show the performance of signal extraction capability for biometric diagnosis purpose, Lead III signals are measured after drinking, smoking, or exercise to consider various monitoring conditions and it showed 99.92% of accuracy and 99.97% of sensitivity.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.6
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• pp.43-50
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• 2011

Currently, conventional security methods including IC card or password type method are quickly switched into biometric security systems in various applications and the electrocardiogram (ECG) has been considered as one of novel biometrics way. However, conventional ECG based biometrics used lead II signal which conventionally used for formulaic signal to heart disease diagnosis and it is not suitable for biometrics since it is rather difficult to find consistent features for heart disease patents. To overcome this problem, we developed new biometrics system using ECG lead III signals. For wave extraction, signal peak points are extracted through AAV algorithm. For feature selection, extracted waves are categorized into one of four wave types and total twenty two features including number of vertices, wave shapes, amplitude information and interval information are extracted based on their wave types. Experimental results for thirty-six people showed 100% specificity, 95.59% sensitivity and 99.17% of overall identification accuracy.

• Geophysics and Geophysical Exploration
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• v.22 no.2
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• pp.49-55
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• 2019

In this study, we presented a reference-shot subset method for stable convergence of full waveform inversion using a cyclic-shot subsampling technique. Full waveform inversion needs repetitive modeling of wave propagation and thus its calculation time increases as the number of sources increases. In order to reduce the computation time, we can use a cyclic-shot subsampling method; however, it makes the cost function oscillate in the early stage of the inversion and causes a problem in applying the convergence criteria. We introduced a method in which the cost function is calculated using a fixed reference-shot subset while updating the model parameters using the cyclic-shot subsampling method. Through the examples of full waveform inversion using the Marmousi velocity model, we confirmed that the convergence of cost function becomes stable even under the cyclic-shot subsampling method if using a reference-shot subset.

• Journal of the Korean Magnetics Society
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• v.22 no.5
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• pp.178-182
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• 2012

This research suggested that the extraction of respiratory rate could be made possible by using frequency analysis in the data process for clip-type pulsimeter equipped with permanent magnet and Hall sensor. The pulse analysis included of cardiac motion information depending on variation of pulse waveforms is investigated by means of Fast Fourier Transformation (FFT). The peaks of FFT spectrums measured at 15, 20, 30, 40, and 50 tempos are coincided to each respiratory rate having 0.125 Hz, 0.16 Hz, 0.25 Hz, 0.33 Hz, and 0.41 Hz, respectively. The FFT spectrum using algorithm for the extraction of respiratory rate showed the best pulse waves measured during 300 s. Based upon these results, the clip-type pulsimeter could extract the effective respiratory rate reflecting physical effects.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.110-113
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• 2005

본 논문은 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted fuzzy Membership Funcstions, NEWFM)을 이용하여 심전도 신호로부터 조기심실수축(Premature Ventricular Contraction, PVC)을 판별하는 퍼지규칙을 추출하고 있다. NEWFM은 자기적응적(self adaptive) 가중 퍼지소속함수를 가지고 주어진 입력 데이터로부터 학습하여 퍼지규칙을 생성하고 이를 기반으로 정상 파형과 PVC 파형을 구분한다. 분류 성능 평가를 위하여 MIT/BIH 부정맥 데이터 베이스를 사용하였으며, NEWFM의 입력은 심전도의 파형에 웨이블릿 변환을 적용하여 추출된 웨이블릿 계수를 사용하였다. 여기에 비중복면적 분산 측정법을 적용하여 중요도가 낮은 계수를 제거하면서 최소의 m 개 특징입력만을 사용한 하이퍼박스로 단순화 시킨다. 이러한 방법으로 추출된 2개의 웨이블릿 계수를 사용한 퍼지규칙은 $96\%$의 PVC 분류성능을 보여준다.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.127-133
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• 2009

This paper presents an approach to classify normal and epilepsy from electroencephalogram(EEG) using a neural network with weighted fuzzy membership functions(NEWFM). To extract input features used in NEWFM, wavelet transform is used in the first step. In the second step, the frequency distribution of signal and the amount of changes in frequency distribution are used for extracting twenty-four numbers of input features from coefficients and approximations produced by wavelet transform in the previous step. NEWFM classifies normal and epilepsy using twenty four numbers of input features, and then the accuracy rate is 98%.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2531-2533
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• 2004

일반적으로 ECG(electrocardiogram)파형은 정상인의 경우에도 그 형태가 일정하지 않으며, 측정시간 및 측정인의 상태에 따라서도 파형이 변화하기 때문에 표준화된 ECG파형 검사로는 개인의 특성에 따른 정밀 진단이 어려웠다. 따라서 자동화된 개인별 맞춤형 진단을 위해서는 측정대상에 대한 사용자인식 기술이 필수적이다. 본 논문에서는 세 가지 잡음제거법을 이용하여 파형의 잡음성분을 제거하고, ECG Limb Lead III 파형의 다양한 신호간격(interval) 특성치와 미분변화량을 통한 꼭짓점 분석 등을 통하여 파형으로부터 특정인의 특징을 추출한 후 신경회로망을 이용하여 생체인식을 수행하였다. 실험은 동일한 연령대인 7명의 성인남녀를 대상으로 하였고, 재현성을 평가하기 위해서 인위적인 변화(커피, 담배, 알코올 섭취 및 스트레스)후의 ECG파형을 측정, 특정인 인식률을 실험한 결과 실험에 이용된 제한된 변동 내에서 90.9%의 인식률을 보였다.

• Proceedings of the KIEE Conference
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• 2002.11a
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• pp.203-205
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• 2002

기존 부분방전검출법에서는 수십$\sim$수백MHz의 주파수 영역에서 부분방전신호를 관찰하였기 때문에 각 부분방전원별 발생신호파형의 특징을 효율적으로 추출할 수 없었다. 본 논문에서는 수십$\sim$수백MHz의 넓은 주파수대역(UWB)에서 부분방전신호를 관찰하여 각 부분방전원별 신호파형의 특징을 추출한 후 이러한 특징을 토대로 부분방전원을 구별하였으며, 상당히 좋은 분류결과를 보였다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.3
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• pp.195-207
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• 2017

Recently, the SAR-GMTI mode is becoming increasingly essential in airborne radar systems. While SAR requires wideband waveforms for high resolution imaging, GMTI requires narrowband waveforms for doppler processing, which makes general LFM waveforms difficult to use for SAR-GMTI. This paper analyses the FJB(Frequency Jump Burst) waveform, which is studied for the SAR-GMTI waveform, and presents the method for the pulse compression and SAR image formation using FJB waveforms. Simulation results show that there is little difference in performances between the FJB waveform and the LFM waveform.