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이병채(Byung-Chae Lee),유선국(Sun-Kook Yoo),김혜진(Hye-Jin Kim) 대한전자공학회 2013 전자공학회논문지 Vol.50 No.11
집중에 관한 뇌파 해석은 인간의 인지 이해에 기본적인 요소이다. 본 연구에서는 시각자극에 대한 뇌의 집중, 비집중 상태의 차이특성을 비선형 분석하였다. 적은 샘플 데이터와 시간에 따른 변화특성을 해석하기 위하여 반복 정량 분석 법을 사용하였으며, 자극에 동기된 유발 전위의 반복궤적, 색상 반복 궤적을 도식화하였으며, 비선형 특징 파라미터들의 평균특징과 시변특성을 추출하였다. 집중-비집중 도식과 파라미터 쌍은 위상공간 변환의 차원과 시간 지연을 결정하기 위한 정보를 제공하였으며, 집중 시의 뇌가 비집중 시의 뇌보다 복잡하다는 특징을 보였으며, 자극에 동기된 유발전위는 평균적 의미에서 동일 반응을 보이나 국부적으로는 환경과 상태에 따라 변화하였다. 본 실험을 통하여 시각자극에 대한 집중과 비집중 시 뇌의 비선형 현상을 해석하기 위한 가능성을 확인하였다. The analysis of electroencephalographic signal associated with the attention is essential for the understanding of human cognition. In this paper, the characteristic differences between the attention and inattention status in the brain were inspected by nonlinear analysis. The recurrence quantification analysis was applied to the relatively small number of samples of evoked potential having time varying characteristics, where the recurrence plot (RP), the color recurrence plot (CRP), and mean and time-sequential trend parameters were extracted. The dimension and the time delay in phase transformation can be determined by the paired set of extracted parameters. It is observed from RP, CRP, and parameters that the brain dynamics in attention is more complex than that in the inattention, as well as the synchronized brain response is stable in the mean sense but locally time varying. It is feasible that the non-linear analysis method can be useful for the analysis of complex brain dynamics associated during visual attentional task.
이병채,황선철,이명호,Lee, Byung-Chae,Hwang, Seon-Cheol,Lee, Myoung-Ho 대한의용생체공학회 1990 의공학회지 Vol.11 No.1
In this paper, fast Fourier transform and fast Walsh transform algorithm are studied for ECG data compression. ECG data-12 bit samples digitized at 480 samples-are segmented into QRS complexes and 50 intervals by di%ital derivative filter, which used for detection of QS width and difrerenre compressed in Fourler or welsh domain. And also the existing techniques for data compression-TP, MTP, CORTES, AZTEC, MCORTES, which have not been evaluated with a common measurement of goodness, were processed to get absolute terms of values in the same condition.
비선형 반복 패턴과 스펙트럼 분석을 이용한 집중-비집중 분류기의 성능 평가
이지은 ( Jee Eun Lee ),유선국 ( Sun Kook Yoo ),이병채 ( Byung Chae Lee ) 한국감성과학회 2013 감성과학 Vol.16 No.3
집중은 관련된 사건을 선택적으로 주의하고, 관련 없는 사건을 무시하는 인간의 중요한 인지 기능중의 하나이다. 인간의 집중 능력을 관리 이용하는 컴퓨터 기반 장치에 있어서 집중과 비집중 상태를 구분하는 것은 필수적으로 요구되는 조건이다. 본 논문에서는, 뇌파신호로부터 분류기의 입력으로 사용되는 특징을 효율적으로 추출하기 위하여 비선형 반복 패턴 분석기법과 스펙트럼 분석 기법을 새로이 결합하였고(13개 특징 추출), 서포트벡터머신, 역전파 알고리즘, 선형분리, 로지스틱 회귀 분류 기반 분류기들을 포함하는 집중-비집중 분류기들의 성능을 분석하였다. 그중에서 81 %의 정확도를 보이는 서포트벡터머신 분류기가 가장 좋은 성능을 보였다. 또한 스펙트럼 분석으로 추출한 특징만을 사용하였을 경우(76 % 정확도)가 비선형 분석 방법으로 추출한 특징만을 사용했을 경우(67 % 정확도)보다 좀 더 우수한 성능을 보였다. 비선형-스펙트럼 분석법을 복합 적용한 서포트벡터머신 분류기가 추후 집중 관련 장비 설계에 있어서 효율적으로 적용될 수 있을 것이다. Attention is one of important cognitive functions in human affecting on the selectional concentration of relevant events and ignorance of irrelevant events. The discrimination of attentional and inattentional status is the first step to manage human`s attentional capability using computer assisted device. In this paper, we newly combine the non-linear recurrence pattern analysis and spectrum analysis to effectively extract features(total number of 13) from the electroencephalographic signal used in the input to classifiers. The performance of diverse types of attention-inattention classifiers, including supporting vector machine, back-propagation algorithm, linear discrimination, gradient decent, and logistic regression classifiers were evaluated. Among them, the support vector machine classifier shows the best performance with the classification accuracy of 81 %. The use of spectral band feature set alone(accuracy of 76 %) shows better performance than that of non-linear recurrence pattern feature set alone(accuracy of 67 %). The support vector machine classifier with hybrid combination of non-linear and spectral analysis can be used in later designing attention-related devices.
감마선 스펙트럼 비율을 이용한 매립 선원의 깊이 평가 방법론 개발 연구
김준하,정재학,홍상범,서범경,이병채,Kim, Jun-Ha,Cheong, Jea-Hak,Hong, Sang-Bum,Seo, Bum-Kyung,Lee, Byung Chae 한국방사성폐기물학회 2020 방사성폐기물학회지 Vol.18 No.1
This study was conducted to develop a method for depth assessment of embedded sources using gamma-spectrum ratio and for the evaluation of field applicability. To this end, Peak to Compton and Peak to valley ratio changes were evaluated according to <sup>137</sup>Cs, <sup>60</sup>Co, <sup>152</sup>Eu point source depth using HPGe detector and MCNP simulation. The effects of measurement distance of PTV and PTC methods were evaluated. Using the results, the source depth assessment equation using the PTC and PTV methods was derived based on the detection distance of 50 cm. In addition, the sensitivity of detection distance changes was assessed when using PTV and PTC methods, and error increased by 3 to 4 cm when detection distance decreased by 20 cm based on 50 cm. However, it was confirmed that if the detection distance was increased to 100 cm, the effects of detection distance were small. And PTV and PTC methods were compared with the two distance measurement method which evaluates the depth of source by the change of net peak counting rate according to the detection distance. As a result of source depth assessment, the PTV and PTC showed a maximum error of 1.87 cm and the two distance measurement method showed maximum error of 2.69 cm. The results of the experiment confirmed that the accuracy of the PTV and PTC methods was higher than two distance measurement. In addition, Sensitivity evaluation by horizontal position error of source has maximum error of less than 25.59 cm for the two distance measurement method. On the other hand, PTV and PTC method showed high accuracy with maximum error of less than 8.04 cm. In addition, the PTC method has lowest standard deviation for the same time measurement, which is expected to enable rapid measurement.