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정신분열병 환자에서 세가지 청각 자극 "Oddball" 모형에 의한 사건관련전위 P3a와 P3b
진용탁,박이진,남지민,한상익,전양환 大韓神經精神醫學會 2005 신경정신의학 Vol.44 No.5
Objectives : Using 3-stimulus auditory "oddball" paradigm reflecting fronto/central and temporo/parietal functions Simultaneously, we examined patients with schizophrenia. Methods : fifteen patients with schizophrenia from outpatient clinic and fifteen normal controls from hospital staffs were recruited for the study. To elicit P3a and P3b, 3-stimulus auditory oddball paradigm was employed. The 3-stimulus auditory oddball paradigm was composed of standard tone (1,000 Hz, 75 dB, 80%), target tone (2,000 Hz, 75 dB, 10%) and distracter (White noise, 95 dB, 10%). Results : P3a and P3b were prominent in fronto/central and temporo/parietal areas, respectively, in both schizophrenics and normal controls. The P300 amplitude in patients with schizophrenia was reduced across P3a and P3b (p<0.01). The P300latency in patients with schizophrenia was delayed across P3a and P3b (p<0.01). Conclusion : These results were consistent with frontal and temporo-parietal lobe dysfunctions in schizophrenics. The 3-stimulus auditory paradigm could be applied for patients with schizophrenia and useful for further exploration the disorder.
Interval Type-2 RBF 신경회로망 기반 CT 기법을 이용한 강인한 얼굴인식 패턴 분류기 설계
진용탁(Yong-Tak Jin),오성권(Sung-Kwun Oh) 대한전기학회 2015 전기학회논문지 Vol.64 No.5
This paper is concerned with Interval Type-2 Radial Basis Function Neural Network classifier realized with the aid of Census Transform(CT) and (2D)2LDA methods. CT is considered to improve performance of face recognition in a variety of illumination variations. (2D)2LDA is applied to transform high dimensional image into low-dimensional image which is used as input data to the proposed pattern classifier. Receptive fields in hidden layer are formed as interval type-2 membership function. We use the coefficients of linear polynomial function as the connection weights of the proposed networks, and the coefficients and their ensuing spreads are learned through Conjugate Gradient Method(CGM). Moreover, the parameters such as fuzzification coefficient and the number of input variables are optimized by Artificial Bee Colony(ABC). In order to evaluate the performance of the proposed classifier, Yale B dataset which consists of images obtained under diverse state of illumination environment is applied. We show that the results of the proposed model have much more superb performance and robust characteristic than those reported in the previous studies.