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주의력 결핍·과잉행동장애 소년의 자기공명 영상을 이용한 뇌량 및 측뇌실의 계량적 특성분석
이정섭,홍강의,김주한 大韓神經精神醫學會 1997 신경정신의학 Vol.36 No.2
By means of retrospecitive quantitative neuroanatomic imaging, the authors assessed the corpus callosum and the lateral venticle in the boys with attention deficit hyperactivity disorder(ADHD). The midsagittal cross-sectional area of the corpus callosum, divided into seven regions, and the axial ventricle-brain ratio were measured from magnetic resonance images of 18 boys with ADHD and 15 comparison boys. Two anterior regions, the genu and the rostral body, were found to have significantly smaller areas in the ADHD boys. There was no significant difference in ventricle-brain ratio between ADHD and comparison boys. This finding supports the theory of abnormal frontal lobe development in ADHD.
급전선로에 설치되어 있는 피뢰기의 누설전류 측정과 관리방안
한주섭(Han Ju-Seop),서황동(Seo Hwang-Dong),길경석(Kil Gyung-Suk),한문섭(Han Moon-Seob),장동욱(Jang Dong-Uk) 한국철도학회 2005 한국철도학회 학술발표대회논문집 Vol.- No.-
This paper describes the measurement result of leakage current flowing arresters connected in feeder lines to propose an optimal management method. Twenty seven arresters set in seven areas were analyzed on a regular basis for 4 months. The results showed that the RMS and the peak value of the total leakage currents for soundness arresters were a range of ... respectively. During the period of measurement, the magnitude of the leakage current didn"t show conspicious changes and there were impossible places to analyze arrester"s status due to including high THD rate in a feeder line. From the study, leakage current measurement has to be performed at a condition without running an electric train in the line, and the allowable RMS value of soundness arrester is bellow 600uA.
한국형 틸팅열차 열차제어진단장치의 구성품시험에 관한 연구
한주섭(Han Ju Seop),이수길(Lee Su Gil),한성호(Han Seong Ho) 한국철도학회 2006 한국철도학회 학술발표대회논문집 Vol.- No.-
This paper dealt with a component test of train control and management system(TMS) developed for korean tilting train express(TTX). This system that is established on TTX monitors and controls action of various devices by running of the train. Also, to performance estimation of the TMS, it is essential to verify a composition and function of TMS. Therefore, this study reviewed standards related on a component test of the TMS and confirmed test items, test conditions and evaluation basis on a Component Test. Running Test of TTX can verify performance of TMS and communications with other devices and secure reliability of TMS.
한주섭(Ju-Seop Han),송재용(Jae-Yong Song),김일권(Il-Kwon Kim),길경석(Gyung-Suk Kil),한문섭(Moon-Seob Han),장동욱(Dong-Uk Jang) 한국철도학회 2005 한국철도학회 학술발표대회논문집 Vol.- No.-
Detection of small leakage current is the most important technique in arrester diagnosis. Various types of leakage current detector have been commercialized, but they are not available for the railway arresters including voltage harmonics, and do not furnish enough information needed for arrester diagnosis. This paper describes the development of a leakage current detector which is suitable for the railway arresters. The detector is consisted of a sensor unit with amplifier, an optical linker and main analyzer. Frequency bandwidth and measuring range of the detector is 9[㎐]~ 850[㎐] and 50[㎂]~ 5[㎃], respectively.
효과적인 딥러닝 기반 비프로파일링 부채널 분석 모델 설계방안
한재승(JaeSeung Han),심보연(Bo-Yeon Sim),임한섭(Han-Seop Lim),김주환(Ju-Hwan Kim),한동국(Dong-Guk Han) 한국정보보호학회 2020 정보보호학회논문지 Vol.30 No.6
최근 딥러닝 기반 비프로파일링 부채널 분석이 제안됐다. 딥러닝 기반 비프로파일링 분석은 신경망 모델을 모든 추측키에 대해 학습시킨 뒤, 학습된 정도의 차이를 통해 올바른 비밀키를 찾아내는 기법이다. 이때, 신경망 학습모델 설계에 따라 비프로파일링 분석성능이 크게 달라지기 때문에 올바른 모델 설계의 기준이 필요하다. 본 논문은 학습모델 설계에 사용 가능한 2가지 loss 함수와 8가지 label 기법을 설명하고, 비프로파일링 분석과 소비전력모델 관점에서 각 label 기법의 분석성능을 예측했다. 해밍웨이트 소비전력모델을 가정했을 때의 비프로파일링 분석 특징을 고려해서 One-hot인코딩을 적용하지 않은 HW(Hamming Weight) label과 CO(Correlation Optimization) loss를 적용한 학습모델이 가장 좋은 분석성능을 가질 것으로 예측했다. 그리고 AES-128 1라운드 Subbytes 연산 부분 데이터 집합 3가지에 대해 실제 분석을 수행했다. 제시한 각 label 기법과 loss 함수를 적용한 총 16가지 MLP(Multi-Layer Perceptron)기반 학습모델로 두 데이터 집합을 비프로파일링 분석하여 예측에 대해 검증했다. Recently, a deep learning-based non-profiling side-channel analysis was proposed. The deep learning-based non-profiling analysis is a technique that trains a neural network model for all guessed keys and then finds the correct secret key through the difference in the training metrics. As the performance of non-profiling analysis varies greatly depending on the neural network training model design, a correct model design criterion is required. This paper describes the two types of loss functions and eight labeling methods used in the training model design. It predicts the analysis performance of each labeling method in terms of non-profiling analysis and power consumption model. Considering the characteristics of non-profiling analysis and the HW (Hamming Weight) power consumption model is assumed, we predict that the learning model applying the HW label without One-hot encoding and the Correlation Optimization (CO) loss will have the best analysis performance. And we performed actual analysis on three data sets that are Subbytes operation part of AES-128 1 round. We verified our prediction by non-profiling analyzing two data sets with a total 16 of MLP-based model, which we describe.