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김철호,이정훈,이성엽,우영춘,백옥기,원희선,Kim, C.H.,Lee, J.H.,Lee, S.Y.,Woo, Y.C.,Baek, O.K.,Won, H.S. 한국전자통신연구원 2021 전자통신동향분석 Vol.36 No.3
The field of brain science (or neuroscience in a broader sense) has inspired researchers in artificial intelligence (AI) for a long time. The outcomes of neuroscience such as Hebb's rule had profound effects on the early AI models, and the models have developed to become the current state-of-the-art artificial neural networks. However, the recent progress in AI led by deep learning architectures is mainly due to elaborate mathematical methods and the rapid growth of computing power rather than neuroscientific inspiration. Meanwhile, major limitations such as opacity, lack of common sense, narrowness, and brittleness have not been thoroughly resolved. To address those problems, many AI researchers turn their attention to neuroscience to get insights and inspirations again. Biologically plausible neural networks, spiking neural networks, and connectome-based networks exemplify such neuroscience-inspired approaches. In addition, the more recent field of brain network analysis is unveiling complex brain mechanisms by handling the brain as dynamic graph models. We argue that the progress toward the human-level AI, which is the goal of AI, can be accelerated by leveraging the novel findings of the human brain network.
[論文] Computation of the Flow Field around a Butterfly Valve
김철호(C.H.Kim),최정명(J.M.Choi),M.Behnia(M.Behnia),B.E.Milton(B.E.Milton) 한국자동차공학회 1992 오토저널 Vol.14 No.1
대도시의 자동차 배기가스에 의한 대기오염문제는 날로 심화되어 가고 있으며, 그 규제 역시 매우 엄격해지고 있다.<br/> 배기가스의 문제는 연소실내에서의 고효율연소에 의해 개선될 수 있으며, 이를 위해서는 혼합가스 생성장치에서의 잘 미립화된 혼합가스의 생성에 관한 미지적 연구가 필수적이다.<br/> 본 연구에서는 범용 유체 유동해석, 프로그램을 이용하여 Butterfly 밸브 주위에서의 공기의 흐름현상을 예측하였으며, 밸브의 손실계수(Kv)를 심험치와 비교하여 보았다. 또한 압축성과 비압축성 유체 유동해석의 비교, Residual Mass(R.M)의 최적치 정의에 의한 CPU 시간의 최소화, Numerical Grid Size의 해석결과에 미치는 영향 등에 관해 알아보았다.
김철호(C. H. Kim),최세호(S. H. Choi),주원종(W. J. Joo),김기범(K.B. Kim) 한국정밀공학회 2005 한국정밀공학회 학술발표대회 논문집 Vol.2005 No.10월
This paper proposes a new steel strip surface inspection system. The system acquires bright and dark field images of defects by using a stroboscopic IR LED light and area camera system and the defect images are preprocessed and segmented in real time for feature extraction. 4113 defect samples of cold roll steel strips are used to develop KNN (k-Nearest Neighbor) classifier which classifies the defects into 8 different types. The developed KNN classifier demonstrates about 85% classifying performance which is considered very plausible result.