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고태훈,김완진,정용식,김병석 한국공업화학회 2016 한국공업화학회 연구논문 초록집 Vol.2016 No.0
Carbon fiber paper (CFP)-based supercapacitor, a novel and interesting materials for fabricating flexible energy storage devices, are attracting much attention from both industry and academic. CFPs with a unique fiber structure and high surface area properties enable the deposition of conducting polymers and metal oxide nanostructure over CFPs for the construction of flexible supercapacitors with a reasonably good performance at a low price. In this work, poly(3,4-ethyledioxy thiophene) (PEDOT) is deposited on CFP by simple vapor phase polymerization method followed by electrochemical deposition of MnO2 to form MnO2/PEDOT/CFP. The fabricated hybrid CFP has more flexible and high supercapacitance performance. The inexpensive and mass-productive carbon fiber paper as well as simple fabrication technique makes flexible supercapacitors promising candidates for the future green energy and storage device technology.
고태훈,( Kesavan Devarayan ),최웅기,서민강,김영근,김병석 한국공업화학회 2015 한국공업화학회 연구논문 초록집 Vol.2015 No.1
A combination of suitable materials with high stability, conductivity, high surface area, and good capacitance is important to fabricate a high performance supercapacitor. In view of this, in this study, a hybrid nanocomposite of Ag/PANi/MWCNT-NH<sub>2</sub> was prepared. At first, a nylon nanofiber-reinforced cellulose acetate thin film was used as a substrate for layer-by-layer buildup of MWCNT-NH2/PEDOT:PSS up to 10 bi-layers. Then Ag/PANi composite was deposited on to the LBL thin film by means of in situ polymerization. The electrochemical studies revealed exhibited a maximum 289 F/g specific capacitance with better cyclic stabilities over 500 cycles.
박노삼,Park, N.S. 한국전자통신연구원 2021 전자통신동향분석 Vol.36 No.5
This study reviews application of data-driven anomaly detection techniques to the aviation domain. Recent advances in deep learning have inspired significant anomaly detection research, and numerous methods have been proposed. However, some of these advances have not yet been explored in aviation systems. After briefly introducing aviation safety issues, data-driven anomaly detection models are introduced. Along with traditional statistical and well-established machine learning models, the state-of-the-art deep learning models for anomaly detection are reviewed. In particular, the pros and cons of hybrid techniques that incorporate an existing model and a deep model are reviewed. The characteristics and applications of deep learning models are described, and the possibility of applying deep learning methods in the aviation field is discussed.