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기계학습을 이용한 리튬 이온 배터리 고체 전해질의 기계적 물성 예측
최은성(EunSeong Choi),조준호(Joon ho Jo),민경민(Kyoungmin Min) 대한기계학회 2020 대한기계학회 춘추학술대회 Vol.2020 No.12
Li-ion batteries have great output, high energy density and a long battery life, so they are being studied and commercialized. However, in the case of Li SSEs(Solid-State Electrolytes), performance is severely limited due to interfacial contact stability problems and the formation and growth of dendrite. In order to analyze and solve this problem, it is necessary to knowing bulk and shear modulus of that compound. But The number of Li SSEs calculated mechanical properties is not enough. Predicting mechanical properties with Machine Learning techniques is much more efficient than experiments and DFT(Density-functional theory) calculations. So we used machine learning regression algorithm with Materials project database screened. As a result, mechanical properties of candidates were obtained with reasonable high accuracy. This makes it possible to search for a wide range of candidates materials in an economical way to select the ideal one.
이진녕,조준호,편무욱 건국대학교 산업기술연구원 2003 건국기술연구논문지 Vol.28 No.-
The Ubiquitous Technology refers to a new paradigm of intellectualizing physical space and organically integrating its objects through the means of ubiquitous computing and network. The forthcoming ubiquitous revolution is expected to serve a significant role in a sense that intelligent integration of heterogeneous physical space and its components connected to cyberspace will allow communal development of unified space of what can be called as a fourth dimension spatial revolution. And for such development of ubiquitous, technical complicity of location based GIS(Geographic Information System) technology applied to the three-dimensional spatial space construction process is essentially preferential. The manuscript therefore, serves to give systematic methods of illustrating Application of GIS Technology in the Ubiquitous for construction process.