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The Traditional Culture of Northeast Heated Kang and Deyelopment of New Type of Heated Kang
Peng Tt. anxue,June Bong Kim 국제온돌학회 2014 International Journal of Ondol Vol.7 No.1
Heated Kangas one of the most important elements of the northeast mral residence, formed a unique 11 mode ofheated brick bed 11 and 11 culture ofheated brick bed 11 in the long history of more than two thousand years. Kang bed-stove has many functions, for example, heating residential, communication, emotion and so on. Not only it is the carrier of the northeast family's material culture, also it plays a major role to spatial organization and building ofthe mral housing. Kang bed-stove is a way of the repeated skillful use of energy. The traditional heated brick bed built with mud and brick. It connects heat able adobe sleeping platform in its outside and links chimney in its inside. The quantity of heat of Kang bed-stove comes from the waste heat of cooking .In recent years, the new mral constmction of northeast is in the stage of development of high-speed. With the progress of technology and the renewal ofthe concept, based on the traditional heated brick bed, people have made some beneficial attempts to change and have introduced some new heated brick bed: Suspended Kang, energy saving kang and so on. All new heated brick beds both retain the traditional heated brick bed's functions, and conform to the requirements of sustainable development planning. new heated brick beds are the combination oftraditional culture and the new technology. This paper ba<;es on rural housing of northeast area as the research object, discusses the connotation, forms and types, heating means of traditional heated brick bed and the evolution of new type of Heated Kang.
학술연구 : 전자자원의 이용통계 통합 애플리케이션, EBSCONET(R) Usage Consolidation -이용통계 수집, 통합, 분석을 위한 새로운 통합 도구의 제안-
김성곤 ( Sco Tt Kim ) 한국디지틀도서관포럼 2012 디지틀 도서관 Vol.65 No.-
계속되어 온 예산 삭감의 압박과 넘쳐나는 정보의 홍수 속에, 오늘날의 도서관들은 학술 정보의 우선순위를 정해, 취사선택할 수밖에 없는 상황에 놓여있다. 전자 자원에 대한 가치를 부여하고 평가하는데 있어 이용통계의 분석은 그 어느 때 보다도 중요한 평가 수단으로 여겨지고 있다. 하지만 각각의 자원들에 대한 이용통계 데이터를 수집, 취합하여 분석하는 작업은 여간 많은 시간과 노력을 요하는 작업이 아니다. 본고에서는 이러한 사서들의 고충을 덜어줄 수 있는 통합 도구의 출시와 이용방법, 그리고 국내 기관들의 beta test 참여를 통한 기능의 개선 등에 대해 소개하였다. Under the pressure of continued budget cuts and with a flood of overflowing information, libraries nowadays have to choose most important scholarly information among them by setting priorities. In evaluating e-resources, analyzing usage has been regarded as more important means than ever. However it is very time-consuming and much effort required to collect, consolidate, and analyze usage data. This article introduced a new usage tool recently launched for reliving librarian`s predicament at handling this duty, how it works, how it is consolidated into other library`s subscription management solutions and the enhancements from the beta test in which two Korean universities took part.
SchNetPack: A Deep Learning Toolbox For Atomistic Systems
Schü,tt, K. T.,Kessel, P.,Gastegger, M.,Nicoli, K. A.,Tkatchenko, A.,Mü,ller, K.-R. American Chemical Society 2019 Journal of chemical theory and computation Vol.15 No.1
<P>SchNetPack is a toolbox for the development and application of deep neural networks that predict potential energy surfaces and other quantum-chemical properties of molecules and materials. It contains basic building blocks of atomistic neural networks, manages their training, and provides simple access to common benchmark datasets. This allows for an easy implementation and evaluation of new models. For now, SchNetPack includes implementations of (weighted) atom-centered symmetry functions and the deep tensor neural network SchNet, as well as ready-to-use scripts that allow one to train these models on molecule and material datasets. Based on the PyTorch deep learning framework, SchNetPack allows one to efficiently apply the neural networks to large datasets with millions of reference calculations, as well as parallelize the model across multiple GPUs. Finally, SchNetPack provides an interface to the Atomic Simulation Environment in order to make trained models easily accessible to researchers that are not yet familiar with neural networks.</P> [FIG OMISSION]</BR>