By Bachir Achour, Qiyan Wu

ISBN-10: 1138626821

ISBN-13: 9781138626829

ISBN-10: 1315212870

ISBN-13: 9781315212876

ISBN-10: 135181690X

ISBN-13: 9781351816908

This e-book holds the complaints of the 2016 overseas convention on improve in strength and atmosphere learn (ICAEER 2016), which was once hung on August 12-14, 2016 in Guangzhou, China.
ICAEER 2016 introduced jointly leading edge lecturers and commercial specialists within the box of strength and setting learn to a standard discussion board. the first target of the convention used to be to advertise study and developmental actions in strength and setting study and one other target is to advertise medical details interchange among researchers, builders, engineers, scholars, and practitioners operating everywhere in the global. The conference is held each year to make it an incredible platform for individuals to percentage perspectives and reports in strength and setting examine and comparable areas.

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Extra info for Advances in Energy and Environment Research: Proceedings of the International Conference on Advances in Energy and Environment Research (ICAEER2016), Guangzhou City, China, August 12-14, 2016

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Li, C. & Zhang, P. 2016. Improving the electrochemical performance of high voltage spinel cathode at elevated temperature by a novel electrolyte additive. Journal of Power Sources, 303, 41–48. , Sanchez, J. Y. & Passerini, S. 2014. Enabling LiTFSI-based electrolytes for safer lithium-ion batteries by using linear fluorinated carbonates as (Co) solvent. ChemSusChem, 7, 2939–2946. P. & Yang, L. 2014. Challenges and approaches for high-voltage spinel lithium-ion batteries. Chemphyschem, 15, 1940–1954.

0198 Result analysis In order to contrast the performance of PSOLSSVR with other existing prediction methods, a BP neural network prediction model and a simple LSSVR prediction model are concerned, whose outputs are shown in Figures 4 and 5, in which, the solid line indicates original data and the dashedline indicates regression and prediction data. In the BP neural network prediction model, the sample data are normalized before being used in the model. In the LSSVR prediction model, a grid search and cross-validation method is adopted to find the optimal value of the model parameters.

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Advances in Energy and Environment Research: Proceedings of the International Conference on Advances in Energy and Environment Research (ICAEER2016), Guangzhou City, China, August 12-14, 2016 by Bachir Achour, Qiyan Wu


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