5篇
您的检索式：作者名="Mario Conte"
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| 1 | 国际合作情况下的锂电池的寿命测试程序(英文)显示文摘为了满足不同的技术和经济目标,从轻度混合动力、插电式混合动力到全电池动力的电动汽车,都将依赖于新型的、先进的(如基于锂的)蓄电池。这些电池在各种应用条件下的性能预测和寿命表征费工、费时,目前尚未得到充分的发展。一些国家已投入资金和人力进行相关的研究,其实通过国际合作,这些努力和花费也许能发挥更大的作用,例如目前正在国际能源机构(The International Energy Agency,IEA)框架内开展的准备工作。正在致力于开发一套标准化的、加速的测试程序,将允许各个测试机构合作分析电池的测量数据。该文评述了欧洲、日本和美国在加速寿命测试程序上的最新进展。以国际合作为目标,搜集、对比和分析现有的测试程序。 | Mario Conte Fiorentino V. Conte Ira D. Bloom Kenji Morita Tomohiko Ikeya Jeffrey R. Belt | 2011 | 汽车安全与节能学报2011，2，2： | 1 |
| 2 | Inflammation and Cardiovascular Disease: From Pathogenesis to Therapeutic Target显示文摘 | Enrica Golia Giuseppe Limongelli Francesco Natale Fabio Fimiani Valeria Maddaloni Ivana Pariggiano Renatomaria Bianchi Mario Crisci Ludovica D’Acierno Roberto Giordano Gaetano Palma Marianna Conte Paolo Golino Maria Giovanna Russo Raffaele Calabrò Paolo C | 2014 | Current Atherosclerosis Reports2014，，9： | 1 |
| 3 | Crime and Corruption Observatory: Big Questions behind Big Data 显示文摘 | Giulia Bonelli Mario Paolucci Rosaria Conte | 2012 | ER- CIM-NEWS2012，89，： | 1 |
| 4 | Prospective evaluation of early endoscopic ultrasonography for triage in suspected choledocholithiasis: Results from a large single centre series显示文摘 | Andrea Anderloni Marco Ballarè Michela Pagliarulo Dario Conte Marianna Galeazzi Marco Orsello Silvano Andorno Mario Del Piano | 2013 | Digestive and Liver Disease2013，，： | 1 |
| 5 | A new hybrid AI optimal management method for renewable energycommunities显示文摘In this study, we propose a hybrid AI optimal method to improve the efficiency of energy managementin a smart grid such as Renewable Energy Community. This method adopts a Time Delay Neural Networkto forecast the future values of the energy features in the community. Then, these forecasts are used by astochastic Model Predictive Control to optimize the community operations with a proper control strategy ofBattery Energy Storage System. The results of the predictions performed on a public dataset with a predictionhorizon of 24 h return a Mean Absolute Error of 1.60 kW, 2.15 kW, and 0.30 kW for photovoltaic generation,total energy consumption, and common services, respectively. The model predictive control fed with suchpredictions generates maximum income compared to the competitors. The total income is increased by 18.72%compared to utilizing the same management system without exploiting predictions from a forecasting method. | Francesco Conte Federico D’Antoni Gianluca Natrella Mario Merone | 2022 | Energy and AI2022，10，4： | 0 |
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