文献类型：期刊文献

英文题名：Federated learning based atmospheric source term estimation in urban environments

作者：Xu, Jinjin[1];Du, Wenli[1];Xu, Qiaoyi[1];Dong, Jikai[1];Wang, Bing[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China

年份：2021

卷号：155

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：20213710877113);WOS:【SCI-EXPANDED(收录号:WOS:000703984100015)】；

基金：The work is supported by National Natural Science Founda-tion of China (Basic Science Center Program: 61988101) , National Natural Science Fund for Distinguished Young Scholars (61725301) and International (Regional) Cooperation and Exchange Project (61720106008) .

语种：英文

外文关键词：Source term estimation; Federated learning; Multiple obstacles; Deep learning; Data-driven

摘要：Establishing an accurate and efficient source term estimation (STE) system is of great significance for identifying unknown gas leakage sources in urban environments. Many successful STE methods have been proposed, while most of them assume there is only one point source. However, the complicated urban atmospheric dispersion and the massive sensor data in distributed edge devices pose new challenges. To address these issues, this paper proposes a method to convert measured concentrations into visual features, which retains the characteristics of diffusion and the layout of obstacles. Then, a federated STE (FL-STE) framework is proposed to extract knowledge from local models collaboratively without collecting all privacy data, in which a deep neural network is used to recognize the relationship between visual features and source terms. Furthermore, we construct an urban dispersion dataset with multiple obstacles and sources by FDS simulation. Various empirical studies prove the efficiency of the proposed method. (c) 2021 Elsevier Ltd. All rights reserved.