GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users

doi:10.3390/s21217010

HFCAS OpenIR

	GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users
	Li, Xuefei 1,2,3; Song, Liangtu1,2 ; Liu, Liu 2; Zhou, Linli1
	2021-11-01
发表期刊	SENSORS
通讯作者	Li, Xuefei(lcyyyy@mail.ustc.edu.cn)
摘要	Gas supply system risk assessment is a serious and important problem in cities. Existing methods tend to manually build mathematical models to predict risk value from single-modal information, i.e., pipeline parameters. In this paper, we attempt to consider this problem from a deep-learning perspective and define a novel task, Urban Gas Supply System Risk Assessment (GSS-RA). To drive deep-learning techniques into this task, we collect and build a domain-specific dataset GSS-20K containing multi-modal data. Accompanying the dataset, we design a new deep-learning framework named GSS-RiskAsser to learn risk prediction. In our method, we design a parallel-transformers Vision Embedding Transformer (VET) and Score Matrix Transformer (SMT) to process multi-modal information, and then propose a Multi-Modal Fusion (MMF) module to fuse the features with a cross-attention mechanism. Experiments show that GSS-RiskAsser could work well on GSS-RA task and facilitate practical applications. Our data and code will be made publicly available.
关键词	natural gas supply system risk assessment multi-modal fusion deep learning cross-attention mechanism
DOI	10.3390/s21217010
关键词[WOS]	PIPELINE RISK ; VULNERABILITY ; CORROSION
收录类别	SCI
语种	英语
资助项目	key research and development plan projects of Anhui Province[201904a06020056]
项目资助者	key research and development plan projects of Anhui Province
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000719989800001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/126685
专题	中国科学院合肥物质科学研究院
通讯作者	Li, Xuefei
作者单位	1.Chinese Acad Sci, Hefei Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Hefei 230027, Peoples R China 3.Anhui Jianzhu Univ, Coll Environm & Energy Engn, Hefei 230601, Peoples R China
推荐引用方式 GB/T 7714	Li, Xuefei,Song, Liangtu,Liu, Liu,et al. GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users[J]. SENSORS,2021,21.
APA	Li, Xuefei,Song, Liangtu,Liu, Liu,&Zhou, Linli.(2021).GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users.SENSORS,21.
MLA	Li, Xuefei,et al."GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users".SENSORS 21(2021).