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

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 perspect...

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Autores principales:	Xuefei Li, Liangtu Song, Liu Liu, Linli Zhou
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	natural gas supply system risk assessment multi-modal fusion deep learning cross-attention mechanism Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/2ecc1a3b6fcf4264bcc4626b768de9de
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spelling	oai:doaj.org-article:2ecc1a3b6fcf4264bcc4626b768de9de2021-11-11T19:02:53ZGSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users10.3390/s212170101424-8220https://doaj.org/article/2ecc1a3b6fcf4264bcc4626b768de9de2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7010https://doaj.org/toc/1424-8220Gas 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.Xuefei LiLiangtu SongLiu LiuLinli ZhouMDPI AGarticlenatural gas supply system risk assessmentmulti-modal fusiondeep learningcross-attention mechanismChemical technologyTP1-1185ENSensors, Vol 21, Iss 7010, p 7010 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	natural gas supply system risk assessment multi-modal fusion deep learning cross-attention mechanism Chemical technology TP1-1185
spellingShingle	natural gas supply system risk assessment multi-modal fusion deep learning cross-attention mechanism Chemical technology TP1-1185 Xuefei Li Liangtu Song Liu Liu Linli Zhou GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users
description	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.
format	article
author	Xuefei Li Liangtu Song Liu Liu Linli Zhou
author_facet	Xuefei Li Liangtu Song Liu Liu Linli Zhou
author_sort	Xuefei Li
title	GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users
title_short	GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users
title_full	GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users
title_fullStr	GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users
title_full_unstemmed	GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users
title_sort	gss-riskasser: a multi-modal deep-learning framework for urban gas supply system risk assessment on business users
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2ecc1a3b6fcf4264bcc4626b768de9de
work_keys_str_mv	AT xuefeili gssriskasseramultimodaldeeplearningframeworkforurbangassupplysystemriskassessmentonbusinessusers AT liangtusong gssriskasseramultimodaldeeplearningframeworkforurbangassupplysystemriskassessmentonbusinessusers AT liuliu gssriskasseramultimodaldeeplearningframeworkforurbangassupplysystemriskassessmentonbusinessusers AT linlizhou gssriskasseramultimodaldeeplearningframeworkforurbangassupplysystemriskassessmentonbusinessusers
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GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users

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