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Risk Assessment of Urban Gas Pipeline Based on Different Unknown Measure Functions

2021 Tehnički Vjesnik  
To explore the uncertain factors in the process of gas pipeline risk assessment, and propose a practical assessment method, a three-layer index system for the risk assessment of urban gas pipeline was  ...  Several risk factors threaten the safety of urban gas pipeline.  ...  Based on unascertained measurement theory, the risk evaluation model of urban gas pipeline was established.  ... 
doi:10.17559/tv-20201021110548 fatcat:6n4cxedcy5ezdciwgw33iq2yhq

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

Xuefei Li, Liangtu Song, Liu Liu, Linli Zhou
2021 Sensors  
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).  ...  Gas supply system risk assessment is a serious and important problem in cities.  ...  Related Work Urban Natural Gas Risk Assessment Early works for gas risk assessment consider it as a mathematical problem.  ... 
doi:10.3390/s21217010 pmid:34770315 pmcid:PMC8588040 fatcat:utejoeaxwvgfjblu4atowookum

A Review of Underground Pipeline Leakage and Sinkhole Monitoring Methods Based on Wireless Sensor Networking

Haibat Ali, Jae-ho Choi
2019 Sustainability  
The aim of this work is to review the existing methods for monitoring leakage in underground pipelines, the sinkholes caused by these leakages, and the viability of wireless sensor networking (WSN) for  ...  Herein, the authors have discussed the methods based on different objectives and their applicability via various approaches—(1) patent analysis; (2) web-of-science analysis; (3) WSN-based pipeline leakage  ...  called the faster region based convolutional neural network (faster R-CNN) [81] .  ... 
doi:10.3390/su11154007 fatcat:vuwmuzkvgjhjxct5stgy5yblam

An Approach on the Evaluation of LNG Tank Container Transportation Safety

Feiyu Meng, Li Ma, Xuefeng Wang
2019 International Journal of Engineering and Management Research  
Therefore, this paper proposes a model based on the Recurrent Neural Networks(RNN) to evaluate the safety performance.  ...  As a clean energy source, liquefied natural gas (LNG) has been widely accepted all around the world. As a way to transport LNG, tank container transportation is becoming more and more popular.  ...  For example, safety assessment, risk assessment etc., Goldarag construct a financial credit risk evaluation index system of small and medium-sized enterprise based on supply chain, then use the BP neural  ... 
doi:10.31033/ijemr.9.5.8 fatcat:vlhcl46lqvg6ljl5i3plnj4kxy

Development of Methods for Diagnosing the Operating Conditions of Water Supply Networks over the Last Two Decades

Justyna Stańczyk, Ewa Burszta-Adamiak
2022 Water  
The review carried out by the authors shows that there is a need for research on the detection of operating conditions of water supply networks under the operating conditions of real systems.  ...  This review is a compendium of knowledge on the detection and diagnosis of water supply networks.  ...  At the stage of creating prognostic tools, the naive Bayes classifier (NBC), statistical regression, artificial neural networks (ANNs), and the genetic algorithm (GA) were used.  ... 
doi:10.3390/w14050786 fatcat:oui2wq3s5vdefbq2c2uyu7zym4

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

Maryam Pishgar, Salah Fuad Issa, Margaret Sietsema, Preethi Pratap, Houshang Darabi
2021 International Journal of Environmental Research and Public Health  
The majority of evidence of AI in OSH research within the oil/gas and transportation sectors focused on the development of sensors to detect hazardous situations.  ...  exposure risks in a worker's immediate environment.  ...  A major class of ML algorithms are constructed based on Neural Networks (NN). NN are designed based on the human brain with interconnected neurons.  ... 
doi:10.3390/ijerph18136705 pmid:34206378 pmcid:PMC8296875 fatcat:rjbt6vdevre47dviox32sifkzy

Table of Contents

2020 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2)  
Current 204 Pa07-01 Protection Scheme for Collector Lines Based on the Current Amplitude Ratio with Spectrum Index 209 Pa07-02 Fast Security Analysis for Urban Power System Based on Full Voltage  ...  Distributive Devices Online Status Assessment of Distribution Network Transformer Based on Random Matrix Research on Signature Verification of Two-Tickets Based on Siamese Convolutional Neural Particle  ... 
doi:10.1109/ei250167.2020.9347098 fatcat:uzijufuzb5ab3blgftr5niughe

Solving Management Problems in Water Distribution Networks: A Survey of Approaches and Mathematical Models

Oladipupo Bello, Adnan Abu-Mahfouz, Yskandar Hamam, Philip Page, Kazeem Adedeji, Olivier Piller
2019 Water  
Also, new directions for future research studies are suggested to enable water utility managers and researchers to improve the performance of water distribution networks.  ...  The management problems in such complex networks may be classified into short-term, medium-term, and long-term, depending on the duration at which the problems are solved or considered.  ...  Acknowledgments: This research work was supported by Tshwane University of Technology, Pretoria and the Council for Scientific and Industrial Research (CSIR), Pretoria, South Africa.  ... 
doi:10.3390/w11030562 fatcat:xdrzv5j65vcdhdvq3iqtiqz4si

Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – A state-of-the-art review

Yongliang (Harry) Yan, Tohid Borhani, Gokul Subraveti, Nagesh Pai, Vinay Prasad, Arvind Rajendran, Paula Nkulikiyinka, Jude Odianosen Asibor, Zhien Zhang, Ding Shao, Lijuan Wang, Wenbiao Zhang (+7 others)
2021 Energy & Environmental Science  
Bromhal et al. 242 introduced a work to summarise how the National Risk Assessment Partnership (NRAP) handles the longterm quantitative risk assessment for carbon storage.  ...  Sparse Grid polynomials and artificial neural networks were used as data-based models to approximate decision variableprocesses output mapping.  ... 
doi:10.1039/d1ee02395k fatcat:oherbaerwfcarc744bn77pu77y

A Hybrid Meta-heuristic for a Bi-objective Stochastic Optimization of Urban Water Supply System

Azadeh Dogani, Arash Dourandish, Mohammad Ghorbani, Mohammad Reza Shahbazbegian
2020 IEEE Access  
The restoration and remodeling of the urban water supply system are traditional challenges for water companies due to either aged existing water supply networks or lodging expansion.  ...  INDEX TERMS Optimization, water supply, hybrid metaheuristic, red deer algorithm (RDA), simulated annealing (SA).  ...  The leakage rate for each recently installed pipeline is represented by LR.  ... 
doi:10.1109/access.2020.3009885 fatcat:7gq4kcfnizf53l7u7m2k4cpjpq

Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems—A Review

Simone Buffa, Mohammad Hossein Fouladfar, Giuseppe Franchini, Ismael Lozano Gabarre, Manuel Andrés Chicote
2021 Applied Sciences  
Accordingly, to address these shortcomings, researchers have developed plenty of innovative methods based on their applications and features.  ...  both fourth and fifth generation district heating and cooling networks.  ...  By way of conclusion, even though it seems that every time more algorithms used for fault detection are based on machine learning (ML) techniques such as artificial neural networks (ANNs), a recent study  ... 
doi:10.3390/app11010455 fatcat:5xur4nlqv5cydg2g5xrk5jay6u

Development of wastewater pipe performance index and performance prediction model

Thiti Angkasuwansiri, Sunil K. Sinha
2014 International Journal of Sustainable Materials and Structural Systems  
The performance index were evaluated based on artificial data and field data to ensure that the index could be implemented to real scenarios.  ...  This research presents a development of a performance index for wastewater pipes.  ...  neural network models (Najafi, 2005) Najafi (2005) proposed a method for predicting the pipeline condition based on historical data using Artificial Neural Networks, intended for the Sewer System  ... 
doi:10.1504/ijsmss.2014.062767 fatcat:zl2brykysnbvxchetac5xzhlae

ARAMIS heritage, 10 years after the end of the project [chapter]

2017 Risk Analysis and Management - Trends, Challenges and Emerging Issues  
Although all care is taken to ensure integrity and the quality of this publication and the information herein, no responsibility is assumed by the publishers nor the author for any damage to the property  ...  MATRIX (New methodologies for multi-hazard and multi-risk assessment methods for Europe), EU funded FP7 project. Merz, B., Thieken, A.H. (2005) .  ...  Flood Risk Assessment: A Methodological Framework, Water Resources Management: New Approaches and Technologies, European Water Resources  ... 
doi:10.1201/9781315265339-9 fatcat:rs6apsg5rndttgq7fi5huiukry

Evaluating risk of water mains failure using a Bayesian belief network model

Golam Kabir, Solomon Tesfamariam, Alex Francisque, Rehan Sadiq
2015 European Journal of Operational Research  
This paper presents a Bayesian Belief Network (BBN) model to evaluate the risk of failure of metallic water mains using structural integrity, hydraulic capacity, water quality, and consequence factors.  ...  The proposed model is capable of ranking water mains within distribution network that can identify vulnerable and sensitive pipes to justify proper decision action for maintenance/rehabilitation/replacement  ...  Acknowledgements The financial support to the second and fourth authors from Natural Sciences and Engineering Research Council of Canada (NSERC) under Discovery and CRD grant programs is acknowledged.  ... 
doi:10.1016/j.ejor.2014.06.033 fatcat:ucfw4vzypzfo5lj55ljmhp5c4e

Tackling Climate Change with Machine Learning [article]

David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli (+6 others)
2019 arXiv   pre-print
Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate.  ...  We call on the machine learning community to join the global effort against climate change.  ...  The authors gratefully acknowledge support from National Science Foundation grant 1803547, the Center for Climate and Energy Decision Making through a cooperative agreement between the National Science  ... 
arXiv:1906.05433v2 fatcat:ykmqsivkbfcazaz3wl5f7srula
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