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Recent Developments in Sensor Network-Based Data-Driven Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 2386

Special Issue Editors


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Guest Editor
Mary Kay O'Connor Process Safety Center (MKOPSC), Texas A&M University, College Station, TX 77843, USA
Interests: fault diagnosis; safety analysis; data-driven models; cyber-physical system safety; risk assessment; machine learning
Special Issues, Collections and Topics in MDPI journals
Centre for Offshore Engineering and Safety Technology (COEST), China University of Petroleum (East China), Qingdao 266580, China
Interests: physics-guided machine (deep) learning; leaks detection; real-time prediction and forecasting; digital twin of emergency management

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Guest Editor
Department of Ocean and Naval Architectural Engineering, Memorial University of Newfoundland, St. John’s, NL, Canada
Interests: design; analysis; failure prediction; multiscale modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advent and advancement of sensor technologies, industries are receiving an enormous amount of data. The degree of data handling has enhanced due to the emergence of Big Data, the Internet of Things, and Industry 4.0. The success of a business operation significantly depends on how these data are analyzed. Developing data-driven monitoring models for fault detection and diagnosis, failure prediction, alarm management, remaining useful life prediction, real-time operational condition assessment, and dynamic safety assessment is pivotal for event-free operations. In addition, a growing trend has been noticed in recent years to overcome the computational load of conventionally established tools such as FEA and CFD using data-driven machine learning models.

This Special Issue aims to put together original contributions in terms of the theory and application of data-driven methods in solving earlier-mentioned engineering problems. We welcome both reviews and research articles.

Dr. Md Tanjin Amin
Dr. Jihao Shi
Dr. Ahmed Youssri Elruby
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data-driven models
  • data analytics
  • surrogate models
  • soft sensors
  • artificial intelligence

Published Papers (1 paper)

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Research

19 pages, 9645 KiB  
Article
Advanced Computer Vision-Based Subsea Gas Leaks Monitoring: A Comparison of Two Approaches
by Hongwei Zhu, Weikang Xie, Junjie Li, Jihao Shi, Mingfu Fu, Xiaoyuan Qian, He Zhang, Kaikai Wang and Guoming Chen
Sensors 2023, 23(5), 2566; https://doi.org/10.3390/s23052566 - 25 Feb 2023
Cited by 4 | Viewed by 2051
Abstract
Recent years have witnessed the increasing risk of subsea gas leaks with the development of offshore gas exploration, which poses a potential threat to human life, corporate assets, and the environment. The optical imaging-based monitoring approach has become widespread in the field of [...] Read more.
Recent years have witnessed the increasing risk of subsea gas leaks with the development of offshore gas exploration, which poses a potential threat to human life, corporate assets, and the environment. The optical imaging-based monitoring approach has become widespread in the field of monitoring underwater gas leakage, but the shortcomings of huge labor costs and severe false alarms exist due to related operators’ operation and judgment. This study aimed to develop an advanced computer vision-based monitoring approach to achieve automatic and real-time monitoring of underwater gas leaks. A comparison analysis between the Faster Region Convolutional Neural Network (Faster R-CNN) and You Only Look Once version 4 (YOLOv4) was conducted. The results demonstrated that the Faster R-CNN model, developed with an image size of 1280 × 720 and no noise, was optimal for the automatic and real-time monitoring of underwater gas leakage. This optimal model could accurately classify small and large-shape leakage gas plumes from real-world datasets, and locate the area of these underwater gas plumes. Full article
(This article belongs to the Special Issue Recent Developments in Sensor Network-Based Data-Driven Systems)
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