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Sensors
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How Intelligent Sensors Will Make a Difference in Industry 4.0
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How Intelligent Sensors Will Make a Difference in Industry 4.0

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

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 3501

Special Issue Editors

Prof. Dr. Maysam Abbod

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Guest Editor
Department of Electronic and Electrical Engineering, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, UB8 3PH, United Kingdom
Interests: artificial intelligence; modelling and control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jiann-Shing Shieh

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Guest Editor
Department of Mechanical Engineering, Graduate School of Biotechnology and Bioengineering, Graduate Program in Biomedical Informatics, Yuan Ze University, Taoyuan, Chung-Li 32003, Taiwan
Interests: intelligent analysis and control in industrial processes; bio-signal processing; anaesthesia monitoring and control; pain model and control; medical automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Industry 4.0 standard will change the landscape of all production-based industries. Digitalization and minimization devices will be the key driving factors of future factory concepts, and together with IoT, AI, big data analysis, edge computing, fog computing, and cloud computing, real-time intelligent sensors (including not only monitoring sensors but also modelling, critics, fault detection and isolation, and over the air update algorithms) will open a new era of process control. The aim of real-time intelligent sensor design is to improve performance under uncertain environmental conditions. In order to complete certain tasks without the intervention of any other systems or supervisor (human), these intelligent sensors should be able to adapt to their surroundings when facing different conditions (i.e., external interfere or internal variations in parameters).

We invite researchers to contribute relevant original research papers or comprehensive reviews to this Special Issue on the “How intelligent sensors will make a difference in Industry 4.0”. Your contributions will help to improve and advance the methodologies used to process and analyze systems data. This information can be used to produce new intelligent sensor applications, allowing the formation of solutions integrated with new technologies (with IoT, A.I., big data analysis, edge computing, fog computing, and cloud computing) for use in the implementation of Industry 4.0 in important industrial systems.

Dr. Maysam Abbod
Prof. Dr. Jiann-Shing Shieh
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

  • Smart sensors
  • Intelligent sensors
  • Real-time automation
  • Intelligent modelling
  • Modelling evaluation and analysis
  • Fault detection and isolation
  • Intelligent maintenance system

Published Papers (1 paper)

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Research

21 pages, 5879 KiB  
Article
Smart Soft Sensor Design with Hierarchical Sampling Strategy of Ensemble Gaussian Process Regression for Fermentation Processes
by Xiaochen Sheng, Junxia Ma and Weili Xiong
Sensors 2020, 20(7), 1957; https://doi.org/10.3390/s20071957 - 31 Mar 2020
Cited by 14 | Viewed by 2822
Abstract
Accurate and real-time quality prediction to realize the optimal process control at a competitive price is an important issue in Industrial 4.0. This paper shows a successful engineering application of how smart soft sensors can be combined with machine learning technique to significantly [...] Read more.
Accurate and real-time quality prediction to realize the optimal process control at a competitive price is an important issue in Industrial 4.0. This paper shows a successful engineering application of how smart soft sensors can be combined with machine learning technique to significantly save human resources and improve performance under complex industrial conditions. Ensemble learning based soft sensors succeed in capturing complex nonlinearities, frequent dynamic changes, as well as time-varying characteristics in industrial processes. However, local model regions under traditional ensemble modelling methods are highly dependent on labeled data samples and, hence, their prediction accuracy might get affected when labeled samples are limited. A novel active learning (AL) framework upon the ensemble Gaussian process regression (GPR) model is proposed for smart soft sensor design in order to overcome this drawback. Firstly, to iteratively select the most informative unlabeled samples for labeling with hierarchical sampling based AL strategy, to then apply Gaussian mixture model (GMM) technique to autonomously identify operation phases, to further construct local GPR models without human involvement, and finally to integrate the base predictors by applying the Bayesian fusion strategy. Comparative studies for the penicillin fermentation process demonstrate the reliability and superiority of the recommended smart soft sensing. The cost of human annotation can be dramatically reduced by at least half while the prediction performance simultaneously keeps high. Full article
(This article belongs to the Special Issue How Intelligent Sensors Will Make a Difference in Industry 4.0)
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