Deep Learning-Based Soft Sensors
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 22030
Special Issue Editors
Interests: artificial intelligence; neural networks; soft sensors; ionic polymeric transducers; sensor modelling and characterization; mechanical sensors; energy harvesting; smart materials; smart sensing systems
Special Issues, Collections and Topics in MDPI journals
Interests: automatic control; system identification; nonlinear control; industrial automation; soft sensors; soft computing; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The implementation of Industry 4.0 enforces the need for processes monitoring. Plant monitoring, control policies, and asset management require the estimation of plant working conditions. Efficient measurement and data elaboration systems are, therefore, required. A key role will be played by sensing systems with somewhat contrasting constraints on sensing capability, efficiency, redundancy, and costs. Physical or economic reasons, e.g., can limit the nature, number, and location of measuring systems.
Soft Sensors (SSs) are becoming a penetrating solution in the industry, capable of overcoming physical limitations that can be involved with the problems mentioned above. SSs are software tools that elaborate data on easy-to-measure process variables (SS inputs) and estimate hard-to-measure quantities (SS outputs). They are used for many purposes, such as hardware sensing back-up, real-time estimation for monitoring and control, sensor validation, and fault detection. Notwithstanding the interest in SSs, unsolved problems have so far hindered the full success of the data-driven designing approach. Deep learning has emerged as a valuable approach for alleviating some such problems. Nevertheless, the complex and nonlinear nature of industrial processes still poses interesting challenges that are the focus of the Special Issue. Real case studies will be a valuable contribution to the significativity of the Issue.
Contributes are invited on the following topics:
-Data-driven methods for SS design;
-Deep belief networks for SS design;
-Autoencoders for SS design;
-Long- and short-term memory networks for SS design;
-Convolution neural networks for SS design;
-Ensemble methods;
-Model structure selection;
-Small and large dataset;
-Semisupervised learning;
-Model validation;
-Hyperparameters design;
-Learning methods;
-Computational complexity;
-Feature selection in SS design;
-SSs for industrial applications;
-SSs for inferential control;
-SSs for monitoring and fault detection.
Prof. Dr. Salvatore Graziani
Prof. Dr. Maria Gabriella Xibilia
Guest Editors
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