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Applied Sciences
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Recent Advancements in Artificial Intelligence (AI) in Instrumentation and Control...
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Recent Advancements in Artificial Intelligence (AI) in Instrumentation and Control Systems (AI-ICS)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 2652

Special Issue Editor

Prof. Desineni Subbaram Naidu

E-Mail Website
Guest Editor
Minnesota Power Jack Rowe Endowed Chair & Professor in Electrical Engineering, University of Minnesota Duluth (UMD), Duluth, MN 55812, USA
Interests: measurement and control engineering; optimal control; time scales; energy; cyber, physical, and life systems; resilience; convergence and integration igniting innovation between physical and life (& biomedical) sciences, arts (& humanities), natural sciences and engineering and technology (PLANET)

Special Issue Information

Dear Colleagues,

Instrumentation encompasses measurement techniques and the associated equipment. Control is basically making a “change” of present status to desired status of any cyber, physical, biological systems. Artificial Intelligence (AI) in Instrumentation and Control Systems (AI-ICS) addresses advanced AI-embedded and secure technologies for modeling, analysis, synthesis, experimentation, validation and deployment of sensor, control, monitoring, and decision systems As the society moves towards Convergence and Integration Igniting Innovation between Physical and Life (& Biomedical) Sciences, Arts (& Humanities), Natural Sciences and Engineering and Technology (PLANET) leading to the Internet of Things (IoT), Internet of Everything (IoET) and The Social Networking of Humans and Machines, resilience-embedded cyber security is of paramount importance to protect critical infrastructure such as Electric, Oil, Natural Gas, Water, Finance and Communication Grids from natural and/or intentional adversary actions.

Contributions (including new and work-in-progress, state-of-the-art overviews, surveys, etc.) are invited in these and other related areas for this special issue on AI-ICS.

Prof. Desineni Subbaram Naidu
Guest Editor

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. Applied Sciences 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 2400 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

  • Artificial Intelligence (AI) including Machine Learning and Deep Learning in Instrumentation/ Measurement and Control Systems
  • Cyber, Physical, Bio Systems (CPBS)
  • Cyber Security and Resilience
  • Optimal, Adaptive, Robust, Nonlinear and Intelligent Control
  • Biomedical Sciences and Engineering
  • Applications, not limited to, Critical Infrastructure such as Electric, Natural Gas, Oil, Water, Financial and Communication Grids

Published Papers (1 paper)

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Research

15 pages, 4286 KiB  
Article
Continual Learning for Addressing Optimization Problems with a Snake-Like Robot Controlled by a Self-Organizing Model
by Jong-Chen Chen
Appl. Sci. 2020, 10(14), 4848; https://doi.org/10.3390/app10144848 - 15 Jul 2020
Cited by 5 | Viewed by 1771
Abstract
We have entered a new era, “Industry 4.0”, that sees the overall industry marching toward an epoch of man–machine symbiosis and intelligent production. The developers of so-called “intelligent” systems must attempt to seriously take into account all possible situations that might occur in [...] Read more.
We have entered a new era, “Industry 4.0”, that sees the overall industry marching toward an epoch of man–machine symbiosis and intelligent production. The developers of so-called “intelligent” systems must attempt to seriously take into account all possible situations that might occur in the real world, to minimize unexpected errors. By contrast, biological systems possess comparatively better “adaptability” than man-made machines, as they possess a self-organizing learning that plays an indispensable role. The objective of this study was to apply a malleable learning system to the movement control of a snake-like robot, to investigate issues related to self-organizing dynamics. An artificial neuromolecular (ANM) system previously developed in our laboratory was used to control the movements of an eight-joint snake-like robot (called Snaky). The neuromolecular model is a multilevel neural network that abstracts biological structure–function relationships into the system’s structure, in particular into its intraneuronal structure. With this feature, the system possesses structure richness in generating a broad range of dynamics that allows it to learn how to complete the assigned tasks in a self-organizing manner. The activation and rotation angle of each motor are dependent on the firing activity of neurons that control the motor. An evolutionary learning algorithm is used to train the system to complete the assigned tasks. The key issues addressed include the self-organizing learning capability of the ANM system in a physical environment. The experimental results show that Snaky was capable of learning in a continuous manner. We also examined how the ANM system controlled the angle of each of Snaky’s joints, to complete each assigned task. The result might provide us with another dimension of information on how to design the movement of a snake-like robot. Full article
(This article belongs to the Special Issue Recent Advancements in Artificial Intelligence (AI) in Instrumentation and Control Systems (AI-ICS))
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