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Intelligent Control and Testing Systems and Applications
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Intelligent Control and Testing Systems and Applications

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 18038

Special Issue Editors

Dr. Ovidiu P. Stan

E-Mail Website
Guest Editor
Faculty of Automation and Computer Science, Department of Automation, Technical University of Cluj-Napoca, Memorandumului 28, 400014 Cluj-Napoca, Romania
Interests: semantic interoperability; information management in the age of the Internet; cloud-fog-edge; dependable systems
Special Issues, Collections and Topics in MDPI journals
Dr. Sanislav Teodora

E-Mail Website
Guest Editor
Department of Automation, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 26-28 G. Baritiu, 400027 Cluj-Napoca, Romania
Interests: information systems (in particular Cyber-Physical Systems); dependability (availability, reliability, safety, integrity and maintainability); security (confidentially); artificial intelligence techniques; Internet of Things; intelligent systems; sensors; wireless sensor network
Dr. Dan Gota

E-Mail Website
Guest Editor
Faculty of Automation and Computer Science, Department of Automation, Technical University of Cluj-Napoca, Memorandumului 28, 400014 Cluj-Napoca, Romania
Interests: IoT systems; cyber-physical systems; multiagent systems; computer aided design; energy systems; neural networks; artificial intelligence

Special Issue Information

Dear Colleagues,

This Special Issue will act as a forum for research in automation, quality, testing and robotics. It will discuss the current trends and future directions of control and testing technologies and their industrial and social applications in the private and the public sectors.

Topics of interest include:

  • Control systems: fault-detection and isolation; fault models; control methods; real-time systems; decision-support systems; distributed systems; intelligent systems; and automotive systems.
  • Quality and Testing: IoT and CPS dependability; automatic test generation; design for testability; built-in self-test/repair; reliability; software QA; system testing; machine learning and testing; quality of experiments; field trials and deployment; advanced metering infrastructure; wide-area monitoring; and testing, reliability and security of emerging technologies.
  • Robot control: mobile robots; cooperative robots; parallel structures; machine vision; and sensor fusion.
  • Applications: cyber-physical systems; industry 4.0 applications; software applications; network sensors; image processing; and eHealth; eLearning; eCitizen and smart cities.

Dr. Ovidiu P. Stan
Dr. Sanislav Teodora
Dr. Dan Gota
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.

Published Papers (7 papers)

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Research

20 pages, 4437 KiB  
Article
Safe Decision Controller for Autonomous DrivingBased on Deep Reinforcement Learning inNondeterministic Environment
by Hongyi Chen, Yu Zhang, Uzair Aslam Bhatti and Mengxing Huang
Sensors 2023, 23(3), 1198; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031198 - 20 Jan 2023
Cited by 3 | Viewed by 1964
Abstract
Autonomous driving systems are crucial complicated cyber–physical systems that combine physical environment awareness with cognitive computing. Deep reinforcement learning is currently commonly used in the decision-making of such systems. However, black-box-based deep reinforcement learning systems do not guarantee system safety and the interpretability [...] Read more.
Autonomous driving systems are crucial complicated cyber–physical systems that combine physical environment awareness with cognitive computing. Deep reinforcement learning is currently commonly used in the decision-making of such systems. However, black-box-based deep reinforcement learning systems do not guarantee system safety and the interpretability of the reward-function settings in the face of complex environments and the influence of uncontrolled uncertainties. Therefore, a formal security reinforcement learning method is proposed. First, we propose an environmental modeling approach based on the influence of nondeterministic environmental factors, which enables the precise quantification of environmental issues. Second, we use the environment model to formalize the reward machine’s structure, which is used to guide the reward-function setting in reinforcement learning. Third, we generate a control barrier function to ensure a safer state behavior policy for reinforcement learning. Finally, we verify the method’s effectiveness in intelligent driving using overtaking and lane-changing scenarios. Full article
(This article belongs to the Special Issue Intelligent Control and Testing Systems and Applications)
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16 pages, 2042 KiB  
Article
A Hybrid Software and Hardware SDN Simulation Testbed
by Sorin Buzura, Adrian Peculea, Bogdan Iancu, Emil Cebuc, Vasile Dadarlat and Rudolf Kovacs
Sensors 2023, 23(1), 490; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010490 - 02 Jan 2023
Cited by 4 | Viewed by 2292
Abstract
In recent years, the software-defined networking (SDN) paradigm has been deployed in various types of networks, including wireless sensor networks (WSN), wide area networks (WAN) and data centers. Given the wide range of SDN domain applicability and the large-scale environments where the paradigm [...] Read more.
In recent years, the software-defined networking (SDN) paradigm has been deployed in various types of networks, including wireless sensor networks (WSN), wide area networks (WAN) and data centers. Given the wide range of SDN domain applicability and the large-scale environments where the paradigm is being deployed, creating a full real test environment is a complex and costly task. To address these problems, software-based simulations are employed to validate the proposed solutions before they are deployed in real networks. However, simulations are constrained by relying on replicating previously saved logs and datasets and do not use real time hardware data. The current article addresses this limitation by creating a novel hybrid software and hardware SDN simulation testbed where data from real hardware sensors are directly used in a Mininet emulated network. The article conceptualizes a new approach for expanding Mininet’s capabilities and provides implementation details on how to perform simulations in different contexts (network scalability, parallel computations and portability). To validate the design proposals and highlight the benefits of the proposed hybrid testbed solution, specific scenarios are provided for each design idea. Furthermore, using the proposed hybrid testbed, new datasets can be easily generated for specific scenarios and replicated in more complex research. Full article
(This article belongs to the Special Issue Intelligent Control and Testing Systems and Applications)
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32 pages, 23939 KiB  
Article
Comparative Performance Analysis of the DC-AC Converter Control System Based on Linear Robust or Nonlinear PCH Controllers and Reinforcement Learning Agent
by Marcel Nicola and Claudiu-Ionel Nicola
Sensors 2022, 22(23), 9535; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239535 - 06 Dec 2022
Cited by 2 | Viewed by 1349
Abstract
Starting from the general topology and the main elements that connect a microgrid represented by a DC power source to the main grid, this article presents the performance of the control system of a DC-AC converter. The main elements of this topology are [...] Read more.
Starting from the general topology and the main elements that connect a microgrid represented by a DC power source to the main grid, this article presents the performance of the control system of a DC-AC converter. The main elements of this topology are the voltage source inverter represented by a DC-AC converter and the network filters. The active Insulated Gate Bipolar Transistor (IGBT) or Metal–Oxide–Semiconductor Field-Effect Transistor (MOSFET) elements of the DC-AC converter are controlled by robust linear or nonlinear Port Controlled Hamiltonian (PCH) controllers. The outputs of these controllers are modulation indices which are inputs to a Pulse-Width Modulation (PWM) system that provides the switching signals for the active elements of the DC-AC converter. The purpose of the DC-AC converter control system is to maintain ud and uq voltages to the prescribed reference values where there is a variation of the three-phase load, which may be of balanced/unbalanced or nonlinear type. The controllers are classic PI, robust or nonlinear PCH, and their performance is improved by the use of a properly trained Reinforcement Learning-Twin Delayed Deep Deterministic Policy Gradient (RL-TD3) agent. The performance of the DC-AC converter control systems is compared using performance indices such as steady-state error, error ripple and Total Harmonic Distortion (THD) current value. Numerical simulations are performed in Matlab/Simulink and conclude the superior performance of the nonlinear PCH controller and the improvement of the performance of each controller presented by using an RL-TD3 agent, which provides correction signals to improve the performance of the DC-AC converter control systems when it is properly trained. Full article
(This article belongs to the Special Issue Intelligent Control and Testing Systems and Applications)
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23 pages, 5421 KiB  
Article
Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network
by Ovidiu-Constantin Novac, Mihai Cristian Chirodea, Cornelia Mihaela Novac, Nicu Bizon, Mihai Oproescu, Ovidiu Petru Stan and Cornelia Emilia Gordan
Sensors 2022, 22(22), 8872; https://0-doi-org.brum.beds.ac.uk/10.3390/s22228872 - 16 Nov 2022
Cited by 9 | Viewed by 2908
Abstract
In this paper, we present an analysis of important aspects that arise during the development of neural network applications. Our aim is to determine if the choice of library can impact the system’s overall performance, either during training or design, and to extract [...] Read more.
In this paper, we present an analysis of important aspects that arise during the development of neural network applications. Our aim is to determine if the choice of library can impact the system’s overall performance, either during training or design, and to extract a set of criteria that could be used to highlight the advantages and disadvantages of each library under consideration. To do so, we first extracted the previously mentioned aspects by comparing two of the most popular neural network libraries—PyTorch and TensorFlow—and then we performed an analysis on the obtained results, with the intent of determining if our initial hypothesis was correct. In the end, the results of the analysis are gathered, and an overall picture of what tasks are better suited for what library is presented. Full article
(This article belongs to the Special Issue Intelligent Control and Testing Systems and Applications)
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24 pages, 447 KiB  
Article
A Low-Latency Optimization of a Rust-Based Secure Operating System for Embedded Devices
by Ioana Culic, Alexandru Vochescu and Alexandru Radovici
Sensors 2022, 22(22), 8700; https://0-doi-org.brum.beds.ac.uk/10.3390/s22228700 - 10 Nov 2022
Viewed by 2644
Abstract
Critical systems such as drone control or power grid control applications rely on embedded devices capable of a real-time response. While much research and advancements have been made to implement low-latency and real-time characteristics, the security aspect has been left aside. All current [...] Read more.
Critical systems such as drone control or power grid control applications rely on embedded devices capable of a real-time response. While much research and advancements have been made to implement low-latency and real-time characteristics, the security aspect has been left aside. All current real-time operating systems available for industrial embedded devices are implemented in the C programming language, which makes them prone to memory safety issues. As a response to this, Tock, an innovative secure operating system for embedded devices written completely in Rust, has recently appeared. The only downside of Tock is that it lacks the low-latency real-time component. Therefore, the purpose of this research is to leverage the extended Berkeley Packet Filter technology used for efficient network traffic processing and to add the low-latency capability to Tock. The result is a secure low-latency operating system for embedded devices and microcontrollers capable of handling interrupts at latencies as low as 60 µs. Full article
(This article belongs to the Special Issue Intelligent Control and Testing Systems and Applications)
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23 pages, 13597 KiB  
Article
Estimation of the Kinematics and Workspace of a Robot Using Artificial Neural Networks
by Cătălin Boanta and Cornel Brișan
Sensors 2022, 22(21), 8356; https://0-doi-org.brum.beds.ac.uk/10.3390/s22218356 - 31 Oct 2022
Cited by 5 | Viewed by 1268
Abstract
At present, in specific and complex industrial operations, robots have to respect certain requirements and criteria as high kinematic or dynamic performance, specific dimensions of the workspace, or limitation of the dimensions of the mobile elements of the robot. In order to respect [...] Read more.
At present, in specific and complex industrial operations, robots have to respect certain requirements and criteria as high kinematic or dynamic performance, specific dimensions of the workspace, or limitation of the dimensions of the mobile elements of the robot. In order to respect these criteria, a proper design of the robots has to be achieved, which requires years of practice and a proper knowledge and experience of a human designer. In order to assist the human designer in the process of designing the robots, several methods (including optimization methods) have been developed. The scientific problem addressed in this paper is the development of an artificial intelligence method to estimate the size of the workspace and the kinematics of a robot using a feedforward neural network. The method is applied on a parallel robot composed of a base platform, a mobile platform and six kinematic rotational-universal-spherical open loops. The numerical results show that, with proper training and topology, a feedforward neural network is able to estimate properly values of the volume of the workspace and the values of the generalized coordinates based on the pose of the end effector. Full article
(This article belongs to the Special Issue Intelligent Control and Testing Systems and Applications)
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18 pages, 5805 KiB  
Article
Method for Continuous Integration and Deployment Using a Pipeline Generator for Agile Software Projects
by Ionut-Catalin Donca, Ovidiu Petru Stan, Marius Misaros, Dan Gota and Liviu Miclea
Sensors 2022, 22(12), 4637; https://0-doi-org.brum.beds.ac.uk/10.3390/s22124637 - 20 Jun 2022
Cited by 11 | Viewed by 4515
Abstract
Lately, the software development industry is going through a slow but real transformation. Software is increasingly a part of everything, and, software developers, are trying to cope with this exploding demand through more automation. The pipelining technique of continuous integration (CI) and continuous [...] Read more.
Lately, the software development industry is going through a slow but real transformation. Software is increasingly a part of everything, and, software developers, are trying to cope with this exploding demand through more automation. The pipelining technique of continuous integration (CI) and continuous delivery (CD) has developed considerably due to the overwhelming demand for the deployment and deliverability of new features and applications. As a result, DevOps approaches and Agile principles have been developed, in which developers collaborate closely with infrastructure engineers to guarantee that their applications are deployed quickly and reliably. Thanks to pipeline approach thinking, the efficiency of projects has greatly improved. Agile practices represent the introduction to the system of new features in each sprint delivery. Those practices may contain well-developed features or can contain bugs or failures which impact the delivery. The pipeline approach, depicted in this paper, overcomes the problems of delivery, improving the delivery timeline, the test load steps, and the benchmarking tasks. It decreases system interruption by integrating multiple test steps and adds stability and deliverability to the entire process. It provides standardization which means having an established, time-tested process to use, and can also decrease ambiguity and guesswork, guarantee quality and boost productivity. This tool is developed with an interpreted language, namely Bash, which offers an easier method to integrate it into any platform. Based on the experimental results, we demonstrate the value that this solution currently creates. This solution provides an effective and efficient way to generate, manage, customize, and automate Agile-based CI and CD projects through automated pipelines. The suggested system acts as a starting point for standard CI/CD processes, caches Docker layers for subsequent usage, and implements highly available deliverables in a Kubernetes cluster using Helm. Changing the principles of this solution and expanding it into multiple platforms (windows) will be addressed in a future discussion. Full article
(This article belongs to the Special Issue Intelligent Control and Testing Systems and Applications)
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