Cyber-Physical Systems: Recent Developments and Emerging Trends

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 880

Special Issue Editors


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Guest Editor
Department of Information Engineering, University of Pisa, 56122 Pisa, PI, Italy
Interests: computer arithmetic; RISC-V platforms; digital twins; CPSs; AI/machine learning

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Guest Editor
Department of Information Engineering, University of Pisa, 56122 Pisa, PI, Italy
Interests: dependable systems; model-based design of control systems; formal methods; co-simulation; cybersecurity

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Guest Editor
Department of Information Engineering, University of Florence, 50139 Florence, FI, Italy
Interests: intelligent transportation systems; formal methods for model-driven development of real-time software; requirement analysis techniques to detect ambiguities and to improve industrial development processes; quantitative evaluation of software performance; reliability and safety

Special Issue Information

Dear Colleagues,

Cyber-physical systems (CPSs) are a revolutionary combination of computational algorithms and physical components that are transforming several industries, including manufacturing, transportation, and healthcare. This Special Issue explores the most recent developments and new directions in CPSs to shed light on the field's changing terrain and future directions.

There are several different CPS-related topics covered in this Special Issue. Recent advancements in CPS infrastructure and technology are examined, highlighting the state-of-the-art ideas for this sector. Topics of interest for submission include but are not limited to the following:

  1. How might machine learning approaches be integrated into CPS applications? Machine learning plays a crucial role in enhancing overall system performance and operational efficiency by using algorithms to evaluate large datasets and optimize system processes. Machine learning opens new areas of use for CPSs, such as route optimization in transportation and predictive maintenance in manufacturing.
  2. Emerging trends, illuminating ground-breaking advancements including blockchain integration, autonomous systems, and the changing nature of human–CPS interaction, redefine the capabilities and functionalities of CPSs across several areas, while also pushing the bounds of innovation. Amidst these advancements, however, lie significant challenges and opportunities.
  3. Safety, security, and ethical considerations loom large in the deployment of CPSs, prompting a critical examination of risk mitigation strategies and regulatory frameworks. Moreover, the intersection of CPS with societal values and norms necessitates careful deliberation to ensure responsible and equitable technological adoption.

To the end, this Special Issue guides readers through the challenging landscape of CPS research and developments. It provides priceless insights into the future course of CPSs by highlighting important trends, obstacles, and opportunities.

Dr. Federico Rossi
Dr. Cinzia Bernardeschi
Dr. Gloria Gori
Guest Editors

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Keywords

  • internet of things
  • artificial intelligence in CPSs
  • real-time monitoring
  • autonomous decision making
  • model-based design of CPSs
  • verification of CPSs
  • safety
  • security
  • intelligent transportation systems
  • simulation tools for CPSs

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Published Papers (1 paper)

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Research

23 pages, 1627 KiB  
Article
Hybrid State Estimation: Integrating Physics-Informed Neural Networks with Adaptive UKF for Dynamic Systems
by J. de Curtò and I. de Zarzà
Electronics 2024, 13(11), 2208; https://doi.org/10.3390/electronics13112208 - 5 Jun 2024
Cited by 1 | Viewed by 598
Abstract
In this paper, we present a novel approach to state estimation in dynamic systems by combining Physics-Informed Neural Networks (PINNs) with an adaptive Unscented Kalman Filter (UKF). Recognizing the limitations of traditional state estimation methods, we refine the PINN architecture with hybrid loss [...] Read more.
In this paper, we present a novel approach to state estimation in dynamic systems by combining Physics-Informed Neural Networks (PINNs) with an adaptive Unscented Kalman Filter (UKF). Recognizing the limitations of traditional state estimation methods, we refine the PINN architecture with hybrid loss functions and Monte Carlo Dropout for enhanced uncertainty estimation. The Unscented Kalman Filter is augmented with an adaptive noise covariance mechanism and incorporates model parameters into the state vector to improve adaptability. We further validate this hybrid framework by integrating the enhanced PINN with the UKF for a seamless state prediction pipeline, demonstrating significant improvements in accuracy and robustness. Our experimental results show a marked enhancement in state estimation fidelity for both position and velocity tracking, supported by uncertainty quantification via Bayesian inference and Monte Carlo Dropout. We further extend the simulation and present evaluations on a double pendulum system and state estimation on a quadcopter drone. This comprehensive solution is poised to advance the state-of-the-art in dynamic system estimation, providing unparalleled performance across control theory, machine learning, and numerical optimization domains. Full article
(This article belongs to the Special Issue Cyber-Physical Systems: Recent Developments and Emerging Trends)
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