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Processes
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Modeling and Simulation of Complex Networks for Automation in Systems...
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Modeling and Simulation of Complex Networks for Automation in Systems Engineering

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 3300

Special Issue Editors

Dr. Silvia Carpitella

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Guest Editor
Department of Manufacturing Systems Engineering and Management​, California State University, Northridge, CA, USA
Interests: innovation engineering; mathematics; decision-making processes; sustainable manufacturing technologies
Dr. Manuel Herrera

E-Mail Website
Guest Editor
Institute for Manufacturing – Department of Engineering, University of Cambridge, 17 Charles Babbage Road, Cambridge CB3 0FS, UK
Interests: network analysis; industrial statistics; digital water; infrastructure resilience; complex systems
Special Issues, Collections and Topics in MDPI journals
Dr. Bruno Melo Brentan

E-Mail Website
Guest Editor
Hydraulic Engineering and Water Resources Department, Federal University of Minas Gerais. Avenida Presidente Antonio Carlos 6467, Belo Horizonte, Brazil
Interests: hydraulic modelling; data mining; complex network theory; hydropower generation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Joaquín Izquierdo

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Guest Editor
Institute for Multidisciplinary Mathematics, Department of Applied Mathematics, Universitat Politècnica de València, Camí de Vera, s/n, 46022 València, Spain
Interests: mathematical modeling; knowledge-based systems; DSSs in engineering (mainly urban hydraulics)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Paradigms in systems engineering, such as industrial processes, infrastructure management, and service assurance, are interrelated, and quickly adapt to the complexities of automated systems, helping them to achieve optimal performance. Technology breakthroughs related to cyber-physical systems, such as sensors and smart meters, are the key to real-time automated management of complex and interconnected services and infrastructure, providing efficient industrial processes as well as basic commodities and services such as energy, water, transportation, and telecommunications. These degrees of automation and system interconnection, both for physical and digital industry and infrastructure, generate new levels of complexity for which the methodology used should match the technological and the end-users’ requirements.

This Special Issue on “Modeling and Simulation of Complex Networks for Automation in Systems Engineering” aims to present novel advances on methodologies to improve the development and use of a complexity science framework for automated digital management of industry and infrastructure systems. In recent years, network science has become a popular approach to model complex systems. The latest advances in research related to network dynamics and structure provide an excellent framework to understand, control and predict complex systems, such as those related to Industrial, Manufacturing, Electrical, and Civil engineering. Network models which are specifically adapted to capture spatiotemporal dimensions of an engineering system, such as spatial networks and temporal networks, are of particular interest. New directions on graph signal processing and graph machine learning are providing innovative research in complex systems, blending powerful AI and data analytics tools with the graph-based structure of the problem.

The scope of this Special Issue includes (but is not limited to):

  • Complexity science for systems engineering.
  • Dynamics on networks and dynamics of networks.
  • Decision-making support in complex systems.
  • Diffusion processes and dynamics in complex networks.
  • Swarm intelligence applications in networked systems.
  • Intelligent infrastructure and asset management.
  • Approaches and bounded strategies for learning in multi-agent systems at different scales.
  • Multi-agent learning solutions for near-real time decision making.
  • Automation in complex systems.
  • Graph signal processing in engineering systems operations and management.
  • Graph machine learning and graph neural networks models in systems operations and management.
  • Sustainable supply chain management.

Dr. Silvia Carpitella
Dr. Manuel Herrera
Dr. Bruno Melo Brentan
Prof. Dr. Joaquín Izquierdo
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. Processes is an international peer-reviewed open access monthly 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

  • complex networks
  • multi-agent systems
  • automation
  • decision support
  • systems engineering

Published Papers (2 papers)

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Research

19 pages, 551 KiB  
Article
Analysis of Controllability in Cyber–Physical Power Systems under a Novel Load-Capacity Model
by Yaodong Ge, Yan Li, Tianqi Xu, Zhaolei He and Quancong Zhu
Processes 2023, 11(10), 3046; https://doi.org/10.3390/pr11103046 - 23 Oct 2023
Viewed by 929
Abstract
In cyber–physical power systems (CPPSs), system collapse can occur as a result of a failure in a particular component. In this paper, an approach is presented to build the load-capacity model of CPPSs using the concept of electrical betweenness and information entropy, which [...] Read more.
In cyber–physical power systems (CPPSs), system collapse can occur as a result of a failure in a particular component. In this paper, an approach is presented to build the load-capacity model of CPPSs using the concept of electrical betweenness and information entropy, which takes into account real-time node loads and the allocation of power and information flows within CPPSs. By introducing an innovative load redistribution strategy and comparing it with conventional load distribution strategies, the superior effectiveness of the proposed strategy in minimizing system failures and averting system collapses has been demonstrated. The controllability of the system after cascading failures under different coupling strategies and capacity parameters is investigated through the analysis of different information network topologies and network parameters. It was observed that CPPSs constructed using small-world networks, which couple high-degree nodes from the information network to high-betweenness nodes from the power grid, exhibit improved resilience. Furthermore, increasing the capacity parameter of the power network yields more favorable results compared to increasing the capacity parameter of the information network. In addition, our research results are validated using the IEEE 39-node system and the Chinese 132-node system. Full article
(This article belongs to the Special Issue Modeling and Simulation of Complex Networks for Automation in Systems Engineering)
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17 pages, 2275 KiB  
Article
An Enhanced Method for Nanosecond Time Synchronization in IEEE 1588 Precision Time Protocol
by Fei Li, Wenyi Liu, Yueyan Qi, Qiang Li and Gaigai Liu
Processes 2023, 11(5), 1328; https://doi.org/10.3390/pr11051328 - 25 Apr 2023
Cited by 1 | Viewed by 1701
Abstract
The performance of time-critical systems depends heavily on time synchronization accuracy. Therefore, it is crucial to have a synchronization method that can achieve high time synchronization accuracy. In this paper, we propose a new underlying transmission architecture and new synchronization messages. On the [...] Read more.
The performance of time-critical systems depends heavily on time synchronization accuracy. Therefore, it is crucial to have a synchronization method that can achieve high time synchronization accuracy. In this paper, we propose a new underlying transmission architecture and new synchronization messages. On the basis of these, aiming at the time error problem of the slave clock, we propose an enhanced time synchronization method based on new synchronization messages. Furthermore, we evaluate the performance of the enhanced time synchronization method on the OMNeT++ simulator. In addition, we compare the impact of different crystal oscillator accuracies and different crystal oscillator frequencies on time synchronization accuracy, respectively. Simulation results show that the time offset is at most ±1 clock period using the enhanced time synchronization method. We realize the purpose of timing the master clock and the slave clock by counting the period of the clock signal. Therefore, we needed to round down the time count to an integer. This is the reason why −1 and 1 appear at the same time. When the crystal oscillator frequency used is 80 MHz, the system can achieve a time synchronization accuracy of ±12.5 ns; that is, a nanosecond-level time synchronization accuracy can be achieved. With the reduction of the crystal oscillator accuracy of the slave clock, the synchronization accuracy of ±1 clock period can still be achieved. With the increase in the crystal oscillator frequency, the time synchronization accuracy that can be achieved also improves. The method proposed in this paper provides a new way of thinking and has certain guiding significance for improving the time synchronization accuracy of time-critical systems. Full article
(This article belongs to the Special Issue Modeling and Simulation of Complex Networks for Automation in Systems Engineering)
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