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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: 31 December 2024 | Viewed by 8887

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
Department of Engineering, University of Cambridge, Cambridge, UK
Interests: water systems; time series analysis; network science
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

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Published Papers (6 papers)

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Research

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14 pages, 1551 KiB  
Article
Towards the Implementation and Integration of a Digital Twin in a Discrete Manufacturing Context
by Michela Lanzini, Ivan Ferretti and Simone Zanoni
Processes 2024, 12(11), 2384; https://doi.org/10.3390/pr12112384 - 30 Oct 2024
Viewed by 672
Abstract
In the context of enhanced decision making related to Industry 4.0 and 5.0, this work examines the first step toward the implementation of a Digital Twin (DT) in a discrete manufacturing firm. It will be required that the DT be adequately integrated with [...] Read more.
In the context of enhanced decision making related to Industry 4.0 and 5.0, this work examines the first step toward the implementation of a Digital Twin (DT) in a discrete manufacturing firm. It will be required that the DT be adequately integrated with the information systems, especially the Manufacturing Execution System (MES), because the virtual counterpart of the DT itself, a Discrete Event Simulator (DES) model, will exploit the MES data for the validation and monitoring. The objective of the DT is to enhance the decision making related to production planning in particular, achieving better on-time delivery to customers. Therefore, the DT intends to depict material flows within the production department to enhance the monitoring and control, facilitating the prompt identification of deviations from the plan and supporting the decision-makers, enabling a more responsive and informed management of delay alerts. The first goal to achieve the DT implementation and integration is to establish a conceptual framework that improves material flow data synchronization. A conceptual integration and implementation framework for the DT will be proposed and discussed, underlying the technical decisions chosen to achieve the functional and integration requirements. Full article
(This article belongs to the Special Issue Modeling and Simulation of Complex Networks for Automation in Systems Engineering)
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25 pages, 4874 KiB  
Article
Monitoring and Predicting Air Quality with IoT Devices
by Claudia Banciu, Adrian Florea and Razvan Bogdan
Processes 2024, 12(9), 1961; https://doi.org/10.3390/pr12091961 - 12 Sep 2024
Viewed by 1571
Abstract
The growing concern about air quality and its influence on human health has prompted the development of sophisticated monitoring and forecast systems. This article gives a thorough investigation into forecasting the air quality index (AQI) with an Internet of Things (IoT) device that [...] Read more.
The growing concern about air quality and its influence on human health has prompted the development of sophisticated monitoring and forecast systems. This article gives a thorough investigation into forecasting the air quality index (AQI) with an Internet of Things (IoT) device that analyzes temperature, humidity, PM10, and PM2.5 levels. The dataset used for this analysis comprises 5869 data points across six critical parameters essential for accurate air quality prediction. The data from these sensors is sent to the ThingSpeak cloud platform for storage and preliminary analysis. The system forecasts AQI using a TensorFlow-based regression model, delivering real-time insights. The combination of IoT technology and machine learning improves the accuracy and responsiveness of air quality monitoring systems, making it a useful tool for environmental management and public health protection. This work presents comparatively the effectiveness of feedforward neural network models trained with the ‘adam’ and ‘RMSprop’ optimizers over different epochs, as well as the machine learning algorithm random forest with varying numbers of estimators to forecast AQI. The models were trained using both types of regression analysis: linear regression and random forest regression. The findings show that the model achieves a high degree of accuracy, with the predictions closely aligning with the actual AQI values, thus having the potential to significantly reduce the negative health impact associated with poor air quality, protecting public health and alerting users when pollution levels are higher than allowed. Specifically, the random forest model with 100 estimators delivers the best overall performance for both AQI 10 and AQI 2.5, achieving the lowest Mean Absolute Error (MAE) of 0.2785 for AQI 10 and 0.2483 for AQI 2.5. This integration of IoT technology and advanced predictive analysis addresses the significant worldwide issue of air pollution by identifying the pollution hotspots and allowing decision-makers for quick reactions, and the development of effective strategies to reduce pollution sources. Full article
(This article belongs to the Special Issue Modeling and Simulation of Complex Networks for Automation in Systems Engineering)
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13 pages, 2837 KiB  
Article
A Hybrid Data-Driven and Model-Based Approach for Leak Reduction in Water Distribution Systems Using LQR and Genetic Algorithms
by José-Roberto Bermúdez, Leonardo Gómez-Coronel, Francisco-Ronay López-Estrada, Gildas Besançon and Ildeberto Santos-Ruiz
Processes 2024, 12(9), 1805; https://doi.org/10.3390/pr12091805 - 25 Aug 2024
Cited by 1 | Viewed by 983
Abstract
This paper presents a pressure management technique for the reduction of leaks considering as a case study a branched water distribution system. The proposed technique is based on the detection and location of the leak using a genetic algorithm (GA) and pressure control [...] Read more.
This paper presents a pressure management technique for the reduction of leaks considering as a case study a branched water distribution system. The proposed technique is based on the detection and location of the leak using a genetic algorithm (GA) and pressure control using a Linear Quadratic Regulator (LQR). The validation of the proposed method uses measured pressure and flow data from a laboratory-scale water distribution system and its dynamic model. Full article
(This article belongs to the Special Issue Modeling and Simulation of Complex Networks for Automation in Systems Engineering)
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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
Cited by 2 | Viewed by 1408
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 3 | Viewed by 2692
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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Review

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32 pages, 775 KiB  
Review
A Comprehensive Synthesis on Analytical Algorithms for Assessing Elastic Buckling Loads of Thin-Walled Isotropic and Laminated Cylindrical Shells
by Maria Tănase
Processes 2024, 12(10), 2120; https://doi.org/10.3390/pr12102120 - 29 Sep 2024
Viewed by 433
Abstract
A comprehensive review is presented on the main analytical methods used in the specialized literature to evaluate the buckling loads of thin-walled cylindrical shells (TWCS) subjected to different mechanical loads or load combinations. The analytical formulations are first presented for unstiffened TWCS, followed [...] Read more.
A comprehensive review is presented on the main analytical methods used in the specialized literature to evaluate the buckling loads of thin-walled cylindrical shells (TWCS) subjected to different mechanical loads or load combinations. The analytical formulations are first presented for unstiffened TWCS, followed by stiffened TWCS in different configurations (stiffeners in the axial direction, circumferential direction or both axial and circumferential directions, placed on the external or internal surface of the shell). This research can serve as a helpful resource for researchers investigating this field, allowing the analytical methods to be used as a reference basis for numerical and experimental results regarding the behavior of structures in the category of TWCS. Full article
(This article belongs to the Special Issue Modeling and Simulation of Complex Networks for Automation in Systems Engineering)
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