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Electronics
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Modeling and Simulation Methods: Recent Advances and Applications
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Modeling and Simulation Methods: Recent Advances and Applications

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 9148

Special Issue Editors

Dr. Anastasia Angelopoulou

E-Mail Website
Guest Editor
TSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USA
Interests: modeling and simulation; serious games; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Konstantinos Mykoniatis

E-Mail Website
Guest Editor
Industrial and Systems Engineering, Auburn University, Auburn, AL 36849, USA
Interests: modeling and simulation; industry 4.0; robotics technology; extended reality for training applications
Dr. Sean Mondesire

E-Mail Website
Guest Editor
Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
Interests: machine learning; big data analytics; simulation-based training
Dr. Athanasios Aris Panagopoulos

E-Mail Website
Guest Editor
Computer Science at California State University, Fresno, CA 91355, USA
Interests: artificial intelligence; machine learning; intelligent systems

Special Issue Information

Dear Colleagues,

Modeling and simulation methods and applications provide powerful tools for designing, evaluating, and/or predicting new or existing complex systems and processes. The purpose of this Special Issue is to present recent advances in methods and applications that focus on any aspect of system or process modeling and simulation. We are seeking the latest original contributions that have not been published and are not currently under process in any other journal or conference. The potential topics of interest include but are not limited to: conceptual and theoretical models; discrete-event simulation; agent-based modeling; system dynamics; hybrid and multi-paradigm simulation; mathematical models; simulation optimization; neural and fuzzy modeling; and simulation languages, tools, methods, and applications. 

Dr. Anastasia Angelopoulou
Dr. Konstantinos Mykoniatis
Dr. Sean Mondesire
Dr. Athanasios Aris Panagopoulos
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. Electronics 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

  • modeling
  • simulation
  • agent-based modeling
  • system dynamics
  • discrete event simulation
  • hybrid simulation
  • mathematical modeling
  • optimization

Published Papers (4 papers)

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Research

17 pages, 1759 KiB  
Article
Mitigating Catastrophic Forgetting with Complementary Layered Learning
by Sean Mondesire and R. Paul Wiegand
Electronics 2023, 12(3), 706; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics12030706 - 31 Jan 2023
Cited by 1 | Viewed by 1352
Abstract
Catastrophic forgetting is a stability–plasticity imbalance that causes a machine learner to lose previously gained knowledge that is critical for performing a task. The imbalance occurs in transfer learning, negatively affecting the learner’s performance, particularly in neural networks and layered learning. This work [...] Read more.
Catastrophic forgetting is a stability–plasticity imbalance that causes a machine learner to lose previously gained knowledge that is critical for performing a task. The imbalance occurs in transfer learning, negatively affecting the learner’s performance, particularly in neural networks and layered learning. This work proposes a complementary learning technique that introduces long- and short-term memory to layered learning to reduce the negative effects of catastrophic forgetting. In particular, this work proposes the dual memory system in the non-neural network approaches of evolutionary computation and Q-learning instances of layered learning because these techniques are used to develop decision-making capabilities for physical robots. Experiments evaluate the new learning augmentation in a multi-agent system simulation, where autonomous unmanned aerial vehicles learn to collaborate and maneuver to survey an area effectively. Through these direct-policy and value-based learning experiments, the proposed complementary layered learning is demonstrated to significantly improve task performance over standard layered learning, successfully balancing stability and plasticity. Full article
(This article belongs to the Special Issue Modeling and Simulation Methods: Recent Advances and Applications)
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24 pages, 798 KiB  
Article
Agent-Based Models Assisted by Supervised Learning: A Proposal for Model Specification
by Alejandro Platas-López, Alejandro Guerra-Hernández, Marcela Quiroz-Castellanos and Nicandro Cruz-Ramírez
Electronics 2023, 12(3), 495; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics12030495 - 18 Jan 2023
Cited by 4 | Viewed by 3249
Abstract
Agent-based modeling (ABM) has become popular since it allows a direct representation of heterogeneous individual entities, their decisions, and their interactions, in a given space. With the increase in the amount of data in different domains, an opportunity to support the design, implementation, [...] Read more.
Agent-based modeling (ABM) has become popular since it allows a direct representation of heterogeneous individual entities, their decisions, and their interactions, in a given space. With the increase in the amount of data in different domains, an opportunity to support the design, implementation, and analysis of these models, using Machine Learning techniques, has emerged. A vast and diverse literature evidences the interest and benefits of this symbiosis, but also exhibits the inadequacy of current specification standards, such as the Overview, Design concepts and Details (ODD) protocol, to cover such diversity and, in consequence, its lack of use. Given the relevance of standard specifications for the sake of reproducible ABMs, this paper proposes an extension to the ODD Protocol to provide a standardized description of the uses of Machine Learning (ML) in supporting agent-based modeling. The extension is based on categorization, a result of a broad, but integrated, review of the literature, considering the purpose of learning, the moment when the learning process is executed, the components of the model affected by learning, and the algorithms and data used in learning. The proposed extension of the ODD protocol allows orderly and transparent communication of ML workflows in ABM, facilitating its understanding and potential replication in other investigations. The presentation of a full-featured agent-based model of tax evasion illustrates the application of the proposed approach where the adoption of machine learning results in an error statistically significantly lower, with a p-value of 0.02 in the Wilcoxon signed-rank test. Furthermore, our analysis provides numerical estimates that reveal the strong impact of the penalty and tax rate on tax evasion. Future work considers other kinds of learning applications, e.g., the calibration of parameters and the analysis of the ABM results. Full article
(This article belongs to the Special Issue Modeling and Simulation Methods: Recent Advances and Applications)
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28 pages, 6604 KiB  
Article
A Modified RL-IGWO Algorithm for Dynamic Weapon-Target Assignment in Frigate Defensing UAV Swarms
by Mingyu Nan, Yifan Zhu, Li Kang, Tao Wang and Xin Zhou
Electronics 2022, 11(11), 1796; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11111796 - 06 Jun 2022
Cited by 3 | Viewed by 2347
Abstract
Unmanned aerial vehicle (UAV) swarms have significant advantages in terms of cost, number, and intelligence, constituting a serious threat to traditional frigate air defense systems. Ship-borne short-range anti-air weapons undertake terminal defense tasks against UAV swarms. In traditional air defense fire control systems, [...] Read more.
Unmanned aerial vehicle (UAV) swarms have significant advantages in terms of cost, number, and intelligence, constituting a serious threat to traditional frigate air defense systems. Ship-borne short-range anti-air weapons undertake terminal defense tasks against UAV swarms. In traditional air defense fire control systems, a dynamic weapon-target assignment (DWTA) is disassembled into several static weapon target assignments (SWTAs), but the relationship between DWTAs and SWTAs is not supported by effective analytical proof. Based on the combat scenario between a frigate and UAV swarms, a model-based reinforcement learning framework was established, and a DWAT problem was disassembled into several static combination optimization (SCO) problems by means of the dynamic programming method. In addition, several variable neighborhood search (VNS) operators and an opposition-based learning (OBL) operator were designed to enhance the global search ability of the original Grey Wolf Optimizer (GWO), thereby solving SCO problems. An improved grey wolf algorithm based on reinforcement learning (RL-IGWO) was established for solving DWTA problems in the defense of frigates against UAV swarms. The experimental results show that RL-IGWO had obvious advantages in both the decision making time and solution quality. Full article
(This article belongs to the Special Issue Modeling and Simulation Methods: Recent Advances and Applications)
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12 pages, 2146 KiB  
Article
Non-Invasive Diagnosis of Liver Fibrosis in Chronic Hepatitis C using Mathematical Modeling and Simulation
by Nehal Shukla, Anastasia Angelopoulou and Rania Hodhod
Electronics 2022, 11(8), 1260; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11081260 - 16 Apr 2022
Cited by 2 | Viewed by 1506
Abstract
Hepatitis C is a viral infection (HCV) that causes liver inflammation, and it was found that it affects over 170 million people around the world, with Egypt having the highest rate in the world. Unfortunately, serial liver biopsies, which can be invasive, expensive, [...] Read more.
Hepatitis C is a viral infection (HCV) that causes liver inflammation, and it was found that it affects over 170 million people around the world, with Egypt having the highest rate in the world. Unfortunately, serial liver biopsies, which can be invasive, expensive, risky, and inconvenient to patients, are typically used for the diagnosis of liver fibrosis progression. This study presents the development, validation, and evaluation of a prediction mathematical model for non-invasive diagnosis of liver fibrosis in chronic HCV. The proposed model in this article uses a set of nonlinear ordinary differential equations as its core and divides the population into six groups: Susceptible, Treatment, Responder, Non-Responder, Cured, and Fibrosis. The validation approach involved the implementation of two equivalent simulation models that examine the proposed process from different perspectives. A system dynamics model was developed to understand the nonlinear behavior of the diagnosis process over time. The system dynamics model was then transformed to an equivalent agent-based model to examine the system at the individual level. The numerical analysis and simulation results indicate that the earlier the HCV treatment is implemented, the larger the group of people who will become responders, and less people will develop complications such as fibrosis. Full article
(This article belongs to the Special Issue Modeling and Simulation Methods: Recent Advances and Applications)
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