Topic Editors

Department of Advanced Computational Methods, Faculty of Science and Technology Jan Dlugosz University in Czestochowa 13/15 Armii Krajowej Av., 42-200 Czestochowa, Poland
Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland
Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, Armii Krajowej 13/15, 42-200 Czestochowa, Poland
Faculty of Science and Technology, Jan Dlugosz University in Czestochowa, 13/15 Armii Krajowej Av., 42-200 Czestochowa, Poland
Dr. Ghulam Moeen Uddin
Department of Mechanical Engineering, University of Engineering & Technology, Lahore, Punjab 54890, Pakistan

AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity

Abstract submission deadline
30 November 2024
Manuscript submission deadline
30 January 2025
Viewed by
5819

Topic Information

Dear Colleagues,

Due to the increasing computational capability of current data processing systems, new opportunities emerge in the modelling, simulations and optimization of complex systems and devices. Difficult-to-apply, highly demanding and time-consuming methods may now be considered when developing complete and sophisticated models in many areas of science and technology. Combining AI algorithms and computational methods, including numerical and other methods, allows for conducting multi-threaded analyses to solve advanced and interdisciplinary problems.

This article collection aims to bring together research on advances in the modelling, simulations and optimization issues of complex systems, considering the great interest received for the part I of the topic.

Original research, review articles and short communications focusing on (but not limited to) artificial intelligence and other computational methods are welcome.

Prof. Dr. Jaroslaw Krzywanski
Dr. Marcin Sosnowski
Dr. Karolina Grabowska
Dr. Dorian Skrobek
Dr. Ghulam Moeen Uddin
Topic Editors

Keywords

  • artificial intelligence
  • artificial neural networks
  • deep learning
  • genetic and evolutionary algorithms
  • artificial immune systems
  • fuzzy logic
  • information theory
  • expert systems
  • bio-inspired methods
  • CFD
  • fractal and fractional problems
  • fractional and fractal dynamics
  • functional analysis
  • quantum mechanics
  • micro and nano-mechanics
  • fluidics and nano-fluidics
  • modelling
  • simulation
  • optimization
  • complex systems

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Algorithms
algorithms
2.3 3.7 2008 15 Days CHF 1600 Submit
Energies
energies
3.2 5.5 2008 16.1 Days CHF 2600 Submit
Entropy
entropy
2.7 4.7 1999 20.8 Days CHF 2600 Submit
Fractal and Fractional
fractalfract
5.4 3.6 2017 18.9 Days CHF 2700 Submit
Machine Learning and Knowledge Extraction
make
3.9 8.5 2019 19.9 Days CHF 1800 Submit
Materials
materials
3.4 5.2 2008 13.9 Days CHF 2600 Submit

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Published Papers (8 papers)

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20 pages, 6707 KiB  
Article
Towards Multi-Objective Object Push-Grasp Policy Based on Maximum Entropy Deep Reinforcement Learning under Sparse Rewards
by Tengteng Zhang and Hongwei Mo
Entropy 2024, 26(5), 416; https://doi.org/10.3390/e26050416 - 12 May 2024
Viewed by 532
Abstract
In unstructured environments, robots need to deal with a wide variety of objects with diverse shapes, and often, the instances of these objects are unknown. Traditional methods rely on training with large-scale labeled data, but in environments with continuous and high-dimensional state spaces, [...] Read more.
In unstructured environments, robots need to deal with a wide variety of objects with diverse shapes, and often, the instances of these objects are unknown. Traditional methods rely on training with large-scale labeled data, but in environments with continuous and high-dimensional state spaces, the data become sparse, leading to weak generalization ability of the trained models when transferred to real-world applications. To address this challenge, we present an innovative maximum entropy Deep Q-Network (ME-DQN), which leverages an attention mechanism. The framework solves complex and sparse reward tasks through probabilistic reasoning while eliminating the trouble of adjusting hyper-parameters. This approach aims to merge the robust feature extraction capabilities of Fully Convolutional Networks (FCNs) with the efficient feature selection of the attention mechanism across diverse task scenarios. By integrating an advantage function with the reasoning and decision-making of deep reinforcement learning, ME-DQN propels the frontier of robotic grasping and expands the boundaries of intelligent perception and grasping decision-making in unstructured environments. Our simulations demonstrate a remarkable grasping success rate of 91.6%, while maintaining excellent generalization performance in the real world. Full article
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14 pages, 430 KiB  
Article
A Neural Network Forecasting Approach for the Smart Grid Demand Response Management Problem
by Slim Belhaiza and Sara Al-Abdallah
Energies 2024, 17(10), 2329; https://doi.org/10.3390/en17102329 - 11 May 2024
Viewed by 451
Abstract
Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds [...] Read more.
Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds significance as it can substantially impact the final cost, mitigate risks, and support informed decision-making. In this paper, a forecasting approach employing neural networks for smart grid demand-side management is proposed. The study explores various enhanced artificial neural network (ANN) architectures for forecasting smart grid consumption. The performance of the ANN approach in predicting energy demands is evaluated through a comparison with three statistical models: a time series model, an auto-regressive model, and a hybrid model. Experimental results demonstrate the ability of the proposed neural network framework to deliver accurate and reliable energy demand forecasts. Full article
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21 pages, 7237 KiB  
Article
Analysis of Grid Performance with Diversified Distributed Resources and Storage Integration: A Bilevel Approach with Network-Oriented PSO
by Ahmad El Sayed and Gokturk Poyrazoglu
Energies 2024, 17(10), 2270; https://doi.org/10.3390/en17102270 - 8 May 2024
Viewed by 456
Abstract
The growing deployment of distributed resources significantly affects the distribution grid performance in most countries. The optimal sizing and placement of these resources have become increasingly crucial to mitigating grid issues and reducing costs. Particle Swarm Optimization (PSO) is widely used to address [...] Read more.
The growing deployment of distributed resources significantly affects the distribution grid performance in most countries. The optimal sizing and placement of these resources have become increasingly crucial to mitigating grid issues and reducing costs. Particle Swarm Optimization (PSO) is widely used to address such problems but faces computational inefficiency due to its numerical convergence behavior. This limits its effectiveness, especially for power system problems, because the numerical distance between two nodes in power systems might be different from the actual electrical distance. In this paper, a scalable bilevel optimization problem with two novel algorithms enhances PSO’s computational efficiency. While the resistivity-driven algorithm strategically targets low-resistivity regions and guides PSO toward areas with lower losses, the connectivity-driven algorithm aligns solution spaces with the grid’s physical topology. It prioritizes actual physical neighbors during the search to prevent local optima traps. The tests of the algorithms on the IEEE 33-bus and the 69-bus and Norwegian networks show significant reductions in power losses (up to 74% for PV, wind, and storage) and improved voltage stability (a 21% reduction in mean voltage deviation index) with respect to the results of classical PSO. The proposed network-oriented PSO outperforms classical PSO by achieving a 2.84% reduction in the average fitness value for the IEEE 69-bus case with PV, wind, and storage deployment. The Norwegian case study affirms the effectiveness of the proposed approach in real-world applications through significant improvements in loss reduction and voltage stability. Full article
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27 pages, 2009 KiB  
Review
A Comprehensive Summary of the Application of Machine Learning Techniques for CO2-Enhanced Oil Recovery Projects
by Xuejia Du, Sameer Salasakar and Ganesh Thakur
Mach. Learn. Knowl. Extr. 2024, 6(2), 917-943; https://doi.org/10.3390/make6020043 - 29 Apr 2024
Viewed by 718
Abstract
This paper focuses on the current application of machine learning (ML) in enhanced oil recovery (EOR) through CO2 injection, which exhibits promising economic and environmental benefits for climate-change mitigation strategies. Our comprehensive review explores the diverse use cases of ML techniques in [...] Read more.
This paper focuses on the current application of machine learning (ML) in enhanced oil recovery (EOR) through CO2 injection, which exhibits promising economic and environmental benefits for climate-change mitigation strategies. Our comprehensive review explores the diverse use cases of ML techniques in CO2-EOR, including aspects such as minimum miscible pressure (MMP) prediction, well location optimization, oil production and recovery factor prediction, multi-objective optimization, Pressure–Volume–Temperature (PVT) property estimation, Water Alternating Gas (WAG) analysis, and CO2-foam EOR, from 101 reviewed papers. We catalog relative information, including the input parameters, objectives, data sources, train/test/validate information, results, evaluation, and rating score for each area based on criteria such as data quality, ML-building process, and the analysis of results. We also briefly summarized the benefits and limitations of ML methods in petroleum industry applications. Our detailed and extensive study could serve as an invaluable reference for employing ML techniques in the petroleum industry. Based on the review, we found that ML techniques offer great potential in solving problems in the majority of CO2-EOR areas involving prediction and regression. With the generation of massive amounts of data in the everyday oil and gas industry, machine learning techniques can provide efficient and reliable preliminary results for the industry. Full article
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17 pages, 4829 KiB  
Article
Crushing Response and Optimization of a Modified 3D Re-Entrant Honeycomb
by Jun Zhang, Bo-Qiang Shi, Bo Wang and Guo-Qing Yu
Materials 2024, 17(9), 2083; https://doi.org/10.3390/ma17092083 - 28 Apr 2024
Viewed by 551
Abstract
A modified 3D re-entrant honeycomb is designed and fabricated utilizing Laser Cladding Deposition (LCD) technology, the mechanical properties of which are systematically investigated by experimental and finite element (FE) methods. Firstly, the influences of honeycomb angle on localized deformation and the response of [...] Read more.
A modified 3D re-entrant honeycomb is designed and fabricated utilizing Laser Cladding Deposition (LCD) technology, the mechanical properties of which are systematically investigated by experimental and finite element (FE) methods. Firstly, the influences of honeycomb angle on localized deformation and the response of force are studied by an experiment. Experimental results reveal that the honeycomb angles have a significant effect on deformation and force. Secondly, a series of numerical studies are conducted to analyze stress characteristics and energy absorption under different angles (α) and velocities (v). It is evident that two variables play an important role in stress and energy. Thirdly, response surface methodology (RSM) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are implemented with high precision to solve multi-objective optimization. Finally, the final compromise solution is determined based on the fitness function, with an angle of 49.23° and an impact velocity of 16.40 m/s. Through simulation verification, the errors of energy absorption (EA) and peak crush stress (PCS) are 9.26% and 0.4%, respectively. The findings of this study offer valuable design guidance for selecting the optimal design parameters under the same mass conditions to effectively enhance the performance of the honeycomb. Full article
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21 pages, 10954 KiB  
Article
An Efficient Image Cryptosystem Utilizing Difference Matrix and Genetic Algorithm
by Honglian Shen and Xiuling Shan
Entropy 2024, 26(5), 351; https://doi.org/10.3390/e26050351 - 23 Apr 2024
Viewed by 625
Abstract
Aiming at addressing the security and efficiency challenges during image transmission, an efficient image cryptosystem utilizing difference matrix and genetic algorithm is proposed in this paper. A difference matrix is a typical combinatorial structure that exhibits properties of discretization and approximate uniformity. It [...] Read more.
Aiming at addressing the security and efficiency challenges during image transmission, an efficient image cryptosystem utilizing difference matrix and genetic algorithm is proposed in this paper. A difference matrix is a typical combinatorial structure that exhibits properties of discretization and approximate uniformity. It can serve as a pseudo-random sequence, offering various scrambling techniques while occupying a small storage space. The genetic algorithm generates multiple ciphertext images with strong randomness through local crossover and mutation operations, then obtains high-quality ciphertext images through multiple iterations using the optimal preservation strategy. The whole encryption process is divided into three stages: first, the difference matrix is generated; second, it is utilized for initial encryption to ensure that the resulting ciphertext image has relatively good initial randomness; finally, multiple rounds of local genetic operations are used to optimize the output. The proposed cryptosystem is demonstrated to be effective and robust through simulation experiments and statistical analyses, highlighting its superiority over other existing algorithms. Full article
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12 pages, 3122 KiB  
Article
Numerical Simulation of Soliton Propagation Behavior for the Fractional-in-Space NLSE with Variable Coefficients on Unbounded Domain
by Fengzhou Tian, Yulan Wang and Zhiyuan Li
Fractal Fract. 2024, 8(3), 163; https://doi.org/10.3390/fractalfract8030163 - 12 Mar 2024
Viewed by 1072
Abstract
The soliton propagation of the fractional-in-space nonlinear Schrodinger equation (NLSE) is much more complicated than that of the corresponding integer NLSE. The aim of this paper is to discover some novel fractal soliton propagation behaviors (FSPBs) of this fractional-in-space NLSE. Firstly, the exact [...] Read more.
The soliton propagation of the fractional-in-space nonlinear Schrodinger equation (NLSE) is much more complicated than that of the corresponding integer NLSE. The aim of this paper is to discover some novel fractal soliton propagation behaviors (FSPBs) of this fractional-in-space NLSE. Firstly, the exact solution is compared with the present numerical solution, and the validity and accuracy of the present numerical method are verified. Secondly, the effect of fractional derivatives on soliton propagation is explored through the present numerical simulation results. At the same time, the present method is extended to the three-dimensional fractional-order NLSE. Finally, some novel FSPBs of the fractional-in-space NLSE are given. Full article
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19 pages, 6293 KiB  
Article
Reaction Curve-Assisted Rule-Based PID Control Design for Islanded Microgrid
by T. K. Bashishtha, V. P. Singh, U. K. Yadav and T. Varshney
Energies 2024, 17(5), 1110; https://doi.org/10.3390/en17051110 - 26 Feb 2024
Cited by 1 | Viewed by 663
Abstract
In a renewable energy-based islanded microgrid system, frequency control is one of the major challenges. In general, frequency oscillations occur in islanded microgrids due to the stochastic nature of load and variable output power of distributed generating units (DGUs). In the presented research [...] Read more.
In a renewable energy-based islanded microgrid system, frequency control is one of the major challenges. In general, frequency oscillations occur in islanded microgrids due to the stochastic nature of load and variable output power of distributed generating units (DGUs). In the presented research proposal, frequency oscillations are suppressed by implementing the proportional integral derivative (PID) controller-based control design strategy for an islanded microgrid. The modeling of the islanded microgrid is firstly presented in the form of a linearized transfer function. Further, the derived transfer function is approximated into its equivalent first-order plus dead time (FOPDT) form. The approximated FOPDT transfer function is obtained by employing the reaction curve method to calculate the parameters of the FOPDT transfer function. Furthermore, the desired frequency regulation is achieved for the manifested FOPDT transfer function by incorporating PID control design. For PID controller tuning, different rule-based methods are implemented. Additionally, comparative analysis is also performed to ensure the applicability of the comparatively better rule-based tuning method. The Wang–Chan–Juang (WCJ) method is found effective over other rule-based tuning methods. The efficacy of the WCJ method is proved in terms of transient response and frequency deviation. The tabulated data of tuning parameters, time domain specifications, and error indices along with responses are provided in support of the presented control strategy. Full article
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