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Mathematics
Volume 11
Issue 5
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Mathematics, Volume 11, Issue 5 (March-1 2023) – 209 articles

Cover Story (view full-size image): In differential geometry of manifolds with additional tensor structures, an important role is played by those affine connections which preserve the structure tensors and the metric connections, also known as natural connections. In the present work, we study the almost paracontact and almost paracomplex Riemannian manifolds, called Riemannian Π-manifolds. We define the concept of natural connection on these manifolds, and we prove a necessary and sufficient condition—an affine connection—to be natural. Moreover, we introduce a non-symmetric natural connection, and we call it the first natural connection on the Riemannian Π-manifold. We obtain relations between this connection and the Levi-Civita connection, and we study some of its curvature characteristics. We support the results using an explicit example of dimension 5. View this paper
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29 pages, 4100 KiB  
Article
Analytically Computing the Moments of a Conic Combination of Independent Noncentral Chi-Square Random Variables and Its Application for the Extended Cox–Ingersoll–Ross Process with Time-Varying Dimension
by Sanae Rujivan, Athinan Sutchada, Kittisak Chumpong and Napat Rujeerapaiboon
Mathematics 2023, 11(5), 1276; https://doi.org/10.3390/math11051276 - 06 Mar 2023
Cited by 1 | Viewed by 1512
Abstract
This paper focuses mainly on the problem of computing the γth, γ>0, moment of a random variable Yn:=∑i=1nαiXi in which the αi’s are positive [...] Read more.
This paper focuses mainly on the problem of computing the γth, γ>0, moment of a random variable Yn:=∑i=1nαiXi in which the αi’s are positive real numbers and the Xi’s are independent and distributed according to noncentral chi-square distributions. Finding an analytical approach for solving such a problem has remained a challenge due to the lack of understanding of the probability distribution of Yn, especially when not all αi’s are equal. We analytically solve this problem by showing that the γth moment of Yn can be expressed in terms of generalized hypergeometric functions. Additionally, we extend our result to computing the γth moment of Yn when Xi is a combination of statistically independent Zi2 and Gi in which the Zi’s are distributed according to normal or Maxwell–Boltzmann distributions and the Gi’s are distributed according to gamma, Erlang, or exponential distributions. Our paper has an immediate application in interest rate modeling, where we can explicitly provide the exact transition probability density function of the extended Cox–Ingersoll–Ross (ECIR) process with time-varying dimension as well as the corresponding γth conditional moment. Finally, we conduct Monte Carlo simulations to demonstrate the accuracy and efficiency of our explicit formulas through several numerical tests. Full article
(This article belongs to the Special Issue Probability, Statistics and Their Applications 2021)
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20 pages, 2643 KiB  
Article
JQPro:Join Query Processing in a Distributed System for Big RDF Data Using the Hash-Merge Join Technique
by Nahla Mohammed Elzein, Mazlina Abdul Majid, Ibrahim Abaker Targio Hashem, Ashraf Osman Ibrahim, Anas W. Abulfaraj and Faisal Binzagr
Mathematics 2023, 11(5), 1275; https://doi.org/10.3390/math11051275 - 06 Mar 2023
Cited by 1 | Viewed by 1547
Abstract
In the last decade, the volume of semantic data has increased exponentially, with the number of Resource Description Framework (RDF) datasets exceeding trillions of triples in RDF repositories. Hence, the size of RDF datasets continues to grow. However, with the increasing number of [...] Read more.
In the last decade, the volume of semantic data has increased exponentially, with the number of Resource Description Framework (RDF) datasets exceeding trillions of triples in RDF repositories. Hence, the size of RDF datasets continues to grow. However, with the increasing number of RDF triples, complex multiple RDF queries are becoming a significant demand. Sometimes, such complex queries produce many common sub-expressions in a single query or over multiple queries running as a batch. In addition, it is also difficult to minimize the number of RDF queries and processing time for a large amount of related data in a typical distributed environment encounter. To address this complication, we introduce a join query processing model for big RDF data, called JQPro. By adopting a MapReduce framework in JQPro, we developed three new algorithms, which are hash-join, sort-merge, and enhanced MapReduce-join for join query processing of RDF data. Based on an experiment conducted, the result showed that the JQPro model outperformed the two popular algorithms, gStore and RDF-3X, with respect to the average execution time. Furthermore, the JQPro model was also tested against RDF-3X, RDFox, and PARJs using the LUBM benchmark. The result showed that the JQPro model had better performance in comparison with the other models. In conclusion, the findings showed that JQPro achieved improved performance with 87.77% in terms of execution time. Hence, in comparison with the selected models, JQPro performs better. Full article
(This article belongs to the Special Issue Machine Learning, Statistics and Big Data)
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18 pages, 982 KiB  
Article
Optimization Model and Algorithm of Logistics Vehicle Routing Problem under Major Emergency
by Kangye Tan, Weihua Liu, Fang Xu and Chunsheng Li
Mathematics 2023, 11(5), 1274; https://doi.org/10.3390/math11051274 - 06 Mar 2023
Cited by 7 | Viewed by 2942
Abstract
The novel coronavirus pandemic is a major global public health emergency, and has presented new challenges and requirements for the timely response and operational stability of emergency logistics that were required to address the major public health events outbreak in China. Based on [...] Read more.
The novel coronavirus pandemic is a major global public health emergency, and has presented new challenges and requirements for the timely response and operational stability of emergency logistics that were required to address the major public health events outbreak in China. Based on the problems of insufficient timeliness and high total system cost of emergency logistics distribution in major epidemic situations, this paper takes the minimum vehicle distribution travel cost, time cost, early/late punishment cost, and fixed cost of the vehicle as the target, the soft time window for receiving goods at each demand point, the rated load of the vehicle, the volume, maximum travel of the vehicle in a single delivery as constraints, and an emergency logistics vehicle routing problem optimization model for major epidemics was constructed. The convergence speed improvement strategy, particle search improvement strategy, and elite retention improvement strategy were introduced to improve the particle swarm optimization (PSO) algorithm for it to be suitable for solving global optimization problems. The simulation results prove that the improved PSO algorithm required to solve the emergency medical supplies logistics vehicle routing problem for the major emergency can reach optimal results. Compared with the basic PSO algorithm, the total cost was reduced by 20.09%. Full article
(This article belongs to the Special Issue Mathematics-Based Methods in Artificial Intelligence, Pattern Recognition and Deep Learning)
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36 pages, 21841 KiB  
Article
Mountaineering Team-Based Optimization: A Novel Human-Based Metaheuristic Algorithm
by Iman Faridmehr, Moncef L. Nehdi, Iraj Faraji Davoudkhani and Alireza Poolad
Mathematics 2023, 11(5), 1273; https://doi.org/10.3390/math11051273 - 06 Mar 2023
Cited by 16 | Viewed by 2319
Abstract
This paper proposes a novel optimization method for solving real-world optimization problems. It is inspired by a cooperative human phenomenon named the mountaineering team-based optimization (MTBO) algorithm. Proposed for the first time, the MTBO algorithm is mathematically modeled to achieve a robust optimization [...] Read more.
This paper proposes a novel optimization method for solving real-world optimization problems. It is inspired by a cooperative human phenomenon named the mountaineering team-based optimization (MTBO) algorithm. Proposed for the first time, the MTBO algorithm is mathematically modeled to achieve a robust optimization algorithm based on the social behavior and human cooperation needed in considering the natural phenomena to reach a mountaintop, which represents the optimal global solution. To solve optimization problems, the proposed MTBO algorithm captures the phases of the regular and guided movement of climbers based on the leader’s experience, obstacles against reaching the peak and getting stuck in local optimality, and the coordination and social cooperation of the group to save members from natural hazards. The performance of the MTBO algorithm was tested with 30 known CEC 2014 test functions, as well as on classical engineering design problems, and the results were compared with that of well-known methods. It is shown that the MTBO algorithm is very competitive in comparison with state-of-art metaheuristic methods. The superiority of the proposed MTBO algorithm is further confirmed by statistical validation, as well as the Wilcoxon signed-rank test with advanced optimization algorithms. Compared to the other algorithms, the MTBO algorithm is more robust, easier to implement, exhibits effective optimization performance for a wide range of real-world test functions, and attains faster convergence to optimal global solutions. Full article
(This article belongs to the Special Issue Advances in Machine Learning, Optimization, and Control Applications)
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16 pages, 435 KiB  
Article
High-Dimensional Distributionally Robust Mean-Variance Efficient Portfolio Selection
by Zhonghui Zhang, Huarui Jing and Chihwa Kao
Mathematics 2023, 11(5), 1272; https://doi.org/10.3390/math11051272 - 06 Mar 2023
Cited by 2 | Viewed by 1422
Abstract
This paper introduces a novel distributionally robust mean-variance portfolio estimator based on the projection robust Wasserstein (PRW) distance. This approach addresses the issue of increasing conservatism of portfolio allocation strategies due to high-dimensional data. Our simulation results show the robustness of the PRW-based [...] Read more.
This paper introduces a novel distributionally robust mean-variance portfolio estimator based on the projection robust Wasserstein (PRW) distance. This approach addresses the issue of increasing conservatism of portfolio allocation strategies due to high-dimensional data. Our simulation results show the robustness of the PRW-based estimator in the presence of noisy data and its ability to achieve a higher Sharpe ratio than regular Wasserstein distances when dealing with a large number of assets. Our empirical study also demonstrates that the proposed portfolio estimator outperforms classic “plug-in” methods using various covariance estimators in terms of risk when evaluated out of sample. Full article
(This article belongs to the Special Issue Mathematical Economics and Spatial Econometrics)
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14 pages, 418 KiB  
Article
A Flexible Class of Two-Piece Normal Distribution with a Regression Illustration to Biaxial Fatigue Data
by Hugo Salinas, Hassan Bakouch, Najla Qarmalah and Guillermo Martínez-Flórez
Mathematics 2023, 11(5), 1271; https://doi.org/10.3390/math11051271 - 06 Mar 2023
Viewed by 1319
Abstract
Using a two-piece normal distribution for modeling univariate data that exhibits symmetry, and uni/bimodality is notably effective. In this respect, the shape parameter value determines whether unimodality or bimodality is present. This paper proposes a flexible uni/bimodal distribution with platykurtic density, which can [...] Read more.
Using a two-piece normal distribution for modeling univariate data that exhibits symmetry, and uni/bimodality is notably effective. In this respect, the shape parameter value determines whether unimodality or bimodality is present. This paper proposes a flexible uni/bimodal distribution with platykurtic density, which can be used to simulate a variety of data. The concept is based on the transforming of a random variable into a folded distribution. Further, the proposed class includes the normal distribution as a sub-model. In the current study, the maximum likelihood method is considered for deriving the main structural properties and for the estimation of parameters. In addition, simulation experiments are presented to evaluate the behavior of estimators. Finally, fitting and regression applications are presented to illustrate the usefulness of the proposed distribution for data modeling in different real-life scenarios. Full article
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30 pages, 7582 KiB  
Article
An Improved Fick’s Law Algorithm Based on Dynamic Lens-Imaging Learning Strategy for Planning a Hybrid Wind/Battery Energy System in Distribution Network
by Mohana Alanazi, Abdulaziz Alanazi, Ahmad Almadhor and Hafiz Tayyab Rauf
Mathematics 2023, 11(5), 1270; https://doi.org/10.3390/math11051270 - 06 Mar 2023
Cited by 3 | Viewed by 1191
Abstract
In this paper, optimal and multi-objective planning of a hybrid energy system (HES) with wind turbine and battery storage (WT/Battery) has been proposed to drop power loss, smooth voltage profile, enhance customers reliability, as well as minimize the net present cost of the [...] Read more.
In this paper, optimal and multi-objective planning of a hybrid energy system (HES) with wind turbine and battery storage (WT/Battery) has been proposed to drop power loss, smooth voltage profile, enhance customers reliability, as well as minimize the net present cost of the hybrid system plus the battery degradation cost (BDC). Decision variables include the installation site of the hybrid system and size of the wind farm and battery storage. These variables are found with the help of a novel metaheuristic approach called improved Fick’s law algorithm (IFLA). To enhance the exploration performance and avoid the early incomplete convergence of the conventional Fick’s law (FLA) algorithm, a dynamic lens-imaging learning strategy (DLILS) based on opposition learning has been adopted. The planning problem has been implemented in two approaches without and considering BDC to analyze its impact on the reserve power level and the amount and quality of power loss, voltage profile, and reliability. A 33-bus distribution system has also been employed to validate the capability and efficiency of the suggested method. Simulation results have shown that the multi-objective planning of the hybrid WT/Battery energy system improves voltage and reliability and decreases power loss by managing the reserve power based on charging and discharging battery units and creating electrical planning with optimal power injection into the network. The results of simulations and evaluation of statistic analysis indicate the superiority of the IFLA in achieving the optimal solution with faster convergence than conventional FLA, particle swarm optimization (PSO), manta ray foraging optimizer (MRFO), and bat algorithm (BA). It has been observed that the proposed methodology based on IFLA in different approaches has obtained lower power loss and more desirable voltage profile and reliability than its counterparts. Simulation reports demonstrate that by considering BDC, the values of losses and voltage deviations are increased by 2.82% and 1.34%, respectively, and the reliability of network customers is weakened by 5.59% in comparison with a case in which this cost is neglected. Therefore, taking into account this parameter in the objective function can lead to the correct and real calculation of the improvement rate of each of the objectives, especially the improvement of the reliability level, as well as making the correct decisions of network planners based on these findings. Full article
(This article belongs to the Special Issue Numerical Modeling and Hybrid Methods for Thermal Management, Storage, and Optimization)
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18 pages, 1477 KiB  
Article
A Tripartite Evolutionary Game Analysis of Participant Decision-Making Behavior in Mobile Crowdsourcing
by Hanyun Hao, Jian Yang and Jie Wang
Mathematics 2023, 11(5), 1269; https://doi.org/10.3390/math11051269 - 06 Mar 2023
Cited by 3 | Viewed by 1461
Abstract
With the rapid development of the Internet of Things and the popularity of numerous sensing devices, Mobile crowdsourcing (MCS) has become a paradigm for collecting sensing data and solving problems. However, most early studies focused on schemes of incentive mechanisms, task allocation and [...] Read more.
With the rapid development of the Internet of Things and the popularity of numerous sensing devices, Mobile crowdsourcing (MCS) has become a paradigm for collecting sensing data and solving problems. However, most early studies focused on schemes of incentive mechanisms, task allocation and data quality control, which did not consider the influence and restriction of different behavioral strategies of stakeholders on the behaviors of other participants, and rarely applied dynamic system theory to analysis of participant behavior in mobile crowdsourcing. In this paper, we first propose a tripartite evolutionary game model of crowdsourcing workers, crowdsourcing platforms and task requesters. Secondly, we focus on the evolutionary stability strategies and evolutionary trends of different participants, as well as the influential factors, such as participants’ irrational personality, conflict of interest, punishment intensity, technical level and awareness of rights protection, to analyze the influence of different behavioral strategies on other participants. Thirdly, we verify the stability of the equilibrium point of the tripartite game system through simulation experiments. Finally, we summarize our work and provide related recommendations for governing agencies and different stakeholders to facilitate the continuous operation of the mobile crowdsourcing market and maximize social welfare. Full article
(This article belongs to the Special Issue Evolutionary Games, Propagation Processes and Control in Complex Systems)
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17 pages, 4882 KiB  
Article
Mathematical and Experimental Model of Neuronal Oscillator Based on Memristor-Based Nonlinearity
by Ivan Kipelkin, Svetlana Gerasimova, Davud Guseinov, Dmitry Pavlov, Vladislav Vorontsov, Alexey Mikhaylov and Victor Kazantsev
Mathematics 2023, 11(5), 1268; https://doi.org/10.3390/math11051268 - 06 Mar 2023
Cited by 5 | Viewed by 1735
Abstract
This article presents a mathematical and experimental model of a neuronal oscillator with memristor-based nonlinearity. The mathematical model describes the dynamics of an electronic circuit implementing the FitzHugh–Nagumo neuron model. A nonlinear component of this circuit is the Au/Zr/ZrO2(Y)/TiN/Ti memristive device. [...] Read more.
This article presents a mathematical and experimental model of a neuronal oscillator with memristor-based nonlinearity. The mathematical model describes the dynamics of an electronic circuit implementing the FitzHugh–Nagumo neuron model. A nonlinear component of this circuit is the Au/Zr/ZrO2(Y)/TiN/Ti memristive device. This device is fabricated on the oxidized silicon substrate using magnetron sputtering. The circuit with such nonlinearity is described by a three-dimensional ordinary differential equation system. The effect of the appearance of spontaneous self-oscillations is investigated. A bifurcation scenario based on supercritical Andronov–Hopf bifurcation is found. The dependence of the critical point on the system parameters, particularly on the size of the electrode area, is analyzed. The self-oscillating and excitable modes are experimentally demonstrated. Full article
(This article belongs to the Special Issue Mathematical Modeling in Biophysics, Biochemistry and Physical Chemistry)
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13 pages, 2862 KiB  
Article
Delay-Dependent Stability Region for the Distributed Coordination of Delayed Fractional-Order Multi-Agent Systems
by Abbasali Koochakzadeh, Mojtaba Naderi Soorki, Aydin Azizi, Kamran Mohammadsharifi and Mohammadreza Riazat
Mathematics 2023, 11(5), 1267; https://doi.org/10.3390/math11051267 - 06 Mar 2023
Cited by 2 | Viewed by 1052
Abstract
Delay and especially delay in the transmission of agents’ information, is one of the most important causes of disruption to achieving consensus in a multi-agent system. This paper deals with achieving consensus in delayed fractional-order multi-agent systems (FOMAS). The aim in the present [...] Read more.
Delay and especially delay in the transmission of agents’ information, is one of the most important causes of disruption to achieving consensus in a multi-agent system. This paper deals with achieving consensus in delayed fractional-order multi-agent systems (FOMAS). The aim in the present note is to find the exact maximum allowable delay in a FOMAS with non-uniform delay, i.e., the case in which the interactions between agents are subject to non-identical communication time-delays. By proving a stability theorem, the results available for non-delayed networked fractional-order systems are extended for the case in which interaction links have nonequal communication time-delays. In this extension by considering a time-delay coordination algorithm, necessary and sufficient conditions on the time delays and interaction graph are presented to guarantee the coordination. In addition, the delay-dependent stability region is also obtained. Finally, the dependency of the maximum allowable delay on two parameters, the agent fractional-order and the largest eigenvalue of the graph Laplacian matrix, is exactly determined. Numerical simulation results are given to confirm the proposed methodologies. Full article
(This article belongs to the Special Issue Dynamical Systems and System Analysis)
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15 pages, 719 KiB  
Article
Significance of Nanoparticle Radius and Gravity Modulation on Dynamics of Nanofluid over Stretched Surface via Finite Element Simulation: The Case of Water-Based Copper Nanoparticles
by Bagh Ali, Anum Shafiq, Meznah M. Alanazi, Awatif A. Hendi, Ahmed Kadhim Hussein and Nehad Ali Shah
Mathematics 2023, 11(5), 1266; https://doi.org/10.3390/math11051266 - 06 Mar 2023
Cited by 3 | Viewed by 1427
Abstract
This communication studies the importance of varying the radius Dp of Copper nanoparticles for microgravity-modulated mixed convection in micropolar nanofluid flux under an inclined surface subject magnetic field and heat source. In the current era, extremely pervasive modernized technical implementations have drawn [...] Read more.
This communication studies the importance of varying the radius Dp of Copper nanoparticles for microgravity-modulated mixed convection in micropolar nanofluid flux under an inclined surface subject magnetic field and heat source. In the current era, extremely pervasive modernized technical implementations have drawn attention to free convection governed by g-jitter force connected with microgravity. Therefore, fixed inter-spacing of nanoparticles and effects of g-jitter on the inclined surface are taken into consideration. A mathematical formulation based on conservation principles was non-dimensionalized by enforcement of similarity transformation, yielding a related set of ODEs. The convective non-linearity and coupling, an FE discretization, was implemented and executed on the Matlab platform. The numerical process’ credibility was ensured for its acceptable adoption with the defined outcomes. Then, the computational endeavor was continued to elucidate the impacts of various inputs of Dp, the amplitude of modulation ϵ, material parameter β, mixed convection parameter λ, inclination angle γ, and magnetic parameter M. The enlarging size of nanoparticles accelerated the nanofluid flow due to the depreciation of viscosity and receded the fluid temperature by reducing the surface area for heat transportation. The modulated Nusselt number, couple stress, and skin friction coefficient are significantly affected by the variation of Dp, M, β, λ, and ϵ. These results would benefit experts dealing with upper space transportation and materials’ performance, such as the effectualness of chemical catalytic reactors and crystals. Full article
(This article belongs to the Special Issue Numerical and Analytical Study of Fluid Dynamics)
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13 pages, 484 KiB  
Article
Acoustic Wind in a Hyperbolic Predator—Prey System
by Andrey Morgulis
Mathematics 2023, 11(5), 1265; https://doi.org/10.3390/math11051265 - 06 Mar 2023
Viewed by 960
Abstract
We address a hyperbolic model for prey-sensitive predators interacting with purely diffusive prey. We adopt the Cattaneo formulation for describing the predators’ transport. Given the hyperbolicity, the long-lived short-wave patterns occur for sufficiently weak prey diffusivities. The main result is that the non-linear [...] Read more.
We address a hyperbolic model for prey-sensitive predators interacting with purely diffusive prey. We adopt the Cattaneo formulation for describing the predators’ transport. Given the hyperbolicity, the long-lived short-wave patterns occur for sufficiently weak prey diffusivities. The main result is that the non-linear interplay of the short waves generically excites the slowly growing amplitude modulation for wide ranges of the model parameters. We have observed such a feature in the numerical experiments and support our conclusions with a short-wave asymptotic solution in the limit of vanishing prey diffusivity. Our reasoning relies on the so-called homogenized system that governs slow evolutions of the amplitudes of the short-wave parcels. It includes a term (called wind) which is absent in the original model and only comes from averaging over the short waves. It is the wind that (unlike any of the other terms!) is capable of exciting the instability and pumping the growth of solutions. There is quite a definite relationship between the predators’ transport coefficients to be held for getting rid of the wind. Interestingly, this relationship had been introduced in prior studies of small-scale mosaics in the spatial distributions of some real-life populations. Full article
(This article belongs to the Collection Theoretical and Mathematical Ecology)
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17 pages, 2461 KiB  
Article
Periodically Intermittent Control of Memristor-Based Hyper-Chaotic Bao-like System
by Kun Li, Rongfeng Li, Longzhou Cao, Yuming Feng and Babatunde Oluwaseun Onasanya
Mathematics 2023, 11(5), 1264; https://doi.org/10.3390/math11051264 - 06 Mar 2023
Cited by 18 | Viewed by 1578
Abstract
In this paper, based on a three-dimensional Bao system, a memristor-based hyper-chaotic Bao-like system is successfully constructed, and a simulated equivalent circuit is designed, which is used to verify the chaotic behaviors of the system. Meanwhile, a control method called periodically intermittent control [...] Read more.
In this paper, based on a three-dimensional Bao system, a memristor-based hyper-chaotic Bao-like system is successfully constructed, and a simulated equivalent circuit is designed, which is used to verify the chaotic behaviors of the system. Meanwhile, a control method called periodically intermittent control with variable control width is proposed. The control width sequence in the proposed method is not only variable, but also monotonically decreasing, and the method can effectively stabilize most existing nonlinear systems. Moreover, the memristor-based hyper-chaotic Bao-like system is controlled by combining the proposed method with the Lyapunov stability principle. Finally, we should that the proposed method can effectively control and stabilize not only the proposed hyper-chaotic system, but also the Chua’s oscillator. Full article
(This article belongs to the Special Issue Nonlinear Systems: Dynamics, Control, Optimization and Applications to the Science and Engineering, 2nd Edition)
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35 pages, 2336 KiB  
Review
Modeling Languages for Internet of Things (IoT) Applications: A Comparative Analysis Study
by Sadik Arslan, Mert Ozkaya and Geylani Kardas
Mathematics 2023, 11(5), 1263; https://doi.org/10.3390/math11051263 - 06 Mar 2023
Cited by 5 | Viewed by 2798
Abstract
Modeling languages have gained ever-increasing importance for the Internet of Things (IoT) domain for improving the productivity and quality of IoT developments. In this study, we analyzed 32 different modeling languages that have been designed for IoT software development in terms of a [...] Read more.
Modeling languages have gained ever-increasing importance for the Internet of Things (IoT) domain for improving the productivity and quality of IoT developments. In this study, we analyzed 32 different modeling languages that have been designed for IoT software development in terms of a set of requirements that were categorized into three groups: language definition, language features, and tool support. Some key findings are as follows: (1) performance is the most supported quality property (28%); (2) most languages offer a visual notation set only, while 6% provide both textual and visual notation sets; (3) most languages (88%) lack formally precise semantic definitions; (4) most languages (94%) support the physical, deployment, and logical modeling viewpoints, while the behavior, logical, and information viewpoints are rarely supported; (5) almost none of the languages enable extensibility; (6) Java (34%) and C (21%) are the most preferred programming languages for model transformation; (7) consistency (77%) and completeness (64%) are the most supported properties for the automated checking of models; and (8) most languages (81%) are not supported with any websites for sharing case studies, source code, tools, tutorials, etc. The analysis results can be useful for language engineers, practitioners, and tool vendors for better understanding the existing languages for IoT, their weak and strong points, and IoT industries’ needs in future language and modeling toolset developments. Full article
(This article belongs to the Special Issue Mathematics: 10th Anniversary)
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9 pages, 326 KiB  
Article
Multi de Bruijn Sequences and the Cross-Join Method
by Abbas Alhakim and Janusz Szmidt
Mathematics 2023, 11(5), 1262; https://doi.org/10.3390/math11051262 - 06 Mar 2023
Viewed by 1021
Abstract
We show a method to construct binary multi de Bruijn sequences using the cross-join method. We extend the proof given by Alhakim for ordinary de Bruijn sequences to the case of multi de Bruijn sequences. In particular, we establish that all multi de [...] Read more.
We show a method to construct binary multi de Bruijn sequences using the cross-join method. We extend the proof given by Alhakim for ordinary de Bruijn sequences to the case of multi de Bruijn sequences. In particular, we establish that all multi de Bruijn sequences can be obtained by cross-joining an ordinary de Bruijn sequence concatenated with itself an appropriate number of times. We implemented the generation of all multi de Bruijn sequences of type C(2,2,2) and C(3,2,2). We experimentally confirm that some multi de Bruijn sequences can be generated by Galois Nonlinear Feedback Shift Registers (NLFSRs). It is supposed that all multi de Bruijn sequences can be generated using Galois NLFSRs. Full article
(This article belongs to the Special Issue Advanced Graph Theory and Combinatorics)
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17 pages, 3844 KiB  
Review
Multiview-Learning-Based Generic Palmprint Recognition: A Literature Review
by Shuping Zhao, Lunke Fei and Jie Wen
Mathematics 2023, 11(5), 1261; https://doi.org/10.3390/math11051261 - 06 Mar 2023
Cited by 5 | Viewed by 1869
Abstract
Palmprint recognition has been widely applied to security authentication due to its rich characteristics, i.e., local direction, wrinkle, and texture. However, different types of palmprint images captured from different application scenarios usually contain a variety of dominant features. Specifically, the palmprint recognition performance [...] Read more.
Palmprint recognition has been widely applied to security authentication due to its rich characteristics, i.e., local direction, wrinkle, and texture. However, different types of palmprint images captured from different application scenarios usually contain a variety of dominant features. Specifically, the palmprint recognition performance will be degraded by the interference factors, i.e., noise, rotations, and shadows, while palmprint images are acquired in the open-set environments. Seeking to handle the long-standing interference information in the images, multiview palmprint feature learning has been proposed to enhance the feature expression by exploiting multiple characteristics from diverse views. In this paper, we first introduced six types of palmprint representation methods published from 2004 to 2022, which described the characteristics of palmprints from a single view. Afterward, a number of multiview-learning-based palmprint recognition methods (2004–2022) were listed, which discussed how to achieve better recognition performances by adopting different complementary types of features from multiple views. To date, there is no work to summarize the multiview fusion for different types of palmprint features. In this paper, the aims, frameworks, and related methods of multiview palmprint representation will be summarized in detail. Full article
(This article belongs to the Special Issue Mathematics-Based Methods in Artificial Intelligence, Pattern Recognition and Deep Learning)
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21 pages, 9977 KiB  
Article
Evaluating the Privacy and Utility of Time-Series Data Perturbation Algorithms
by Adrian-Silviu Roman
Mathematics 2023, 11(5), 1260; https://doi.org/10.3390/math11051260 - 05 Mar 2023
Cited by 1 | Viewed by 1743
Abstract
Data collected from sensor-rich systems may reveal user-related patterns that represent private information. Sensitive patterns from time-series data can be protected using diverse perturbation methods; however, choosing the method that provides the desired privacy and utility level is challenging. This paper proposes a [...] Read more.
Data collected from sensor-rich systems may reveal user-related patterns that represent private information. Sensitive patterns from time-series data can be protected using diverse perturbation methods; however, choosing the method that provides the desired privacy and utility level is challenging. This paper proposes a new procedure for evaluating the utility and privacy of perturbation techniques and an algorithm for comparing perturbation methods. The contribution is significant for those involved in protecting time-series data collected from various sensors as the approach is sensor-type-independent, algorithm-independent, and data-independent. The analysis of the impact of data integrity attacks on the perturbed data follows the methodology. Experimental results obtained using actual data collected from a VW Passat vehicle via the OBD-II port demonstrate the applicability of the approach to measuring the utility and privacy of perturbation algorithms. Moreover, important benefits have been identified: the proposed approach measures both privacy and utility, various distortion and perturbation methods can be compared (no matter how different), and an evaluation of the impact of data integrity attacks on perturbed data is possible. Full article
(This article belongs to the Special Issue Recent Advances in Security, Privacy, and Applied Cryptography)
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24 pages, 2986 KiB  
Article
Hidden Markov Mixture of Gaussian Process Functional Regression: Utilizing Multi-Scale Structure for Time Series Forecasting
by Tao Li and Jinwen Ma
Mathematics 2023, 11(5), 1259; https://doi.org/10.3390/math11051259 - 05 Mar 2023
Viewed by 1416
Abstract
The mixture of Gaussian process functional regressions (GPFRs) assumes that there is a batch of time series or sample curves that are generated by independent random processes with different temporal structures. However, in real situations, these structures are actually transferred in a random [...] Read more.
The mixture of Gaussian process functional regressions (GPFRs) assumes that there is a batch of time series or sample curves that are generated by independent random processes with different temporal structures. However, in real situations, these structures are actually transferred in a random manner from a long time scale. Therefore, the assumption of independent curves is not true in practice. In order to get rid of this limitation, we propose the hidden-Markov-based GPFR mixture model (HM-GPFR) by describing these curves with both fine- and coarse-level temporal structures. Specifically, the temporal structure is described by the Gaussian process model at the fine level and the hidden Markov process at the coarse level. The whole model can be regarded as a random process with state switching dynamics. To further enhance the robustness of the model, we also give a priori parameters to the model and develop a Bayesian-hidden-Markov-based GPFR mixture model (BHM-GPFR). The experimental results demonstrated that the proposed methods have both high prediction accuracy and good interpretability. Full article
(This article belongs to the Special Issue Interpretable Models and Their Applications in Neural Computation and Statistical Learning)
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13 pages, 1962 KiB  
Article
Do Statistics Show Differences between Distance Estimations of 3D Objects in the Traffic Environment Using Glances, Side View Mirrors, and Camera Display?
by Aleksandar Trifunović, Tijana Ivanišević, Svetlana Čičević, Sreten Simović, Vedran Vukšić and Živana Slović
Mathematics 2023, 11(5), 1258; https://doi.org/10.3390/math11051258 - 05 Mar 2023
Cited by 1 | Viewed by 1187
Abstract
The driver’s task in traffic is to evaluate traffic situations and act in accordance with the estimate. One of the most common causes of road crashes is “incorrect estimated of the traffic situation”. Correct perception of surroundings is one of the prerequisites for [...] Read more.
The driver’s task in traffic is to evaluate traffic situations and act in accordance with the estimate. One of the most common causes of road crashes is “incorrect estimated of the traffic situation”. Correct perception of surroundings is one of the prerequisites for safe and successful driving. To investigate the mentioned issue, the authors of this paper conducted an experimental study with the aim of determining what affects the estimation of the object distance. In contrast to previous studies known from the available literature, our study presents experimental research of the estimated distance of 3D stimuli in three environments by direct observation, a rear-view mirror, and a camera display in a vehicle. One-hundred-and-sixty-four participants participated in the experiment. The research results show statistically significant differences in the estimation of the distance of 3D objects for different colors. Participants, for the largest number of stimuli, best estimate the distance from direct observation than through the rear-view mirror, while they make the most mistakes when estimating the distance of 3D objects using the camera display in a vehicle. On the other hand, in all described conditions, the respondents estimated the distance to the blue and green objects with the most significant errors. Full article
(This article belongs to the Special Issue Mathematics and Computer Programming in 2D and 3D Open Source Software)
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17 pages, 3616 KiB  
Article
Numerical Analysis and Structure Optimization of Concentric GST Ring Resonator Mounted over SiO2 Substrate and Cr Ground Layer
by Khaled Aliqab, Bo Bo Han, Ammar Armghan, Meshari Alsharari, Jaymit Surve and Shobhit K. Patel
Mathematics 2023, 11(5), 1257; https://doi.org/10.3390/math11051257 - 05 Mar 2023
Cited by 2 | Viewed by 1377
Abstract
Since the introduction of Metal-Insulator-Metal (MIM) absorbers, most of the structures demonstrated a narrowband absorption response which is not suitable for potential applications in photovoltaic systems, as it requires higher energy to enhance its performance. Very little research is being conducted in this [...] Read more.
Since the introduction of Metal-Insulator-Metal (MIM) absorbers, most of the structures demonstrated a narrowband absorption response which is not suitable for potential applications in photovoltaic systems, as it requires higher energy to enhance its performance. Very little research is being conducted in this direction; to address this issue, we exhibit a broadband solar absorber designed using a concentric GST ring resonator placed upon a silicon dioxide substrate layer with chromium used as a ground plane. It was analyzed using the finite element method. The design is also optimized by using a nonlinear parametric optimization algorithm. Comparatively less work has been focused on solar absorbers designed with the help of GST material, and here we have compared the effect of two different phases of GST, i.e., amorphous (aGST) and crystalline (cGST); the results indicate the higher performance of aGST phase. Parametric optimization has been adapted to identify the optimal design to attain high performance at minimal resources. The absorption response is angle insensitive for 0 to 60 degrees, and at the same time for both TE and TM modes, the design provides identical results, indicating the polarization-insensitive properties. The electric field intensity changes at the six peak wavelengths are also demonstrated for the authentication of the high performance. Thus, the proposed concentric GST ring resonator solar absorber can present a higher solar energy absorption rate than other solar structure designs. This design can be applied for improving the performance of photovoltaic systems. Full article
(This article belongs to the Special Issue Computational Methods and Applications for Numerical Analysis)
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42 pages, 19875 KiB  
Article
Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems
by Jinhua You, Heming Jia, Di Wu, Honghua Rao, Changsheng Wen, Qingxin Liu and Laith Abualigah
Mathematics 2023, 11(5), 1256; https://doi.org/10.3390/math11051256 - 05 Mar 2023
Cited by 3 | Viewed by 2431
Abstract
The artificial Gorilla Troop Optimization (GTO) algorithm (GTO) is a metaheuristic optimization algorithm that simulates the social life of gorillas. This paper proposes three innovative strategies considering the GTO algorithm’s insufficient convergence accuracy and low convergence speed. First, a shrinkage control factor fusion [...] Read more.
The artificial Gorilla Troop Optimization (GTO) algorithm (GTO) is a metaheuristic optimization algorithm that simulates the social life of gorillas. This paper proposes three innovative strategies considering the GTO algorithm’s insufficient convergence accuracy and low convergence speed. First, a shrinkage control factor fusion strategy is proposed to expand the search space and reduce search blindness by strengthening the communication between silverback gorillas and other gorillas to improve global optimization performance. Second, a sine cosine interaction fusion strategy based on closeness is proposed to stabilize the performance of silverback gorillas and other gorilla individuals and improve the convergence ability and speed of the algorithm. Finally, a gorilla individual difference identification strategy is proposed to reduce the difference between gorilla and silverback gorillas to improve the quality of the optimal solution. In order to verify the optimization effect of the modified artificial gorilla troop optimization (MGTO) algorithm, we used 23 classic benchmark functions, 30 CEC2014 benchmark functions, and 10 CEC2020 benchmark functions to test the performance of the proposed MGTO algorithm. In this study, we used a total of 63 functions for algorithm comparison. At the same time, we carried out the exploitation and exploration balance experiment of 30 CEC2014 and 10 CEC2020 functions for the MGTO algorithm. In addition, the MGTO algorithm was also applied to test seven practical engineering problems, and it achieved good results. Full article
(This article belongs to the Special Issue Computational Intelligence Methods in Bioinformatics)
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28 pages, 3894 KiB  
Article
Ranking of Service Quality Solution for Blended Design Teaching Using Fuzzy ANP and TOPSIS in the Post-COVID-19 Era
by Chia-Liang Lin, Jwu-Jenq Chen and Yu-Yu Ma
Mathematics 2023, 11(5), 1255; https://doi.org/10.3390/math11051255 - 05 Mar 2023
Cited by 5 | Viewed by 1381
Abstract
The blended educational method has become a common way of teaching and learning in the post-COVID-19 era. However, the related research on the selection model for the blended design teaching service quality solution is still an important research gap during this period. Therefore, [...] Read more.
The blended educational method has become a common way of teaching and learning in the post-COVID-19 era. However, the related research on the selection model for the blended design teaching service quality solution is still an important research gap during this period. Therefore, this study proposed a hybrid method of fuzzy analytic network process (FANP) and technique for order preference by similarity to ideal solution (TOPSIS) to analyse the dimensions, indicators and alternatives of blended design teaching service quality. As for the findings of this research, the dimension of assurance is the most vital factor, followed by responsiveness, reliability and empathy. Meanwhile, this research discovered that the top three significant alternatives are “Employees are trustworthy”, “Safe transaction mechanism and environment” and “Personalised needs of customers”. Also, we found that dimensions utilised to evaluate the quality of education service are similar whether in the post COVID-19 era, in the COVID-19 epidemic or prior to the COVID-19 pandemic. The main contribution of this study is to establish a multi-criteria decision-making (MCDM) model for the ranking of the blended design teaching service quality index and solution under a fuzzy environment. Finally, the research findings of this study have a guiding role, thereby becoming a guide for the industries related to hybrid design education to maintain good service quality in similar scenarios in the future. Full article
(This article belongs to the Special Issue Practice and Application of Emergency Management in Social Systems: Mathematical Modeling and the Use of Information Technology)
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20 pages, 10239 KiB  
Article
Realization of Intelligent Observer for Sensorless PMSM Drive Control
by Dwi Sudarno Putra, Seng-Chi Chen, Hoai-Hung Khong and Chin-Feng Chang
Mathematics 2023, 11(5), 1254; https://doi.org/10.3390/math11051254 - 05 Mar 2023
Cited by 2 | Viewed by 1406
Abstract
An observer is a crucial part of the sensorless control of a permanent magnet synchronous motor (PMSM). An observer, based on mathematical equations, depends on information regarding several parameters of the controlled motor. If the motor is replaced, then we need to know [...] Read more.
An observer is a crucial part of the sensorless control of a permanent magnet synchronous motor (PMSM). An observer, based on mathematical equations, depends on information regarding several parameters of the controlled motor. If the motor is replaced, then we need to know the motor parameter values and reset the observer’s parameters. This article discusses an intelligent observer that can be used for several motors with different parameters. The proposed intelligent observer was developed using machine learning methods. This observer’s core algorithm is a modified Jordan neural network. It processes Iα, Iβ, vα, and vβ to produce Sin θ and Cos θ values. It is combined with a phase-locked loop function to generate position and speed feedback information. The offline learning process is carried out using data acquired from the simulations of PMSM motors. This study used five PMSMs with different parameters, three as the learning reference sources and two as testing sources. The proposed intelligent observer was successfully used to control motors with different parameters in both simulation and experimental hardware. The average error in position estimated for the simulation was 0.0078 p.u and the error was 0.0100 p.u for the experimental realization. Full article
(This article belongs to the Special Issue Advanced Mathematical Approaches to Engineering and Computational Problems)
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15 pages, 526 KiB  
Article
An Innovative Approach to Nonlinear Fractional Shock Wave Equations Using Two Numerical Methods
by Meshari Alesemi
Mathematics 2023, 11(5), 1253; https://doi.org/10.3390/math11051253 - 05 Mar 2023
Viewed by 871
Abstract
In this research, we propose a combined approach to solving nonlinear fractional shock wave equations using an Elzaki transform, the homotopy perturbation method, and the Adomian decomposition method. The nonlinear fractional shock wave equation is first transformed into an equivalent integral equation using [...] Read more.
In this research, we propose a combined approach to solving nonlinear fractional shock wave equations using an Elzaki transform, the homotopy perturbation method, and the Adomian decomposition method. The nonlinear fractional shock wave equation is first transformed into an equivalent integral equation using the Elzaki transform. The homotopy perturbation method and Adomian decomposition method are then utilized to approximate the solution of the integral equation. To evaluate the effectiveness of the proposed method, we conduct several numerical experiments and compare the results with existing methods. The numerical results show that the combined method provides accurate and efficient solutions for nonlinear fractional shock wave equations. Overall, this research contributes to the development of a powerful tool for solving nonlinear fractional shock wave equations, which has potential applications in many fields of science and engineering. This study presents a solution approach for nonlinear fractional shock wave equations using a combination of an Elzaki transform, the homotopy perturbation method, and the Adomian decomposition method. The Elzaki transform is utilized to transform the nonlinear fractional shock wave equation into an equivalent integral equation. The homotopy perturbation method and Adomian decomposition method are then employed to approximate the solution of the integral equation. The effectiveness of the combined method is demonstrated through several numerical examples and compared with other existing methods. The results show that the proposed method provides accurate and efficient solutions for nonlinear fractional shock wave equations. Full article
(This article belongs to the Special Issue Numerical Methods for Fractional Differential Equations and Applications)
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18 pages, 5775 KiB  
Article
An Analysis of Actors in Malay Films: Small Worlds, Centralities and Genre Diversity
by Nurun Najwa Bahari, Paul Expert and Fatimah Abdul Razak
Mathematics 2023, 11(5), 1252; https://doi.org/10.3390/math11051252 - 04 Mar 2023
Cited by 1 | Viewed by 3284
Abstract
This article utilizes social network analysis in addition to a measure of genre diversity to quantify the quality and capacity of actors in the Malay language film industry. We built a dataset by collecting data from various websites pertaining to Malay films. The [...] Read more.
This article utilizes social network analysis in addition to a measure of genre diversity to quantify the quality and capacity of actors in the Malay language film industry. We built a dataset by collecting data from various websites pertaining to Malay films. The data consists of 180 Malay films released from 2015 until 2020. The actor network is then built by connecting actors co-starring in a movie together and is compared to small world networks. We quantified the quality of actors in the network using five measures: number of films (TFA), degree centrality (DC), strength centrality (SC), betweenness centrality (BC), and normalized Herfindahl–Hirschman Index (NHHI). TFA, DC and SC indicate experience in the industry, since a high TFA shows that an actor has acted in more films. A high DC shows an actor has worked with many co-stars, and a high SC reflects an actor’s frequency of co-occurrence relationship. Actors with high TFA, DC, and SC are popular in this sense. Meanwhile, BC highlights the social importance of an actor in the network where they are the middlemen that connect actors from different genres of movies in the network, and we found that high BC actors are voice actors that may not have a high TFA, DC, or SC. NHHI highlights the actor’s capability to work with different types of film, and it serves as an important measure of an actor’s versatility. Moreover, we also calculated the average shortest path in the network to search for the “Kevin Bacon” of the Malay language film actor network. Using NHHI as an indicator of genre diversity, we also show that most of the actors diversify their work over the years and that genre diversity is an important benchmark for an actor. Full article
(This article belongs to the Topic Complex Systems and Network Science)
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20 pages, 1343 KiB  
Article
A Dendritic Neuron Model Optimized by Meta-Heuristics with a Power-Law-Distributed Population Interaction Network for Financial Time-Series Forecasting
by Yuxin Zhang, Yifei Yang, Xiaosi Li, Zijing Yuan, Yuki Todo and Haichuan Yang
Mathematics 2023, 11(5), 1251; https://doi.org/10.3390/math11051251 - 04 Mar 2023
Cited by 4 | Viewed by 1674
Abstract
The famous McCulloch–Pitts neuron model has been criticized for being overly simplistic in the long term. At the same time, the dendritic neuron model (DNM) has been shown to be effective in prediction problems, and it accounts for the nonlinear information-processing capacity of [...] Read more.
The famous McCulloch–Pitts neuron model has been criticized for being overly simplistic in the long term. At the same time, the dendritic neuron model (DNM) has been shown to be effective in prediction problems, and it accounts for the nonlinear information-processing capacity of synapses and dendrites. Furthermore, since the classical error back-propagation (BP) algorithm typically experiences problems caused by the overabundance of saddle points and local minima traps, an efficient learning approach for DNMs remains desirable but difficult to implement. In addition to BP, the mainstream DNM-optimization methods include meta-heuristic algorithms (MHAs). However, over the decades, MHAs have developed a large number of different algorithms. How to screen suitable MHAs for optimizing DNMs has become a hot and challenging area of research. In this study, we classify MHAs into different clusters with different population interaction networks (PINs). The performance of DNMs optimized by different clusters of MHAs is tested in the financial time-series-forecasting task. According to the experimental results, the DNM optimized by MHAs with power-law-distributed PINs outperforms the DNM trained based on the BP algorithm. Full article
(This article belongs to the Special Issue Dynamics in Neural Networks)
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13 pages, 769 KiB  
Article
Solving Optimal Power Flow Problem via Improved Constrained Adaptive Differential Evolution
by Wenchao Yi, Zhilei Lin, Youbin Lin, Shusheng Xiong, Zitao Yu and Yong Chen
Mathematics 2023, 11(5), 1250; https://doi.org/10.3390/math11051250 - 04 Mar 2023
Cited by 2 | Viewed by 1692
Abstract
The optimal power flow problem is one of the most widely used problems in power system optimizations, which are multi-modal, non-linear, and constrained optimization problems. Effective constrained optimization methods can be considered in tackling the optimal power flow problems. In this paper, an [...] Read more.
The optimal power flow problem is one of the most widely used problems in power system optimizations, which are multi-modal, non-linear, and constrained optimization problems. Effective constrained optimization methods can be considered in tackling the optimal power flow problems. In this paper, an ϵ-constrained method-based adaptive differential evolution is proposed to solve the optimal power flow problems. The ϵ-constrained method is improved to tackle the constraints, and a p-best selection method based on the constraint violation is implemented in the adaptive differential evolution. The single and multi-objective optimal power flow problems on the IEEE 30-bus test system are used to verify the effectiveness of the proposed and improved εadaptive differential evolution algorithm. The comparison between state-of-the-art algorithms illustrate the effectiveness of the proposed and improved εadaptive differential evolution algorithm. The proposed algorithm demonstrates improvements in nine out of ten cases. Full article
(This article belongs to the Special Issue Optimization in Scheduling and Control Problems)
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17 pages, 321 KiB  
Article
Stress–Strength Inference on the Multicomponent Model Based on Generalized Exponential Distributions under Type-I Hybrid Censoring
by Tzong-Ru Tsai, Yuhlong Lio, Jyun-You Chiang and Ya-Wen Chang
Mathematics 2023, 11(5), 1249; https://doi.org/10.3390/math11051249 - 04 Mar 2023
Cited by 2 | Viewed by 1137
Abstract
The stress–strength analysis is investigated for a multicomponent system, where all strength variables of components follow a generalized exponential distribution and are subject to the generalized exponential distributed stress. The estimation methods of the maximum likelihood and Bayesian are utilized to infer the [...] Read more.
The stress–strength analysis is investigated for a multicomponent system, where all strength variables of components follow a generalized exponential distribution and are subject to the generalized exponential distributed stress. The estimation methods of the maximum likelihood and Bayesian are utilized to infer the system reliability. For the Bayesian estimation method, informative and non-informative priors combined with three loss functions are considered. Because the computational difficulty on working posteriors, the Markov chain Monte Carlo method is adopted to obtain the approximation of the reliability estimator posterior. In addition, the bootstrap method and highest probability density interval are used to obtain the reliability confidence intervals. The simulation study shows that the Bayes estimator with informative prior is superior to other competitors. Finally, two real examples are given to illustrate the proposed estimation methods. Full article
(This article belongs to the Special Issue Recent Advances of Computational Statistics in Industry and Business III)
0 pages, 15947 KiB  
Article
Optimized Sizing of Energy Management System for Off-Grid Hybrid Solar/Wind/Battery/Biogasifier/Diesel Microgrid System
by Ali M. Jasim, Basil H. Jasim, Florin-Constantin Baiceanu and Bogdan-Constantin Neagu
Mathematics 2023, 11(5), 1248; https://doi.org/10.3390/math11051248 - 04 Mar 2023
Cited by 13 | Viewed by 2510 | Correction
Abstract
Recent advances in electric grid technology have led to sustainable, modern, decentralized, bidirectional microgrids (MGs). The MGs can support energy storage, renewable energy sources (RESs), power electronics converters, and energy management systems. The MG system is less costly and creates less CO2 [...] Read more.
Recent advances in electric grid technology have led to sustainable, modern, decentralized, bidirectional microgrids (MGs). The MGs can support energy storage, renewable energy sources (RESs), power electronics converters, and energy management systems. The MG system is less costly and creates less CO2 than traditional power systems, which have significant operational and fuel expenses. In this paper, the proposed hybrid MG adopts renewable energies, including solar photovoltaic (PV), wind turbines (WT), biomass gasifiers (biogasifier), batteries’ storage energies, and a backup diesel generator. The energy management system of the adopted MG resources is intended to satisfy the load demand of Basra, a city in southern Iraq, considering the city’s real climate and demand data. For optimal sizing of the proposed MG components, a meta-heuristic optimization algorithm (Hybrid Grey Wolf with Cuckoo Search Optimization (GWCSO)) is applied. The simulation results are compared with those achieved using Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), and Antlion Optimization (ALO) to evaluate the optimal sizing results with minimum costs. Since the adopted GWCSO has the lowest deviation, it is more robust than the other algorithms, and their optimal number of component units, annual cost, and Levelized Cost Of Energy (LCOE) are superior to the other ones. According to the optimal annual analysis, LCOE is 0.1192 and the overall system will cost about USD 2.6918 billion. Full article
(This article belongs to the Special Issue Artificial Intelligence Techniques Applications on Power Systems)
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18 pages, 3458 KiB  
Article
Multi-Agent Deep Q-Network Based Dynamic Controller Placement for Node Variable Software-Defined Mobile Edge-Cloud Computing Networks
by Chenglin Xu, Cheng Xu and Bo Li
Mathematics 2023, 11(5), 1247; https://doi.org/10.3390/math11051247 - 04 Mar 2023
Cited by 1 | Viewed by 1077
Abstract
Software-defined networks (SDN) can use the control plane to manage heterogeneous devices efficiently, improve network resource utilization, and optimize Mobile Edge-Cloud Computing Networks (MECCN) network performance through decisions based on global information. However, network traffic in MECCNs can change over time and affect [...] Read more.
Software-defined networks (SDN) can use the control plane to manage heterogeneous devices efficiently, improve network resource utilization, and optimize Mobile Edge-Cloud Computing Networks (MECCN) network performance through decisions based on global information. However, network traffic in MECCNs can change over time and affect the performance of the SDN control plane. Moreover, the MECCN network may need to temporarily add network access points when the network load is excessive, and it is difficult for the control plane to form effective management of temporary nodes. This paper investigates the dynamic controller placement problem (CPP) in SDN-enabled Mobile Edge-Cloud Computing Networks (SD-MECCN) to enable the control plane to continuously and efficiently serve the network under changing network load and network access points. We consider the deployment of a two-layer structure with a control plane and construct the CPP based on this control plane. Subsequently, we solve this problem based on multi-agent DQN (MADQN), in which multiple agents cooperate to solve CPP and adjust the number of controllers according to the network load. The experimental results show that the proposed dynamic controller deployment algorithm based on MADQN for node-variable networks in this paper can achieve better performance in terms of delay, load difference, and control reliability than the Louvain-based algorithm, single-agent DQN-based algorithm, and MADQN- (without node-variable networks consideration) based algorithm. Full article
(This article belongs to the Topic Complex Systems and Network Science)
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