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Mathematics, Volume 10, Issue 23 (December-1 2022) – 235 articles

Cover Story (view full-size image): The most recent literature contains two theories: one on the construction of probability measures from fractal structures and another one on cumulative distribution functions on separable linearly ordered topological spaces. Indeed, it is possible to construct a probability measure from a pre-measure defined on a fractal structure on a space, but in order to define a cumulative distribution function, a compatible order is needed. In this paper, it is shown how to define such order and the relationship between the pre-measure and the cumulative distribution function with respect to that order. Hence, we can use both theories interchangeably in both topological contexts: when working with a fractal structure and the topological structures induced by it, and when considering a separable linearly ordered topological space. View this paper
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11 pages, 410 KiB  
Article
Implementation of the Hindmarsh–Rose Model Using Stochastic Computing
by Oscar Camps, Stavros G. Stavrinides, Carol de Benito and Rodrigo Picos
Mathematics 2022, 10(23), 4628; https://doi.org/10.3390/math10234628 - 06 Dec 2022
Cited by 1 | Viewed by 1392
Abstract
The Hindmarsh–Rose model is one of the most used models to reproduce spiking behaviour in biological neurons. However, since it is defined as a system of three coupled differential equations, its implementation can be burdensome and impractical for a large number of elements. [...] Read more.
The Hindmarsh–Rose model is one of the most used models to reproduce spiking behaviour in biological neurons. However, since it is defined as a system of three coupled differential equations, its implementation can be burdensome and impractical for a large number of elements. In this paper, we present a successful implementation of this model within a stochastic computing environment. The merits of the proposed approach are design simplicity, due to stochastic computing, and the ease of implementation. Simulation results demonstrated that the approximation achieved is equivalent to introducing a noise source into the original model, in order to reproduce the actual observed behaviour of the biological systems. A study for the level of noise introduced, according to the number of bits in the stochastic sequence, has been performed. Additionally, we demonstrate that such an approach, even though it is noisy, reproduces the behaviour of biological systems, which are intrinsically noisy. It is also demonstrated that using some 18–19 bits are enough to provide a speedup of x2 compared to biological systems, with a very small number of gates, thus paving the road for the in silico implementation of large neuron networks. Full article
(This article belongs to the Special Issue Neural Networks and Learning Systems II)
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17 pages, 2924 KiB  
Article
An Optimized Double-Nested Anti-Missile Force Deployment Based on the Deep Kuhn–Munkres Algorithm
by Wen Sun, Zeyang Cao, Gang Wang, Yafei Song and Xiangke Guo
Mathematics 2022, 10(23), 4627; https://doi.org/10.3390/math10234627 - 06 Dec 2022
Viewed by 2215
Abstract
In view of a complex multi-factor interaction relationship and high uncertainty of a battlefield environment in the anti-missile troop deployment, this paper analyzes the relationships between the defending stronghold, weapon system, incoming target, and ballistic missile. In addition, a double nested optimization architecture [...] Read more.
In view of a complex multi-factor interaction relationship and high uncertainty of a battlefield environment in the anti-missile troop deployment, this paper analyzes the relationships between the defending stronghold, weapon system, incoming target, and ballistic missile. In addition, a double nested optimization architecture is designed by combining deep learning hierarchy concept and hierarchical dimensionality reduction processing. Moreover, a deployment model based on the double nested optimization architecture is constructed with the interception arc length as an optimization goal and based on the basic deployment model, kill zone model, and cover zone model. Further, by combining the target full coverage adjustment criterion and depth-first search, a deep Kuhn–Munkres algorithm is proposed. The model is validated by simulations of typical scenes. The results verify the rationality and feasibility of the proposed model, high adaptability of the proposed algorithm. The research of this paper has important enlightenment and reference function for solving the force deployment optimization problems in uncertain battlefield environment. Full article
(This article belongs to the Special Issue Mathematical Applications of Complex Evidence Theory in Engineering)
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9 pages, 529 KiB  
Article
On Grill Sβ-Open Set in Grill Topological Spaces
by Nagarajan Kalaivani, Khaleel Fayaz Ur Rahman, Lenka Čepová and Robert Čep
Mathematics 2022, 10(23), 4626; https://doi.org/10.3390/math10234626 - 06 Dec 2022
Cited by 1 | Viewed by 1165
Abstract
In this article we originate a new class of Grill Set, namely GSβ-Open Set, which is parallel to the β Open Set in Grill Topological Space (X, θ, G). In addition, we entitle [...] Read more.
In this article we originate a new class of Grill Set, namely GSβ-Open Set, which is parallel to the β Open Set in Grill Topological Space (X, θ, G). In addition, we entitle GSβ-continuous and GSβ-open functions by applying a GSβ-Open Set and we review some of its important properties. Many examples are given to explain the concept lucidly. The properties of GSβ open sets are investigated and studied. The theorems based on the arbitrary union and finite intersections are discussed with counter examples. Moreover, some operators like GSβclosure and GSβinterior are introduced and investigated. The concept of GSβcontinuous functions are compared with the idea of GSemi Continuous function. The theorems based on GSβcontinunity have been proved. Full article
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22 pages, 3346 KiB  
Article
An Innovative Hunter-Prey-Based Optimization for Electrically Based Single-, Double-, and Triple-Diode Models of Solar Photovoltaic Systems
by Mostafa Elshahed, Ali M. El-Rifaie, Mohamed A. Tolba, Ahmed Ginidi, Abdullah Shaheen and Shazly A. Mohamed
Mathematics 2022, 10(23), 4625; https://doi.org/10.3390/math10234625 - 06 Dec 2022
Cited by 27 | Viewed by 1432
Abstract
The derivation of PV model parameters is crucial for the optimization, control, and simulation of PV systems. Although many parameter extraction algorithms have been developed to address this issue, they might have some limitations. This work presents an efficient hybrid optimization approach for [...] Read more.
The derivation of PV model parameters is crucial for the optimization, control, and simulation of PV systems. Although many parameter extraction algorithms have been developed to address this issue, they might have some limitations. This work presents an efficient hybrid optimization approach for reliably and effectively extracting PV parameters based on the hunter–prey optimizer (HPO) technique. The proposed HPO technique is a new population-based optimizer inspired by the behavior of prey and predator animals. In the proposed HPO mechanism, the predator attacks the prey that leaves the prey population. Accordingly, the position of a hunter is adjusted toward this distant prey, while the position of the prey is adjusted towards a secure place. The search agent’s position, which represents the best fitness function value, is considered a secure place. The proposed HPO technique worked as suggested when parameters are extracted from several PV models, including single-, double-, and triple-diode models. Moreover, a statistical error analysis was used to demonstrate the superiority of the proposed method. The proposed HPO technique outperformed other recently reported techniques in terms of convergence speed, dependability, and accuracy, according to simulation data. Full article
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33 pages, 3466 KiB  
Article
Matrix Approach for Analyzing n-Site Generalized ASIP Systems: PGF and Site Occupancy Probabilities
by Uri Yechiali and Yaron Yeger
Mathematics 2022, 10(23), 4624; https://doi.org/10.3390/math10234624 - 06 Dec 2022
Cited by 1 | Viewed by 907
Abstract
The Asymmetric Simple Inclusion Process (ASIP) is an n-site tandem stochastic network with a Poisson arrival influx into the first site. Each site has an unlimited buffer with a gate in front of it. Each gate opens, independently of all other gates, [...] Read more.
The Asymmetric Simple Inclusion Process (ASIP) is an n-site tandem stochastic network with a Poisson arrival influx into the first site. Each site has an unlimited buffer with a gate in front of it. Each gate opens, independently of all other gates, following a site-dependent Exponential inter-opening time. When a site’s gate opens, all particles occupying the site move simultaneously to the next site. In this paper, a Generalized ASIP network is analyzed where the influx is to all sites, while gate openings are determined by a general renewal process. A compact matrix approach—instead of the conventional (and tedious) successive substitution method—is constructed for the derivation of the multidimensional probability-generating function (PGF) of the site occupancies. It is shown that the set of (2nn) linear equations required to obtain the PGF of an n-site network can be first cut by half into a set of 2n1n equations, and then further reduced to a set of 2nn+1 equations. The latter set can be additionally split into several smaller triangular subsets. It is also shown how the PGF of an n+1-site network can be derived from the corresponding PGF of an n-site system. Explicit results for networks with n=3 and n=4 sites are obtained. The matrix approach is utilized to explicitly calculate the probability that site k k=1,2,,n is occupied. We show that, in the case where arrivals occur to the first site only, these probabilities are functions of both the site’s index and the arrival flux and not solely of the site’s index. Consequently, refined formulas for the latter probabilities and for the mean conditional site occupancies are derived. We further show that in the case where the arrival process to the first site is Poisson with rate λ, the following interesting property holds: Psite k is occupied | λ=1=Psite k+1 is occupied | λ. The case where the inter-gate opening intervals are Gamma distributed is investigated and explicit formulas are obtained. Mean site occupancy and mean total load of the first k sites are calculated. Numerical results are presented. Full article
(This article belongs to the Special Issue Queue and Stochastic Models for Operations Research II)
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18 pages, 5118 KiB  
Article
Performance of a Vector-Controlled PMSM Drive without Using Current Sensors
by Sai Shiva Badini, Vimlesh Verma, Mohd Tariq, Shabana Urooj and Lucian Mihet-Popa
Mathematics 2022, 10(23), 4623; https://doi.org/10.3390/math10234623 - 06 Dec 2022
Viewed by 1692
Abstract
The current sensorless vector-controlled permanent-magnet synchronous motor (PMSM) drive using a single sensor (i.e., speed sensor) is presented in this work. The current sensors are removed, and the estimated currents are used to close the current loop to minimize the drive cost and [...] Read more.
The current sensorless vector-controlled permanent-magnet synchronous motor (PMSM) drive using a single sensor (i.e., speed sensor) is presented in this work. The current sensors are removed, and the estimated currents are used to close the current loop to minimize the drive cost and make it fault-tolerant against current sensor failure. A classical vector-control PMSM drive requires at least three sensors, i.e., two current sensors and one speed/position sensor. This paper presents a new current estimation technique that is free from inverter switching states, an integrator, and differentiator terms. The drive is suitable for retrofit applications, as it does not require any additional hardware. The reference voltages (vds and vqs) are used to estimate the rotor reference frame currents (i.e., iqs and ids). The presented algorithm depends on the stator resistance (Ɍs). The online Ɍs estimation algorithm is used for compensation to overcome the effect of the Ɍs on the estimated currents. The sensitivity analysis for the currents against the speed is verified and presented. The speed loop is closed with actual speed information, which will try to maintain the reference speed under any circumstances. The proposed current sensorless PMSM drive was validated using MATLAB/Simulink and also verified on a hardware prototype. The presented technique was verified for various operation conditions, and some of the extensive results are presented. Full article
(This article belongs to the Special Issue Control, Modeling and Optimization for Multiphase Machines and Drives)
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14 pages, 1714 KiB  
Article
Event-Triggered Optimal Consensus of Heterogeneous Nonlinear Multi-Agent Systems
by Yunfeng Ji, Gang Wang, Qingdu Li and Chaoli Wang
Mathematics 2022, 10(23), 4622; https://doi.org/10.3390/math10234622 - 06 Dec 2022
Cited by 3 | Viewed by 884
Abstract
This paper deals with optimal consensus problems of a general heterogeneous nonlinear multi-agent system. A novel filter is proposed for each agent by integrating local gradients with neighboring output information. Using this filter and introducing an appropriate auxiliary variable, the event-triggered control algorithm [...] Read more.
This paper deals with optimal consensus problems of a general heterogeneous nonlinear multi-agent system. A novel filter is proposed for each agent by integrating local gradients with neighboring output information. Using this filter and introducing an appropriate auxiliary variable, the event-triggered control algorithm is obtained within the framework of the prescribed performance control. One of the remarkable properties of the proposed algorithm is that it can save resources by updating control signals only when necessary rather than periodically while achieving optimal consensus. Theoretical and simulation verifications of the algorithm without the Zeno behavior are carefully studied. Instructions are also presented for control parameter selection to keep the residual errors as small as desired. Full article
(This article belongs to the Section Dynamical Systems)
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23 pages, 374 KiB  
Article
Optimal Per-Loss Reinsurance for a Risk Model with a Thinning-Dependence Structure
by Fudong Wang and Zhibin Liang
Mathematics 2022, 10(23), 4621; https://doi.org/10.3390/math10234621 - 06 Dec 2022
Cited by 1 | Viewed by 1056
Abstract
In this paper, we consider the optimal reinsurance problem for a risk model with a thinning-dependence structure, where the stochastic sources related to claim occurrence are classified into different groups, and each group may cause a claim in each insurance class with some [...] Read more.
In this paper, we consider the optimal reinsurance problem for a risk model with a thinning-dependence structure, where the stochastic sources related to claim occurrence are classified into different groups, and each group may cause a claim in each insurance class with some probability. We assume that the insurer can manage the risk by purchasing per-loss reinsurance, and their aim is to maximize the expected utility of the terminal wealth. By using the technique of stochastic control, we obtain the corresponding Hamilton–Jaccobi–Bellman equation. From the perspective of game theory, we derive the closed-form expression of the optimal strategy for each class of business, which is actually the best response to other given strategies. We also investigate the necessary conditions for optimal strategies and transfer the original optimization problem into a system of equations. Furthermore, we prove that the solution of the system of equations always exists, but may not be unique, and we also study some features of the optimal strategies in special cases and derive several interesting results. Finally, some numerical examples are given to show the impacts of some important parameters on the optimal strategies. Full article
(This article belongs to the Section Computational and Applied Mathematics)
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11 pages, 1667 KiB  
Article
Equiareal Parameterization of Triangular Bézier Surfaces
by Jun Chen, Xiang Kong and Huixia Xu
Mathematics 2022, 10(23), 4620; https://doi.org/10.3390/math10234620 - 06 Dec 2022
Viewed by 1024
Abstract
Parameterization is the key property of a parametric surface and significantly affects many kinds of applications. To improve the quality of parameterization, equiareal parameterization minimizes the equiareal energy, which is presented as a measure to describe the uniformity of iso-parametric curves. With the [...] Read more.
Parameterization is the key property of a parametric surface and significantly affects many kinds of applications. To improve the quality of parameterization, equiareal parameterization minimizes the equiareal energy, which is presented as a measure to describe the uniformity of iso-parametric curves. With the help of the binary Möbius transformation, the equiareal parameterization is extended to the triangular Bézier surface on the triangular domain for the first time. The solution of the corresponding nonlinear minimization problem can be equivalently converted into solving a system of bivariate polynomial equations with an order of three. All the exact solutions of the equations can be obtained, and one of them is chosen as the global optimal solution of the minimization problem. Particularly, the coefficients in the system of equations can be explicitly formulated from the control points. Equiareal parameterization keeps the degree, control points, and shape of the triangular Bézier surface unchanged. It improves the distribution of iso-parametric curves only. The iso-parametric curves from the new expression are more uniform than the original one, which is displayed by numerical examples. Full article
(This article belongs to the Special Issue Computer-Aided Geometric Design)
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18 pages, 426 KiB  
Article
Statistical Descriptions of Inhomogeneous Anisotropic Turbulence
by J. J. H. Brouwers
Mathematics 2022, 10(23), 4619; https://doi.org/10.3390/math10234619 - 06 Dec 2022
Viewed by 892
Abstract
Descriptions are given of the Langevin and diffusion equation of passively marked fluid particles in turbulent flow with spatially varying and anisotropic statistical properties. The descriptions consist of the first two terms of an expansion in powers of C01, [...] Read more.
Descriptions are given of the Langevin and diffusion equation of passively marked fluid particles in turbulent flow with spatially varying and anisotropic statistical properties. The descriptions consist of the first two terms of an expansion in powers of C01, where C0 is an autonomous Lagrangian-based Kolmogorov constant: C07. Solutions involve the application of methods of stochastic analysis while complying with the basic laws of physics. The Lagrangian-based descriptions are converted into Eulerian-based fixed-point expressions through asymptotic matching. This leads to novel descriptions for the mean values of the fluctuating convective terms of the conservation laws of continua. They can be directly implemented in CFD codes for calculating fluid flows in engineering and environmental analysis. The solutions are verified in detail through comparison with direct numerical simulations of turbulent channel flows at large Reynolds numbers. Full article
(This article belongs to the Special Issue Analysis and Applications of Mathematical Fluid Dynamics)
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14 pages, 1333 KiB  
Article
Project-Based STEM Learning Using Educational Robotics as the Development of Student Problem-Solving Competence
by Petr Coufal
Mathematics 2022, 10(23), 4618; https://doi.org/10.3390/math10234618 - 06 Dec 2022
Cited by 7 | Viewed by 3429
Abstract
The study focuses on teaching students using educational robots in the field of STEM. The study focused on the influence of project-based teaching on the development of student competences, especially problem-solving competences. The research part of the study describes the conducted pedagogical experiment—teaching [...] Read more.
The study focuses on teaching students using educational robots in the field of STEM. The study focused on the influence of project-based teaching on the development of student competences, especially problem-solving competences. The research part of the study describes the conducted pedagogical experiment—teaching pupils the programming of educational robots. The experiment compared two groups of students in the 8th grade of elementary school, using the “Skills for Life” test, which is used to test student competencies. Project-based teaching in STEM fields using educational robotics is very popular among students and, according to research results, has an impact on the development of student competencies. The results of the presented study clearly demonstrate the positive influence of project-based teaching using educational robots on the development of student competencies, especially the important key competencies for solving problems. The key competence to solve problems is applicable both in the areas of STEM education, but also in the everyday life of the student. Full article
(This article belongs to the Special Issue Selected Papers from the Innovative STEM Education)
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32 pages, 5065 KiB  
Article
Photovoltaic Models’ Parameter Extraction Using New Artificial Parameterless Optimization Algorithm
by Mohana Alanazi, Abdulaziz Alanazi, Ahmad Almadhor and Hafiz Tayyab Rauf
Mathematics 2022, 10(23), 4617; https://doi.org/10.3390/math10234617 - 06 Dec 2022
Cited by 6 | Viewed by 1352
Abstract
Identifying parameters in photovoltaic (PV) cell and module models is one of the primary challenges of the simulation and design of photovoltaic systems. Metaheuristic algorithms can find near-optimal solutions within a reasonable time for such challenging real-world optimization problems. Control parameters must be [...] Read more.
Identifying parameters in photovoltaic (PV) cell and module models is one of the primary challenges of the simulation and design of photovoltaic systems. Metaheuristic algorithms can find near-optimal solutions within a reasonable time for such challenging real-world optimization problems. Control parameters must be adjusted with many existing algorithms, making them difficult to use. In real-world problems, many of these algorithms must be combined or hybridized, which results in more complex and time-consuming algorithms. This paper presents a new artificial parameter-less optimization algorithm (APLO) for parameter estimation of PV models. New mutation operators are designed in the proposed algorithm. APLO’s exploitation phase is enhanced by each individual searching for the best solution in this updating operator. Moreover, the current best, the old best, and the individual’s current position are utilized in the differential term of the mutation operator to assist the exploration phase and control the convergence speed. The algorithm uses a random step length based on a normal distribution to ensure population diversity. We present the results of a comparative study using APLO and well-known existing parameter-less meta-heuristic algorithms such as grey wolf optimization, the salp swarm algorithm, JAYA, teaching-learning based optimization, colliding body optimization, as well as three major parameter-based algorithms such as differential evolution, genetic algorithm, and particle swarm optimization to estimate the parameters of PV the modules. The results revealed that the proposed algorithm could provide excellent exploration–exploitation balance and consistency during the iterations. Furthermore, the APLO algorithm shows high reliability and accuracy in identifying the parameters of PV cell models. Full article
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18 pages, 1368 KiB  
Article
MACA: Multi-Agent with Credit Assignment for Computation Offloading in Smart Parks Monitoring
by Liang She, Jianyuan Wang, Yifan Bo and Yangyan Zeng
Mathematics 2022, 10(23), 4616; https://doi.org/10.3390/math10234616 - 06 Dec 2022
Viewed by 1097
Abstract
Video monitoring has a wide range of applications in a variety of scenarios, especially in smart parks. How to improve the efficiency of video data processing and reduce resource consumption have become of increasing concern. The high complexity of traditional computation offloading algorithms [...] Read more.
Video monitoring has a wide range of applications in a variety of scenarios, especially in smart parks. How to improve the efficiency of video data processing and reduce resource consumption have become of increasing concern. The high complexity of traditional computation offloading algorithms makes it difficult to apply them to real-time decision-making scenarios. Thus, we propose a multi-agent deep reinforcement learning algorithm with credit assignment (MACA) for computation offloading in smart park monitoring. By making online decisions after offline training, the agent can give consideration to both decision time and accuracy in effectively solving the problem of the curse of dimensionality. Via simulation, we compare the performance of MACA with traditional deep Q-network reinforcement learning algorithm and other methods. Our results show that MACA performs better in scenarios where there are a higher number of agents and can minimize request delay and reduce task energy consumption. In addition, we also provide results from a generalization capability verified experiment and ablation study, which demonstrate the contribution of MACA algorithm to each component. Full article
(This article belongs to the Special Issue Computational Methods and Application in Machine Learning)
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16 pages, 3032 KiB  
Article
Reliability Analysis of the Multi-State k-out-of-n: F Systems with Multiple Operation Mechanisms
by Yanbo Song and Xiaoyue Wang
Mathematics 2022, 10(23), 4615; https://doi.org/10.3390/math10234615 - 05 Dec 2022
Cited by 10 | Viewed by 1263
Abstract
Modern engineering systems are designed and utilized to realize complicated functions, and their operation mechanisms are becoming more complex. Nevertheless, prior related research mainly focused on the reliability evaluations of the systems with a single operation mechanism, which are not appropriate to depict [...] Read more.
Modern engineering systems are designed and utilized to realize complicated functions, and their operation mechanisms are becoming more complex. Nevertheless, prior related research mainly focused on the reliability evaluations of the systems with a single operation mechanism, which are not appropriate to depict the operation process of systems with multiple operation mechanisms. Faced with the research gaps and practical needs, this paper establishes a new reliability model for the multi-state k-out-of-n: F system composed of n subsystems, which runs under multiple interactive operation mechanisms, including performance sharing, balanced requirement, and protection strategy. The units in each subsystem can share the performance via a common bus, with the purpose of regulating the performance of all equal units. A new triggering criterion of the protection device in each subsystem is proposed based on the total performance of the units. Due to the protection from the device, the degradation rate of the units between two adjacent states decreases to a lower rate. Each subsystem breaks down when the total performance of the units reaches a critical value. According to the number of failed subsystems, the state of the entire system can be divided into multiple states. The Markov process imbedding method combined with the finite Markov chain imbedding approach is developed to obtain the probabilistic indexes of each subsystem and the entire system. The applicability of the proposed model and the effectiveness of the method can be sufficiently demonstrated by illustrative examples and sensitivity analyses. Full article
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16 pages, 298 KiB  
Article
Evolution for First Eigenvalue of LT,f on an Evolving Riemannian Manifold
by Apurba Saha, Shahroud Azami, Daniel Breaz, Eleonora Rapeanu and Shyamal Kumar Hui
Mathematics 2022, 10(23), 4614; https://doi.org/10.3390/math10234614 - 05 Dec 2022
Cited by 2 | Viewed by 889
Abstract
In this paper, evolution formulas for the first non-zero eigenvalue of the operator LT,f on a weighted closed Riemannian manifold along the Ricci flow as well as along the Yamabe flow are formulated. Some monotonic quantities are also derived for [...] Read more.
In this paper, evolution formulas for the first non-zero eigenvalue of the operator LT,f on a weighted closed Riemannian manifold along the Ricci flow as well as along the Yamabe flow are formulated. Some monotonic quantities are also derived for the normalized Ricci flow on Bianchi classes. Full article
22 pages, 3389 KiB  
Article
Important Arguments Nomination Based on Fuzzy Labeling for Recognizing Plagiarized Semantic Text
by Ahmed Hamza Osman and Hani Moaiteq Aljahdali
Mathematics 2022, 10(23), 4613; https://doi.org/10.3390/math10234613 - 05 Dec 2022
Cited by 3 | Viewed by 1363
Abstract
Plagiarism is an act of intellectual high treason that damages the whole scholarly endeavor. Many attempts have been undertaken in recent years to identify text document plagiarism. The effectiveness of researchers’ suggested strategies to identify plagiarized sections needs to be enhanced, particularly when [...] Read more.
Plagiarism is an act of intellectual high treason that damages the whole scholarly endeavor. Many attempts have been undertaken in recent years to identify text document plagiarism. The effectiveness of researchers’ suggested strategies to identify plagiarized sections needs to be enhanced, particularly when semantic analysis is involved. The Internet’s easy access to and copying of text content is one factor contributing to the growth of plagiarism. The present paper relates generally to text plagiarism detection. It relates more particularly to a method and system for semantic text plagiarism detection based on conceptual matching using semantic role labeling and a fuzzy inference system. We provide an important arguments nomination technique based on the fuzzy labeling method for identifying plagiarized semantic text. The suggested method matches text by assigning a value to each phrase within a sentence semantically. Semantic role labeling has several benefits for constructing semantic arguments for each phrase. The approach proposes nominating for each argument produced by the fuzzy logic to choose key arguments. It has been determined that not all textual arguments affect text plagiarism. The proposed fuzzy labeling method can only choose the most significant arguments, and the results were utilized to calculate similarity. According to the results, the suggested technique outperforms other current plagiarism detection algorithms in terms of recall, precision, and F-measure with the PAN-PC and CS11 human datasets. Full article
(This article belongs to the Special Issue Applied Computing and Artificial Intelligence)
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18 pages, 2799 KiB  
Article
A Data-Driven System Based on Deep Learning for Diagnosis Fetal Cavum Septum Pellucidum in Ultrasound Images
by Yuzhou Wu, Cheng Peng, Xuechen Chen, Xin Yao and Zhigang Chen
Mathematics 2022, 10(23), 4612; https://doi.org/10.3390/math10234612 - 05 Dec 2022
Viewed by 1705
Abstract
Cavum septum pellucidum (CSP) is one of the most important physiologic structures that should be detected in Ultrasound (US) scanning for the normal development of the fetal central nervous system. However, manual measurement of CSP is still a difficult and time-consuming task due [...] Read more.
Cavum septum pellucidum (CSP) is one of the most important physiologic structures that should be detected in Ultrasound (US) scanning for the normal development of the fetal central nervous system. However, manual measurement of CSP is still a difficult and time-consuming task due to the high noise of US images, even for experienced sonographers. Especially considering that maternal mortality remains high in many developing countries, a data-driven system with a medical diagnosis can help sonographers and obstetricians make decisions rapidly and improve their work efficiency. In this study, we propose a novel data-driven system based on deep learning for the diagnosis of CSP called CA-Unet, which consists of a channel attention network to segment the CSP and a post-processing module to measure and diagnose the anomalies of CSP. We collected the US data from three hospitals in China from 2012 to 2018 year to validate the effectiveness of our system. Experiments on a fetal US dataset demonstrated that our proposed system is able to help doctors make decisions and has achieved the highest precision of 79.5% and the largest Dice score of 77.5% in the segmentation of CSP. Full article
(This article belongs to the Special Issue Advances of Data-Driven Science in Artificial Intelligence)
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16 pages, 1776 KiB  
Article
Cryptanalysis of Two Recent Ultra-Lightweight Authentication Protocols
by Mohammad Reza Servati, Masoumeh Safkhani, Saqib Ali, Mazhar Hussain Malik, Omed Hassan Ahmed, Mehdi Hosseinzadeh and Amir H. Mosavi
Mathematics 2022, 10(23), 4611; https://doi.org/10.3390/math10234611 - 05 Dec 2022
Cited by 1 | Viewed by 1529
Abstract
Radio Frequency Identification (RFID) technology is a critical part of many Internet of Things (IoT) systems, including Medical IoT (MIoT) for instance. On the other hand, the IoT devices’ numerous limitations (such as memory space, computing capability, and battery capacity) make it difficult [...] Read more.
Radio Frequency Identification (RFID) technology is a critical part of many Internet of Things (IoT) systems, including Medical IoT (MIoT) for instance. On the other hand, the IoT devices’ numerous limitations (such as memory space, computing capability, and battery capacity) make it difficult to implement cost- and energy-efficient security solutions. As a result, several researchers attempted to address this problem, and several RFID-based security mechanisms for the MIoT and other constrained environments were proposed. In this vein, Wang et al. and Shariq et al. recently proposed CRUSAP and ESRAS ultra-lightweight authentication schemes. They demonstrated, both formally and informally, that their schemes meet the required security properties for RFID systems. In their proposed protocols, they have used a very lightweight operation called Cro(·) and Rank(·), respectively. However, in this paper, we show that those functions are not secure enough to provide the desired security. We show that Cro(·) is linear and reversible, and it is easy to obtain the secret values used in its calculation. Then, by exploiting the vulnerability of the Cro(·) function, we demonstrated that CRUSAP is vulnerable to secret disclosure attacks. The proposed attack has a success probability of "1" and is as simple as a CRUSAP protocol run. Other security attacks are obviously possible by obtaining the secret values of the tag and reader. In addition, we present a de-synchronization attack on the CRUSAP protocol. Furthermore, we provide a thorough examination of ESRAS and its Rank(·) function. We first present a de-synchronization attack that works for any desired Rank(·) function, including Shariq et al.’s proposed Rank(·) function. We also show that Rank(·) does not provide the desired confusion and diffusion that is claimed by the designers. Finally, we conduct a secret disclosure attack against ESRAS. Full article
(This article belongs to the Topic Safe and Secure Autonomous Systems)
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16 pages, 2718 KiB  
Article
TwoViewDensityNet: Two-View Mammographic Breast Density Classification Based on Deep Convolutional Neural Network
by Mariam Busaleh, Muhammad Hussain, Hatim A. Aboalsamh, Fazal-e-Amin and Sarah A. Al Sultan
Mathematics 2022, 10(23), 4610; https://doi.org/10.3390/math10234610 - 05 Dec 2022
Cited by 2 | Viewed by 2190
Abstract
Dense breast tissue is a significant factor that increases the risk of breast cancer. Current mammographic density classification approaches are unable to provide enough classification accuracy. However, it remains a difficult problem to classify breast density. This paper proposes TwoViewDensityNet, an end-to-end deep [...] Read more.
Dense breast tissue is a significant factor that increases the risk of breast cancer. Current mammographic density classification approaches are unable to provide enough classification accuracy. However, it remains a difficult problem to classify breast density. This paper proposes TwoViewDensityNet, an end-to-end deep learning-based method for mammographic breast density classification. The craniocaudal (CC) and mediolateral oblique (MLO) views of screening mammography provide two different views of each breast. As the two views are complementary, and dual-view-based methods have proven efficient, we use two views for breast classification. The loss function plays a key role in training a deep model; we employ the focal loss function because it focuses on learning hard cases. The method was thoroughly evaluated on two public datasets using 5-fold cross-validation, and it achieved an overall performance (F-score of 98.63%, AUC of 99.51%, accuracy of 95.83%) on DDSM and (F-score of 97.14%, AUC of 97.44%, accuracy of 96%) on the INbreast. The comparison shows that the TwoViewDensityNet outperforms the state-of-the-art methods for classifying breast density into BI-RADS class. It aids healthcare providers in providing patients with more accurate information and will help improve the diagnostic accuracy and reliability of mammographic breast density evaluation in clinical care. Full article
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12 pages, 593 KiB  
Article
Random Motions at Finite Velocity on Non-Euclidean Spaces
by Francesco Cybo Ottone and Enzo Orsingher
Mathematics 2022, 10(23), 4609; https://doi.org/10.3390/math10234609 - 05 Dec 2022
Viewed by 997
Abstract
In this paper, random motions at finite velocity on the Poincaré half-plane and on the unit-radius sphere are studied. The moving particle at each Poisson event chooses a uniformly distributed direction independent of the previous evolution. This implies that the current distance [...] Read more.
In this paper, random motions at finite velocity on the Poincaré half-plane and on the unit-radius sphere are studied. The moving particle at each Poisson event chooses a uniformly distributed direction independent of the previous evolution. This implies that the current distance d(P0,Pt) from the starting point P0 is obtained by applying the hyperbolic Carnot formula in the Poincaré half-plane and the spherical Carnot formula in the analysis of the motion on the sphere. We obtain explicit results of the conditional and unconditional mean distance in both cases. Some results for higher-order moments are also presented for a small number of changes of direction. Full article
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30 pages, 7408 KiB  
Article
Data-Mining-Based Real-Time Optimization of the Job Shop Scheduling Problem
by Anran Zhao, Peng Liu, Xiyu Gao, Guotai Huang, Xiuguang Yang, Yuan Ma, Zheyu Xie and Yunfeng Li
Mathematics 2022, 10(23), 4608; https://doi.org/10.3390/math10234608 - 05 Dec 2022
Cited by 3 | Viewed by 1600
Abstract
In the job-shop scheduling field, timely and proper updating of the original scheduling strategy is an effective way to avoid the negative impact of disturbances on manufacturing. In this paper, a pure reactive scheduling method for updating the scheduling strategy is proposed to [...] Read more.
In the job-shop scheduling field, timely and proper updating of the original scheduling strategy is an effective way to avoid the negative impact of disturbances on manufacturing. In this paper, a pure reactive scheduling method for updating the scheduling strategy is proposed to deal with the disturbance of the uncertainty of the arrival of new jobs in the job shop. The implementation process is as follows: combine data mining, discrete event simulation, and dispatching rules (DRs), take makespan and machine utilization as scheduling criteria, divide the manufacturing system production period into multiple scheduling subperiods, and build a dynamic scheduling model that assigns DRs to subscheduling periods in real-time; the scheduling strategies are generated at the beginning of each scheduling subperiod. The experiments showed that the method proposed enables a reduction in the makespan of 2–17% and an improvement in the machine utilization of 2–21%. The constructed scheduling model can assign the optimal DR to each scheduling subperiod in real-time, which realizes the purpose of locally updating the scheduling strategy and enhancing the overall scheduling effect of the manufacturing system. Full article
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28 pages, 5928 KiB  
Article
An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II
by Aidin Delgoshaei, Mohd Khairol Anuar Bin Mohd Ariffin and Zulkiflle B. Leman
Mathematics 2022, 10(23), 4607; https://doi.org/10.3390/math10234607 - 05 Dec 2022
Cited by 3 | Viewed by 1415
Abstract
Improving the performance of manufacturing systems is a vital issue in today’s rival market. For this purpose, during the last decade, scientists have considered more than one objective function while scheduling a production line. This paper develops a 4-phased fuzzy framework to identify [...] Read more.
Improving the performance of manufacturing systems is a vital issue in today’s rival market. For this purpose, during the last decade, scientists have considered more than one objective function while scheduling a production line. This paper develops a 4-phased fuzzy framework to identify effective factors, determine their weights on multi-objective functions, and, accordingly, schedule manufacturing systems in a fuzzy environment. The aim is to optimize product completion time and operational and product defect costs in a job-shop-based multi-objective fuzzy scheduling problem. In the first and second phases of the proposed framework, it was shown that the existing uncertainty of the internal factors for the studied cases causes the weights of factors to change up to 44.5%. Then, a fuzzy-weighted NSGA-II is proposed (FW-NSGA-II) to address the developed Non-linear Fuzzy Multi-objective Dual resource-constrained scheduling problem. Comparing the outcomes of the proposed method with other solving algorithms, such as the Sine Cosine Algorithm, Simulated Annealing, Tabu Search, and TLBO heuristic, using seven series of comprehensive computational experiments, indicates the superiority of the proposed framework in scheduling manufacturing systems. The outcomes indicated that using the proposed method for the studied cases saved up to 5% in the objective function for small-scale, 11.2% for medium-scale, and 3.8% for large-scale manufacturing systems. The outcomes of this study can help production planning managers to provide more realistic schedules by considering fuzzy factors in their manufacturing systems. Further investigating the proposed method for dynamic product conditions is another direction for future research. Full article
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21 pages, 8596 KiB  
Article
Slow–Fast Dynamics Behaviors under the Comprehensive Effect of Rest Spike Bistability and Timescale Difference in a Filippov Slow–Fast Modified Chua’s Circuit Model
by Shaolong Li, Weipeng Lv, Zhenyang Chen, Miao Xue and Qinsheng Bi
Mathematics 2022, 10(23), 4606; https://doi.org/10.3390/math10234606 - 05 Dec 2022
Cited by 1 | Viewed by 1208
Abstract
Since the famous slow–fast dynamical system referred to as the Hodgkin–Huxley model was proposed to describe the threshold behaviors of neuronal axons, the study of various slow–fast dynamical behaviors and their generation mechanisms has remained a popular topic in modern nonlinear science. The [...] Read more.
Since the famous slow–fast dynamical system referred to as the Hodgkin–Huxley model was proposed to describe the threshold behaviors of neuronal axons, the study of various slow–fast dynamical behaviors and their generation mechanisms has remained a popular topic in modern nonlinear science. The primary purpose of this paper is to introduce a novel transition route induced by the comprehensive effect of special rest spike bistability and timescale difference rather than a common bifurcation via a modified Chua’s circuit model with an external low-frequency excitation. In this paper, we attempt to explain the dynamical mechanism behind this novel transition route through quantitative calculations and qualitative analyses of the nonsmooth dynamics on the discontinuity boundary. Our work shows that the whole system responses may tend to be various and complicated when this transition route is triggered, exhibiting rich slow–fast dynamics behaviors even with a very slight change in excitation frequency, which is described well by using Poincaré maps in numerical simulations. Full article
(This article belongs to the Special Issue Modeling and Analysis in Dynamical Systems and Bistability)
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18 pages, 12585 KiB  
Article
Methodology of Plasma Shape Reachability Area Estimation in D-Shaped Tokamaks
by Yuri V. Mitrishkin, Valerii I. Kruzhkov and Pavel S. Korenev
Mathematics 2022, 10(23), 4605; https://doi.org/10.3390/math10234605 - 05 Dec 2022
Cited by 1 | Viewed by 1147
Abstract
This paper suggests and develops a new methodology of estimation for a multivariable reachability region of a plasma separatrix shape on the divertor phase of a plasma discharge in D-shaped tokamaks. The methodology is applied to a spherical Globus-M/M2 tokamak, including the estimation [...] Read more.
This paper suggests and develops a new methodology of estimation for a multivariable reachability region of a plasma separatrix shape on the divertor phase of a plasma discharge in D-shaped tokamaks. The methodology is applied to a spherical Globus-M/M2 tokamak, including the estimation of a controllability region of a vertical unstable plasma position on the basis of the experimental data. An assessment of the controllability region and the reachability region of the plasma is important for the design of tokamak poloidal field coils and the synthesis of a plasma magnetic control system. When designing a D-shaped tokamak, it is necessary to avoid the small controllability region of the vertically unstable plasma, because such cases occur in practice at a restricted voltage on a horizon field coil. To make the estimations mentioned above robust, PID-controllers for vertical and horizontal plasma position control were designed using the Quantitative Feedback Theory approach, which stabilizes the system and provides satisfactory control indexes (stability margins, setting time, overshoot) during plasma discharges. The controllers were tested on a series of plasma models and nonlinear models of current inverters in auto-oscillation mode as actuators for plasma position control. The estimations were made on these models, taking into account limitations on control actions, i.e., voltages on poloidal field coils. This research is the first step in the design of the plasma shape feedback control system for the operation of the Globus-M2 spherical tokamak. The developed methodology may be used in the design of poloidal field coil systems in tokamak projects in order to avoid weak achievability and controllability regions in magnetic plasma control. It was found that there is a strong cross-influence from the PF-coils currents and the CC current on the plasma shape; hence, these coils should be used to control the plasma shape simultaneously. Full article
(This article belongs to the Special Issue Dynamics and Control Theory with Applications)
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24 pages, 6989 KiB  
Article
LDA-CNN: Linear Discriminant Analysis Convolution Neural Network for Periocular Recognition in the Wild
by Amani Alahmadi, Muhammad Hussain and Hatim Aboalsamh
Mathematics 2022, 10(23), 4604; https://doi.org/10.3390/math10234604 - 05 Dec 2022
Cited by 1 | Viewed by 2568
Abstract
Due to the COVID-19 pandemic, the necessity for a contactless biometric system able to recognize masked faces drew attention to the periocular region as a valuable biometric trait. However, periocular recognition remains challenging for deployments in the wild or in unconstrained environments where [...] Read more.
Due to the COVID-19 pandemic, the necessity for a contactless biometric system able to recognize masked faces drew attention to the periocular region as a valuable biometric trait. However, periocular recognition remains challenging for deployments in the wild or in unconstrained environments where images are captured under non-ideal conditions with large variations in illumination, occlusion, pose, and resolution. These variations increase within-class variability and between-class similarity, which degrades the discriminative power of the features extracted from the periocular trait. Despite the remarkable success of convolutional neural network (CNN) training, CNN requires a huge volume of data, which is not available for periocular recognition. In addition, the focus is on reducing the loss between the actual class and the predicted class but not on learning the discriminative features. To address these problems, in this paper we used a pre-trained CNN model as a backbone and introduced an effective deep CNN periocular recognition model, called linear discriminant analysis CNN (LDA-CNN), where an LDA layer was incorporated after the last convolution layer of the backbone model. The LDA layer enforced the model to learn features so that the within-class variation was small, and the between-class separation was large. Finally, a new fully connected (FC) layer with softmax activation was added after the LDA layer, and it was fine-tuned in an end-to-end manner. Our proposed model was extensively evaluated using the following four benchmark unconstrained periocular datasets: UFPR, UBIRIS.v2, VISOB, and UBIPr. The experimental results indicated that LDA-CNN outperformed the state-of-the-art methods for periocular recognition in unconstrained environments. To interpret the performance, we visualized the discriminative power of the features extracted from different layers of the LDA-CNN model using the t-distributed Stochastic Neighboring Embedding (t-SNE) visualization technique. Moreover, we conducted cross-condition experiments (cross-light, cross-sensor, cross-eye, cross-pose, and cross-database) that proved the ability of the proposed model to generalize well to different unconstrained conditions. Full article
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17 pages, 3823 KiB  
Article
Manifold Regularized Principal Component Analysis Method Using L2,p-Norm
by Minghua Wan, Xichen Wang, Hai Tan and Guowei Yang
Mathematics 2022, 10(23), 4603; https://doi.org/10.3390/math10234603 - 05 Dec 2022
Cited by 2 | Viewed by 1228
Abstract
The main idea of principal component analysis (PCA) is to transform the problem of high-dimensional space into low-dimensional space, and obtain the output sample set after a series of operations on the samples. However, the accuracy of the traditional principal component analysis method [...] Read more.
The main idea of principal component analysis (PCA) is to transform the problem of high-dimensional space into low-dimensional space, and obtain the output sample set after a series of operations on the samples. However, the accuracy of the traditional principal component analysis method in dimension reduction is not very high, and it is very sensitive to outliers. In order to improve the robustness of image recognition to noise and the importance of geometric information in a given data space, this paper proposes a new unsupervised feature extraction model based on l2,p-norm PCA and manifold learning method. To improve robustness, the model method adopts l2,p-norm to reconstruct the distance measure between the error and the original input data. When the image is occluded, the projection direction will not significantly deviate from the expected solution of the model, which can minimize the reconstruction error of the data and improve the recognition accuracy. To verify whether the algorithm proposed by the method is robust, the data sets used in this experiment include ORL database, Yale database, FERET database, and PolyU palmprint database. In the experiments of these four databases, the recognition rate of the proposed method is higher than that of other methods when p=0.5. Finally, the experimental results show that the method proposed in this paper is robust and effective. Full article
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19 pages, 5719 KiB  
Article
Deep Learning Activation Layer-Based Wall Quality Recognition Using Conv2D ResNet Exponential Transfer Learning Model
by Bubryur Kim, Yuvaraj Natarajan, Shyamala Devi Munisamy, Aruna Rajendran, K. R. Sri Preethaa, Dong-Eun Lee and Gitanjali Wadhwa
Mathematics 2022, 10(23), 4602; https://doi.org/10.3390/math10234602 - 05 Dec 2022
Cited by 6 | Viewed by 1459
Abstract
Crack detection is essential for observing structural health and guaranteeing structural safety. The manual crack and other damage detection process is time-consuming and subject to surveyors’ biased judgments. The proposed Conv2D ResNet Exponential model for wall quality detection was trained with 5000 wall [...] Read more.
Crack detection is essential for observing structural health and guaranteeing structural safety. The manual crack and other damage detection process is time-consuming and subject to surveyors’ biased judgments. The proposed Conv2D ResNet Exponential model for wall quality detection was trained with 5000 wall images, including various imperfections such as cracks, holes, efflorescence, damp patches, and spalls. The model was trained with initial weights to form the trained layers of the base model and was integrated with Xception, VGG19, DenseNet, and ResNet convolutional neural network (CNN) models to retrieve the general high-level features. A transfer deep-learning-based approach was implemented to create a custom layer of CNN models. The base model was combined with custom layers to estimate wall quality. Xception, VGG19, DenseNet, and ResNet models were fitted with different activation layers such as softplus, softsign, tanh, selu, elu, and exponential, along with transfer learning. The performance of Conv2D was evaluated using model loss, precision, accuracy, recall, and F-score measures. The model was validated by comparing the performances of Xception, VGG19, DenseNet, ResNet, and Conv2D ResNet Exponential. The experimental results show that the Conv2D ResNet model with an exponential activation layer outperforms it with an F-score value of 0.9978 and can potentially be a viable substitute for classifying various wall defects. Full article
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26 pages, 968 KiB  
Article
Numerical and Stability Investigations of the Waste Plastic Management Model in the Ocean System
by Mohammad Izadi, Mahmood Parsamanesh and Waleed Adel
Mathematics 2022, 10(23), 4601; https://doi.org/10.3390/math10234601 - 05 Dec 2022
Cited by 2 | Viewed by 1498
Abstract
This study investigates the solution of an ocean waste plastic management system model. The model is represented by a nonlinear system which is divided into three compartments: the waste plastic materials W(τ), marine debris M(τ), [...] Read more.
This study investigates the solution of an ocean waste plastic management system model. The model is represented by a nonlinear system which is divided into three compartments: the waste plastic materials W(τ), marine debris M(τ), and the process of recycling R(τ). These compartments form a simulated model that is solved using two collocation techniques based on a shifted version of the Morgan-Voyce (MV) functions, while the first matrix collocation procedure is directly applied to the given model, in the second approach we fuse the technique of quasilinearization together with the shifted MV (SMV) collocation strategy. Moreover, we give the basic reproduction number and discuss the existence of equilibria and the local stability of equilibria are investigated. The basic definitions of the SMV polynomials are introduced and detailed convergence analysis of the related power series expansion in both weighted L2 and L norms are presented. Diverse numerical simulations are performed to prove the accurateness and effectiveness of the presented approaches and the results ate illustrated through tables and figures. Full article
(This article belongs to the Section Computational and Applied Mathematics)
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11 pages, 540 KiB  
Article
Validation of Parallel Distributed Adaptive Signal Processing (PDASP) Framework through Processing-Inefficient Low-Cost Platforms
by Hasan Raza, Ishtiaq Ahmad, Noor M. Khan, Waseem Abbasi, Muhammad Shahid Anwar, Sadique Ahmad and Mohammed A. El-Affendi
Mathematics 2022, 10(23), 4600; https://doi.org/10.3390/math10234600 - 05 Dec 2022
Viewed by 1106
Abstract
The computational complexity of the multiple-input and multiple-output (MIMO) based least square algorithm is very high and it cannot be run on processing-inefficient low-cost platforms. To overcome complexity-related problems, a parallel distributed adaptive signal processing (PDASP) architecture is proposed, which is a distributed [...] Read more.
The computational complexity of the multiple-input and multiple-output (MIMO) based least square algorithm is very high and it cannot be run on processing-inefficient low-cost platforms. To overcome complexity-related problems, a parallel distributed adaptive signal processing (PDASP) architecture is proposed, which is a distributed framework used to efficiently run the adaptive filtering algorithms having high computational cost. In this paper, a communication load-balancing procedure is introduced to validate the PDASP architecture using low-cost wireless sensor nodes. The PDASP architecture with the implementation of a multiple-input multiple-output (MIMO) based Recursive Least Square (RLS) algorithm is deployed on the processing-inefficient low-cost wireless sensor nodes to validate the performance of the PDASP architecture in terms of computational cost, processing time, and memory utilization. Furthermore, the processing time and memory utilization provided by the PDASP architecture are compared with sequentially operated RLS-based MIMO channel estimator on 2×2, 3×3, and 4×4 MIMO communication systems. The measurement results show that the sequentially operated MIMO RLS algorithm based on 3×3 and 4×4 MIMO communication systems is unable to work on a single unit; however, these MIMO systems can efficiently be run on the PDASP architecture with reduced memory utilization and processing time. Full article
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22 pages, 2453 KiB  
Article
Impact Analysis of the External Shocks on the Prices of Malaysian Crude Palm Oil: Evidence from a Structural Vector Autoregressive Model
by Mohd Syafiq Sabri, Norlin Khalid, Abdul Hafizh Mohd Azam and Tamat Sarmidi
Mathematics 2022, 10(23), 4599; https://doi.org/10.3390/math10234599 - 05 Dec 2022
Cited by 1 | Viewed by 2182
Abstract
Palm oil prices, similar to other edible oils and commodity prices, are highly sensitive to external shocks which have become particularly prominent in the wake of COVID-19 pandemic. The non-stationary nature of the palm oil price complicates the modelling and forecasting of its [...] Read more.
Palm oil prices, similar to other edible oils and commodity prices, are highly sensitive to external shocks which have become particularly prominent in the wake of COVID-19 pandemic. The non-stationary nature of the palm oil price complicates the modelling and forecasting of its behaviour. This study investigates the impact of the external and internal shocks on Malaysian palm oil (MPO) prices using the SVAR methodology. The SVAR model utilised in this study is unique in that it employs the news-based indices called the Infectious Disease Volatility Tracker (IDVT) and the Economic Policy Uncertainty Index (EPUI) as parts of the time series. News-based indices can potentially uncover essential proxies for economic and policy conditions, as well as portend the investment decision-making and in turn the commodity prices. The rationale behind this choice is to capture the impact from perception and news-based indices on the Malaysian palm oil prices. The empirical result from impulse–response function (IRF) shows that the shock in IDVT has a significant positive impact on Malaysian palm oil prices suggesting the MPO is exposed to the external factor. In addition, amongst the external variables tested, IDVT shows the longest lasting and highest positive impact on Malaysian palm oil prices. These results are in accordance with forecast error variance decomposition which indicates that IDVT shock can explain a huge portion of MPO prices especially over a longer period. The model specified in this study is also sufficiently stable and robust. This study contributes to the literature the significance of news-based indices and their capability in influencing public perception on the current macroeconomic condition, hence influencing the decision-making process of economic agents. Full article
(This article belongs to the Special Issue Time Series Analysis and Econometrics with Applications)
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