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Optimization Models and Applications

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 21347

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Special Issue Editors


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Guest Editor
1. IISRI, Deakin University, Waurn Ponds, VIC, Australia
2. Faculty of Design, Tabriz Islamic Art University, Tabriz, Iran
Interests: structural modeling and dynamics; mechanical vibrations and modal analysis; data analysis and optimization; parallel mechanisms and machine tool design; nanocomposite FSP/FSW
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
IISRI, Deakin University, Waurn Ponds, VIC, Australia
Interests: motion cueing; manipulator; AI in manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Optimization models have been widely used in applications across many fields, including engineering, economics, environment, health, systems of systems, business, etc. Optimizations can be divided into four groups: physic-based optimization algorithms, swarm-based optimization algorithms, game-based optimization algorithms, and evolutionary-based optimization algorithms. Optimization models have been implemented intensively in different applications over the last fifty years. This Special Issue will focus on the connection between optimization models and their applications to solve the basic science real-world problem.

We cordially and earnestly invite researchers to contribute their original and high-quality research papers, which will inspire advances in optimization and their applications. The scope of this Special Issue includes, but is not limited to:

  • Functional analysis;
  • Critical point theory;
  • Bifurcation theory;
  • Set-valued analysis;
  • Calculus of variations and PDEs;
  • Variational and topological methods for ODEs and PDEs;
  • Fixed point, coincidence point, and best proximity point theory;
  • Non smooth analysis and optimisation;
  • Graph theory and optimisation;
  • Game theory;
  • Convex analysis;
  • Matrix theory;
  • Control theory;
  • Inverse and ill-posed problems;
  • Finite element method;
  • Dynamical systems;
  • Image and signal processing;
  • Data mining;
  • Update quality of products;
  • Reinforcement learning;
  • Cost-effective mathematical model.

Dr. Siamak Pedrammehr
Dr. Mohammad Reza Chalak Qazani
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

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Published Papers (12 papers)

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Editorial

Jump to: Research

3 pages, 148 KiB  
Editorial
Special Issue “Optimisation Models and Applications”
by Siamak Pedrammehr and Mohammad Reza Chalak Qazani
Axioms 2024, 13(1), 45; https://doi.org/10.3390/axioms13010045 - 11 Jan 2024
Viewed by 1227
Abstract
Optimisation models have transcended their origins to become indispensable tools across many fields, including engineering, economics, the environment, health, systems of systems, businesses, and beyond [...] Full article
(This article belongs to the Special Issue Optimization Models and Applications)

Research

Jump to: Editorial

14 pages, 292 KiB  
Article
On Mond–Weir-Type Robust Duality for a Class of Uncertain Fractional Optimization Problems
by Xiaole Guo
Axioms 2023, 12(11), 1029; https://doi.org/10.3390/axioms12111029 - 2 Nov 2023
Viewed by 982
Abstract
This article is focused on the investigation of Mond–Weir-type robust duality for a class of semi-infinite multi-objective fractional optimization with uncertainty in the constraint functions. We first establish a Mond–Weir-type robust dual problem for this fractional optimization problem. Then, by combining a new [...] Read more.
This article is focused on the investigation of Mond–Weir-type robust duality for a class of semi-infinite multi-objective fractional optimization with uncertainty in the constraint functions. We first establish a Mond–Weir-type robust dual problem for this fractional optimization problem. Then, by combining a new robust-type subdifferential constraint qualification condition and a generalized convex-inclusion assumption, we present robust ε-quasi-weak and strong duality properties between this uncertain fractional optimization and its uncertain Mond–Weir-type robust dual problem. Moreover, we also investigate robust ε-quasi converse-like duality properties between them. Full article
(This article belongs to the Special Issue Optimization Models and Applications)
21 pages, 25832 KiB  
Article
Designing a Secure Mechanism for Image Transferring System Based on Uncertain Fractional Order Chaotic Systems and NLFPID Sliding Mode Controller
by Mohammad Rasouli, Assef Zare, Hassan Yaghoubi and Roohallah Alizadehsani
Axioms 2023, 12(9), 828; https://doi.org/10.3390/axioms12090828 - 28 Aug 2023
Viewed by 931
Abstract
A control method for the robust synchronization of a class of chaotic systems with unknown time delay, unknown uncertainty, and unknown disturbance is presented. The robust controller was designed using a nonlinear fractional order PID sliding surface. The Lyapunov method was used to [...] Read more.
A control method for the robust synchronization of a class of chaotic systems with unknown time delay, unknown uncertainty, and unknown disturbance is presented. The robust controller was designed using a nonlinear fractional order PID sliding surface. The Lyapunov method was used to determine the update laws, prove the stability of the proposed mechanism, and guarantee the convergence of the synchronization errors to zero. The simulation was performed using MATLAB software to evaluate the performance of the proposed mechanism, and the results showed that it was efficient. Finally, the proposed method was combined with a secure communication application to encrypt images, and the results obtained were favorable regarding the standard criteria of correlation, NPCR, PSNR, and information entropy. Full article
(This article belongs to the Special Issue Optimization Models and Applications)
Show Figures

Figure 1

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<p>Sigma function behavior diagram.</p>
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<p>Chaotic behavior of the fractional order Jerk master and slave systems without applying the controller.</p>
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<p>The behavior of the master and slave system states without applying the control signal.</p>
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<p>Synchronization of chaotic jerk systems with the help of the proposed control mechanism and application of the control signal at t = 5 s.</p>
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<p>Synchronization error of the master and slave systems using the proposed adaptive sliding mode control mechanism.</p>
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<p>Control signal based on the proposed adaptive sliding mode control mechanism.</p>
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<p>System parameter estimation error including time delay, disturbance bound, and uncertainty bound.</p>
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<p>Uncertainties and disturbances in the master and slave systems.</p>
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<p>Block diagram of chaotic masking for image encryption.</p>
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<p>The original, encrypted, and decrypted color image.</p>
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<p>Histogram of the original, encrypted, and decrypted color image.</p>
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<p>Original, encrypted, and decrypted color image.</p>
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<p>Histogram of the original, encrypted, and decrypted color image.</p>
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<p>The original, encrypted, and decrypted black and white image.</p>
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<p>Histogram of the original, encrypted, and decrypted black and white image.</p>
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<p>The original, encrypted, and decrypted black and white image.</p>
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<p>Histogram of the original, encrypted, and decrypted black and white image.</p>
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<p>Original, encrypted, and decrypted medical image.</p>
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<p>Histogram of the original, encrypted, and decrypted medical image.</p>
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<p>The original, encrypted, and decrypted medical image.</p>
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<p>Histogram of the original, encrypted, and decrypted medical image.</p>
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16 pages, 532 KiB  
Article
Robust Multi-Criteria Traffic Network Equilibrium Problems with Path Capacity Constraints
by Xing-Xing Ma and Yang-Dong Xu
Axioms 2023, 12(7), 662; https://doi.org/10.3390/axioms12070662 - 3 Jul 2023
Cited by 1 | Viewed by 1005
Abstract
With the progress of society and the diversification of transportation modes, people are faced with more and more complicated travel choices, and thus, multi-criteria route choosing optimization problems have drawn increased attention in recent years. A number of multi-criteria traffic network equilibrium problems [...] Read more.
With the progress of society and the diversification of transportation modes, people are faced with more and more complicated travel choices, and thus, multi-criteria route choosing optimization problems have drawn increased attention in recent years. A number of multi-criteria traffic network equilibrium problems have been proposed, but most of them do not involve data uncertainty nor computational methods. This paper focuses on the methods for solving robust multi-criteria traffic network equilibrium problems with path capacity constraints. The concepts of the robust vector equilibrium and the robust vector equilibrium with respect to the worst case are introduced, respectively. For the robust vector equilibrium, an equivalent min–max optimization problem is constructed. A direct search algorithm, in which the step size without derivatives and redundant parameters, is proposed for solving this min–max problem. In addition, we construct a smoothing optimization problem based on a variant version of ReLU activation function to compute the robust weak vector equilibrium flows with respect to the worst case and then find robust vector equilibrium flows with respect to the worst case by using the heaviside step function. Finally, extensive numerical examples are given to illustrate the excellence of our algorithms compared with existing algorithms. It is shown that the proposed min–max algorithm may take less time to find the robust vector equilibrium flows and the smoothing method can more effectively generate a subset of the robust vector equilibrium with respect to the worst case. Full article
(This article belongs to the Special Issue Optimization Models and Applications)
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<p>Network topology for Example 2.</p>
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<p>Network topology for Example 3.</p>
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<p>Network topology for Example 4.</p>
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<p>Network topology for Example 5.</p>
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<p>Network topology for Example 6.</p>
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13 pages, 304 KiB  
Article
A Nonconstant Gradient Constrained Problem for Nonlinear Monotone Operators
by Sofia Giuffrè
Axioms 2023, 12(6), 605; https://doi.org/10.3390/axioms12060605 - 18 Jun 2023
Viewed by 1106
Abstract
The purpose of the research is the study of a nonconstant gradient constrained problem for nonlinear monotone operators. In particular, we study a stationary variational inequality, defined by a strongly monotone operator, in a convex set of gradient-type constraints. We investigate the relationship [...] Read more.
The purpose of the research is the study of a nonconstant gradient constrained problem for nonlinear monotone operators. In particular, we study a stationary variational inequality, defined by a strongly monotone operator, in a convex set of gradient-type constraints. We investigate the relationship between the nonconstant gradient constrained problem and a suitable double obstacle problem, where the obstacles are the viscosity solutions to a Hamilton–Jacobi equation, and we show the equivalence between the two variational problems. To obtain the equivalence, we prove that a suitable constraint qualification condition, Assumption S, is fulfilled at the solution of the double obstacle problem. It allows us to apply a strong duality theory, holding under Assumption S. Then, we also provide the proof of existence of Lagrange multipliers. The elements in question can be not only functions in L2, but also measures. Full article
(This article belongs to the Special Issue Optimization Models and Applications)
27 pages, 1033 KiB  
Article
New Class of K-G-Type Symmetric Second Order Vector Optimization Problem
by Chetan Swarup, Ramesh Kumar, Ramu Dubey and Dowlath Fathima
Axioms 2023, 12(6), 571; https://doi.org/10.3390/axioms12060571 - 8 Jun 2023
Viewed by 1015
Abstract
In this paper, we present meanings of K-Gf-bonvexity/K-Gf-pseudobonvexity and their generalization between the above-notice functions. We also construct various concrete non-trivial examples for existing these types of functions. We formulate K-Gf [...] Read more.
In this paper, we present meanings of K-Gf-bonvexity/K-Gf-pseudobonvexity and their generalization between the above-notice functions. We also construct various concrete non-trivial examples for existing these types of functions. We formulate K-Gf-Wolfe type multiobjective second-order symmetric duality model with cone objective as well as cone constraints and duality theorems have been established under these aforesaid conditions. Further, we have validates the weak duality theorem under those assumptions. Our results are more generalized than previous known results in the literature. Full article
(This article belongs to the Special Issue Optimization Models and Applications)
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Figure 1
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<p><inline-formula><mml:math id="mm625"><mml:semantics><mml:mrow><mml:msub><mml:mo>Φ</mml:mo><mml:mn>1</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mfenced separators="" open="{" close="}"><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:msup><mml:mi>s</mml:mi><mml:mn>4</mml:mn></mml:msup><mml:mi>φ</mml:mi><mml:mo>−</mml:mo><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:msup><mml:mi>s</mml:mi><mml:mn>4</mml:mn></mml:msup><mml:mi>δ</mml:mi><mml:mo>+</mml:mo><mml:mo>−</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msup><mml:mi>δ</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow><mml:mn>4</mml:mn><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:msup><mml:mi>s</mml:mi><mml:mn>3</mml:mn></mml:msup><mml:mi>δ</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mo>−</mml:mo><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>δ</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mfenced></mml:mrow></mml:semantics></mml:math></inline-formula>.</p>
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<p><inline-formula><mml:math id="mm626"><mml:semantics><mml:mstyle scriptlevel="0" displaystyle="true"><mml:mrow><mml:msub><mml:mo>Φ</mml:mo><mml:mn>2</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mn>2</mml:mn></mml:mfrac><mml:msup><mml:mi>p</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mfenced separators="" open="[" close="]"><mml:mn>12</mml:mn><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:msup><mml:mi>s</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mi>δ</mml:mi><mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mo>−</mml:mo><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>δ</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mo>+</mml:mo><mml:mn>4</mml:mn><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:msup><mml:mi>s</mml:mi><mml:mn>3</mml:mn></mml:msup><mml:mi>δ</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mo>−</mml:mo><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>s</mml:mi><mml:mi>δ</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mi>p</mml:mi><mml:mfenced separators="" open="(" close=")"><mml:mn>12</mml:mn><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:msup><mml:mi>s</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mi>δ</mml:mi><mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mo>−</mml:mo><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>δ</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mo>+</mml:mo><mml:mn>4</mml:mn><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:msup><mml:mi>s</mml:mi><mml:mn>3</mml:mn></mml:msup><mml:mi>δ</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mo>−</mml:mo><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>s</mml:mi><mml:mi>δ</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mfenced></mml:mfenced></mml:mrow></mml:mstyle></mml:semantics></mml:math></inline-formula>.</p>
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<p><inline-formula><mml:math id="mm627"><mml:semantics><mml:mrow><mml:mi>φ</mml:mi><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mfrac><mml:mn>1</mml:mn><mml:mi>φ</mml:mi></mml:mfrac><mml:mo>−</mml:mo><mml:mi>δ</mml:mi><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mfrac><mml:mn>1</mml:mn><mml:mi>δ</mml:mi></mml:mfrac><mml:mo>−</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msup><mml:mi>δ</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mfrac><mml:mn>1</mml:mn><mml:mi>δ</mml:mi></mml:mfrac><mml:mo>−</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mi>δ</mml:mi></mml:mfrac><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>s</mml:mi><mml:mfrac><mml:mn>1</mml:mn><mml:mi>δ</mml:mi></mml:mfrac></mml:mrow></mml:semantics></mml:math></inline-formula>.</p>
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<p><inline-formula><mml:math id="mm628"><mml:semantics><mml:mrow><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>s</mml:mi><mml:mi>φ</mml:mi><mml:mo>−</mml:mo><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>s</mml:mi><mml:mi>δ</mml:mi><mml:mo>+</mml:mo><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msup><mml:mi>δ</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>δ</mml:mi></mml:mrow></mml:semantics></mml:math></inline-formula>.</p>
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<p><inline-formula><mml:math id="mm629"><mml:semantics><mml:mfenced separators="" open="(" close=")"><mml:msup><mml:mrow><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mi>φ</mml:mi><mml:mo>+</mml:mo><mml:mn>4</mml:mn><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:msup><mml:mi>s</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mi>φ</mml:mi><mml:mo>,</mml:mo><mml:msup><mml:mi>e</mml:mi><mml:mrow><mml:mn>3</mml:mn><mml:mi>φ</mml:mi></mml:mrow></mml:msup><mml:mo>+</mml:mo><mml:mn>17</mml:mn></mml:mfenced></mml:semantics></mml:math></inline-formula>.</p>
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22 pages, 11952 KiB  
Article
Predicting Sit-to-Stand Body Adaptation Using a Simple Model
by Sarra Gismelseed, Amur Al-Yahmedi, Riadh Zaier, Hassen Ouakad and Issam Bahadur
Axioms 2023, 12(6), 559; https://doi.org/10.3390/axioms12060559 - 5 Jun 2023
Viewed by 1302
Abstract
Mathematical models that simulate human motion are used widely due to their potential in predicting basic characteristics of human motion. These models have been involved in investigating various aspects of gait and human-related tasks, especially walking and running. This study uses a simple [...] Read more.
Mathematical models that simulate human motion are used widely due to their potential in predicting basic characteristics of human motion. These models have been involved in investigating various aspects of gait and human-related tasks, especially walking and running. This study uses a simple model to study the impact of different factors on sit-to-stand motion through the formulation of an optimization problem that aims at minimizing joint torques. The simulated results validated experimental results reported in the literature and showed the ability of the model to predict the changes in kinetic and kinematic parameters as adaptation to any change in the speed of motion, reduction in the joint strength, and change in the seat height. The model discovered that changing one of these determinants would affect joint angular displacement, joint torques, joint angular velocities, center of mass position, and ground reaction force. Full article
(This article belongs to the Special Issue Optimization Models and Applications)
Show Figures

Figure 1

Figure 1
<p>Three-links model, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>u</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> is the torque applied at each joint, and each link is assigned with a number: 1 = stance tibia, 2 = stance femur, 3 = trunk.</p>
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<p>The normal pattern of STS can be explained using the ground force reaction and the velocity of center of mass by dividing the movement into phases starting from the model sitting and ending when the velocity of center of mass approaches zero.</p>
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<p>The profile of GRF directed forward under the feet from simulation and experimental results [<a href="#B30-axioms-12-00559" class="html-bibr">30</a>].</p>
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<p>The velocity of COM of the model and the experimental results [<a href="#B29-axioms-12-00559" class="html-bibr">29</a>].</p>
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<p>(<b>a</b>) The model performing the sit-to-stand task within different timeframes; (<b>b</b>) Torso angle during standing from sitting position within different timeframes.</p>
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<p>Torso angular velocity at different STS speeds.</p>
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<p>Hip joint velocity and knee joint velocity at different STS speeds.</p>
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<p>Ankle, knee, and hip torques at different STS speeds.</p>
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<p>The model standing up with reduced knee joint strength corresponds to different coefficients of knee joint torque (α<sub>2</sub> = 1, α<sub>2</sub> = 3, α<sub>2</sub> = 5, and α<sub>2</sub> = 7).</p>
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<p>The angular velocity of the model with reduced knee joint strength corresponds to different coefficients of knee joint torque (α<sub>2</sub> = 1, α<sub>2</sub> = 3, α<sub>2</sub> = 5, and α<sub>2</sub> = 7).</p>
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<p>The Hip and knee joint torques of the model with reduced knee joint strength correspond to different coefficients of knee joint torque (α<sub>2</sub> = 1, α<sub>2</sub> = 3, α<sub>2</sub> = 5, and α<sub>2</sub> = 7).</p>
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<p>Hip angle, Knee angle, and Ankle angle at different STS speeds with normal and reduced knee joint strength (α<sub>2</sub> = 1 and α<sub>2</sub> = 5, respectively).</p>
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<p>Horizontal and vertical positions of the center of mass at different STS speeds with normal and reduced knee joint strength (α<sub>2</sub> = 1 and α<sub>2</sub> = 5, respectively).</p>
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<p>Hip joint velocity and knee joint velocity at different STS speeds with normal and reduced knee joint strength (α<sub>2</sub> = 1 and α<sub>2</sub> = 5, respectively).</p>
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<p>(<b>a</b>) Model standing up from different seat heights; (<b>b</b>) Torso angle of the model standing up from four seat heights (30 cm, 33 cm, 37 cm, and 44 cm).</p>
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<p>Hip and knee joint velocities of the model standing up from four seat heights (30 cm, 33 cm, 37 cm, and 44 cm).</p>
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<p>Ground Reaction Force of the model (as a percentage of body weight) standing up from four seat heights (30 cm, 33 cm, 37 cm, and 44 cm).</p>
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<p>Hip joint and knee joint torque of the model standing up from four seat heights (30 cm, 33 cm, 37 cm, and 44 cm).</p>
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<p>COM of body segments in the sagittal plane.</p>
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29 pages, 789 KiB  
Article
Efficient Method to Solve the Monge–Kantarovich Problem Using Wavelet Analysis
by Juan Rafael Acosta-Portilla, Carlos González-Flores, Raquiel Rufino López-Martínez and Armando Sánchez-Nungaray
Axioms 2023, 12(6), 555; https://doi.org/10.3390/axioms12060555 - 4 Jun 2023
Viewed by 1360
Abstract
In this paper, we present and justify a methodology to solve the Monge–Kantorovich mass transfer problem through Haar multiresolution analysis and wavelet transform with the advantage of requiring a reduced number of operations to carry out. The methodology has the following steps. We [...] Read more.
In this paper, we present and justify a methodology to solve the Monge–Kantorovich mass transfer problem through Haar multiresolution analysis and wavelet transform with the advantage of requiring a reduced number of operations to carry out. The methodology has the following steps. We apply wavelet analysis on a discretization of the cost function level j and obtain four components comprising one corresponding to a low-pass filter plus three from a high-pass filter. We obtain the solution corresponding to the low-pass component in level j1 denoted by μj1*, and using the information of the high-pass filter components, we get a solution in level j denoted by μ^j. Finally, we make a local refinement of μ^j and obtain the final solution μjσ. Full article
(This article belongs to the Special Issue Optimization Models and Applications)
Show Figures

Figure 1

Figure 1
<p>Functions <math display="inline"><semantics> <msub> <mo>Φ</mo> <mrow> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msubsup> <mo>Ψ</mo> <mrow> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> </mrow> <mrow> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> </semantics></math>, <math display="inline"><semantics> <msubsup> <mo>Ψ</mo> <mrow> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> </mrow> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> </semantics></math> and <math display="inline"><semantics> <msubsup> <mo>Ψ</mo> <mrow> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> </mrow> <mrow> <mo stretchy="false">(</mo> <mn>3</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> </semantics></math>.</p>
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<p>Support of <math display="inline"><semantics> <mi>μ</mi> </semantics></math> and the proximity criteria.</p>
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<p>Classification of the points in <math display="inline"><semantics> <mrow> <mo form="prefix">support</mo> <mo stretchy="false">(</mo> <mi>μ</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>: Type I points.</p>
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<p>Classification of the points in <math display="inline"><semantics> <mrow> <mo form="prefix">support</mo> <mo stretchy="false">(</mo> <mi>μ</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>: Type II points.</p>
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<p><b>Step 1</b>. Discretization of the cost function to the level <span class="html-italic">j</span>, which is denoted by <math display="inline"><semantics> <msub> <mi>c</mi> <mi>j</mi> </msub> </semantics></math>. In particular, the cost function is <math display="inline"><semantics> <mrow> <mi>c</mi> <mrow> <mo stretchy="false">(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo stretchy="false">)</mo> </mrow> <mo>=</mo> <mn>4</mn> <msup> <mi>x</mi> <mn>2</mn> </msup> <mi>y</mi> <mo>−</mo> <mi>x</mi> <msup> <mi>y</mi> <mn>2</mn> </msup> </mrow> </semantics></math> for lever <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 6
<p><math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mn>5</mn> </msub> <mrow> <mo stretchy="false">(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p><math display="inline"><semantics> <mrow> <msup> <mo>Ψ</mo> <mrow> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </msup> <mrow> <mo stretchy="false">(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p><math display="inline"><semantics> <mrow> <msup> <mo>Ψ</mo> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mo stretchy="false">)</mo> </mrow> </msup> <mrow> <mo stretchy="false">(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p><math display="inline"><semantics> <mrow> <msup> <mo>Ψ</mo> <mrow> <mo stretchy="false">(</mo> <mn>3</mn> <mo stretchy="false">)</mo> </mrow> </msup> <mrow> <mo stretchy="false">(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo stretchy="false">)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p><b>Step 3</b>. We obtain a solution <math display="inline"><semantics> <msubsup> <mi>μ</mi> <mn>5</mn> <mo>*</mo> </msubsup> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>M</mi> <msup> <mi>K</mi> <mn>5</mn> </msup> </mrow> </semantics></math> associated with the cost function <math display="inline"><semantics> <msub> <mi>c</mi> <mn>5</mn> </msub> </semantics></math> given in <a href="#axioms-12-00555-f006" class="html-fig">Figure 6</a>.</p>
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<p>Division of the components of <math display="inline"><semantics> <mrow> <mo form="prefix">support</mo> <mo stretchy="false">(</mo> <msubsup> <mi>μ</mi> <mrow> <mi>j</mi> <mo>−</mo> <mn>1</mn> </mrow> <mo>*</mo> </msubsup> <mo stretchy="false">)</mo> </mrow> </semantics></math> into four parts.</p>
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<p>Refinement of grid from level <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> to <span class="html-italic">j</span> of discretization.</p>
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<p>Supports for refinement of the element <math display="inline"><semantics> <msub> <mi>S</mi> <mrow> <msub> <mi>k</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> </mrow> </msub> </semantics></math> corresponding to the level <span class="html-italic">j</span>:Option I.</p>
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<p>Supports for refinement of the element <math display="inline"><semantics> <msub> <mi>S</mi> <mrow> <msub> <mi>k</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>k</mi> <mn>2</mn> </msub> </mrow> </msub> </semantics></math> corresponding to the level <span class="html-italic">j</span>: Option II.</p>
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<p><b>Step 5</b>. Using the solution <math display="inline"><semantics> <msubsup> <mi>μ</mi> <mn>5</mn> <mo>*</mo> </msubsup> </semantics></math> which is given by <a href="#axioms-12-00555-f010" class="html-fig">Figure 10</a>, the information of the high-pass components (<a href="#axioms-12-00555-f007" class="html-fig">Figure 7</a>, <a href="#axioms-12-00555-f008" class="html-fig">Figure 8</a> and <a href="#axioms-12-00555-f009" class="html-fig">Figure 9</a>) and Lemma 1, we obtain a feasible solution for Level 6, which is denoted by <math display="inline"><semantics> <msub> <mover accent="true"> <mi>μ</mi> <mo stretchy="false">^</mo> </mover> <mn>6</mn> </msub> </semantics></math>.</p>
Full article ">Figure 16
<p><b>Step 4.</b> We classify the points of the support of the solution <math display="inline"><semantics> <msubsup> <mi>μ</mi> <mn>5</mn> <mo>*</mo> </msubsup> </semantics></math> by proximity criteria as points of Type I <span style="color: #0000FF"><span class="html-italic">■</span></span> or Type II <span style="color: #FF0000"><span class="html-italic">■</span></span> (the measure <math display="inline"><semantics> <msubsup> <mi>μ</mi> <mn>5</mn> <mo>*</mo> </msubsup> </semantics></math> corresponds to <a href="#axioms-12-00555-f010" class="html-fig">Figure 10</a>).</p>
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<p><b>Step 6</b>. Classification of the points of <math display="inline"><semantics> <msubsup> <mi>μ</mi> <mn>5</mn> <mo>*</mo> </msubsup> </semantics></math> induces classification of the points in <math display="inline"><semantics> <msub> <mover accent="true"> <mi>μ</mi> <mo stretchy="false">^</mo> </mover> <mn>6</mn> </msub> </semantics></math> by contention in the support. Over the points of Type I of the solution <math display="inline"><semantics> <msub> <mover accent="true"> <mi>μ</mi> <mo stretchy="false">^</mo> </mover> <mn>6</mn> </msub> </semantics></math>, we do not move those points. For the points of Type II, we apply a permutation to the solution over the two points that improve the solution and repeat the process with the rest of the points.</p>
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<p>Discretization of the cost function <math display="inline"><semantics> <mrow> <mi>c</mi> <mrow> <mo stretchy="false">(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo stretchy="false">)</mo> </mrow> <mo>=</mo> <msup> <mi>x</mi> <mn>2</mn> </msup> <mi>y</mi> <mo>−</mo> <mi>x</mi> <msup> <mi>y</mi> <mn>2</mn> </msup> </mrow> </semantics></math> at level <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>.</p>
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<p>Filtering of the cost function <span class="html-italic">c</span> at level <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>−</mo> <mn>1</mn> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p>
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<p><math display="inline"><semantics> <mi>MK</mi> </semantics></math> solution for the filtered function <span class="html-italic">c</span> at level <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>−</mo> <mn>1</mn> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p>
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<p>Classification of points of <math display="inline"><semantics> <mrow> <mo form="prefix">support</mo> <mo stretchy="false">(</mo> <msubsup> <mi>μ</mi> <mn>5</mn> <mo>*</mo> </msubsup> <mo stretchy="false">)</mo> </mrow> </semantics></math> into Type I <span style="color: #0000FF"><span class="html-italic">■</span></span> and Type II <span style="color: #FF0000"><span class="html-italic">■</span></span>.</p>
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<p>Solution <math display="inline"><semantics> <msub> <mover accent="true"> <mi>μ</mi> <mo stretchy="false">^</mo> </mover> <mn>6</mn> </msub> </semantics></math> from refinement of <math display="inline"><semantics> <msubsup> <mi>μ</mi> <mn>5</mn> <mo>*</mo> </msubsup> </semantics></math>.</p>
Full article ">Figure 23
<p>Final result <math display="inline"><semantics> <msubsup> <mi>μ</mi> <mn>6</mn> <msup> <mi>σ</mi> <mo>*</mo> </msup> </msubsup> </semantics></math>.</p>
Full article ">Figure 24
<p>Discretization of the cost function <math display="inline"><semantics> <mrow> <mi>c</mi> <mo stretchy="false">(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo stretchy="false">)</mo> <mo>=</mo> <mi>x</mi> <mi>y</mi> </mrow> </semantics></math> at level <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 25
<p>Apply the filter to the cost function <span class="html-italic">c</span> at level <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>−</mo> <mn>1</mn> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 26
<p>Solution of the <math display="inline"><semantics> <mi>MK</mi> </semantics></math> for the function <span class="html-italic">c</span> at level <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>−</mo> <mn>1</mn> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p>
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<p>In this example there are only Type I points <span style="color: #0000FF"><span class="html-italic">■</span></span>.</p>
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<p>Solution <math display="inline"><semantics> <msub> <mover accent="true"> <mi>μ</mi> <mo stretchy="false">^</mo> </mover> <mn>6</mn> </msub> </semantics></math> from refinement of <math display="inline"><semantics> <msubsup> <mi>μ</mi> <mn>5</mn> <mo>*</mo> </msubsup> </semantics></math>.</p>
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<p>Final result <math display="inline"><semantics> <msubsup> <mi>μ</mi> <mn>6</mn> <msup> <mi>σ</mi> <mo>*</mo> </msup> </msubsup> </semantics></math>.</p>
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<p>Discretization of cost function <math display="inline"><semantics> <mrow> <mi>c</mi> <mrow> <mo stretchy="false">(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo stretchy="false">)</mo> </mrow> <mo>=</mo> <msup> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mi>y</mi> <mo>−</mo> <mi>x</mi> <mo>−</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> <mn>2</mn> </msup> <msup> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mi>y</mi> <mo>−</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math> for level <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>.</p>
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<p>Filtered cost function for <math display="inline"><semantics> <mrow> <mi>j</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p>
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<p>Solution of the <math display="inline"><semantics> <mrow> <mi>M</mi> <msub> <mi>K</mi> <mn>5</mn> </msub> </mrow> </semantics></math> problem.</p>
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<p>In this example, there are only Type II points <span style="color: #FF0000"><span class="html-italic">■</span></span>.</p>
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<p>Feasible solution <math display="inline"><semantics> <msub> <mover accent="true"> <mi>μ</mi> <mo stretchy="false">^</mo> </mover> <mn>6</mn> </msub> </semantics></math> from refinement of <math display="inline"><semantics> <msubsup> <mi>μ</mi> <mn>5</mn> <mo>*</mo> </msubsup> </semantics></math>.</p>
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<p>Final result <math display="inline"><semantics> <msubsup> <mi>μ</mi> <mn>6</mn> <msup> <mi>σ</mi> <mo>*</mo> </msup> </msubsup> </semantics></math>.</p>
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12 pages, 7662 KiB  
Article
A License Plate Recognition System with Robustness against Adverse Environmental Conditions Using Hopfield’s Neural Network
by Saman Rajebi, Siamak Pedrammehr and Reza Mohajerpoor
Axioms 2023, 12(5), 424; https://doi.org/10.3390/axioms12050424 - 26 Apr 2023
Cited by 3 | Viewed by 3339
Abstract
License plates typically have unique color, size, and shape characteristics in each country. This paper presents a general method for character extraction and pattern matching in license plate recognition systems. The proposed method is based on a combination of morphological operations and edge [...] Read more.
License plates typically have unique color, size, and shape characteristics in each country. This paper presents a general method for character extraction and pattern matching in license plate recognition systems. The proposed method is based on a combination of morphological operations and edge detection techniques, along with the bounding box method for identifying and revealing license plate characters while removing unwanted artifacts such as dust and fog. The mathematical model of foggy images is presented and the sum of gradients of the image, which represents the visibility of the image, is improved. Previous works on license plate recognition have utilized non-intelligent pattern matching techniques. The proposed technique can be applied in a variety of settings, including traffic monitoring, parking management, and law enforcement, among others. The applied algorithm, unlike SOTA-based methods, does not need a huge set of training data and is implemented only by applying standard templates. The main advantages of the proposed algorithm are the lack of a need for a training set, the high speed of the training process, the ability to respond to different standards, the high response speed, and higher accuracy compared to similar tasks. Full article
(This article belongs to the Special Issue Optimization Models and Applications)
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<p>Typical configuration of an LPR system.</p>
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<p>The proposed five main steps of license plate recognition.</p>
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<p>The border of the area where it is possible for a license plate to exist.</p>
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<p>A typical image affected by Blur filter; non-useful areas have been blurred.</p>
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<p>Implemented algorithm for removing or reducing the image blurring.</p>
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<p>(<b>a</b>) Artificially and exaggeratedly fogged image; (<b>b</b>) corrected version of fogged image.</p>
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<p>Detecting the license plate frame: (<b>a</b>) the initial image; (<b>b</b>) edge detection for black–white mode of initial image; (<b>c</b>) filling of closed-path detected edges; (<b>d</b>) the main objects in the image and finding the object corresponding to the standard license plate; (<b>e</b>) detecting the main object’s position from the initial image determined the license plate frame.</p>
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<p>License plate image prepared for extracting its character segments.</p>
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<p>Cut segments of the license plate—black and white mode image.</p>
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<p>A typical Hopfield neural network; its position depends on the previous time’s position and its energy is decreasing.</p>
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<p>Detecting procedures of noisy license plate characters using Hopfield’s neural network: (<b>a</b>) initial image; (<b>b</b>) detected license plate box; (<b>c</b>) the cut frame of the license plate; (<b>d</b>) black and white mode of cut frame; (<b>e</b>) cut noisy segments.</p>
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<p>A set of selected license plates from different standards to determine the CCR of the proposed algorithm.</p>
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19 pages, 12092 KiB  
Article
Application of Evolutionary Optimization Techniques in Reverse Engineering of Helical Gears: An Applied Study
by Vahid Pourmostaghimi, Farshad Heidari, Saman Khalilpourazary and Mohammad Reza Chalak Qazani
Axioms 2023, 12(3), 252; https://doi.org/10.3390/axioms12030252 - 1 Mar 2023
Cited by 8 | Viewed by 2412
Abstract
Reverse engineering plays an important role in the manufacturing and automobile industries in designing complicated spare parts, reducing actual production time, and allowing for multiple redesign possibilities, including shape alterations, different materials, and changes to other significant parameters of the component. Using reverse [...] Read more.
Reverse engineering plays an important role in the manufacturing and automobile industries in designing complicated spare parts, reducing actual production time, and allowing for multiple redesign possibilities, including shape alterations, different materials, and changes to other significant parameters of the component. Using reverse engineering methodology, damaged gears can be identified and modeled meticulously. Influential parameters can be obtained in the shortest time. Because most of the time it is impossible to solve gear-related inverse equations mathematically, metaheuristic methods can be used to reverse-engineer gears. This paper presents a methodology based on measurement over balls and span measurement along with evolutionary optimization techniques to determine the geometry of a pure involute of a cylindrical helical gear. Advanced optimization techniques, i.e., Grey Wolf Optimization, Whale Optimization, Particle Swarm Optimization, and Genetic Algorithm, were applied for the considered reverse engineering case, and the effectiveness and accuracy of the proposed algorithms were compared. Confirmatory calculations and experiments reveal the remarkable efficiency of Grey Wolf Optimization and Particle Swarm Optimization techniques in the reverse engineering of helical gears compared to other techniques and in obtaining influential gear design parameters. Full article
(This article belongs to the Special Issue Optimization Models and Applications)
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<p>A schematic view of span measurement and over-balls measurement for a gear.</p>
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<p>Flowchart of the proposed RE of gear framework.</p>
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<p>Pseudocode of the proposed algorithmic framework.</p>
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<p>Position update in GWO.</p>
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<p>Hunting process of humpback whales: (<b>a</b>) movement of whales in a random direction or toward leader; (<b>b</b>) encircling process after finding the prey; (<b>c</b>) reaching the prey in spiral route.</p>
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<p>Search mechanism and whales’ movement in WOA: (<b>a</b>) shrinking encircling process; (<b>b</b>) position update in the spiral way.</p>
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<p>Updating the position of particles in PSO.</p>
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<p>Crossover and mutation operators in GA.</p>
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<p>Measuring of over two balls and span size.</p>
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<p>The algorithms’ convergent curves reaching the minimum defined objective function for GWO, WOA, PSO, and GA.</p>
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<p>Boxplots of the iterations for GWO, WOA and PSO.</p>
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17 pages, 3624 KiB  
Article
The Synchronization of a Class of Time-Delayed Chaotic Systems Using Sliding Mode Control Based on a Fractional-Order Nonlinear PID Sliding Surface and Its Application in Secure Communication
by Mohammad Rasouli, Assef Zare, Majid Hallaji and Roohallah Alizadehsani
Axioms 2022, 11(12), 738; https://doi.org/10.3390/axioms11120738 - 16 Dec 2022
Cited by 6 | Viewed by 1772
Abstract
A novel approach for the synchronization of a class of chaotic systems with uncertainty, unknown time delays, and external disturbances is presented. The control method given here is expressed by combining sliding mode control approaches with adaptive rules. A sliding surface of fractional [...] Read more.
A novel approach for the synchronization of a class of chaotic systems with uncertainty, unknown time delays, and external disturbances is presented. The control method given here is expressed by combining sliding mode control approaches with adaptive rules. A sliding surface of fractional order has been developed to construct the control strategy of the abovementioned sliding mode by employing the structure of nonlinear fractional PID (NLPID) controllers. The suggested control mechanism using Lyapunov’s theorem developed robust adaptive rules in such a way that the estimation error of the system’s unknown parameters and time delays tends to be zero. Furthermore, the proposed robust control approach’s stability has been demonstrated using Lyapunov stability criteria and Lipschitz conditions. Then, in order to assess the performance of the proposed mechanism, the presented control approach was used to simulate the synchronization of two chaotic jerk systems with uncertainty, unknown time delays, and external distortion. The results of the simulation confirm the robust and desirable synchronization performance. Finally, a secure communications mechanism based on the proposed technique is shown as a practical implementation of the introduced control strategy, in which the message signal is disguised in the transmitter with high security and well recovered in the receiver with high quality, according to the mean squared error (MES) criteria. Full article
(This article belongs to the Special Issue Optimization Models and Applications)
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<p>Phase diagram of the jerk master and slave systems without applying the controller.</p>
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<p>Behavior of master and slave system states without applying the control signal.</p>
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<p>Synchronization of jerk systems using the proposed mechanism and applying the control signal at t = 5 s.</p>
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<p>Synchronization error of the master and slave systems.</p>
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<p>The control signal based on the proposed adaptive sliding mode control.</p>
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<p>Estimation error of the system parameters.</p>
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<p>Uncertainty and disturbances of the master and slave systems.</p>
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<p>Chaotic secure communication structure based on the proposed approach.</p>
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<p>Message 1, masked and retrieved, and the error between these two signals.</p>
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<p>Message 2, masked and retrieved, and the error between these two signals.</p>
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13 pages, 2435 KiB  
Article
Analysis and Design of Robust Controller for Polynomial Fractional Differential Systems Using Sum of Squares
by Hassan Yaghoubi, Assef Zare and Roohallah Alizadehsani
Axioms 2022, 11(11), 623; https://doi.org/10.3390/axioms11110623 - 7 Nov 2022
Cited by 1 | Viewed by 1379
Abstract
This paper discusses the robust stability and stabilization of polynomial fractional differential (PFD) systems with a Caputo derivative using the sum of squares. In addition, it presents a novel method of stability and stabilization for PFD systems. It demonstrates the feasibility of designing [...] Read more.
This paper discusses the robust stability and stabilization of polynomial fractional differential (PFD) systems with a Caputo derivative using the sum of squares. In addition, it presents a novel method of stability and stabilization for PFD systems. It demonstrates the feasibility of designing problems that cannot be represented in LMIs (linear matrix inequalities). First, sufficient conditions of stability are expressed for the PFD equation system. Based on the results, the fractional differential system is Mittag–Leffler stable when there is a polynomial function to satisfy the inequality conditions. These functions are obtained from the sum of the square (SOS) approach. The result presents a valuable method to select the Lyapunov function for the stability of PFD systems. Then, robust Mittag–Leffler stability conditions were able to demonstrate better convergence performance compared to asymptotic stabilization and a robust controller design for a PFD equation system with unknown system parameters, and design performance based on a polynomial state feedback controller for PFD-controlled systems. Finally, simulation results indicate the effectiveness of the proposed theorems. Full article
(This article belongs to the Special Issue Optimization Models and Applications)
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<p>The proposed method.</p>
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<p>Phase portrait of <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo> </mo> </mrow> </semantics></math>for PFDE nonlinear system (28).</p>
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<p>Phase portrait of <span class="html-italic">x</span><sub>1</sub>(<span class="html-italic">t</span>), <span class="html-italic">x</span><sub>2</sub>(<span class="html-italic">t</span>) for PFDE nonlinear system (34).</p>
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<p>Surface <span class="html-italic">h</span>(<span class="html-italic">x</span>) for large range of <span class="html-italic">x</span><sub>1</sub>(<span class="html-italic">t</span>), <span class="html-italic">x</span><sub>2</sub>(<span class="html-italic">t</span>) and check the condition <span class="html-italic">h</span>(<span class="html-italic">x</span>) &gt; 0 for stability of nonlinear system (34).</p>
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<p>Surface <span class="html-italic">h</span>(<span class="html-italic">x</span>) for small range of <span class="html-italic">x</span><sub>1</sub>(<span class="html-italic">t</span>), <span class="html-italic">x</span><sub>2</sub>(<span class="html-italic">t</span>) and check the condition <span class="html-italic">h</span>(<span class="html-italic">x</span>) &gt; 0 for stability of nonlinear system (34).</p>
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<p>Phase portrait of <span class="html-italic">x</span><sub>1</sub>(<span class="html-italic">t</span>), <span class="html-italic">x</span><sub>2</sub>(<span class="html-italic">t</span>) for nonlinear time-variant system (39).</p>
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<p>Surface <span class="html-italic">h</span>(<span class="html-italic">x</span>) for small range of <span class="html-italic">x</span><sub>1</sub>(<span class="html-italic">t</span>), <span class="html-italic">x</span><sub>2</sub>(<span class="html-italic">t</span>) and check the condition <span class="html-italic">h</span>(<span class="html-italic">x</span>) &gt; 0 for stability of nonlinear time-variant system (31).</p>
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<p>Surface <span class="html-italic">h</span>(<span class="html-italic">x</span>) for small range of <span class="html-italic">x</span><sub>1</sub>(<span class="html-italic">t</span>), <span class="html-italic">x</span><sub>2</sub>(<span class="html-italic">t</span>) and check the condition <span class="html-italic">h</span>(<span class="html-italic">x</span>) &gt; 0 for stability nonlinear time-variant system (39).</p>
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