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21 pages, 19569 KiB  
Article
The General Solution to a System of Tensor Equations over the Split Quaternion Algebra with Applications
by Zong-Ru Jia and Qing-Wen Wang
Mathematics 2025, 13(4), 644; https://doi.org/10.3390/math13040644 - 16 Feb 2025
Viewed by 151
Abstract
This paper presents a systematic investigation into the solvability and the general solution of a tensor equation system within the split quaternion algebra framework. As an extension of classical quaternions with distinctive pseudo-Euclidean properties, split quaternions offer unique advantages in multidimensional signal processing [...] Read more.
This paper presents a systematic investigation into the solvability and the general solution of a tensor equation system within the split quaternion algebra framework. As an extension of classical quaternions with distinctive pseudo-Euclidean properties, split quaternions offer unique advantages in multidimensional signal processing applications. We establish rigorous necessary and sufficient conditions for the existence of solutions to the proposed tensor equation system, accompanied by explicit formulations for general solutions when solvability criteria are satisfied. The theoretical framework is further strengthened by the development of computational algorithms and numerical validations through concrete examples. Notably, we demonstrate the practical implementation of our theoretical findings through encryption/decryption algorithms for color video data. Full article
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<p>Flowcharts of Algorithms 5 and 6.</p>
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<p>Frames before and after Encryption and Decryption.</p>
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<p>Frames before and after Block Encryption and Decryption.</p>
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19 pages, 386 KiB  
Article
Strained Graphene as Pristine Graphene with a Deformed Momentum Operator
by David Valenzuela, Alfredo Raya and Juan D. García-Muñoz
Condens. Matter 2025, 10(1), 10; https://doi.org/10.3390/condmat10010010 - 7 Feb 2025
Viewed by 328
Abstract
We explore the equivalence between the low-energy dynamics of strained graphene and a quantum mechanical framework for the 2D Dirac equation in flat space with a deformed momentum operator. By considering some common forms of the anisotropic Fermi velocity tensor emerging from the [...] Read more.
We explore the equivalence between the low-energy dynamics of strained graphene and a quantum mechanical framework for the 2D Dirac equation in flat space with a deformed momentum operator. By considering some common forms of the anisotropic Fermi velocity tensor emerging from the elasticity theory, we associate such tensor forms with a deformation of the momentum operator. We first explore the bound states of charge carriers in a background uniform magnetic field in this framework and quantify the impact of strain in the energy spectrum. Then, we use a quadrature algebra formula as a mathematical tool to analyze the impact of the deformation attached to the momentum operator and identify physical consequences of such deformation in terms of energy modifications due to the applied strain. Full article
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<p>Effect of uniform uniaxial strain on the Landau levels. Solid curves represent the levels <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> (blue), <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> (gold), <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> (green), <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> red and <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> (purple) without strain. Dashed curves (in the same color) represent strain with <math display="inline"><semantics> <mrow> <mi>ξ</mi> <mo>=</mo> <mn>1.1</mn> </mrow> </semantics></math> and dotted curves with <math display="inline"><semantics> <mrow> <mi>ξ</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>.</p>
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<p>Effect of uniform shear strain on the <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> Landau level (1LL). The solid black curve represents the unstrained case. Dashed curves represent <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>2</mn> <mo>−</mo> <mn>5</mn> </mrow> </semantics></math> outward from the 1LL. Dotted curves represent <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>0.2</mn> <mo>−</mo> <mn>0.5</mn> </mrow> </semantics></math> increasing toward the 1LL curve.</p>
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<p>Energy eigenvalues from Equation (<a href="#FD74-condensedmatter-10-00010" class="html-disp-formula">74</a>) as a function of the parameter <span class="html-italic">a</span> for fixed <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>y</mi> </msub> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math> in arbitrary units. The blue curve corresponds to <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, gold to <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, green to <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, red to <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> and purple to <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p>
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<p>Energy eigenvalue for the first excited state in from Equation (<a href="#FD90-condensedmatter-10-00010" class="html-disp-formula">90</a>) as a function of the parameter <span class="html-italic">a</span> for different values of <span class="html-italic">b</span> at fixed <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>y</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> in arbitrary units. The lue curve corresponds to <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, long-dashed gold to <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, dashed green to <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, dot-dashed red to <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> and dotted purple to <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p>
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<p>Energy eigenvalues from Equation (<a href="#FD110-condensedmatter-10-00010" class="html-disp-formula">110</a>) as a function of the parameter <span class="html-italic">a</span> for fixed values <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>k</mi> <mi>y</mi> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> in arbitrary units. The blue curve corresponds to <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, long-dashed gold to <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, dashed green to <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, dot-dashed red to <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> and dotted purple to <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p>
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27 pages, 1810 KiB  
Article
Efficient Tensor Robust Principal Analysis via Right-Invertible Matrix-Based Tensor Products
by Zhang Huang, Jun Feng and Wei Li
Axioms 2025, 14(2), 99; https://doi.org/10.3390/axioms14020099 - 28 Jan 2025
Viewed by 428
Abstract
In this paper, we extend the definition of tensor products from using an invertible matrix to utilising right-invertible matrices, exploring the algebraic properties of these new tensor products. Based on this novel definition, we define the concepts of tensor rank and tensor nuclear [...] Read more.
In this paper, we extend the definition of tensor products from using an invertible matrix to utilising right-invertible matrices, exploring the algebraic properties of these new tensor products. Based on this novel definition, we define the concepts of tensor rank and tensor nuclear norm, ensuring consistency with their matrix counterparts, and derive a singular value thresholding (L,R SVT) formula to approximately solve the subproblems in the alternating direction method of multipliers (ADMM), which is integral to our proposed tensor robust principal component analysis (LR TRPCA) algorithm. The computational complexity of the LR TRPCA algorithm is O(k·(n1n2n3+p·min(n12n2,n1n22))) for k iterations. According to this complexity analysis, by using a right-invertible matrix that selects p rows from the n3 rows of the invertible matrix used in the tensor product with an invertible matrix, the computational load is approximately reduced to p/n3 of what it would be with an invertible matrix, highlighting the efficiency gain in terms of computational resources. We apply this efficient algorithm to grayscale video denoising and motion detection problems, where it demonstrates significant improvements in processing speed while maintaining comparable quality levels to existing methods, thereby providing a promising approach for handling multi-linear data and offering valuable insights for advanced data analysis tasks. Full article
(This article belongs to the Special Issue Advances in Linear Algebra with Applications, 2nd Edition)
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<p>Comparative analysis of PSNR values for <math display="inline"><semantics> <msub> <mo>∗</mo> <mrow> <mi>L</mi> <mo>,</mo> <mi>R</mi> </mrow> </msub> </semantics></math> TRPCA algorithms (TRPCA-(20/200)C, TRPCA-(40/200)C, TRPCA-(60/200)C, and TRPCA-(100/200)C), and classical TRPCA algorithm (TRPCA-C) across frames on random tensor.</p>
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<p>Comparative analysis of RSE values for <math display="inline"><semantics> <msub> <mo>∗</mo> <mrow> <mi>L</mi> <mo>,</mo> <mi>R</mi> </mrow> </msub> </semantics></math> TRPCA algorithms (TRPCA-(20/200)C, TRPCA-(40/200)C, TRPCA-(60/200)C, and TRPCA-(100/200)C), and classical TRPCA algorithm (TRPCA-C) across frames on random tensor.</p>
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<p>Comparative analysis of PSNR values for TRPAC-C, TRPCA-(100/200)C, TRPCA-(40/200)C, and TRPCA-(20/200)C algorithms across video frames.</p>
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<p>Comparative analysis of RSE values for TRPAC-C, TRPCA-(100/200)C, TRPCA-(40/200)C, and TRPCA-(20/200)C algorithms across video frames.</p>
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<p>Comparison of denoised images with TRPAC-C (classic TRPCA) and TRPCA-(20/200)C(<math display="inline"><semantics> <msub> <mo>∗</mo> <mrow> <mi>L</mi> <mo>,</mo> <mi>R</mi> </mrow> </msub> </semantics></math> TRPCA) and frames = 40, 70, 120.</p>
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<p>Comparison of residuals after denoising with TRPAC-C (classic TRPCA) and TRPCA-(20/200)C(<math display="inline"><semantics> <msub> <mo>∗</mo> <mrow> <mi>L</mi> <mo>,</mo> <mi>R</mi> </mrow> </msub> </semantics></math> TRPCA) and frames = 40, 70, 120.</p>
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20 pages, 304 KiB  
Article
Derivation of Tensor Algebra as a Fundamental Operation—The Fermi Derivative in a General Metric Affine Space
by Michael Tsamparlis
Symmetry 2025, 17(1), 81; https://doi.org/10.3390/sym17010081 - 7 Jan 2025
Viewed by 557
Abstract
The aim of this work is to demonstrate that all linear derivatives of the tensor algebra over a smooth manifold M can be viewed as specific cases of a broader concept—the operation of derivation. This approach reveals the universal role of differentiation, which [...] Read more.
The aim of this work is to demonstrate that all linear derivatives of the tensor algebra over a smooth manifold M can be viewed as specific cases of a broader concept—the operation of derivation. This approach reveals the universal role of differentiation, which simplifies and generalizes the study of tensor derivatives, making it a powerful tool in Differential Geometry and related fields. To perform this, the generic derivative is introduced, which is defined in terms of the quantities Qk(i)(X). Subsequently, the transformation law of these quantities is determined by the requirement that the generic derivative of a tensor is a tensor. The quantities Qk(i)(X) and their transformation law define a specific geometric object on M, and consequently, a geometric structure on M. Using the generic derivative, one defines the tensor fields of torsion and curvature and computes them for all linear derivatives in terms of the quantities Qk(i)(X). The general model is applied to the cases of Lie derivative, covariant derivative, and Fermi derivative. It is shown that the Lie derivative has non-zero torsion and zero curvature due to the Jacobi identity. For the covariant derivative, the standard results follow without any further calculations. Concerning the Fermi derivative, this is defined in a new way, i.e., as a higher-order derivative defined in terms of two derivatives: a given derivative and the Lie derivative. Being linear derivative, it has torsion and curvature tensor. These fields are computed in a general affine space from the corresponding general expressions of the generic derivative. Applications of the above considerations are discussed in a number of cases. Concerning the Lie derivative, it is been shown that the Poisson bracket is in fact a Lie derivative. Concerning the Fermi derivative, two applications are considered: (a) the explicit computation of the Fermi derivative in a general affine space and (b) the consideration of Freedman–Robertson–Walker spacetime endowed with a scalar torsion field, which satisfies the Cosmological Principle and the computation of Fermi derivative of the spatial directions defining a spatial frame along the cosmological fluid of comoving observers. It is found that torsion, even in this highly symmetric case, induces a kinematic rotation of the space axes, questioning the interpretation of torsion as a spin. Finally it is shown that the Lie derivative of the dynamical equations of an autonomous conservative dynamical system is equivalent to the standard Lie symmetry method. Full article
(This article belongs to the Special Issue Advances in Nonlinear Systems and Symmetry/Asymmetry)
19 pages, 313 KiB  
Article
A Closed Form of Higher-Order Cayley Transforms and Generalized Rodrigues Vectors Parameterization of Rigid Motion
by Daniel Condurache, Mihail Cojocari and Ioan-Adrian Ciureanu
Mathematics 2025, 13(1), 114; https://doi.org/10.3390/math13010114 - 30 Dec 2024
Viewed by 480
Abstract
This paper introduces a novel closed-form coordinate-free expression for the higher-order Cayley transform, a concept that has not been explored in depth before. The transform is defined by the Lie algebra of three-dimensional vectors into the Lie group of proper orthogonal Euclidean tensors. [...] Read more.
This paper introduces a novel closed-form coordinate-free expression for the higher-order Cayley transform, a concept that has not been explored in depth before. The transform is defined by the Lie algebra of three-dimensional vectors into the Lie group of proper orthogonal Euclidean tensors. The approach uses only elementary algebraic calculations with Euclidean vectors and tensors. The analytical expressions are given by rational functions by the Euclidean norm of vector parameterization. The inverse of the higher-order Cayley map is a multi-valued function that recovers the higher-order Rodrigues vectors (the principal parameterization and their shadows). Using vector parameterizations of the Euler and higher-order Rodrigues vectors, we determine the instantaneous angular velocity (in space and body frame), kinematics equations, and tangent operator. The analytical expressions of the parameterized quantities are identical for both the principal vector and shadows parameterization, showcasing the novelty and potential of our research. Full article
(This article belongs to the Special Issue Geometric Methods in Contemporary Engineering)
13 pages, 701 KiB  
Article
Practical Canonical Labeling of Multi-Digraphs via Computer Algebra
by Jiang Liu, Siyu Yang, Wencheng Liu, Feng Ni and Chenfan Zhu
Symmetry 2024, 16(12), 1638; https://doi.org/10.3390/sym16121638 - 11 Dec 2024
Viewed by 533
Abstract
Practical algorithms for computing canonical forms of multi-digraphs do not exist in the literature. This paper proposes two practical approaches for finding canonical forms, from the perspective of nD symbolic computation. Initially, the approaches turn the problem of finding canonical forms of [...] Read more.
Practical algorithms for computing canonical forms of multi-digraphs do not exist in the literature. This paper proposes two practical approaches for finding canonical forms, from the perspective of nD symbolic computation. Initially, the approaches turn the problem of finding canonical forms of multi-digraphs into computing canonical forms of indexed monomials in computer algebra. Then, the first approach utilizes the double coset representative method in computational group theory for canonicalization of indexed monomials and shows that finding the canonical forms of a class of multi-digraphs in practice has polynomial complexity of approximately O((k+p)2) or O(k2.1) by the computer algebra system (CAS) tool Tensor-canonicalizer. The second approach verifies the equivalence of canonicalization of indexed monomials and finding canonical forms of (simple) colored tripartite graphs. It is found that the proposed algorithm takes approximately O((k+2p)4.803) time for a class of multi-digraphs in practical implementation, combined with one of the best known graph isomorphism tools Traces, where k and p are the vertex number and edge number of a multi-digraph, respectively. Full article
(This article belongs to the Special Issue Applications Based on AI in Mathematics and Asymmetry/Symmetry)
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<p>An example of a multi-digraph.</p>
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<p>An example of computing canonical forms of multi-digraphs. (<b>a</b>) A multi-digraph <span class="html-italic">G</span>. (<b>b</b>) The canonical form of <span class="html-italic">G</span>.</p>
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<p>Graphical description of Example 4 (red = 1, green = 2, yellow = 3).</p>
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<p>Performance of computing canonical forms of (non-disjoint) union of tripartite graphs by <span class="html-italic"><tt>Traces</tt></span> (horizontal: number of vertices; vertical: time in seconds).</p>
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23 pages, 619 KiB  
Article
A System of Tensor Equations over the Dual Split Quaternion Algebra with an Application
by Liuqing Yang, Qing-Wen Wang and Zuliang Kou
Mathematics 2024, 12(22), 3571; https://doi.org/10.3390/math12223571 - 15 Nov 2024
Viewed by 632
Abstract
In this paper, we propose a definition of block tensors and the real representation of tensors. Equipped with the simplification method, i.e., the real representation along with the M-P inverse, we demonstrate the conditions that are necessary and sufficient for the system of [...] Read more.
In this paper, we propose a definition of block tensors and the real representation of tensors. Equipped with the simplification method, i.e., the real representation along with the M-P inverse, we demonstrate the conditions that are necessary and sufficient for the system of dual split quaternion tensor equations (ANX,XSC)=(B,D), when its solution exists. Furthermore, the general expression of the solution is also provided when the solution of the system exists, and we use a numerical example to validate it in the last section. To the best of our knowledge, this is the first time that the aforementioned tensor system has been examined on dual split quaternion algebra. Additionally, we provide its equivalent conditions when its Hermitian solution X=X and η-Hermitian solutions X=Xη exist. Subsequently, we discuss two special dual split quaternion tensor equations. Last but not least, we propose an application for encrypting and decrypting two color videos, and we validate this algorithm through a specific example. Full article
(This article belongs to the Special Issue Advances of Linear and Multilinear Algebra)
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<p>The model for encrypting two videos.</p>
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<p>The original, encrypted, and decrypted images of randomly selected slices from color videos <math display="inline"><semantics> <msub> <mi mathvariant="script">X</mi> <mn>0</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi mathvariant="script">X</mi> <mn>1</mn> </msub> </semantics></math>.</p>
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65 pages, 2635 KiB  
Tutorial
Understanding the Flows of Signals and Gradients: A Tutorial on Algorithms Needed to Implement a Deep Neural Network from Scratch
by Przemysław Klęsk
Appl. Sci. 2024, 14(21), 9972; https://doi.org/10.3390/app14219972 - 31 Oct 2024
Viewed by 812
Abstract
Theano, TensorFlow, Keras, Torch, PyTorch, and other software frameworks have remarkably stimulated the popularity of deep learning (DL). Apart from all the good they achieve, the danger of such frameworks is that they unintentionally spur a black-box attitude. Some practitioners play around with [...] Read more.
Theano, TensorFlow, Keras, Torch, PyTorch, and other software frameworks have remarkably stimulated the popularity of deep learning (DL). Apart from all the good they achieve, the danger of such frameworks is that they unintentionally spur a black-box attitude. Some practitioners play around with building blocks offered by frameworks and rely on them, having a superficial understanding of the internal mechanics. This paper constitutes a concise tutorial that elucidates the flows of signals and gradients in deep neural networks, enabling readers to successfully implement a deep network from scratch. By “from scratch”, we mean with access to a programming language and numerical libraries but without any components that hide DL computations underneath. To achieve this goal, the following five topics need to be well understood: (1) automatic differentiation, (2) the initialization of weights, (3) learning algorithms, (4) regularization, and (5) the organization of computations. We cover all of these topics in the paper. From a tutorial perspective, the key contributions include the following: (a) proposition of R and S operators for tensors—rashape and stack, respectively—that facilitate algebraic notation of computations involved in convolutional, pooling, and flattening layers; (b) a Python project named hmdl (“home-made deep learning”); and (c) consistent notation across all mathematical contexts involved. The hmdl project serves as a practical example of implementation and a reference. It was built using NumPy and Numba modules with JIT and CUDA amenities applied. In the experimental section, we compare hmdl implementation to Keras (backed with TensorFlow). Finally, we point out the consistency of the two in terms of convergence and accuracy, and we observe the superiority of the latter in terms of efficiency. Full article
(This article belongs to the Special Issue Advanced Digital Signal Processing and Its Applications)
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<p>Milestones in the history of neural networks and deep learning [<a href="#B3-applsci-14-09972" class="html-bibr">3</a>,<a href="#B4-applsci-14-09972" class="html-bibr">4</a>,<a href="#B5-applsci-14-09972" class="html-bibr">5</a>,<a href="#B6-applsci-14-09972" class="html-bibr">6</a>,<a href="#B7-applsci-14-09972" class="html-bibr">7</a>,<a href="#B8-applsci-14-09972" class="html-bibr">8</a>,<a href="#B9-applsci-14-09972" class="html-bibr">9</a>,<a href="#B10-applsci-14-09972" class="html-bibr">10</a>,<a href="#B11-applsci-14-09972" class="html-bibr">11</a>,<a href="#B12-applsci-14-09972" class="html-bibr">12</a>,<a href="#B13-applsci-14-09972" class="html-bibr">13</a>,<a href="#B14-applsci-14-09972" class="html-bibr">14</a>,<a href="#B15-applsci-14-09972" class="html-bibr">15</a>,<a href="#B16-applsci-14-09972" class="html-bibr">16</a>,<a href="#B17-applsci-14-09972" class="html-bibr">17</a>,<a href="#B18-applsci-14-09972" class="html-bibr">18</a>,<a href="#B19-applsci-14-09972" class="html-bibr">19</a>,<a href="#B20-applsci-14-09972" class="html-bibr">20</a>,<a href="#B21-applsci-14-09972" class="html-bibr">21</a>,<a href="#B22-applsci-14-09972" class="html-bibr">22</a>,<a href="#B23-applsci-14-09972" class="html-bibr">23</a>,<a href="#B24-applsci-14-09972" class="html-bibr">24</a>,<a href="#B25-applsci-14-09972" class="html-bibr">25</a>,<a href="#B26-applsci-14-09972" class="html-bibr">26</a>,<a href="#B27-applsci-14-09972" class="html-bibr">27</a>,<a href="#B28-applsci-14-09972" class="html-bibr">28</a>,<a href="#B29-applsci-14-09972" class="html-bibr">29</a>,<a href="#B30-applsci-14-09972" class="html-bibr">30</a>,<a href="#B31-applsci-14-09972" class="html-bibr">31</a>,<a href="#B32-applsci-14-09972" class="html-bibr">32</a>,<a href="#B33-applsci-14-09972" class="html-bibr">33</a>,<a href="#B34-applsci-14-09972" class="html-bibr">34</a>,<a href="#B35-applsci-14-09972" class="html-bibr">35</a>].</p>
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<p>Example medium-sized deep network built with popular layer types: convolutional, max pooling, dropout, flattening, and dense (prepared for the CIFAR-10 data set; see experiment with ID 3392187021 in <a href="#sec8-applsci-14-09972" class="html-sec">Section 8</a>).</p>
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<p>Example of a simple neural network for 10-class classification.</p>
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<p>Illustration of forward and backward computations at the junction of two convolutional layers <math display="inline"><semantics> <mi>α</mi> </semantics></math> and <math display="inline"><semantics> <mi>β</mi> </semantics></math> for the network structure from <a href="#applsci-14-09972-f003" class="html-fig">Figure 3</a>.</p>
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<p>Forward computations of max and average pooling for <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>.</p>
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<p>Illustration of backward computations for flattening and dropout layers. High dropout rate <math display="inline"><semantics> <mrow> <msup> <mi>r</mi> <mo>*</mo> </msup> <mo>=</mo> <mn>0.875</mn> </mrow> </semantics></math> chosen for readability of surviving connections.</p>
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<p>Illustration of two stages of backward computations for a dense layer using a softmax activation function.</p>
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<p>Forward pass leading to exploding signals: a fake network consisting of 100 dense layers with 512 neurons each (no activations) and intial weights drawn from a standard normal distribution.</p>
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<p>Fake forward pass leading to vanishing signals due to intial weights drawn from a normal distribution with standard deviation scaled by <math display="inline"><semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </semantics></math>, regardless of <span class="html-italic">N</span>.</p>
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<p>Fake forward pass leading to a stable numerical behavior due to initial weights drawn from a properly scaled normal distribution with standard deviation <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <msqrt> <mi>N</mi> </msqrt> </mrow> </semantics></math>.</p>
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<p>Training costs (losses) of Adam and other SGD algorithms on MNIST (<b>a</b>) and CIFAR-10 (<b>b</b>) data sets. Source: (Kingma and Ba, 2014) [<a href="#B25-applsci-14-09972" class="html-bibr">25</a>].</p>
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<p>UML class diagram of <span class="html-italic">hdml</span> project (<a href="https://github.com/pklesk/hmdl/blob/main/uml/classes.pdf" target="_blank">https://github.com/pklesk/hmdl/blob/main/uml/classes.pdf</a>) (accessed on 17 October 2024).</p>
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<p>Functions executing forward and backward computations from the abstract class <tt>Layer</tt>.</p>
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<p>The core of the <tt>fit</tt> function of <tt>SequentialClassifier</tt>. The main training loop (over epochs and batches) executes forward and backward computations through layers for each batch.</p>
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<p>Forward and backward passes for <tt>SequentialClassifier</tt>.</p>
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<p>Automatic differentiation for class <tt>Flatten</tt>.</p>
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<p>Automatic differentiation for class <tt>Dropout</tt>.</p>
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<p>Automatic differentiation for class <tt>Dense</tt>.</p>
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<p>Forward computations for class <tt>MaxPool2D</tt>: the simplest <tt>numpy</tt>-based variant.</p>
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<p>Backward computations for class <tt>MaxPool2D</tt>: the simplest <tt>numpy</tt>-based variant.</p>
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<p>Backward computations of gradient for class <tt>Conv2D</tt>: the simplest <tt>numpy</tt>-based variant (GEMM).</p>
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<p>Backward computations of error propagation for class <tt>Conv2D</tt>: the simplest <tt>numpy</tt>-based variant (GEMM).</p>
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<p>Execution times of convolutional layers (64 input and 64 output channels; batch size: 32) for different implementation variants, filter sizes, and image sizes. For details of the hardware and software environment see page 42.</p>
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<p>Functions for Glorot initalization of weights in the hmdl project.</p>
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<p>Functions for He initalization of weights in the hmdl project.</p>
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<p>Implementation of Adam (coupled with regularization) in the hmdl project.</p>
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<p>Sample images from data sets applied in experiments.</p>
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<p>Main settings for an experiment in script <tt>experimenter.py</tt>.</p>
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<p>Description of a network structure in the hmdl project, declared in <tt>experimenter.py</tt>.</p>
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<p>Choice of data set, randomization seed, and other settings in the script <tt>experimenter.py</tt>.</p>
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<p>Forward computations for class <tt>MaxPool2D</tt>: variant based on <tt>numba</tt>’s just-in-time compilation.</p>
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<p>Backward computations for class <tt>MaxPool2D</tt>: variant based on <tt>numba</tt>’s just-in-time compilation.</p>
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<p>Forward computations for class <tt>MaxPool2D</tt>: variant implemented for GPU computations using <tt>numba.cuda</tt> module.</p>
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<p>CUDA kernel function <tt>do_forward_numba_cuda_direct_job</tt> for forward max pooling computations invoked via function <tt>do_forward_numba_cuda_direct</tt>.</p>
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<p>Backward computations for class <tt>MaxPool2D</tt>: variant implemented for GPU computations using <tt>numba.cuda</tt> module.</p>
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<p>CUDA kernel function <tt>do_backward_numba_cuda_direct_job</tt> for backward max pooling computations invoked via function <tt>do_backward_numba_cuda_direct</tt>.</p>
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<p>Backward computations of gradient for class <tt>Conv2D</tt>: variant based on <tt>numba</tt>’s just-in-time compilation.</p>
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<p>Backward computations of gradient for class <tt>Conv2D</tt>: variant based directly on definition, using GPU computations and <tt>numba.cuda</tt>.</p>
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<p>CUDA kernel function <tt>do_backward_numba_cuda_direct_job</tt> for backward convolutional computations invoked via function <tt>do_backward_numba_cuda_direct</tt>.</p>
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<p>Backward computations of gradient for class <tt>Conv2D</tt>: variant based on tiles, using GPU and <tt>numba.cuda</tt>.</p>
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<p>CUDA kernel function <tt>do_backward_numba_cuda_tiles_job</tt> for backward convolutional computations invoked via function <tt>do_backward_numba_cuda_tiles</tt>.</p>
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21 pages, 1612 KiB  
Article
Effects of Anisotropy, Convection, and Relaxation on Nonlinear Reaction-Diffusion Systems
by Juan I. Ramos
Computation 2024, 12(11), 214; https://doi.org/10.3390/computation12110214 - 25 Oct 2024
Viewed by 685
Abstract
The effects of relaxation, convection, and anisotropy on a two-dimensional, two-equation system of nonlinearly coupled, second-order hyperbolic, advection–reaction–diffusion equations are studied numerically by means of a three-time-level linearized finite difference method. The formulation utilizes a frame-indifferent constitutive equation for the heat and mass [...] Read more.
The effects of relaxation, convection, and anisotropy on a two-dimensional, two-equation system of nonlinearly coupled, second-order hyperbolic, advection–reaction–diffusion equations are studied numerically by means of a three-time-level linearized finite difference method. The formulation utilizes a frame-indifferent constitutive equation for the heat and mass diffusion fluxes, taking into account the tensorial character of the thermal diffusivity of heat and mass diffusion. This approach results in a large system of linear algebraic equations at each time level. It is shown that the effects of relaxation are small although they may be noticeable initially if the relaxation times are smaller than the characteristic residence, diffusion, and reaction times. It is also shown that the anisotropy associated with one of the dependent variables does not have an important role in the reaction wave dynamics, whereas the anisotropy of the other dependent variable results in transitions from spiral waves to either large or small curvature reaction fronts. Convection is found to play an important role in the reaction front dynamics depending on the vortex circulation and radius and the anisotropy of the two dependent variables. For clockwise-rotating vortices of large diameter, patterns similar to those observed in planar mixing layers have been found for anisotropic diffusion tensors. Full article
Show Figures

Figure 1

Figure 1
<p>(Color online) <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> at (from <b>left</b> to <b>right</b> and <b>top</b> to <b>bottom</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>, 55, 60, 65, 70, 75, 80, 85, and 90 for parameter set 1001.</p>
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<p>(Color online) <math display="inline"><semantics> <mrow> <mi>v</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> at (from <b>left</b> to <b>right</b> and <b>top</b> to <b>bottom</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>, 55, 60, 65, 70, 75, 80, 85, and 90 for parameter set 1001.</p>
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<p>(Color online) <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> at (from <b>left</b> to <b>right</b> and <b>top</b> to <b>bottom</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>, 55, 60, 65, 70, 75, 80, 85, and 90 for parameter set 1002.</p>
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<p>(Color online) <math display="inline"><semantics> <mrow> <mi>v</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> at (from <b>left</b> to <b>right</b> and <b>top</b> to <b>bottom</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>, 55, 60, 65, 70, 75, 80, 85, and 90 for parameter set 1002.</p>
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<p>(Color online) <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> (<b>left</b>) and <math display="inline"><semantics> <mrow> <mi>v</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> (<b>right</b>) at <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced separators="" open="(" close=")"> <mo>−</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> <mo>,</mo> <mo>−</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> </mfenced> </mrow> </semantics></math> (continuous line, red), <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math> (dashed line, green) and <math display="inline"><semantics> <mfenced separators="" open="(" close=")"> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> <mo>,</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> </mfenced> </semantics></math> (dashed-dotted line, blue) for parameter set 1000 (first row), 1001 (second row), 1002 (third row), and 1003 (third row).</p>
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<p>(Color online) <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> (<b>left</b>) and <math display="inline"><semantics> <mrow> <mi>v</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> (<b>right</b>) at <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced separators="" open="(" close=")"> <mo>−</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> <mo>,</mo> <mo>−</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> </mfenced> </mrow> </semantics></math> (continuous line, red), <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math> (dashed line, green) and <math display="inline"><semantics> <mfenced separators="" open="(" close=")"> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> <mo>,</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> </mfenced> </semantics></math> (dashed-dotted line, blue) for parameter set 1011 (first row), 2008 (second row), 2009 (third row) and 2010 (third row).</p>
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<p>(Color online) <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> (<b>left</b>) and <math display="inline"><semantics> <mrow> <mi>v</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> (<b>right</b>) at <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced separators="" open="(" close=")"> <mo>−</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> <mo>,</mo> <mo>−</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> </mfenced> </mrow> </semantics></math> (continuous line, red), <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math> (dashed line, green) and <math display="inline"><semantics> <mfenced separators="" open="(" close=")"> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> <mo>,</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> </mfenced> </semantics></math> (dashed-dotted line, blue) for parameter set 1023 (first row), 2020 (second row), 2021 (third row) and 2022 (third row).</p>
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<p>(Color online) <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> (<b>left</b>) and <math display="inline"><semantics> <mrow> <mi>v</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> (<b>right</b>) at <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced separators="" open="(" close=")"> <mo>−</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> <mo>,</mo> <mo>−</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> </mfenced> </mrow> </semantics></math> (continuous line, red), <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math> (dashed line, green) and <math display="inline"><semantics> <mfenced separators="" open="(" close=")"> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> <mo>,</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mi>L</mi> <mn>2</mn> </mfrac> </mstyle> </mfenced> </semantics></math> (dashed-dotted line, blue) for parameter set 2009 (first row), 2013 (second row), 2021 (third row), and 2025 (third row).</p>
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<p>(Color online) <math display="inline"><semantics> <mrow> <mi>v</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> at (from <b>left</b> to <b>right</b> and <b>top</b> to <b>bottom</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>, 55, 60, 65, 70, 75, 80, 85, and 90 for parameter set 1024.</p>
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<p>(Color online) <math display="inline"><semantics> <mrow> <mi>v</mi> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </semantics></math> at (from <b>left</b> to <b>right</b> and <b>top</b> to <b>bottom</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>, 55, 60, 65, 70, 75, 80, 85, and 90 for parameter set 1025.</p>
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24 pages, 6178 KiB  
Article
An Exploratory Study of a Choreographic Approach to Golf Swing Dynamics: Bridging Biomechanics and Laban Movement Analysis
by Wangdo Kim, Albert H. Vette, Wanda Ottes and Colleen Wahl
Sensors 2024, 24(21), 6845; https://doi.org/10.3390/s24216845 - 24 Oct 2024
Viewed by 1969
Abstract
This study introduces an innovative integration of Laban Movement Analysis (LMA) with biomechanical principles to examine the golf swing dynamics from an ecological perspective. Traditionally, LMA focuses on the qualitative aspects of movement, often isolated from external influences. This research bridges that gap [...] Read more.
This study introduces an innovative integration of Laban Movement Analysis (LMA) with biomechanical principles to examine the golf swing dynamics from an ecological perspective. Traditionally, LMA focuses on the qualitative aspects of movement, often isolated from external influences. This research bridges that gap by investigating how golfers manage and adapt to the inertial forces of the club throughout the swing. Using motion tracking sensors and screw theory, we analyzed the spatial movement pattern in the Kinesphere (mapped as an icosahedron) and related it to force dynamics in the Effort Cube through the inertia tensor. The results showed significant differences between skilled and novice golfers in terms of how efficiently they align their movements with the club’s inertia. Skilled golfers demonstrated smoother Instantaneous Screw Axes (ISAs) and better synchronization with inertia forces, while novice golfers exhibited more abrupt deviations. These findings suggest that integrating qualitative movement descriptors with biomechanical models provides deeper insights into swing efficiency, performance improvement, and injury prevention. This combined framework offers a novel method to enhance both qualitative and quantitative analysis of golf swings. Full article
(This article belongs to the Section Biomedical Sensors)
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Figure 1

Figure 1
<p>(<b>a</b>) Laban’s Effort Graph. This Effort graph, created by Rudolf Laban, illustrates the organization of inner intent or motivation behind a movement. The graph delineates the polarities of the four Effort qualities: Weight, Time, Space, and Flow. Each Effort quality has two opposing characteristics: Weight Effort ranges from light to strong, indicating the force exerted in a movement. Time Effort ranges from sustained to quick, reflecting the speed and acceleration of a movement. Space Effort ranges from direct to indirect, representing the focus and clarity of the movement’s path. Flow Effort ranges from free to bound, describing the continuity and control of the movement. The bottom-left panels show true 3D directions, illustrating the spatial orientation of movements, while the bottom-right panel shows the “Effort cube”, representing the combination of Effort elements in a three-dimensional framework. Effort quality in the context of the swing also directly impacts efficiency and timing. For instance, a ‘free flow’ in the effort action ‘press’—strong, direct, and sustained—can be seen in the even application of force through the impact with the ball, maximizing transfer of energy without unnecessary resistance. The implementation of LMA in this context serves as an innovative method to convey the complex biomechanical and qualitative nuances of the golf swing. The inclusion of these Effort Actions within training regimens may enhance a golfer’s understanding of the physical and psychological elements at play, potentially leading to improved performance and a deeper appreciation for the subtleties of the sport. (<b>b</b>) The ‘A’ scale inclinations illustrate the specific body alignments and movements associated with various phases of the golf swing. These inclinations represent the movements across the three spatial planes—horizontal, vertical, and sagittal. The scale integrates these planes through a series of 12 transversal units, comprising six on the right and six on the left, capturing a range of motions from flat and steep to flowing. This structure effectively links the angular dimensions of the three planes to the dynamic movements of the golfer.</p>
Full article ">Figure 1 Cont.
<p>(<b>a</b>) Laban’s Effort Graph. This Effort graph, created by Rudolf Laban, illustrates the organization of inner intent or motivation behind a movement. The graph delineates the polarities of the four Effort qualities: Weight, Time, Space, and Flow. Each Effort quality has two opposing characteristics: Weight Effort ranges from light to strong, indicating the force exerted in a movement. Time Effort ranges from sustained to quick, reflecting the speed and acceleration of a movement. Space Effort ranges from direct to indirect, representing the focus and clarity of the movement’s path. Flow Effort ranges from free to bound, describing the continuity and control of the movement. The bottom-left panels show true 3D directions, illustrating the spatial orientation of movements, while the bottom-right panel shows the “Effort cube”, representing the combination of Effort elements in a three-dimensional framework. Effort quality in the context of the swing also directly impacts efficiency and timing. For instance, a ‘free flow’ in the effort action ‘press’—strong, direct, and sustained—can be seen in the even application of force through the impact with the ball, maximizing transfer of energy without unnecessary resistance. The implementation of LMA in this context serves as an innovative method to convey the complex biomechanical and qualitative nuances of the golf swing. The inclusion of these Effort Actions within training regimens may enhance a golfer’s understanding of the physical and psychological elements at play, potentially leading to improved performance and a deeper appreciation for the subtleties of the sport. (<b>b</b>) The ‘A’ scale inclinations illustrate the specific body alignments and movements associated with various phases of the golf swing. These inclinations represent the movements across the three spatial planes—horizontal, vertical, and sagittal. The scale integrates these planes through a series of 12 transversal units, comprising six on the right and six on the left, capturing a range of motions from flat and steep to flowing. This structure effectively links the angular dimensions of the three planes to the dynamic movements of the golfer.</p>
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<p>Displayed here are the positions of reflective markers on a golfer at the moment the club addresses the ball. This setup was used to capture the biomechanical data originally recorded, detailing the anatomical landmarks critical for analyzing movement dynamics. The separate panel shows detailed placements on the hand, essential for understanding the grip dynamics and the resultant force transmission through the golfer’s body during the swing. The origin of the global frame coincides with the first COP location of the left foot.</p>
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<p>X-Scale positions in a golf swing. This figure illustrates the X-Scale positions specific to a golf swing, mapping key phases of the swing to spatial orientations and movements. Top Diagram: X1 to X11: Symbols representing key positions in the golf swing, indicating directional movements and spatial orientation in three planes (vertical, horizontal, and sagittal). Bottom Diagram: Golfer’s Swing Path: Arrows show the path of the golf club throughout the swing, from the start position to the follow-through, illustrating the continuous motion and flow. X1: Start position, club down in front of the golfer. X2: Right sideward middle (door plane, vertical). X3: Right backward high (table plane, horizontal). X4: Top of backswing, backward high (wheel plane, sagittal). X5: Right backward high (table plane, horizontal). X6: Right sideward middle (door plane, vertical). X7: Forward low (wheel plane, sagittal). X8: Left backward low (table plane, horizontal). X9: Left middle (door plane, vertical). X10: Left backward high (table plane, horizontal). X11: Follow-through, backward high (wheel plane, sagittal).</p>
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<p>Comparison of Effort Cube panels. The figure demonstrates the integration of a, b, and c planes with arrows indicating the correct spatial orientations. The human figures expressing the three planes are labeled with the same a, b, and c labels for clarity. The red, blue, and green colors represent a two-dimensional projection of the Effort Cube, visualizing the “three unequal spatial pulls.” It is important to focus on the arrows to correctly interpret the spatial relationships depicted in the figure. This figure illustrates Laban’s Effort Graph diagram, which integrates three key dimensions of movement: (<b>a</b>) high/deep or the Weight plane, representing vertical movements and the distribution of weight; (<b>b</b>) side-to-side or the Space plane, depicting lateral movements and spatial orientation; (<b>c</b>) forward/backward or the Time plane, indicating movements related to timing and progression. (<b>d</b>) Shows a two-dimensional projection of the Effort Cube, simplifying the analysis by focusing on movement within a single plane. (<b>e</b>) Presents an X-marked schematic within the bounds of the Effort Cube, highlighting specific movement patterns and the intersections of the three dimensions. This figure is crucial for understanding how the dynamic qualities of movement are analyzed and interpreted using Laban’s framework.</p>
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<p>This figure illustrates the grip reference frame attached at the center of the end of the club shaft. The spatial inertia tensor, computed with reference to the origin of the grip coordinate system, is depicted showing its principal axes and moments of inertia. This representation highlights how the inertia tensor transforms at any point along the wrist joint axis ‘A’, emphasizing the consistent eigenvalues irrespective of the positioning. The spatial arrangement allows us to explore how the inertia impacts the golfer’s control over the club during dynamic movements.</p>
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<p>This figure illustrates the Instantaneous Screw Axis (ISA) represented for the golf club (solid lines depicted in blue) and the principal axes of inertia (e3 dashed lines depicted in red) during a novice golfer’s swing. The paths projected onto the medial and superior sides illustrate the motion dynamics, showing the lack of alignment between perceived inertia and actual movement paths, indicative of the novice’s struggle with effective force management and synchronization. The arrow indicates where the subsequent axes have migrated at every 0.0333 s of time step (units in cm). Adapted from [<a href="#B14-sensors-24-06845" class="html-bibr">14</a>], “Haptic Perception-Action Coupling Manifold of Effective Golf Swing”, International Journal of Golf Science, 2(1), 10–32. The axes reference their initial description and orientation as detailed in <a href="#sensors-24-06845-f002" class="html-fig">Figure 2</a>.</p>
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<p>Showcased here is the proficient golfer’s ability to synchronize her swing dynamics effectively, as evidenced by the figure that illustrates alignment between the Instantaneous Screw Axis (ISA) represented for the golf club. and the e3 (dashed lines depicted in red). This alignment demonstrates her adept perception-action coupling, allowing her to voluntarily harness the club’s inertia to optimize swing mechanics and energy flow, contrasted with the novice’s disjointed motion paths. Adapted from [<a href="#B14-sensors-24-06845" class="html-bibr">14</a>] “Haptic Perception-Action Coupling Manifold of Effective Golf Swing”, International Journal of Golf Science, 2(1), 10–32.</p>
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<p>This image embodies the application of the LMA framework, illustrating how the golfer’s body dynamically adapts to the contour of the club’s inertia envelope during the downswing. <a href="#sensors-24-06845-f008" class="html-fig">Figure 8</a> is a projection of <a href="#sensors-24-06845-f007" class="html-fig">Figure 7</a> in the XZ plane, providing a detailed view of the spatial relationships and orientations of the vectors involved. The red dashed contour marks the principal axis of inertia of the club, serving as a guide that the golfer’s movements mold around. The e3 eigenvector, known as the principal axis of inertia, represents the largest principal moment of inertia and the mass distribution along the grey lines indeed represent the longitudinal axis of the golf club, aligned perpendicular to this axis and passing through the mass center of the golf club. The axes are defined as follows, consistent with <a href="#sensors-24-06845-f002" class="html-fig">Figure 2</a>: X-axis is horizontal, Z-axis is vertical, and Y-axis is perpendicular to both the X and Z axes. This adaptive process, akin to a sculptor intuitively shaping clay, highlights the profound integration of body form with the evolving physical forces of the club, showcasing a sophisticated synchronization of movement and inertia for optimal swing efficiency.</p>
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<p>Illustration of the hands holding the drive and making a swing, along with additional information about the torso movements with the Effort elements. This figure uses Motif Writing to provide a simplified representation of key movement elements, rather than a complete notation of the entire swing. As a result, the weight shift is not visually represented in this figure. The central vertical line represents the body’s midline, with symbols on the left denoting body part positions and movements. The large curved arrow on the right depicts the swing arc. At the bottom center, a small circle with an “x” marks the golf ball’s position. Upward and downward arrows indicate the backswing and downswing movements. The sign at the bottom left represents the left hand and the right hand together in one symbol, indicating the coordinated use of both hands during the swing. Curved lines at the top and bottom frame the movement sequence. The legend distinguishes between the ball (open circle) and the touched ball (filled circle). This notation system translates the complex, three-dimensional golf swing into a detailed two-dimensional representation, encompassing spatial pathways, body engagement, and movement flow. The motif provides insights into the golfer’s technique, potentially revealing differences between novice (<b>b</b>) and proficient players (<b>a</b>) in terms of movement fluidity, body alignment, swing path consistency, weight shift, stability, and overall swing efficiency. This method, originating with Rudolf Laban, can become highly detailed. The accompanying sketches and notes provide context and clarity for those unfamiliar with the notation (<a href="#app1-sensors-24-06845" class="html-app">Supplementary Materials</a>).</p>
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<p>Ground Reaction Force (GRF) of the left foot during a golf swing for novice and proficient golfers. Top Panel: Vertical GRF for novice (blue) and proficient (green) golfers. The proficient golfer demonstrates higher and sharper peaks in the vertical GRF, indicating efficient force application during the downswing and impact phases. The novice golfer shows more variable and lower peak forces, reflecting less distinct timing and less efficient force application. Bottom Panel: Horizontal GRF for novice (red) and proficient (orange) golfers. The proficient golfer displays a smoother and more consistent force application with less noise, whereas the novice golfer exhibits more variability and less consistent force patterns, highlighting the differences in balance and coordination between the two skill levels. The normalized time axis represents the progression of the golf swing from start to finish, capturing the critical phases of the swing and their corresponding GRF profiles.</p>
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21 pages, 1166 KiB  
Article
Incorporating Boundary Nonlinearity into Structural Vibration Problems
by Alex J. Elliott and Andrea Cammarano
Vibration 2024, 7(4), 949-969; https://doi.org/10.3390/vibration7040050 - 18 Oct 2024
Viewed by 894
Abstract
This paper presents a methodology for accurately incorporating the nonlinearity of boundary conditions (BCs) into the mode shapes, natural frequencies, and dynamic behaviour of analytical beam models. Such models have received renewed interest in recent years as a result of their successful implementation [...] Read more.
This paper presents a methodology for accurately incorporating the nonlinearity of boundary conditions (BCs) into the mode shapes, natural frequencies, and dynamic behaviour of analytical beam models. Such models have received renewed interest in recent years as a result of their successful implementation in state-of-the-art multiphysics problems. To address the need for this boundary nonlinearity to be more completely captured in the equations of motion, a nonlinear algebra expansion of the classical linear approach for developing solvability conditions for natural frequencies and mode shapes is presented. The method is applicable to any BC that can be accurately represented in polynomial form, either explicitly or through the application of a Taylor expansion; this is the only assumption made in removing the need for the use of analytical approximations of the dynamics themselves. By reducing the BCs of the beam to a system of polynomials, it is possible to utilise the tensor resultant to develop these solvability conditions analogous to the conditions placed on the matrix determinant in linear, classical cases. The approach is first derived for a general set of nonlinear BCs before being applied to two example systems to investigate the importance of including nonlinear tip behaviour in the BCs to accurately predict the system response. In the first, a theoretical, symmetric system, in which a beam is supported by nonlinear springs, is used to explore both the applicability of the methodology and the improvements it can make to the accuracy of the model. Then, the more practical example of a cantilever beam with repulsive magnetic interaction at the tip is used to more explicitly assess the importance of properly incorporating boundary nonlinearity into multiphysics problems. Full article
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<p>Schematic of a beam supported by nonlinear springs at both ends.</p>
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<p>Normalised mode shapes of the first mode of the nonlinear spring-supported beam for <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>∈</mo> </mrow> </semantics></math><math display="inline"><semantics> <mrow> <mo>[</mo> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>9</mn> </mrow> </msup> <mo>,</mo> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> <mo>,</mo> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> <mo>,</mo> <mn>1</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Normalised mode shapes of the second mode of the nonlinear spring-supported beam for <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>∈</mo> </mrow> </semantics></math><math display="inline"><semantics> <mrow> <mo>[</mo> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>9</mn> </mrow> </msup> <mo>,</mo> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> <mo>,</mo> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> <mo>,</mo> <mn>1</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Normalised mode shapes of the third mode of the nonlinear spring-supported beam for <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>∈</mo> </mrow> </semantics></math><math display="inline"><semantics> <mrow> <mo>[</mo> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>9</mn> </mrow> </msup> <mo>,</mo> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> <mo>,</mo> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> <mo>,</mo> <mn>1</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Backbone curves for the spring-supported beams with <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>∈</mo> </mrow> </semantics></math><math display="inline"><semantics> <mrow> <mo>[</mo> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>9</mn> </mrow> </msup> <mo>,</mo> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> <mo>,</mo> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Schematic for a cantilever beam with tip magnet.</p>
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<p>Normalised mode shapes for the first mode of the clamped-free, clamped-pinned, and clamped-spring supported beams.</p>
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<p>Validation of the cubic-order cantilever beam model, with the true solution (created via Ansys) in black and the predicted solution in red.</p>
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<p>Backbone curves for the beam model with various BCs at the tip.</p>
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21 pages, 324 KiB  
Article
(Almost) Ricci Solitons in Lorentzian–Sasakian Hom-Lie Groups
by Esmaeil Peyghan, Leila Nourmohammadifar, Akram Ali and Ion Mihai
Axioms 2024, 13(10), 693; https://doi.org/10.3390/axioms13100693 - 4 Oct 2024
Viewed by 617
Abstract
We study Lorentzian contact and Lorentzian–Sasakian structures in Hom-Lie algebras. We find that the three-dimensional sl(2,R) and Heisenberg Lie algebras provide examples of such structures, respectively. Curvature tensor properties in Lorentzian–Sasakian Hom-Lie algebras are investigated. If v is [...] Read more.
We study Lorentzian contact and Lorentzian–Sasakian structures in Hom-Lie algebras. We find that the three-dimensional sl(2,R) and Heisenberg Lie algebras provide examples of such structures, respectively. Curvature tensor properties in Lorentzian–Sasakian Hom-Lie algebras are investigated. If v is a contact 1-form, conditions under which the Ricci curvature tensor is v-parallel are given. Ricci solitons for Lorentzian–Sasakian Hom-Lie algebras are also studied. It is shown that a Ricci soliton vector field ζ is conformal whenever the Lorentzian–Sasakian Hom-Lie algebra is Ricci semisymmetric. To illustrate the use of the theory, a two-parameter family of three-dimensional Lorentzian–Sasakian Hom-Lie algebras which are not Lie algebras is given and their Ricci solitons are computed. Full article
(This article belongs to the Special Issue Differential Geometry and Its Application, 3rd Edition)
16 pages, 279 KiB  
Article
Solving the QLY Least Squares Problem of Dual Quaternion Matrix Equation Based on STP of Dual Quaternion Matrices
by Ruyu Tao, Ying Li, Mingcui Zhang, Xiaochen Liu and Musheng Wei
Symmetry 2024, 16(9), 1117; https://doi.org/10.3390/sym16091117 - 28 Aug 2024
Viewed by 964
Abstract
Dual algebra plays an important role in kinematic synthesis and dynamic analysis, but there are still few studies on dual quaternion matrix theory. This paper provides an efficient method for solving the QLY least squares problem of the dual quaternion matrix equation [...] Read more.
Dual algebra plays an important role in kinematic synthesis and dynamic analysis, but there are still few studies on dual quaternion matrix theory. This paper provides an efficient method for solving the QLY least squares problem of the dual quaternion matrix equation AXB+CYDE, where X, Y are unknown dual quaternion matrices with special structures. First, we define a semi-tensor product of dual quaternion matrices and study its properties, which can be used to achieve the equivalent form of the dual quaternion matrix equation. Then, by using the dual representation of dual quaternion and the GH-representation of special dual quaternion matrices, we study the expression of QLY least squares Hermitian solution of the dual quaternion matrix equation AXB+CYDE. The algorithm is given and the numerical examples are provided to illustrate the efficiency of the method. Full article
(This article belongs to the Special Issue Exploring Symmetry in Dual Quaternion Matrices and Matrix Equations)
12 pages, 3079 KiB  
Article
Michelson Interferometric Methods for Full Optical Complex Convolution
by Haoyan Kang, Hao Wang, Jiachi Ye, Zibo Hu, Jonathan K. George, Volker J. Sorger, Maria Solyanik-Gorgone and Behrouz Movahhed Nouri
Nanomaterials 2024, 14(15), 1262; https://doi.org/10.3390/nano14151262 - 28 Jul 2024
Cited by 2 | Viewed by 1317
Abstract
Optical real-time data processing is advancing fields like tensor algebra acceleration, cryptography, and digital holography. This technology offers advantages such as reduced complexity through optical fast Fourier transform and passive dot-product multiplication. In this study, the proposed Reconfigurable Complex Convolution Module (RCCM) is [...] Read more.
Optical real-time data processing is advancing fields like tensor algebra acceleration, cryptography, and digital holography. This technology offers advantages such as reduced complexity through optical fast Fourier transform and passive dot-product multiplication. In this study, the proposed Reconfigurable Complex Convolution Module (RCCM) is capable of independently modulating both phase and amplitude over two million pixels. This research is relevant for applications in optical computing, hardware acceleration, encryption, and machine learning, where precise signal modulation is crucial. We demonstrate simultaneous amplitude and phase modulation of an optical two-dimensional signal in a thin lens’s Fourier plane. Utilizing two spatial light modulators (SLMs) in a Michelson interferometer placed in the focal plane of two Fourier lenses, our system enables full modulation in a 4F system’s Fourier domain. This setup addresses challenges like SLMs’ non-linear inter-pixel crosstalk and variable modulation efficiency. The integration of these technologies in the RCCM contributes to the advancement of optical computing and related fields. Full article
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<p>Schematic and working principle of a complex optical 4F convolution module. (<b>a</b>) A detailed schematic representation illustrating the complex convolution process based on Euler’s formula, emphasizing the mathematical underpinnings and optical path integration. (<b>b</b>) An expanded diagram of the experimental setup for executing full optical convolution. SLM1 modulated the illuminating beam’s amplitude, and SLM2 and 3, as a whole, sitting in the Fourier domain, modulated the beam with phase and amplitude controlled simultaneously; see Equation (1).</p>
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<p>Simulated results demonstrating the impact of phase factors in the modulation device. (<b>a</b>) Simulated convolution incorporating both phase and amplitude factors, showcasing the comprehensive modulation capabilities and their combined effects on the resulting optical pattern. (<b>b</b>) Simulated convolution featuring only amplitude factors on the spatial light modulator (SLM), illustrating the distinct optical outcomes when phase factors are absent, thereby emphasizing the unique role of amplitude modulation in the convolution process. This comparison shows the necessity of simultaneously controlling both the phase and amplitude for the featured optical convolution module.</p>
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<p>Experimental setup and basic performance metrics of a complex optical 4F convolution module. (<b>a</b>) A detailed photograph of the experimental setup, highlighting the zoomed-in area showcasing the fabricated amplitude mask and its precise installation within the system. This component is critical for modulating the light patterns as part of the convolution process. (<b>b</b>) A plot of the normalized intensity measurements acquired at the camera, juxtaposed with corresponding simulation data that include a confidence interval. This graph illustrates the convolution of the incoming sequence huyun11111] with the mask huyun10001], with the anticipated nine-digit result huyun111121111]. An inset image displays the actual output captured by the camera, providing a visual confirmation of the simulation accuracy and the experimental effectiveness. The theoretical shade represents the waveform of the classical convolution results of the sequence, and the math line represents the expected results of huyun111121111]. This figure shows evidence of the feasibility and accuracy of the RCCM in an experimental manner of a specific sequence.</p>
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<p>Optical convolution dynamics in a reconfigurable complex convolution module (RCCM) (<b>a</b>) Full experimental results of the RCCM: Convolution of the sequence 11111 with the kernel 10001 displayed on the spatial light modulator (SLM). This panel illustrates the primary convolution process, showcasing the direct optical output. (<b>b</b>) Adjustments with polarization-angle tuning: This modification highlights the effects of polarization-angle adjustments on the convolution output, demonstrating the influence of optical properties on the result. (<b>c</b>) Convolution result of sequence 10111 with kernel 10001: A comparison showing how minor variations in the input sequence affect the convolution outcome, providing insights into the system’s sensitivity and response. (<b>d</b>) Convolution of sequence 10011 with kernel 10001: This panel displays another variant, emphasizing how changes in the initial sequence modify the resulting convolution patterns, illustrating the dynamic capabilities of the convolution system.</p>
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<p>Detailed comparison of modulation techniques in CIFAR-10 neural network performance. (<b>a</b>) Complex modulation (RCCM) performance: Achieved a top accuracy of 51.98%, highlighting the effectiveness of combining amplitude and phase modulations in neural network applications. Color intensity indicates prediction frequency, with darker blue representing higher frequency and lighter shades lower frequency. (<b>b</b>) Amplitude modulation performance: Reached an accuracy of 50.83%, demonstrating its capability, though slightly less effective than complex modulation. (<b>c</b>) Phase modulation performance: Yielded the lowest accuracy of 47.87%, indicating its impact and limitations when used independently in neural networks. (<b>d</b>) Neural network architecture: Depicts the detailed architecture of the simulated neural network, providing insights into the structural elements that contribute to the performance differences observed in the modulation techniques.</p>
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<p>Concept of optical hashing algorithm and the scheme of the proposed full optical setup using RCCM for optical Fourier full convolution accelerator. An example of a 1-D compressed sequence read of the simulated camera reading that corresponds to MNIST input ”5”, ”0”, and ”4”; The scheme of the proposed optical setup, where the source can be a single-mode fiber array collimated into free space or coupled in a photonic chip; diffusion can be achieved by an optical amplitude-only modulation by an SLM; convolution must be performed with complex amplitude-and-phase convolution in the Fourier domain; the optional final step of image classification with a heterogeneous convolutional 4F classifier can be alternatively replaced by a CMOS diode array (DA). All inserted figures along the axis show what pattern is presenting on the SLMs or the current status of the modulated signal carried by the beam.</p>
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13 pages, 323 KiB  
Article
One-Bit Function Perturbation Impact on Robust Set Stability of Boolean Networks with Disturbances
by Lei Deng, Xiujun Cao and Jianli Zhao
Mathematics 2024, 12(14), 2258; https://doi.org/10.3390/math12142258 - 19 Jul 2024
Viewed by 807
Abstract
This paper investigates the one-bit function perturbation (OBFP) impact on the robust set stability of Boolean networks with disturbances (DBNs). Firstly, the dynamics of these networks are converted into the algebraic forms utilizing the semi-tensor product (STP) method. Secondly, OBFP’s impact on the [...] Read more.
This paper investigates the one-bit function perturbation (OBFP) impact on the robust set stability of Boolean networks with disturbances (DBNs). Firstly, the dynamics of these networks are converted into the algebraic forms utilizing the semi-tensor product (STP) method. Secondly, OBFP’s impact on the robust set stability of DBNs is divided into two situations. Then, by constructing a state set and defining an index vector, several necessary and sufficient conditions to guarantee that a DBN under OBFP can stay robust set stable unchanged are provided. Finally, a biological example is proposed to demonstrate the effectiveness of the obtained theoretical results. Full article
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<p>The state trajectory of DBN (25) before function perturbation.</p>
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<p>State trajectory graph of dynamics after function perturbation in Case 1.</p>
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<p>State trajectory graph of dynamics after function perturbation in Case 2.</p>
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