The future of electricity markets is envisioned to be heavily based on renewable generation and distributed flexibility. Yet, integrating existing distributed flexibility into market decisions poses a major challenge,...
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In this paper,the dispersion,attenuation,and bandgap characteristics of in-plane coupled Bloch waves in one-dimensional piezoelectric semiconductor(PSC)phononic crystals are investigated,emphasizing the influence of p...
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In this paper,the dispersion,attenuation,and bandgap characteristics of in-plane coupled Bloch waves in one-dimensional piezoelectric semiconductor(PSC)phononic crystals are investigated,emphasizing the influence of positive-negative(PN)*** piezoelectric phononic crystals,the coupled Bloch waves in PSC phononic crystals are attenuated due to their semiconductor properties,and thus the solution of Bloch waves becomes more *** transfer matrix of the phononic crystal unit cell is obtained using the state transfer *** applying the Bloch theorem for periodic structures,the dispersion relation of the coupled Bloch waves is derived,and the dispersion,attenuation,and bandgap are obtained in the complex wave number *** is found that the influence of the PN junction cannot be ***,the effects of the PN junction under different apparent wave numbers and steady-state carrier concentrations are *** indicates the feasibility of adjusting the propagation characteristics of Bloch waves through the regulation of the PN heterojunction.
Clustering techniques have been instrumental in discerning patterns and relationships within datasets in data analytics and unsupervised machine learning. Traditional clustering algorithms struggle to handle real-worl...
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In this paper, we propose a novel warm restart technique using a new logarithmic step size for the stochastic gradient descent (SGD) approach. For smooth and non-convex functions, we establish an O(1/√T) convergence ...
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In this paper, we propose a novel warm restart technique using a new logarithmic step size for the stochastic gradient descent (SGD) approach. For smooth and non-convex functions, we establish an O(1/√T) convergence rate for the SGD. We conduct a comprehensive implementation to demonstrate the efficiency of the newly proposed step size on the FashionMinst, CIFAR10, and CIFAR100 datasets. Moreover, we compare our results with nine other existing approaches and demonstrate that the new logarithmic step size improves test accuracy by 0.9% for the CIFAR100 dataset when we utilize a convolutional neural network (CNN) model.
We present results of numerical simulations of the tensor-valued elliptic-parabolic PDE model for biological network *** numerical method is based on a nonlinear finite difference scheme on a uniform Cartesian grid in...
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We present results of numerical simulations of the tensor-valued elliptic-parabolic PDE model for biological network *** numerical method is based on a nonlinear finite difference scheme on a uniform Cartesian grid in a two-dimensional(2D)*** focus is on the impact of different discretization methods and choices of regularization parameters on the symmetry of the numerical *** particular,we show that using the symmetric alternating direction implicit(ADI)method for time discretization helps preserve the symmetry of the solution,compared to the(non-symmetric)ADI ***,we study the effect of the regularization by the isotropic background perme-ability r＞0,showing that the increased condition number of the elliptic problem due to decreasing value of r leads to loss of *** show that in this case,neither the use of the symmetric ADI method preserves the symmetry of the ***,we perform the numerical error analysis of our method making use of the Wasserstein distance.
We give a new proof of the Gagliardo–Nirenberg and Sobolev inequalities based on the heat semigroup. Concerning the Gagliardo–Nirenberg inequality, we simplify the previous proof by relying only on the Lp-Lq estimat...
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Robust estimation of the essential matrix, which encodes the relative position and orientation of two cameras, is a fundamental step in structure from motion pipelines. Recent deep-based methods achieved accurate esti...
We develop a thin-film microstructural model that represents structural markers(i.e.,triple junctions in the two-dimensional projections of the structure of films with columnar grains)in terms of a stochastic,marked p...
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We develop a thin-film microstructural model that represents structural markers(i.e.,triple junctions in the two-dimensional projections of the structure of films with columnar grains)in terms of a stochastic,marked point process and the microstructure itself in terms of a grain-boundary *** advantage of this representation is that it is conveniently applicable to the characterization of microstructures obtained from crystal orientation mapping,leading to a picture of an ensemble of interacting triple junctions,while providing results that inform grain-growth models with experimental *** specifically,calculated quantities such as pair,partial pair and mark correlation functions,along with the microstructural mutual information(entropy),highlight effective triple junction interactions that dictate microstructural *** validate this approach,we characterize microstructures from Al thin films via crystal orientation mapping and formulate an approach,akin to classical density functional theory,to describe grain growth that embodies triple-junction interactions.
Fredman proposed in 1976 the following algorithmic problem: Given are a ground set X, some partial order P over X, and some comparison oracle OL that specifies a linear order L over X that extends P. A query to OL has...
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Sparse Neural Networks (SNNs) have emerged as powerful tools for efficient feature selection. Leveraging the dynamic sparse training (DST) algorithms within SNNs has demonstrated promising feature selection capabiliti...
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