In this article, the hybridization of adaptive cross approximation (ACA) algorithm and interpolation-based separation of the kernel function is proposed to accelerate solving the matrix equations resulted in the bound...
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In this article, the hybridization of adaptive cross approximation (ACA) algorithm and interpolation-based separation of the kernel function is proposed to accelerate solving the matrix equations resulted in the boundary element method (BEM) for 3D arbitrary-shaped eddy current nondestructive evaluation problems. The hybrid method combines the advantages of both the ACA algorithm and the interpolation-based methods, and resolves the shortcoming of pure ACA method, when modeling the planar eddy current nondestructive evaluation problems, that it cannot compress the null entries the BEM generated when the testing and basis patches are co-planar. In the proposed method, the submatrices associated with the null entries are compressed by the interpolation-based method, while the others are compressed by the ACA algorithm. Several benchmarks are shown to demonstrate both the robustness and efficiency of the proposed fast and general solver.
This paper presents an interpolation-based method(IBM)for approximating some trigonometric functions or their integrals as *** provides two-sided bounds for each function,which also achieves much better approximation ...
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This paper presents an interpolation-based method(IBM)for approximating some trigonometric functions or their integrals as *** provides two-sided bounds for each function,which also achieves much better approximation effects than those of prevailing *** principle,the IBM can be applied for bounding more bounded smooth functions and their integrals as well,and its applications include approximating the integral of sin(x)/x function and improving the famous square root inequalities.
Semi-supervised learning (SSL) has long been proved to be an effective technique to construct powerful models with limited labels. In the existing literature, consistency regularization-basedmethods, which force the ...
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Semi-supervised learning (SSL) has long been proved to be an effective technique to construct powerful models with limited labels. In the existing literature, consistency regularization-basedmethods, which force the perturbed samples to have similar predictions with the original ones have attracted much attention for their promising accuracy. However, we observe that the performance of such methods decreases drastically when the labels get extremely limited, e.g., 2 or 3 labels for each category. Our empirical study finds that the main problem lies with the drift of semantic information in the procedure of data augmentation. The problem can be alleviated when enough supervision is provided. However, when little guidance is available, the incorrect regularization would mislead the network and undermine the performance of the algorithm. To tackle the problem, we: 1) propose an interpolation-based method to construct more reliable positive sample pairs and 2) design a novel contrastive loss to guide the embedding of the learned network to change linearly between samples so as to improve the discriminative capability of the network by enlarging the margin decision boundaries. Since no destructive regularization is introduced, the performance of our proposed algorithm is largely improved. Specifically, the proposed algorithm outperforms the second best algorithm (Comatch) with 5.3% by achieving 88.73% classification accuracy when only two labels are available for each class on the CIFAR-10 dataset. Moreover, we further prove the generality of the proposed method by improving the performance of the existing state-of-the-art algorithms considerably with our proposed strategy. The corresponding code is available at https://***/xihongyang1999/ICL_SSL.
The behaviour of modern power systems is more and more dictated by smart digital controllers responsible for ensuring the security, optimal operation, and stability of the systems. For the development, testing, and va...
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ISBN:
(纸本)9798350384697;9798350384680
The behaviour of modern power systems is more and more dictated by smart digital controllers responsible for ensuring the security, optimal operation, and stability of the systems. For the development, testing, and validation of these controllers, it is necessary to model and simulate different scenarios in power system dynamic analysis. However, digital controllers introduce discontinuities during the dynamic simulation that force the solver to reduce the time step taken to land on the discontinuity and restart the simulation. Therefore, the simulation of systems containing many digital controllers becomes very time-consuming. The interpolation-based method (IBM) provides a fast but accurate approach for the dynamic simulation of power systems with multiple digital controllers without the need to reduce the time steps. This paper presents a fixed-step implementation of IBM in Modelica that allows to embed this method in the Modelica controller model without the need to modify the solver algorithm. The performance of the method is showcased on a single-machine infinite-bus test system.
To solve the problem that metal artifacts severely damage the clarity of the organization structure in computed tomography（CT） images, a sinogram fusion-based metal artifact correction method is proposed. First, the...
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To solve the problem that metal artifacts severely damage the clarity of the organization structure in computed tomography（CT） images, a sinogram fusion-based metal artifact correction method is proposed. First, the metal image is segmented from the original CT image by the pre-set threshold. The original CT image and metal image are forward projected into the original projection sinogram and metal projection sinogram, respectively. The interpolation-based correction method and mean filter are used to correct the original CT image and preserve the edge of the corrected CT image, respectively. The filtered CT image is forward projected into the filtered image sinogram. According to the position of the metal sinogram in the original sinogram and filtered image sinogram, the corresponding sinograms PM^D （ in the original sinogram） and PM^C （ in the filtered image sinogram）can be acquired from the original sinogram and filtered image sinogram, respectively. Then, PM^D and PM^C are fused into the fused metal sinogram PM^F according to a certain *** final sinogram can be acquired by fusing PM^F , PM^D and the original sinogram P^O. Finally, the final sinogram is reconstructed into the corrected CT image and metal information is compensated into the corrected CT *** on clinical images demonstrate that the proposed method can effectively reduce metal artifacts. A comparison with classical metal artifacts correction methods shows that the proposed metal artifacts correction method performs better in metal artifacts suppression and tissue feature preservation.
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