Learning the Kernel Matrix for Superresolution

2006 ◽  
Author(s):  
Karl Ni ◽  
Sanjeev Kumar ◽  
Truong Nguyen
Keyword(s):  
Kernel Matrix

Numerical simulation of squeezing Cu–Water nanofluid flow by a kernel-based method

2021 ◽  
pp. 2250005
Author(s):  
Mojtaba Fardi ◽  
Yasir Khan
Keyword(s):  
Numerical Simulation ◽  
Hilbert Space ◽  
Linear System ◽  
Numerical Simulations ◽  
Numerical Results ◽  
Kernel Matrix ◽  
Nanofluid Flow ◽  
Parallel Disks ◽  
Engineering Problems ◽  
Well Conditioning

The main aim of this paper is to propose a kernel-based method for solving the problem of squeezing Cu–Water nanofluid flow between parallel disks. Our method is based on Gaussian Hilbert–Schmidt SVD (HS-SVD), which gives an alternate basis for the data-dependent subspace of “native” Hilbert space without ever forming kernel matrix. The well-conditioning linear system is one of the critical advantages of using the alternate basis obtained from HS-SVD. Numerical simulations are performed to illustrate the efficiency and applicability of the proposed method in the sense of accuracy. Numerical results obtained by the proposed method are assessed by comparing available results in references. The results demonstrate that the proposed method can be recommended as a good option to study the squeezing nanofluid flow in engineering problems.

scholarly journals SAR Image Change Detection Based on Multi-scale Feature Extraction

2021 ◽  
Vol 10 (4) ◽  
pp. 077-081
Author(s):  
Xiaoqian Yuan ◽  
Chao Chen ◽  
Shan Tian ◽  
Jiandan Zhong
Keyword(s):  
Feature Extraction ◽  
Change Detection ◽  
Speckle Noise ◽  
Kernel Matrix ◽  
Sar Image ◽  
Scale Feature ◽  
Difference Image ◽  
Multi Scale ◽  
Image Change Detection ◽  
The Difference

In order to improve the contrast of the difference image and reduce the interference of the speckle noise in the synthetic aperture radar (SAR) image, this paper proposes a SAR image change detection algorithm based on multi-scale feature extraction. In this paper, a kernel matrix with weights is used to extract features of two original images, and then the logarithmic ratio method is used to obtain the difference images of two images, and the change area of the images are extracted. Then, the different sizes of kernel matrix are used to extract the abstract features of different scales of the difference image. This operation can make the difference image have a higher contrast. Finally, the cumulative weighted average is obtained to obtain the final difference image, which can further suppress the speckle noise in the image.

scholarly journals Parametric Models for Mutual Kernel Matrix Completion

2018 ◽  
Vol E101.D (12) ◽  
pp. 2976-2983 ◽  
Author(s):  
Rachelle RIVERO ◽  
Tsuyoshi KATO
Keyword(s):  
Matrix Completion ◽  
Parametric Models ◽  
Kernel Matrix

Urban Noise Inference Model Based on Multiple Views and Kernel Tensor Decomposition

2021 ◽  
pp. 2150027
Author(s):  
Junlan Nie ◽  
Ruibo Gao ◽  
Ye Kang
Keyword(s):  
Noise Pollution ◽  
Tensor Decomposition ◽  
Prediction Algorithm ◽  
Kernel Matrix ◽  
Multiple Views ◽  
Tensor Factorization ◽  
Urban Noise ◽  
Model Based ◽  
Factorization Model ◽  
Kernel Distance

Prediction of urban noise is becoming more significant for tackling noise pollution and protecting human mental health. However, the existing noise prediction algorithms neglected not only the correlation between noise regions, but also the nonlinearity and sparsity of the data, which resulted in low accuracy of filling in the missing entries of data. In this paper, we propose a model based on multiple views and kernel-matrix tensor decomposition to predict the noise situation at different times of day in each region. We first construct a kernel tensor decomposition model by using kernel mapping in order to speed decomposition rate and realize stable estimate the prediction system. Then, we analyze and compute the cause of the noise from multiple views including computing the similarity of regions and the correlation between noise categories by kernel distance, which improves the credibility to infer the noise situation and the categories of regions. Finally, we devise a prediction algorithm based on the kernel-matrix tensor factorization model. We evaluate our method with a real dataset, and the experiments to verify the advantages of our method compared with other existing baselines.

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