In this paper, we propose a novel framework for multidomain subspace deformation using node-wise corotational elasticity. With the proper construction of subspaces based on the knowledge of the boundary deformation, we can use the Lagrange multiplier technique to impose coupling constraints at the boundary without overconstraining. In our deformation algorithm, the number of constraint equations to couple two neighboring domains is not related to the number of the nodes on the boundary but is the same as the number of the selected boundary deformation modes. The crack artifact is not present in our simulation result, and the domain decomposition with loops can be easily handled. Experimental results show that the single-core implementation of our algorithm can achieve real-time performance in simulating deformable objects with around quarter million tetrahedral elements.
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http://dx.doi.org/10.1109/TVCG.2013.12 | DOI Listing |
Cogn Neurodyn
February 2024
College of Automation, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang People's Republic of China.
Tensor analysis of electroencephalogram (EEG) can extract the activity information and the potential interaction between different brain regions. However, EEG data varies between subjects, and the existing tensor decomposition algorithms cannot guarantee that the features across subjects are distributed in the same domain, which leads to the non-objectivity of the classification result and analysis, In addition, traditional Tucker decomposition is prone to the explosion of feature dimensions. To solve these problems, combined with the idea of feature transfer, a novel EEG tensor transfer algorithm, Tensor Subspace Learning based on Sparse Regularized Tucker Decomposition (TSL-SRT), is proposed in this paper.
View Article and Find Full Text PDFEntropy (Basel)
August 2023
School of Management, Hefei University of Technology, Hefei 230009, China.
In bearing fault diagnosis, machine learning methods have been proven effective on the basis of the heterogeneous features extracted from multiple domains, including deep representation features. However, comparatively little research has been performed on fusing these multi-domain heterogeneous features while dealing with the interrelation and redundant problems to precisely discover the bearing faults. Thus, in the current study, a novel diagnostic method, namely the method of incorporating heterogeneous representative features into the random subspace, or IHF-RS, is proposed for accurate bearing fault diagnosis.
View Article and Find Full Text PDFNeural Netw
September 2023
School of Electrical Engineering, Chungbuk National University, Chungdae-ro, Seowon-Gu, 28644, Cheongju, South Korea. Electronic address:
In recent years, deep learning super-resolution models for progressive reconstruction have achieved great success. However, these models which refer to multi-resolution analysis basically ignore the information contained in the lower subspaces and do not explore the correlation between features in the wavelet and spatial domain, resulting in not fully utilizing the auxiliary information brought by multi-resolution analysis with multiple domains. Therefore, we propose a super-resolution network based on the wavelet multi-resolution framework (WMRSR) to capture the auxiliary information contained in multiple subspaces and to be aware of the interdependencies between spatial domain and wavelet domain features.
View Article and Find Full Text PDFLung cancer is the leading cause of cancer death worldwide. The best solution for lung cancer is to diagnose the pulmonary nodules in the early stage, which is usually accomplished with the aid of thoracic computed tomography (CT). As deep learning thrives, convolutional neural networks (CNNs) have been introduced into pulmonary nodule detection to help doctors in this labor-intensive task and demonstrated to be very effective.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
November 2023
To solve the user data sparsity problem, which is the main issue in generating user preference prediction, cross-domain recommender systems transfer knowledge from one source domain with dense data to assist recommendation tasks in the target domain with sparse data. However, data are usually sparsely scattered in multiple possible source domains, and in each domain (source/target) the data may be heterogeneous, thus it is difficult for existing cross-domain recommender systems to find one source domain with dense data from multiple domains. In this way, they fail to deal with data sparsity problems in the target domain and cannot provide an accurate recommendation.
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