Facchini L, Betti M, Biagini P (2014) Neural network based modal identification of structural systems through output- only measurement. Comput Struct 138(1):183-194Facchini, L., M. Betti, and P. Biagini, ''Neural network based modal identi- fication of structural systems through output-only...
此外,我们将我们的方法与最近提出的基于subspace alignment (SA)的无监督DA方法(Fernando et al., 2013)进行了比较,该方法在新数据集上易于建立和测试,在与其他“shallow”数据挖掘方法的实验比较中也表现得很好。为了提高这个基线的性能,我们从{2,…, 60}范围选择最重要的free参数(主成分的数量),使目标域上的测...
In this paper, we propose a novel physics-informed neural network approach for nonlinear structural system identification and demonstrate its application in multiphysics cases where the damping term is governed by a separated dynamics equation. The proposed approach, called PIDynNet, improves the ...
M. Large-scale dynamic modeling of task-fMRI signals via subspace system identification. J. Neural Eng. 15, 066016 (2018). PubMed Google Scholar Braun, U. et al. Brain state stability during working memory is explained by network control theory, modulated by dopamine D1/D2 receptor ...
A Two Stream Siamese Convolutional Neural Network For Person Re-Identification Dahjung Chung Khalid Tahboub Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette, Indiana, USA chung123@purdue.edu ktahboub@purdue.edu ...
Enhanced control of a brain–computer interface by tetraplegic participants via neural-network-mediated feature extraction Benyamin Haghi Tyson Aflalo Azita Emami Nature Biomedical Engineering(2024) Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks ...
Because the uncertainty of spurious modes is significantly larger than that of the real ones, a convolutional neural network (CNN) is adopted to automatically analyse the uncertainty diagram and efficiently determine the physical structural modes. The method is then applied to identify modal parameters...
Joint Discriminative and Generative Learning for Person Re-identification [Project] [Paper] [YouTube] [Bilibili] [Poster] [Code] Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face Images [arXiv] Learning a Driving Simulator [arXiv] Learning a Generative Adversarial Network for...
Here we show that this framework elucidates the network dynamics of distributed VRC populations that are critical to generating, patterning and maintaining breathing. The VRC population evolves through rotational trajectories on a low-dimensional manifold of neural activity space that are consistent across...
“confused” by the weak noise in its training in the nonlinear transformation identification. For the most interesting case of high noise, the network works best for samples where the SNR value is the same for the validation and training sets. In such cases, the relative error is about 8–...