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One-Step Multiview Subspace Segmentation via Joint Skinny Tensor Learning and Latent Clustering 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2021, 页码: 15
作者:  Tang, Yongqiang;  Xie, Yuan;  Zhang, Changqing;  Zhang, Zhizhong;  Zhang, Wensheng
收藏  |  浏览/下载：203/0  |  提交时间：2022/01/27
Tensors  Optimization  Clustering methods  Computational modeling  Clustering algorithms  Semantics  Matrix decomposition  Multiview clustering  multiview representations  subspace clustering  tensor singular value decomposition (t-SVD)  
Neural Dynamics for Computing Perturbed Nonlinear Equations Applied to ACP-Based Lower Limb Motion Intention Recognition 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 页码: 9
作者:  Jin, Long;  Li, Jiachang;  Sun, Zhongbo;  Lu, Jingwei;  Wang, Fei-Yue
收藏  |  浏览/下载：278/0  |  提交时间：2022/01/27
Mathematical models  Computational modeling  Numerical models  Nonlinear equations  Convergence  Analytical models  Time-varying systems  Artificial systems  computational experiments  and parallel execution (ACP)  discrete-time neural dynamics (DTND) model  human lower limb physical experiment  nonlinear equations  
Bridging the Micro and Macro: Calibration of Agent-Based Model Using Mean-Field Dynamics 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2021, 卷号: 52, 期号: 11, 页码: 11397-11406
作者:  Ye, Peijun;  Chen, Yuanyuan;  Zhu, Fenghua;  Lv, Yisheng;  Lu, Wanze;  Wang, Fei-Yue
收藏  |  浏览/下载：260/0  |  提交时间：2022/01/27
Calibration  Computational modeling  Mathematical model  Machine learning  Aggregates  Optimization  Bayes methods  Agent-based model (ABM)  calibration  Markovian process  
3D Vehicle Detection With RSU LiDAR for Autonomous Mine 期刊论文
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 卷号: 70, 期号: 1, 页码: 344-355
作者:  Wang, Guojun;  Wu, Jian;  Xu, Tong;  Tian, Bin
Adobe PDF(6851Kb)  |  收藏  |  浏览/下载：190/1  |  提交时间：2021/03/29
Laser radar  Three-dimensional displays  Filtering  Vehicle detection  Detectors  Roads  Feature extraction  Background filtering  3D object detection  deep learning  roadside LiDAR  point cloud