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浏览/检索结果: 共13条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Trustworthy Localization With EM-Based Federated Control Scheme for IIoTs 期刊论文  OAI收割 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2023, 卷号: 19, 期号: 1, 页码: 1069-1079 作者:  Wang, Zhaoyang;  Wang, Song;  Zhao, Zhiyao;  Sun, Muyi   |  收藏  |  浏览/下载：31/0  |  提交时间：2023/03/20 Location awareness  Industrial Internet of Things  Collaboration  Security  Cloud computing  Encryption  Privacy  Collaborative Cloud-Edge-End  expectation maximization (EM)  federated control  industrial Internet of Things (IIoT)  trustworthy localization   A Novel Adaptive Kalman Filter Based on Credibility Measure 期刊论文  OAI收割 IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 1, 页码: 103-120 作者:  Quanbo Ge;  Xiaoming Hu;  Yunyu Li;  Hongli He;  Zihao Song   |  收藏  |  浏览/下载：50/0  |  提交时间：2023/01/03 Credibility  expectation maximization-particle swarm optimization method (EM-PSO)  filter calculated mean square errors (MSE)  inaccurate models  Kalman filter  Sage-Husa  true MSE (TMSE)   An Age-Dependent and State-Dependent Adaptive Prognostic Approach for Hidden Nonlinear Degrading System 期刊论文  OAI收割 IEEE/CAA Journal of Automatica Sinica, 2022, 卷号: 9, 期号: 5, 页码: 907-921 作者:  Zhenan Pang;  Xiaosheng Si;  Changhua Hu;  Zhengxin Zhang   |  收藏  |  浏览/下载：34/0  |  提交时间：2022/04/24 Expectation-maximization (EM)  hidden degradation state  Kalman filter (KF)  remaining useful life (RUL)  unit-to-unit variability   Item Response Theory Based Ensemble in Machine Learning 期刊论文  OAI收割 International Journal of Automation and Computing, 2020, 卷号: 17, 期号: 5, 页码: 621-636 作者:  Ziheng Chen;  Hongshik Ahn   |  收藏  |  浏览/下载：30/0  |  提交时间：2021/02/22 Classification  ensemble learning  item response theory  machine learning  expectation maximization (EM) algorithm.   A Generalized Model for Robust Tensor Factorization With Noise Modeling by Mixture of Gaussians 期刊论文  OAI收割 IEEE Transactions on Neural Networks and Learning Systems, 2018 作者:  Wang Y(王尧);  Han Z(韩志);  Lin, Lin;  Tang YD(唐延东);  Chen XA(陈希爱)   |  收藏  |  浏览/下载：55/0  |  提交时间：2018/03/25 Expectation–maximization (EM) algorithm  generalized weighted low-rank tensor factorization (GWLRTF)  mixture of Gaussians (MoG) model  tensor factorization   Foreground Object Detection Using Top-Down Information Based on EM Framework 期刊论文  OAI收割 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2012, 卷号: 21, 期号: 9, 页码: 4204-4217 作者:  Liu, Zhou;  Huang, Kaiqi;  Tan, Tieniu 收藏  |  浏览/下载：33/0  |  提交时间：2015/08/12 Background model  expectation maximization (EM) framework  foreground detection  Markov random fields (MRFs)   Improved H-infinity channel estimator based on EM for MIMO-OFDM systems 期刊论文  OAI收割 JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 2011, 卷号: 22, 期号: 4, 页码: 572-578 作者:  Xu Peng;  Wang Jinkuan;  Qi Feng   |  收藏  |  浏览/下载：7/0  |  提交时间：2021/02/02 LINEAR-ESTIMATION  WIRELESS SYSTEMS  KREIN SPACES  DESIGN  multiple input multiple output (MIMO)  orthogonal frequency division multiplexing (OFDM)  channel estimation  H-infinity  expectation maximization (EM)  angle domain   Improved H-infinity channel estimator based on EM for MIMO-OFDM systems 期刊论文  OAI收割 JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 2011, 卷号: 22, 期号: 4, 页码: 572-578 作者:  Xu Peng;  Wang Jinkuan;  Qi Feng   |  收藏  |  浏览/下载：13/0  |  提交时间：2021/02/02 LINEAR-ESTIMATION  WIRELESS SYSTEMS  KREIN SPACES  DESIGN  multiple input multiple output (MIMO)  orthogonal frequency division multiplexing (OFDM)  channel estimation  H-infinity  expectation maximization (EM)  angle domain   Exploring Social Annotations with Application to Web Page Recommendation 期刊论文  OAI收割 Journal of Computer Science and Technology, 2009, 卷号: 4, 期号: 6, 页码: 1028-1035 作者:  Hui-Qian Li;  Fen Xia;  Daniel Zeng;  Fei-Yue Wang;  Wen-Ji Mao 收藏  |  浏览/下载：28/0  |  提交时间：2016/10/27 graphic model, EM (expectation-maximization), social annotation, tag, recommendation   Contour extracting with combination particle filtering and em algorithm (EI CONFERENCE) 会议论文  OAI收割 International Symposium on Photoelectronic Detection and Imaging, ISPDI 2007: Related Technologies and Applications, September 9, 2007 - September 12, 2007, Beijing, China Meng B.; Zhu M. 收藏  |  浏览/下载：26/0  |  提交时间：2013/03/25 The problem of extracting continuous structures from images is a difficult issue in early pattern recognition and image processings[1]. Tracking with contours in a filtering framework requires a dynamical model for prediction. Recently  Particle filter  is widely used because its multiple hypotheses and versatility within framework. However  the good choice of the propagation function is still its main problem. In this paper  an improved particle filter  EM-PF algorithm is proposed which using the EM (Expectation-Maximization) algorithm to learn the dynamical models. The EM algorithm can explicitly learn the parameters of the dynamical models from training sequences. The advantage of using the EM algorithm in particle filter is that it is capable of improve tracking contour by having accurate model parameters. Though the experiment results  we show how our EM-PF can be applied to produces more robust and accurate extracting.