Open Access
Issue |
MATEC Web Conf.
Volume 207, 2018
International Conference on Metal Material Processes and Manufacturing (ICMMPM 2018)
|
|
---|---|---|
Article Number | 03008 | |
Number of page(s) | 6 | |
Section | Material Science Engineering | |
DOI | https://doi.org/10.1051/matecconf/201820703008 | |
Published online | 18 September 2018 |
- G Zhang, Jun Sun. Application of Fuzzy Control on the Segment’s Gap Control of Slab Continuous Casting Machine[J]. ELECTRIC DRIVE, 2009, 39, (05): 51–53. [Google Scholar]
- Yiming Fang, Chunyang Hu. Breakout Prediction Classifier for Continuous Casting Based on Active Learning GA-SVM[J]. China Mechanical. [Google Scholar]
- Carlos A Santos. A solidification heat transfer model and a neural network based algorithm applied to the continuous casting of steel billets and blooms[J]. Modelling and Simulation in Materials Science and Engineering. 2005, 10, (13): 1071–1087. [CrossRef] [Google Scholar]
- Nan Wang, Jianhong Dong, Min Chen, Yongkuan Yao. Numerical Simulation on Thermal Stress Field in a Wide Slab Mould of Peritectic Steel Continuous Casting[J]. Journal of iron and steel research, 2012, (2): 909–912. [Google Scholar]
- Qunliang Zhang, Zhaohui Guo, Model control technologies of dynamic secondary cooling and soft reduction for slab continuous casting[J]. Baosteel technology research, 2012, 06, (3):61–64. [Google Scholar]
- Weng J Y, Zhang Y L, Hwang W S. Candid Covariance — free Incremental Principal Component Analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(8): 1034–1040. [CrossRef] [Google Scholar]
- Joseph A, Tokumto T, Azawa S. Online Feature Extraction Based on Accelerated Kernel Principal Component Analysis for Data Stream[J]. Evolving Systems, 2015:1–13. [Google Scholar]
- Martins M, Santos C, Costa L, et al. Feature reduction with PCA/KPCA for gait classification with different assistive devices[J]. International Journal of Intelligent Computing and Cybernetics, 2015, 8(4):363–382. [CrossRef] [Google Scholar]
- Kaur M, Arora AS. Classification of ECG signals using LDA with factor analysis method as feature reduction technique[J]. Journal of Medical Engineering & Technology, 2012, 3 6(8): 411–420. [CrossRef] [Google Scholar]
- Wise B M, Gallagher N B. The Process Chemometrics Approach to Process Monitoring and Fault Detection[J]. Journal of Process Control (S0959—1524), 1996, 6(6): 329–348. [CrossRef] [Google Scholar]
- Dung D, McAvoy T J. Nonlinear Principal Component Analysis Based on Principal Curves and Neural Networks[J]. Computers and Chemical Engineering(S0098—1354), 1996, 20(1): 65–78. [CrossRef] [Google Scholar]
- You C K, Vanrollegghem P A, Lee I-B, Nonlinear Modeling and Adaptive Monitoring with Fuzzy and Multivariate Statistical Methods iu Biological Wastewater Trcatment Plants[J]. Journal of Biotechnology(S0168—1656), 2003, 105:135–163. [Google Scholar]
- Wang Xun, Kruger Uwe, Lennox Barry. Recursive Partial Least Squares Algorithms for Monitoring 1547–1553. 2007. Complex Industrial Processes[J]. Control Engineering Practice(S0967-0661), 2003, 11:613–632. [CrossRef] [Google Scholar]
- Zhang Y, Dudzic M, Vaculik V. Integrated Monitoring Solution to Start — up and Run — time Operations for Continuous Casting[J]. Annual Review of Control(S1367—5788), 2003, 27: 141–149. [CrossRef] [Google Scholar]
- Ivan Milefic, Shannon Quinn, Michael Dudzic, et al. An Industrial Perspective on Implementing On-line Applications of Multivariate Statistics[J]. Journal of Process Control(S0959-1524), 2004, 14: 821–836. [CrossRef] [Google Scholar]
- L.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik. Dimensionality Reduction: A Comparative Review. Tilburg University Technical Report, TiCC-TR 2009–005, 2009. [Google Scholar]
- Li W, Yue H, Valle — Cervantes S, Qin S. Recursive PCA for Adaptive Process Monitoring[J]. Journal of Process Control(S0959—1524), 2000, 10(5): 471–486. [CrossRef] [Google Scholar]
- Wold S. Exponentially Weighted Moving Principal Components Analysis and Projections to Latent Structures[J]. Chemometrics and Intelligent Laboratory Systems(S0169—7439), 1994, 23: 149–161. [CrossRef] [Google Scholar]
- L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579–2605, 2008. [Google Scholar]
- N. Pezzotti, B.P.F. Lelieveldt, L.J.P. van der Maaten, T. Hollt, E. Eisemann, and A. Vilanova. Approximated and User Steerable t-SNE for Progressive Visual Analytics. IEEE Transactions on Visualization and Computer Graphics 23(7), 2017. [Google Scholar]
- L.J.P. van der Maaten. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research 15(Oct):3221–3245, 2014. [Google Scholar]
- Roweis ST, Saul LK. Nonlinear dimensionality reduction by locally linear embedding. SCIENCE.5500(290): 2323-+,2000. [Google Scholar]
- Donoho DL, Grimes C. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA. 100(10): 5591–5596. 2003. [CrossRef] [Google Scholar]
- Chao Yao, Ya-Feng Liu, Jiang Bo. LLE Score: A New Filter-Based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition. IEEE TRANSACTIONS ON IMAGE PROCESSING. 26(11):5257–5269.2017. [CrossRef] [Google Scholar]
- Zhang ZY, Zha HY. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM JOURNAL ON SCIENTIFIC COMPUTING. 26(1): 313–338. 2004. [Google Scholar]
- Zhang Tianhao, Yang Jie, Zhao Deli. Linear local tangent space alignment and application to face recognition. NEUROCOMPUTING. 70(7-9): [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.