ITM Web Conf.
Volume 12, 2017The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|Number of page(s)||6|
|Section||Session 1: Robotics|
|Published online||05 September 2017|
- R. W. Ngigi, C. Pislaru, A. Ball, and F. Gu, “Modern techniques for condition monitoring of railway vehicle dynamics”, In Journal of Physics: Conference Series, vol. 364, no. 1, p. 012016. IOP Publishing, 2012. [CrossRef] [Google Scholar]
- S. D. Milic, “A stationary system of noncontact temperature measurement and hotbox detecting”, IEEE Transactions on Vehicular Technology 57, no. 5 (2008): 2684–2694. [CrossRef] [Google Scholar]
- A. Amini, M. Entezami, and M. Papaelias, “Onboard detection of railway axle bearing defects using envelope analysis of high frequency acoustic emission signals”, Case Studies in Nondestructive Testing and Evaluation Volume 6, Part A, pp. 8–16, 2016. [CrossRef] [Google Scholar]
- W. Ma, S. Tan, X. Hei, J. Zhao and G. Xie, “A Prediction Method Based on Stepwise Regression Analysis for Train Axle Temperature”, 12th International Conference on Computational Intelligence and Security (CIS) IEEE Computer Society, pp.386–390, 2016. [Google Scholar]
- W. Ma, S. Tan, X. Hei, J. Zhao, and G. Xie, “A Prediction Method Based on Stepwise Regression Analysis for Train Axle Temperature”, In Computational Intelligence and Security (CIS), 2016 12th International Conference on, pp. 386–390. IEEE, 2016. [Google Scholar]
- B. Chen, Z. Yan, and W. Chen, “Defect Detection for Wheel-Bearings with Time-Spectral Kurtosis and Entropy.” Entropy, vol. 16, pp. 607–626. 2014. [CrossRef] [Google Scholar]
- P. J. Brockwell, and R. A. Davis, “Introduction to Time Series and Forecasting”. springer, 2016. [CrossRef] [Google Scholar]
- H. Lütkepohl, “New Introduction to Multiple Time Series Analysis”, Springer Science & Business Media, 2005. [CrossRef] [Google Scholar]
- J. Lee, E. Lapira, B. Bagheri and H. A. Kao, “Recent advances and trends in predictive manufacturing systems in big data environment”, Manufacturing Letters, vol.1, pp.38–41, 2013. [CrossRef] [Google Scholar]
- D. Chen, “Study of the Rule of Train Axle Temperature and Infrared Detection Mode of Axle Temperature”, Harbin Engineering University, 2003. [Google Scholar]
- S. Hochreiter, and J. Schmidhuber, “Long short-term memory”, Neural computation 9, no. 8, 1735–1780. 1997. [CrossRef] [PubMed] [Google Scholar]
- N. Srivastava, E. Mansimov, and R. Salakhudinov, “Unsupervised learning of video representations using lstms”, In International Conference on Machine Learning, pp. 843–852. 2015. [Google Scholar]
- H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song, and R. Ward, “Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval”, IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) 24, no. 4, 694–707. 2016. [CrossRef] [Google Scholar]
- I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks”, In Advances in neural information processing systems, pp. 3104–3112. 2014. [EDP Sciences] [Google Scholar]
- V. Pham, T. Bluche, C. Kermorvant, and J. Louradour, “Dropout improves recurrent neural networks for handwriting recognition”, In Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on, pp. 285–290. IEEE, 2014. [Google Scholar]
- P. Malhotra, L. Vig, G. Shroff, and P. Agarwal, “Long short term memory networks for anomaly detection in time series”, In Proceedings, p. 89. Presses universitaires de Louvain, 2015. [Google Scholar]
- Z. C. Lipton, D. C. Kale, and R. C. Wetzell, “Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks”, arXiv preprint arXiv:1510.07641. 2015. [Google Scholar]
- A. ElSaid, B. Wild, J. Higgins, and T. Desell, “Using LSTM recurrent neural networks to predict excess vibration events in aircraft engines”, In e-Science (e-Science), 2016 IEEE 12th International Conference on, pp. 260–269. IEEE, 2016. [CrossRef] [Google Scholar]
- W. Zaremba, I. Sutskever, and O. Vinyals, “Recurrent neural network regularization”, arXiv preprint arXiv:1409.2329. 2014. [Google Scholar]
- D. Kingma, and J. Ba, “Adam: A method for stochastic optimization”, arXiv preprint arXiv:1412.6980 (2014). [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.