@inproceedings{ea175255c97849228f7c7671ab4f0ac7,
title = "The Semaphore Identification and Fault Troubleshooting Modus for Spacecraft Originating from Deep Learning and RF Method",
abstract = "The spacecraft environment and reliability test brings the huge type of data, traditional algorithm has many shortcomings when handling big data especially huge storage space and massive compute time. According to the deep learning network, the writer design a algorithm with the ability that gain the initial parameters of the multi-layer neutral network. After initialize the parameters with gradient descent method, we will select the parameters to help to classifying information more easily. By researching generated data, there is expert information which could help to build a platform to manage the health of spacecraft. Experimental data show that the deep neural network algorithm could be proved to achieve a desired accuracy through classification with multi-kind of spacecraft data environment and reliability experiment.",
keywords = "Data compression, Deep belief network, Deep learning, Prognostic and health management, Random-forest, Signal identification",
author = "Wei Lan and Chun He and Mingju Wang and Ke Li and Zhijian Zhao",
note = "Publisher Copyright: \&\#x00A9; 2019, Springer Nature Singapore Pte Ltd.; 4th International Conference on Cognitive Systems and Information Processing, ICCSIP 2018 ; Conference date: 29-11-2018 Through 01-12-2018",
year = "2019",
doi = "10.1007/978-981-13-7986-4\_19",
language = "英语",
isbn = "9789811379857",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "208--219",
editor = "Dewen Hu and Fuchun Sun and Huaping Liu",
booktitle = "Cognitive Systems and Signal Processing - 4th International Conference, ICCSIP 2018, Revised Selected Papers",
address = "德国",
}