Optical Sensor Behavior Prediction using LSTM Neural Network

被引：1
|
作者：
Zaghloul, Mohamed A. S. [1 ]
Hassan, Amr M. [1 ]
Carpenter, David [2 ]
Calderoni, Pattrick [3 ]
Daw, Joshua [3 ]
Chen, Kevin P. [1 ]
机构：
[1] Univ Pittsburgh, Elect & Comp Engn, Pittsburgh, PA 15260 USA
[2] MIT, Nucl Reactor Lab, Cambridge, MA 02139 USA
[3] Idaho Natl Lab, High Temp Test Lab, Idaho Falls, ID USA
来源：
2019 IEEE PHOTONICS CONFERENCE (IPC) | 2019年
关键词：
Fiber Bragg grating; nuclear reactor core measurements; long-short-term memory; recurrent neural networks;
D O I：
10.1109/ipcon.2019.8908337
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Optical fiber-based-sensors proved capable of enduring various harsh environments. Long-short-term memory (LSTM) neural-networks are often used for datasets with long-dependences. Here, rare FBG measurements collected from a neutron reactor core were used to build a neural-network capable of predicting the future events inside the reactor.
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页数：2
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