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.

引用

页数：2

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