基于LSTM神经网络的光学传感器行为预测

2019 IEEE Photonics Conference (IPC) Pub Date : 2019-09-01 DOI:10.1109/IPCon.2019.8908337

M. Zaghloul, Amr M. Hassan, D. Carpenter, P. Calderoni, J. Daw, Kevin P. Chen

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引用次数: 1

摘要

基于光纤的传感器被证明能够承受各种恶劣环境。长短期记忆(LSTM)神经网络通常用于具有长依赖关系的数据集。在这里，从中子反应堆堆芯收集的罕见的FBG测量数据被用来建立一个能够预测反应堆内部未来事件的神经网络。

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Optical Sensor Behavior Prediction using LSTM Neural Network

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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2019 IEEE Photonics Conference (IPC)

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