A novel method based on adaptive autoencoder and improved long short-term memory and gated recurrent unit for nuclear radiation measurement and monitoring. (August 2022)

• Record Type: Journal Article
• Authors: Liao, Yilin; Wang, Wenhai; Zhang, Zeyin; Zhao, Shunping; Niu, Yunlong; Liu, Xinggao
• Is Part Of: Measurement. Volume 199(2022)
• Publication Date: 2022-08
• Subjects: Nuclear radiation measurement -- Monitoring and warning -- Autoencoder -- Long Short-Term Memory -- Gated Recurrent Unit; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111571
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The proposed adaptive autoencoder can adaptively reduce the data dimension. The proposed improved LSTM and GRU can converge faster. The proposed improved LSTM and GRU do not introduce new parameters. The proposed method has a small experimental error. The proposed method has reliable performance in the real world event. Abstract: One way to implement the defense against nuclear threat is based on the measurement and detection of radiation. To cope with the problems of low precision and slow warning speed in nuclear radiation monitoring and warning, a novel method based on adaptive autoencoder and improved Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) is proposed. Considering the large amount of redundant information in the nuclear radiation energy spectrum, an adaptive autoencoder network is designed to reduce the dimension of data. In order to overcome the problems of slow training speed of LSTM/GRU and insufficient utilization of internal features of the network, the polynomial combinations are increased, which will help to improve the accuracy on the task. The experiments conducted on data measured in real scenario show that the proposed method has high accuracy and reliability for nuclear radiation monitoring and early warning in the real world event.