TAILIEUCHUNG - 3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection

With Europe’s ageing fleet of nuclear reactors operating closer to their safety limits, the monitoring of such reactors through complex models has become of great interest to maintain a high level of availability and safety. | 3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection EPJ Nuclear Sci. Technol. 5 20 2019 Nuclear Sciences A. Durrant et al. published by EDP Sciences 2019 amp Technologies https epjn 2019047 Available online at https REGULAR ARTICLE 3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection Aiden Durrant Georgios Leontidis and Stefanos Kollias University of Lincoln School of Computer Science Machine Learning Group Brayford Pool Lincoln LN6 7TS UK Received 1 July 2019 Accepted 12 July 2019 Abstract. With Europe s ageing fleet of nuclear reactors operating closer to their safety limits the monitoring of such reactors through complex models has become of great interest to maintain a high level of availability and safety. Therefore we propose an extended Deep Learning framework as part of the CORTEX Horizon 2020 EU project for the unfolding of reactor transfer functions from induced neutron noise sources. The unfolding allows for the identification and localisation of reactor core perturbation sources from neutron detector readings in Pressurised Water Reactors. A 3D Convolutional Neural Network 3D-CNN and Long Short-Term Memory LSTM Recurrent Neural Network RNN have been presented each to study the signals presented in frequency and time domain respectively. The proposed approach achieves state-of-the-art results with the classification of perturbation type in the frequency domain reaching accuracy and localisation of the classified perturbation source being regressed to Mean Absolute Error MAE . 1 Introduction Machine learning ML is a data analytical process for the approximation of functions mapping a set of inputs to The early detection classification and localisation of outputs. Therefore the use of ML to approximate such anomalies within the reactors core is vital to ensure the reactor functions given limited detector readings is

• Năng lượng
• Europe’s ageing fleet
• Nuclear reactors operating
• CORTEX Horizon 2020 EU project
• Deep Learning framework
• 3D Convolutional Neural Network
• BMC Bioinformatics
• Protein structural analysis
• Amino acid similarities
• Mutation analysis
• Structural bioinformatics
• Convolutional neural network
• Deep learning
• BMC Genomics
• Copy number aberration
• Somatic mutation
• Cancer type prediction
• Convolutional neural network