基于循环神经网络的LHC超导磁体死后时间序列异常检测
Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets
摘要：
本文提出了一种基于LSTM和GRU深度学习算法的模型，用于促进大型强子对撞机超导磁体的异常检测。我们使用Post Mortem数据库中的高分辨率数据来训练一组模型，并选择最佳的超参数集。使用深度学习方法可以检查大量数据，并提取需要进一步专家检查并被视为异常的片段。所提出的方法不需要繁琐的手动阈值设置和操作员的注意力在系统设置的阶段。取而代之的是，自动的方法，它实现了根据我们的实验准确率为99%。这是针对302 MB的最大数据集和以下网络架构实现的：单层LSTM，128个单元，20个训练时期，look_back=16，look_ahead=128，grid=100和优化器Adam。所有实验均在GPU Nvidia Tesla K80上运行
英文摘要：
This paper presents a model based on Deep Learning algorithms of LSTM and GRU for facilitating an anomaly detection in Large Hadron Collider superconducting magnets. We used high resolution data available in Post Mortem database to train a set of models and chose the best possible set of their hyper-parameters. Using Deep Learning approach allowed to examine a vast body of data and extract the fragments which require further experts examination and are regarded as anomalies. The presented method does not require tedious manual threshold setting and operator attention at the stage of the system setup. Instead, the automatic approach is proposed, which achieves according to our experiments accuracy of 99%. This is reached for the largest dataset of 302 MB and the following architecture of the network: single layer LSTM, 128 cells, 20 epochs of training, look_back=16, look_ahead=128, grid=100 and optimizer Adam. All the experiments were run on GPU Nvidia Tesla K80
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
