论文标题
部分可观测时空混沌系统的无模型预测
A Long-term Dependent and Trustworthy Approach to Reactor Accident Prognosis based on Temporal Fusion Transformer
论文作者
论文摘要
反应堆事故的预后是确保采用适当策略以避免放射性发行的关键方法。但是，在核工业领域的研究非常有限。在本文中，我们提出了一种基于时间融合变压器（TFT）模型具有多头自我注意和门控机制的事故预后的方法。该方法一方面利用多个协变量来提高预测准确性，而对另一方面的分位数回归方法对另一方面进行了不确定性评估。本文提出的方法应用于HPR1000反应堆中冷却液事故（LOCA）后的预后。广泛的实验结果表明，该方法在预测准确性和信心方面超过了新的基于深度学习的预测方法。此外，具有不同信噪比和静态协变量的消融实验的干扰实验进一步说明了鲁棒性来自提取静态和历史协变量特征的能力。总而言之，这项工作首次将新颖的复合深度学习模型TFT应用于反应堆事故后关键参数的预后，并为建立一种更聪明，更聪明，更轻便的反应堆系统做出了积极贡献。
Prognosis of the reactor accident is a crucial way to ensure appropriate strategies are adopted to avoid radioactive releases. However, there is very limited research in the field of nuclear industry. In this paper, we propose a method for accident prognosis based on the Temporal Fusion Transformer (TFT) model with multi-headed self-attention and gating mechanisms. The method utilizes multiple covariates to improve prediction accuracy on the one hand, and quantile regression methods for uncertainty assessment on the other. The method proposed in this paper is applied to the prognosis after loss of coolant accidents (LOCAs) in HPR1000 reactor. Extensive experimental results show that the method surpasses novel deep learning-based prediction methods in terms of prediction accuracy and confidence. Furthermore, the interference experiments with different signal-to-noise ratios and the ablation experiments for static covariates further illustrate that the robustness comes from the ability to extract the features of static and historical covariates. In summary, this work for the first time applies the novel composite deep learning model TFT to the prognosis of key parameters after a reactor accident, and makes a positive contribution to the establishment of a more intelligent and staff-light maintenance method for reactor systems.
