基于LSTM和ELQA框架的失超预测应用程序的概念开发
A Conceptual Development of Quench Prediction App build on LSTM and ELQA framework
摘要：
本文介绍了在CERN的\gls{te-mpe-ee}中开发用于失超预测的Web应用程序。作者描述了一个电气质量保证（ELQA）框架，该平台是专为快速开发Web集成数据分析应用程序的不同分析需要在大型强子对撞机（LHC）的硬件调试。在第二部分中，本文描述了通过使用LSTM递归神经网络对从淬火检测系统收集的数据进行的研究。本文讨论并提出了一个概念性的工作，实现失超预测应用程序的\gls{te-mpe-ee}的基础上ELQA和失超预测算法。
英文摘要：
This article presents a development of web application for quench prediction in \gls{te-mpe-ee} at CERN. The authors describe an ELectrical Quality Assurance (ELQA) framework, a platform which was designed for rapid development of web integrated data analysis applications for different analysis needed during the hardware commissioning of the Large Hadron Collider (LHC). In second part the article describes a research carried out with the data collected from Quench Detection System by means of using an LSTM recurrent neural network. The article discusses and presents a conceptual work of implementing quench prediction application for \gls{te-mpe-ee} based on the ELQA and quench prediction algorithm.
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。
