Complex integrity constraint discovery: measuring trust in modern intelligent railroad systems  

作　　者：Wen-tao HU Da-wei JIANG Sai WU Ke CHEN Gang CHEN 

机构地区：[1]Key Lab of Intelligent Computing Based Big Data of Zhejiang Province,Zhejiang University,Hangzhou 310027,China

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2022年第10期832-837,共6页浙江大学学报（英文版）A辑（应用物理与工程）

摘　　要：1Introduction Data are at the heart of intelligent rail systems in the high-speed transportation sector(Zhou et al.,2020;Ho et al.,2021;Hu et al.,2021;Chen et al.,2022).The core of modern intelligent railroad systems typically includes rail transportation and equipment monitoring models learned from large datasets,which are often optimized for specific data and workloads(Zhu et al.,2019;Tan et al.,2020).While these intelligent railroad systems have been widely adopted and successful,their reliability and proper function will change as the data used changes.If the data used(on which the system operates)deviates from the fundamental constraints of the initial data(on which the system is trained)then,in that case,the system performance degrades,and the results inferred by the system model become unreliable,so the system model must be retrained and redeployed to re-store reliable inference results(Sharma and Chandel,2013).The mechanism for assessing the trustworthiness of intelligent rail system inferences is of paramount importance,especially for rail systems performing safety-critical or high-impact operations.

关 键 词：CONSTRAINT INTELLIGENT operations 

分 类 号：U284.48[交通运输工程—交通信息工程及控制] TP181[交通运输工程—道路与铁道工程]