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典型文献
Complex integrity constraint discovery:measuring trust in modern intelligent railroad systems
文献摘要:
1 Introduction 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 unreli-able, so the system model must be retrained and rede-ployed 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.
文献关键词:
作者姓名:
Wen-tao HU;Da-wei JIANG;Sai WU;Ke CHEN;Gang CHEN
作者机构:
Key Lab of Intelligent Computing Based Big Data of Zhejiang Province,Zhejiang University,Hangzhou 310027,China
引用格式:
[1]Wen-tao HU;Da-wei JIANG;Sai WU;Ke CHEN;Gang CHEN-.Complex integrity constraint discovery:measuring trust in modern intelligent railroad systems)[J].浙江大学学报(英文版)(A辑:应用物理和工程),2022(10):832-837
A类:
Data,unreli,retrained,rede,Chandel
B类:
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AB值:
0.515332
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