Full Text:   <47>

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On-line Access: 2022-10-20

Received: 2022-03-23

Revision Accepted: 2022-08-18

Crosschecked: 2022-10-21

Cited: 0

Clicked: 132

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Gang Chen

https://orcid.org/0000-0002-7483-0045

Wen-tao HU

https://orcid.org/0000-0003-0930-7810

Da-wei JIANG

https://orcid.org/0000-0002-1890-4046

Sai WU

https://orcid.org/0000-0002-7903-1496

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Journal of Zhejiang University SCIENCE A 2022 Vol.23 No.10 P.832-837

http://doi.org/10.1631/jzus.A2200156


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


Author(s):  Wen-tao HU, Da-wei JIANG, Sai WU, Ke CHEN, Gang CHEN

Affiliation(s):  Key Lab of Intelligent Computing Based Big Data of Zhejiang Province, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   cg@zju.edu.cn

Key Words:  Integrity constraint discovery, Intelligent railroad systems, Machine learning


Wen-tao HU, Da-wei JIANG, Sai WU, Ke CHEN, Gang CHEN. Complex integrity constraint discovery: measuring trust in modern intelligent railroad systems[J]. Journal of Zhejiang University Science A, 2022, 23(10): 832-837.

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

复杂完整性约束:在现代智能交通系统中衡量模型可信性

作者:胡文涛,江大伟,伍赛,陈珂,陈刚
机构:浙江大学,浙江省大数据智能计算重点实验室,中国杭州,310027
目的:由于使用的列车运行数据偏离系统应用的数据特征,现代智能交通系统部署的模型推理结果可能不可靠。本文旨在研究部署在系统中的模型使用的数据变化对模型性能的影响,通过研究衡量模型可信性方法,实现在现实场景中无需标注数据实时检测部署在系统中的模型可信性。
创新点:1.提出一种复杂完整性约束概念,在无标注数据的情况下,衡量模型使用数据的不安全程度。2.为实现现代智能交通系统实时检测模型的可信性,我们设计一种新颖的算法,利用位向量索引技术和规则推理系统,快速发现模型应用数据的复杂完整性约束。
方法:1.通过输入部署在现代智能交通系统模型中的训练数据,系统构建面向输入数据的索引向量从而避免对大规模数据进行多次。2.通过规则推理系统和支持度剪枝技术,将语意重复的冗余约束和一些无意义的约束忽略,得到有效的复杂完整性约束。3.利用完整性约束计算违反约束的数据在数据集中的比例从而衡量模型使用的数据不安全程度。4.通过使用真实的列车运行数据集测试,分析复杂完整性约束衡量的数据不安全程度和模型性能的关系,从而验证复杂完整性约束的可行性和有效性。
结论:1.模型使用的数据偏离模型训练数据特征会影响模型的性能。2.通过发现复杂完整性约束,衡量模型使用的数据不安全程度,可以快速检测部署的模型可信性。3.通过对模型可信性的研究,可以无需标注而快速发现不可信的模型,从而及时重新部署可信模型,提升现代智能交通系统的稳定性。

关键词:现代智能交通系统;列车运行控制;约束发现;可信机器学习

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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