Spatial modeling and monitoring considering long-range dependence

Author:

Shao Yunfei1ORCID,Si Wujun1ORCID,Chen Yong2

Affiliation:

1. Department of Industrial, Systems and Manufacturing Engineering, Wichita State University, Wichita, Kansas

2. Department of Industrial and Systems Engineering, University of Iowa, Iowa City, Iowa

Funder

National Science Foundation

Kansas NASA EPSCoR Research Infrastructure Development Program

NASA EPSCoR Program

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management,Safety, Risk, Reliability and Quality

Reference59 articles.

1. Monitoring variations in multimode surface topography

2. Beran, J., Y. Feng, S. Ghosh, and R. Kulik. 2016. Long-memory processes. Berlin, Germany: Springer.

3. Bergmann, P., M. Fauser, D. Sattlegger, and C. Steger. 2019. MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9592–600.

4. Biermé, H. 2019. Introduction to random fields and scale invariance. In Stochastic geometry, 129–80. Basel, Switzerland: Springer.

5. Monitoring for changes in the nature of stochastic textured surfaces

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