Research Output per year
Fingerprint Dive into the research topics where Hao Yan is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Process monitoring
Engineering & Materials Science
Monitoring
Engineering & Materials Science
Decomposition
Engineering & Materials Science
Tensors
Engineering & Materials Science
Process Modeling
Mathematics
Degradation
Engineering & Materials Science
Raman spectroscopy
Engineering & Materials Science
Tensor
Mathematics
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.
Research Output 2014 2020
Multiple Tensor-on-Tensor Regression: An Approach for Modeling Processes With Heterogeneous Sources of Data
Gahrooei, M. R., Yan, H., Paynabar, K. & Shi, J., Jan 1 2020, (Accepted/In press) In : Technometrics.Research output: Contribution to journal › Article
Process Modeling
Tensors
Tensor
Regression
Scalar
AKM2D: An adaptive framework for online sensing and anomaly quantification
Yan, H., Paynabar, K. & Shi, J., Jan 1 2019, (Accepted/In press) In : IISE Transactions.Research output: Contribution to journal › Article
Sampling
Inspection
Coordinate measuring machines
Guided electromagnetic wave propagation
Ultrasonics
Image-based process monitoring via adversarial autoencoder with applications to rolling defect detection
Yan, H., Yeh, H. M. & Sergin, N., Aug 2019, 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019. IEEE Computer Society, p. 311-316 6 p. 8843313. (IEEE International Conference on Automation Science and Engineering; vol. 2019-August).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Process monitoring
Statistics
Monitoring
Defect detection
Performance Evaluation of Production Systems Using Real-Time Machine Degradation Signals
Kang, Y., Yan, H. & Ju, F., Jan 1 2019, (Accepted/In press) In : IEEE Transactions on Automation Science and Engineering.Research output: Contribution to journal › Article
Real time systems
Degradation
Throughput
Markov processes
Productivity
Physics-based deep spatio-temporal metamodeling for cardiac electrical conduction simulation
Yan, H., Zhao, X., Hu, Z. & Du, D., Aug 2019, 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019. IEEE Computer Society, p. 152-157 6 p. 8842902. (IEEE International Conference on Automation Science and Engineering; vol. 2019-August).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Physics
Tissue
Partial differential equations
Neural networks
Fibers
Projects 2018 2021
- 2 Active
ATD: Collaborative Research: Adaptive and Rapid Spatial-temporal Threat Detection over Networks
National Science Foundation (NSF)
9/1/18 → 8/31/21
Project: Research project