Industrial Statistics and Data Analytics Lab

Industrial Statistics and Data Analytics Lab

Industrial Statistics and Data Analytics Lab,, department of industrial engineering, Tsinghua University

Publications

Referred Journals and Transactions

  1. Zhou, P., and Zhang, C.* (2022). Functional state-space model for multi-channel autore- gressive profiles with application in advanced manufacturing, Journal of Manufacturing Systems, https://doi.org/10.1016/j.jmsy.2022.06.014.
  2. Li, Z., Yan, H., Zhang, C., and Tsung, F. (2022). Individualized Passenger Travel Pat- tern Multi-Clustering based on Graph Regularized Tensor Latent Dirichlet Allocation, Data Mining and Knowledge Discovery, 36, pages1247–1278.
  3. Guo, J., Yan, H., and Zhang, C.* (2022). A Bayesian Partially Observable Online Change Detection Approach with Thompson Sampling, Technometrics, online https://doi.org/10.1080/00401706.2022.2127914.
  4. Meng, H., Li, Y. F., and Zhang, C. (2022). Estimation of discharge voltage for lithium- ion batteries through orthogonal experiments at subzero environment, Journal of Energy Storage, 52(C), 10508.
  5. Wu, H., Zhang, C., and Li, Y F. (2021). Monitoring Heterogeneous Multivariate Profiles Based on Heterogeneous Graphical Model. Technometrics, 64(2), 210-223.
  6. Li, Z., Yan, H., Zhang, C., and Tsung, F. (2020). Long-short term spatiotemporal tensor prediction for passenger flow profile. IEEE Robotics and Automation Letters 5.4 (2020): 5010-5017.
  7. Zhang, C. and Hoi, C.H. (2020). A Data-Driven Method for Online Monitoring Tube Wall Thinning Process in Dynamic Noisy Environment, in press, IEEE Transactions on Automation, Systems and Engineering, 2021.
  8. Zhang, C., Hoi, C H, and Tsung, F. (2020). Time-Warped Sparse Non-negative Factorization for Functional Data Analysis. ACM Transactions on Knowledge Discovery from Data, 14(6): 1-23.
  9. Xian, X., Zhang, C., Bonk, S., and Liu, K. (2019). Online Monitoring of Big Data Streams: A Rank-based Sampling Algorithm by Data Augmentation, Journal of Quality Technology, 53.2 (2021): 135-153.
  10. Wu, J., Xu, H., Zhang, C., and Yuan, Y. (2019). A Sequential Bayesian Partitioning Approach for Online Steady-State Detection of Multivariate Systems, IEEE Transactions on Automation Science and Engineering, 16.4 (2019): 1882-1895.
  11. Zhang, C., Chen, N. and Wu, J. (2019). Spatial Rank based High-dimensional Monitoring Through Random Projection, Journal of Quality Technology, 52(2), 111-127.
  12. Zhang, C., Yan, H., Lee, S., and Shi, J. (2021). Dynamic Multivariate Functional data Modeling via Sparse Subspace Learning, Technometrics, 63.3: 370-383. (2017 INFORMS Data Mining Section Best Paper Award).
  13. Zhang, C., and Chen, N. (2018). Statistical Analysis of Simulation Outputs from Parallel Computing, ACM Transactions on Modeling and Computer Simulation (TOMACS), 28(3), 21-35.
  14. Zhang, C., Yan, H., Lee, S., and Shi, J. (2018). Multichannel Profile Monitoring based on Sparse Multichannel Functional Principal Component Analysis, IISE Transactions, 50:10, 878-891(2016 INFORMS Quality, Statistics, and Reliability Section Best Student Poster Award).
  15. Zhang, C., Yan, H., Lee, S., and Shi, J. (2017). Multiple Profiles Sensor-Based Monitoring and Anomaly Detection, accepted, Journal of Quality Technology, 50:4, 344-362.
  16. Zhang, C., Lei, Y., Zhang, L., Chen N. (2017). Modeling Tunnel Profile in Presence of Coordinate Errors: A Gaussian Process Based Approach, IISE Transactions, 49(11), 1065-1077.
  17. Zhang, C., Chen, N., and Li, Z. (2016). State Space Modeling of Autocorrelated Multivariate Poisson Counts, IISE Transactions, 49(5), 518-531.
  18. Zhang, C., Chen, N., and Zou, C. (2016). Robust Multivariate Control Chart Based on Goodness-of-fit Test, Journal of Quality Technology, 48(2), 139-161.

Conferences

  1. Zhang, W., Zhang, C.*, and Tsung, F. (2022). GRELEN: Multivariate Time Series Anomaly Detection from the Perspective of Graph Relational Learning, 31st Interna- tional Joint Conference on Artificial Intelligence, 2022.
  2. Zhang, W., Zhang, C., and Tsung, F., Transformer Based Spatial-Temporal Fusion Network for Metro Passenger Flow Forecasting, 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), 2021, pp. 1515-1520.
  3. He B, Li S, Zhang, C.*, Zheng B., and Tsung, F. Holistic Prediction for Public Transport Crowd Flows: A Spatio Dynamic Graph Network Approach, Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PCDD). Springer, Cham, 2021: 321-336.
  4. Li, Z., Yan, H., Zhang, C., and Tsung, F. Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction, 34th AAAI Conference on Artificial Intelligence (AAAI), 2020.
  5. Zhang, C., and Hoi, C.H. Partially Observable Multi-Sensor Sequential Change Detection: A Combinatorial Multi-Armed Bandit Approach, 33rd AAAI Conference on Artificial Intelligence (AAAI), 2019. code