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A Self-Learning and Online Algorithm for Time Series Anomaly Detection, with Application in CPU Manufacturing.

, , , and . CIKM, page 1823-1832. ACM, (2016)

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A Model Predictive Control framework for robust management of multi-product, multi-echelon demand networks., , , , and . Annu. Rev. Control., 27 (2): 229-245 (2003)Non-Parametric Priors For Generative Adversarial Networks., , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 5838-5847. PMLR, (2019)Application of Model Predictive Control to Robust Management of Multiechelon Demand Networks in Semiconductor Manufacturing., , , and . Simulation, 79 (3): 139-156 (2003)Multifeature, Sparse-Based Approach for Defects Detection and Classification in Semiconductor Units., , , , , and . IEEE Trans Autom. Sci. Eng., 15 (1): 145-159 (2018)Exact variable-length anomaly detection algorithm for univariate and multivariate time series., , , and . Data Min. Knowl. Discov., 32 (6): 1806-1844 (2018)A comparison of supervisory control algorithms for tool/process disturbance tracking., , and . ACC, page 2626-2631. IEEE, (2003)Multi-feature sparse-based defect detection and classification in semiconductor units., , , , and . ICIP, page 754-758. IEEE, (2016)A Mixed Logical Dynamic Model Predictive Control approach for handling industrially relevant transportation constraints., and . CASE, page 966-971. IEEE, (2010)Analysis of the Effects of Truncation on the EWMA Observer., and . CASE, page 472-477. IEEE, (2007)A Self-Learning and Online Algorithm for Time Series Anomaly Detection, with Application in CPU Manufacturing., , , and . CIKM, page 1823-1832. ACM, (2016)