| Real-time fault detection and diagnosis using sparse principal component analysis S Gajjar, M Kulahci, A Palazoglu Journal of Process Control 67, 112-128, 2018 | 148 | 2018 |
| A data-driven multidimensional visualization technique for process fault detection and diagnosis S Gajjar, A Palazoglu Chemometrics and Intelligent Laboratory Systems 154, 122-136, 2016 | 74 | 2016 |
| Selection of non-zero loadings in sparse principal component analysis S Gajjar, M Kulahci, A Palazoglu Chemometrics and Intelligent Laboratory Systems 162, 160-171, 2017 | 35 | 2017 |
| Process knowledge discovery using sparse principal component analysis H Gao, S Gajjar, M Kulahci, Q Zhu, A Palazoglu Industrial & Engineering Chemistry Research 55 (46), 12046-12059, 2016 | 18 | 2016 |
| Use of sparse principal component analysis (SPCA) for fault detection S Gajjar, M Kulahci, A Palazoglu IFAC-PapersOnLine 49 (7), 693-698, 2016 | 18 | 2016 |
| Least squares sparse principal component analysis and parallel coordinates for real-time process monitoring S Gajjar, M Kulahci, A Palazoglu Industrial & Engineering Chemistry Research 59 (35), 15656-15670, 2020 | 17 | 2020 |
| Capitalizing from Data: Real-time Analytics and Knowledge Discovery SG Gajjar University of California, Davis, 2017 | | 2017 |
| A Data-Driven Multidimensional Visualization Technique for Process Fault Detection and Diagnosis AP Shriram Gajjar AIChE 2015 Annual Meeting, 2015 | | 2015 |
Ahmet PalazogluUC DavisE-mail confirmado em ucdavis.edu
