| Deep learning in medical image registration: a survey G Haskins, U Kruger, P Yan Machine Vision and Applications 31 (1), 8, 2020 | 788 | 2020 |
| Developments and applications of nonlinear principal component analysis–a review U Kruger, J Zhang, L Xie Principal manifolds for data visualization and dimension reduction, 1-43, 2008 | 749* | 2008 |
| 3-D convolutional encoder-decoder network for low-dose CT via transfer learning from a 2-D trained network H Shan, Y Zhang, Q Yang, U Kruger, MK Kalra, L Sun, W Cong, G Wang IEEE transactions on medical imaging 37 (6), 1522-1534, 2018 | 503 | 2018 |
| Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction H Shan, A Padole, F Homayounieh, U Kruger, RD Khera, C Nitiwarangkul, ... Nature Machine Intelligence 1 (6), 269-276, 2019 | 417 | 2019 |
| Process monitoring approach using fast moving window PCA X Wang, U Kruger, GW Irwin Industrial & engineering chemistry research 44 (15), 5691-5702, 2005 | 382 | 2005 |
| Moving window kernel PCA for adaptive monitoring of nonlinear processes X Liu, U Kruger, T Littler, L Xie, S Wang Chemometrics and intelligent laboratory systems 96 (2), 132-143, 2009 | 243 | 2009 |
| Recursive partial least squares algorithms for monitoring complex industrial processes X Wang, U Kruger, B Lennox Control Engineering Practice 11 (6), 613-632, 2003 | 233 | 2003 |
| Statistical monitoring of complex multivatiate processes: with applications in industrial process control U Kruger, L Xie John Wiley & Sons, 2012 | 180 | 2012 |
| Learning deep similarity metric for 3D MR–TRUS image registration G Haskins, J Kruecker, U Kruger, S Xu, PA Pinto, BJ Wood, P Yan International journal of computer assisted radiology and surgery 14, 417-425, 2019 | 167 | 2019 |
| Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation DP Howsmon, U Kruger, S Melnyk, SJ James, J Hahn PLoS computational biology 13 (3), e1005385, 2017 | 140 | 2017 |
| Detection of incipient tooth defect in helical gears using multivariate statistics N Baydar, Q Chen, A Ball, U Kruger Mechanical systems and signal processing 15 (2), 303-321, 2001 | 136 | 2001 |
| Cointegration testing method for monitoring nonstationary processes Q Chen, U Kruger, AYT Leung Industrial & Engineering Chemistry Research 48 (7), 3533-3543, 2009 | 126 | 2009 |
| Nonlinear PCA with the local approach for diesel engine fault detection and diagnosis X Wang, U Kruger, GW Irwin, G McCullough, N McDowell IEEE Transactions on Control Systems Technology 16 (1), 122-129, 2007 | 122 | 2007 |
| Statistical‐based monitoring of multivariate non‐Gaussian systems X Liu, L Xie, U Kruger, T Littler, S Wang AIChE journal 54 (9), 2379-2391, 2008 | 121 | 2008 |
| Detecting abnormal situations using the Kullback–Leibler divergence J Zeng, U Kruger, J Geluk, X Wang, L Xie Automatica 50 (11), 2777-2786, 2014 | 118 | 2014 |
| Improved principal component monitoring of large-scale processes U Kruger, Y Zhou, GW Irwin Journal of Process Control 14 (8), 879-888, 2004 | 118 | 2004 |
| Diagnosis of process faults in chemical systems using a local partial least squares approach U Kruger, G Dimitriadis AIChE Journal 54 (10), 2581-2596, 2008 | 116 | 2008 |
| Synthesis of T2 and Q statistics for process monitoring Q Chen, U Kruger, M Meronk, AYT Leung Control Engineering Practice 12 (6), 745-755, 2004 | 110 | 2004 |
| Improved principal component monitoring using the local approach U Kruger, S Kumar, T Littler Automatica 43 (9), 1532-1542, 2007 | 100 | 2007 |
| Extended PLS approach for enhanced condition monitoring of industrial processes U Kruger, Q Chen, DJ Sandoz, RC McFarlane AIChE journal 47 (9), 2076-2091, 2001 | 90 | 2001 |
Lei XieZhejiang UniversityVahvistettu sähköpostiosoite verkkotunnuksessa zju.edu.cn