| Measuring smart grid resilience: Methods, challenges and opportunities L Das, S Munikoti, B Natarajan, B Srinivasan Renewable and Sustainable Energy Reviews 130, 109918, 2020 | 164 | 2020 |
| Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications S Munikoti, D Agarwal, L Das, M Halappanavar, B Natarajan IEEE Transactions on Neural Networks and Learning Systems, 2023 | 88 | 2023 |
| Data-Driven Approaches for Diagnosis of Incipient Faults in DC Motors S Munikoti, L Das, B Natarajan, B Srinivasan IEEE Transactions on Industrial Informatics 15 (9), 5299-5308, 2019 | 61 | 2019 |
| Scalable graph neural network-based framework for identifying critical nodes and links in complex networks S Munikoti, L Das, B Natarajan Neurocomputing 468, 211-221, 2022 | 48 | 2022 |
| Hidden representations in deep neural networks: Part 2. Regression problems L Das, A Sivaram, V Venkatasubramanian Computers & Chemical Engineering 139, 106895, 2020 | 46 | 2020 |
| Hidden representations in deep neural networks: Part 1. Classification problems A Sivaram, L Das, V Venkatasubramanian Computers & Chemical Engineering 134, 106669, 2020 | 31 | 2020 |
| Neuralcompression: a machine learning approach to compress high frequency measurements in smart grid L Das, D Garg, B Srinivasan Applied Energy 257, 113966, 2020 | 27 | 2020 |
| Toward Preventing Accidents in Process Industries by Inferring the Cognitive State of Control Room Operators through Eye Tracking L Das, MU Iqbal, P Bhavsar, B Srinivasan, R Srinivasan ACS Sustainable Chemistry & Engineering 6 (2), 2517-2528, 2018 | 27 | 2018 |
| Multivariate control loop performance assessment with Hurst exponent and Mahalanobis distance L Das, B Srinivasan, R Rengaswamy IEEE Transactions on Control Systems Technology 24 (3), 1067-1074, 2015 | 24 | 2015 |
| A framework for efficient information aggregation in smart grid A Joshi, L Das, B Natarajan, B Srinivasan IEEE Transactions on Industrial Informatics 15 (4), 2233-2243, 2018 | 23 | 2018 |
| Data mining and control loop performance assessment: The multivariate case L Das, R Rengaswamy, B Srinivasan AIChE Journal 63 (8), 3311-3328, 2017 | 19 | 2017 |
| A general framework for quantifying aleatoric and epistemic uncertainty in graph neural networks S Munikoti, D Agarwal, L Das, B Natarajan Neurocomputing 521, 1-10, 2023 | 17 | 2023 |
| A novel framework for integrating data mining with control loop performance assessment L Das, B Srinivasan, R Rengaswamy AIChE Journal 62 (1), 146-165, 2016 | 17 | 2016 |
| A unified theory of emergent equilibrium phenomena in active and passive matter V Venkatasubramanian, A Sivaram, L Das Computers & Chemical Engineering 164, 107887, 2022 | 15 | 2022 |
| Simulation-driven deep learning for locating faulty insulators in a power line B Gjorgiev, L Das, S Merkel, M Rohrer, E Auger, G Sansavini Reliability Engineering & System Safety 231, 108989, 2023 | 13 | 2023 |
| Neural tangent kernel analysis of PINN for advection-diffusion equation MH Saadat, B Gjorgiev, L Das, G Sansavini arXiv preprint arXiv:2211.11716, 2022 | 12 | 2022 |
| Cognitive Behavior Based Framework for Operator Learning: Knowledge and Capability Assessment through Eye Tracking L Das, B Srinivasan, R Srinivasan Computer Aided Chemical Engineering 40, 2977-2982, 2017 | 12 | 2017 |
| Robust and efficient swarm communication topologies for hostile environments V Mann, A Sivaram, L Das, V Venkatasubramanian Swarm and Evolutionary Computation 62, 100848, 2021 | 11 | 2021 |
| On-line performance monitoring of PEM fuel cell using a fast EIS approach L Das, B Srinivasan, R Rengaswamy 2015 American Control Conference (ACC), 1611-1616, 2015 | 11 | 2015 |
| Object detection-based inspection of power line insulators: Incipient fault detection in the low data-regime L Das, MH Saadat, B Gjorgiev, E Auger, G Sansavini arXiv preprint arXiv:2212.11017, 2022 | 7 | 2022 |
Balasubramaniam NatarajanClair N Palmer Sara M Palmer ProfessorПідтверджена електронна адреса в ksu.edu
