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Laya Das
Laya Das
Verified email at iitgn.ac.in
Title
Cited by
Cited by
Year
Measuring smart grid resilience: Methods, challenges and opportunities
L Das, S Munikoti, B Natarajan, B Srinivasan
Renewable and Sustainable Energy Reviews 130, 109918, 2020
1292020
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
522019
Hidden representations in deep neural networks: Part 2. Regression problems
L Das, A Sivaram, V Venkatasubramanian
Computers & Chemical Engineering 139, 106895, 2020
372020
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
332022
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
312023
Hidden representations in deep neural networks: Part 1. Classification problems
A Sivaram, L Das, V Venkatasubramanian
Computers & Chemical Engineering 134, 106669, 2020
262020
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
222018
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
222015
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
212018
Neuralcompression: a machine learning approach to compress high frequency measurements in smart grid
L Das, D Garg, B Srinivasan
Applied Energy 257, 113966, 2020
202020
Data mining and control loop performance assessment: The multivariate case
L Das, R Rengaswamy, B Srinivasan
AIChE Journal 63 (8), 3311-3328, 2017
172017
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
162016
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
132023
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
112017
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
92015
On developing a framework for detection of oscillations in data
MF Ullah, L Das, S Parmar, R Rengaswamy, B Srinivasan
ISA transactions 89, 96-112, 2019
72019
Effect of transformation in compressed sensing of smart grid data
A Joshi, L Das, B Natarajan, B Srinivasan
2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD …, 2019
72019
Data driven approach for performance assessment of linear and nonlinear Kalman filters
L Das, B Srinivasan, R Rengaswamy
2014 American Control Conference, 4127-4132, 2014
72014
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
62023
A novel approach to evaluate state estimation approaches for anaerobic digester units under modeling uncertainties: Application to an industrial dairy unit
L Das, G Kumar, MD Rani, B Srinivasan
Journal of Environmental Chemical Engineering 5 (4), 4004-4013, 2017
62017
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Articles 1–20