受强制性开放获取政策约束的文章 - Laya Das了解详情
无法在其他位置公开访问的文章：2 篇
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
强制性开放获取政策： Department of Science & Technology, India
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
强制性开放获取政策： Department of Science & Technology, India
可在其他位置公开访问的文章：5 篇
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
强制性开放获取政策： US National Science Foundation, US Department of Energy
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
强制性开放获取政策： US National Science Foundation
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
强制性开放获取政策： US National Science Foundation
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
强制性开放获取政策： US National Science Foundation
Bayesian graph neural network for fast identification of critical nodes in uncertain complex networks
S Munikoti, L Das, B Natarajan
2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2021
强制性开放获取政策： US National Science Foundation
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