L
Laya Das
Researcher at Indian Institute of Technology Gandhinagar
Publications - 35
Citations - 314
Laya Das is an academic researcher from Indian Institute of Technology Gandhinagar. The author has contributed to research in topics: Computer science & Complex network. The author has an hindex of 8, co-authored 27 publications receiving 148 citations. Previous affiliations of Laya Das include Columbia University & ETH Zurich.
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Measuring smart grid resilience: Methods, challenges and opportunities
TL;DR: A detailed review and comparative analysis of qualitative frameworks as well as quantitative metrics for studying resilience are provided and the desirable properties of a resilience metric are highlighted and challenges associated with formulating, developing and calculating such a metric in practical scenarios are discussed.
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Data-Driven Approaches for Diagnosis of Incipient Faults in DC Motors
TL;DR: This paper adopts three tools from machine learning to address the challenge of FDI of incipient faults of dc motor with the most commonly and readily measured current data, and adopts the convolutional network as the best method.
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A Framework for Efficient Information Aggregation in Smart Grid
TL;DR: A new measure derived from coefficient of variation is proposed and it is demonstrated that it achieves a better tradeoff between reconstruction performance and compression ratio and is presented as a framework that incorporates dynamic temporal and spatial compression.
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Multivariate Control Loop Performance Assessment With Hurst Exponent and Mahalanobis Distance
TL;DR: A novel data-driven technique for performance assessment of multivariate control loops that takes into account the interactions within the system is proposed, establishing the proposed approach as a promising tool for interactor-matrix-independent MIMO control loop performance assessment.
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Hidden representations in deep neural networks: Part 2. Regression problems
TL;DR: This article is structured in a tutorial-like fashion for the benefit of new practitioners so that they can appreciate nuances and the pitfalls involved in developing a deep neural network models for regression problems.