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pro vyhledávání: '"KUMAR, Ratnesh"'
In many real-world applications where the system dynamics has an underlying interdependency among its variables (such as power grid, economics, neuroscience, omics networks, environmental ecosystems, and others), one is often interested in knowing wh
Externí odkaz:
http://arxiv.org/abs/2404.16326
Autor:
Wilson, Benjamin, Qi, William, Agarwal, Tanmay, Lambert, John, Singh, Jagjeet, Khandelwal, Siddhesh, Pan, Bowen, Kumar, Ratnesh, Hartnett, Andrew, Pontes, Jhony Kaesemodel, Ramanan, Deva, Carr, Peter, Hays, James
We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from se
Externí odkaz:
http://arxiv.org/abs/2301.00493
Most sensor calibrations rely on the linearity and steadiness of their response characteristics, but practical sensors are nonlinear, and their response drifts with time, restricting their choices for adoption. To broaden the realm of sensors to allo
Externí odkaz:
http://arxiv.org/abs/2208.13819
Publikováno v:
IEEE Systems Journal ( Volume: 17, Issue: 3, September 2023)
This paper presents a data-learned linear Koopman embedding of nonlinear networked dynamics and uses it to enable real-time model predictive emergency voltage control in a power network. The approach involves a novel data-driven ``basis-dictionary fr
Externí odkaz:
http://arxiv.org/abs/2206.01272
Autor:
Kumar, Ratnesh1,2, Bora, Bhabani1, Chattopadhyaya, Somnath1, Krolczyk, Grzegorz3, Hloch, Sergej4
Publikováno v:
International Journal of Materials & Product Technology. 2018, Vol. 57 Issue 1-3, p204-229. 26p.
Autor:
Hossain, Ramij R., Kumar, Ratnesh
This article presents a distributed model-predictive control (MPC) design for real-time voltage control in power systems, including an online method to estimate the bus admittance matrix $\mathbf{Y}$ to let it be time-varying and unknown a priori. Th
Externí odkaz:
http://arxiv.org/abs/2202.14014
Autor:
Adesunkanmi, Rahmat, Kumar, Ratnesh
This paper presents a clustering technique that reduces the susceptibility to data noise by learning and clustering the data-distribution and then assigning the data to the cluster of its distribution. In the process, it reduces the impact of noise o
Externí odkaz:
http://arxiv.org/abs/2110.08871
Publikováno v:
In Fusion Engineering and Design July 2024 204
Autor:
Talukder, Soumyabrata, Kumar, Ratnesh
Stability certification and identifying a safe and stabilizing initial set are two important concerns in ensuring operational safety, stability, and robustness of dynamical systems. With the advent of machine-learning tools, these issues need to be a
Externí odkaz:
http://arxiv.org/abs/2109.05710