1 code implementation • Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 2021 • Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, Deva Ramanan, Peter Carr, James Hays
Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category.
no code implementations • 29 Aug 2022 • Soumyabrata Talukder, Souvik Kundu, Ratnesh Kumar
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.
no code implementations • 2 Jun 2022 • Ramij R. Hossain, Rahmat Adesunkanmi, Ratnesh Kumar
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.
no code implementations • 28 Feb 2022 • Ramij R. Hossain, Ratnesh Kumar
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.
no code implementations • 17 Oct 2021 • Rahmat Adesunkanmi, Ratnesh Kumar
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.
no code implementations • 13 Sep 2021 • Soumyabrata Talukder, Ratnesh Kumar
To develop a general theory for stability and stabilizability of a neural network (NN)-controlled nonlinear system subject to bounded parametric variation, a Lyapunov-based stability certificate is proposed and is further used to devise a maximal Lipschitz bound for the NN controller, and also a corresponding maximal region-of-attraction (RoA) inside a given safe operating domain.
no code implementations • 5 Jun 2021 • Ramij Raja Hossain, Ratnesh Kumar
This paper presents a model predictive control (MPC)-based online real-time adaptive control scheme for emergency voltage control in power systems.
8 code implementations • ICCV 2019 • Zheng Tang, Milind Naphade, Stan Birchfield, Jonathan Tremblay, William Hodge, Ratnesh Kumar, Shuo Wang, Xiaodong Yang
In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention.
no code implementations • CVPR 2019 • Zheng Tang, Milind Naphade, Ming-Yu Liu, Xiaodong Yang, Stan Birchfield, Shuo Wang, Ratnesh Kumar, David Anastasiu, Jenq-Neng Hwang
Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking.
no code implementations • 4 Jan 2019 • Ratnesh Kumar, Edwin Weill, Farzin Aghdasi, Parthsarathy Sriram
In this paper we provide an extensive evaluation of these losses applied to vehicle re-identification and demonstrate that using the best practices for learning embeddings outperform most of the previous approaches proposed in the vehicle re-identification literature.