Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Rao, Aniruddha Rajendra"'
Autor:
Backhus, Jana, Rao, Aniruddha Rajendra, Venkatraman, Chandrasekar, Padmanabhan, Abhishek, Kumar, A. Vinoth, Gupta, Chetan
In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks. A key innovation lies in the e
Externí odkaz:
http://arxiv.org/abs/2403.00975
Maritime transport is a pivotal logistics mode for the long-distance and bulk transportation of goods. However, the intricate planning involved in this mode is often hindered by uncertainties, including weather conditions, cargo diversity, and port d
Externí odkaz:
http://arxiv.org/abs/2401.14498
Public utilities are faced with situations where high winds can bring trees and debris into contact with energized power lines and other equipments, which could ignite wildfires. As a result, they need to turn off power during severe weather to help
Externí odkaz:
http://arxiv.org/abs/2311.07569
Autor:
Lee, Xian Yeow, Kumar, Aman, Vidyaratne, Lasitha, Rao, Aniruddha Rajendra, Farahat, Ahmed, Gupta, Chetan
Publikováno v:
2023 IEEE International Conference on Prognostics and Health Management (ICPHM)
This paper focuses on solving a fault detection problem using multivariate time series of vibration signals collected from planetary gearboxes in a test rig. Various traditional machine learning and deep learning methods have been proposed for multiv
Externí odkaz:
http://arxiv.org/abs/2305.05532
Publikováno v:
IEEE BigData 2022
The rise in data has led to the need for dimension reduction techniques, especially in the area of non-scalar variables, including time series, natural language processing, and computer vision. In this paper, we specifically investigate dimension red
Externí odkaz:
http://arxiv.org/abs/2301.00357
Publikováno v:
Statistics and Computing 2023
We introduce a new class of non-linear function-on-function regression models for functional data using neural networks. We propose a framework using a hidden layer consisting of continuous neurons, called a continuous hidden layer, for functional re
Externí odkaz:
http://arxiv.org/abs/2107.14151
Publikováno v:
Journal of Computational and Graphical Statistics, 2023
We introduce a new class of non-linear models for functional data based on neural networks. Deep learning has been very successful in non-linear modeling, but there has been little work done in the functional data setting. We propose two variations o
Externí odkaz:
http://arxiv.org/abs/2104.09371
Publikováno v:
Stat, 2021
This work considers the problem of fitting functional models with sparsely and irregularly sampled functional data. It overcomes the limitations of the state-of-the-art methods, which face major challenges in the fitting of more complex non-linear mo
Externí odkaz:
http://arxiv.org/abs/2011.12509
In the last few decades, building regression models for non-scalar variables, including time series, text, image, and video, has attracted increasing interests of researchers from the data analytic community. In this paper, we focus on a multivariate
Externí odkaz:
http://arxiv.org/abs/2011.12378
Explosive growth in spatio-temporal data and its wide range of applications have attracted increasing interests of researchers in the statistical and machine learning fields. The spatio-temporal regression problem is of paramount importance from both
Externí odkaz:
http://arxiv.org/abs/2009.05665