State estimation with limited sensors – A deep learning based approach

01/27/2021
by   Yash Kumar, et al.
8

The importance of state estimation in fluid mechanics is well-established; it is required for accomplishing several tasks including design/optimization, active control, and future state prediction. A common tactic in this regards is to rely on reduced order models. Such approaches, in general, use measurement data of one-time instance. However, oftentimes data available from sensors is sequential and ignoring it results in information loss. In this paper, we propose a novel deep learning based state estimation framework that learns from sequential data. The proposed model structure consists of the recurrent cell to pass information from different time steps enabling utilization of this information to recover the full state. We illustrate that utilizing sequential data allows for state recovery from only one or two sensors. For efficient recovery of the state, the proposed approached is coupled with an auto-encoder based reduced order model. We illustrate the performance of the proposed approach using two examples and it is found to outperform other alternatives existing in the literature.

READ FULL TEXT

page 10

page 11

page 12

page 13

page 14

page 21

page 22

research
02/25/2022

A deep learning approach for direction of arrival estimation using automotive-grade ultrasonic sensors

In this paper, a deep learning approach is presented for direction of ar...
research
11/03/2021

Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies

Traffic management in a city has become a major problem due to the incre...
research
10/10/2019

Joint Design of Measurement Matrix and Sparse Support Recovery Method via Deep Auto-encoder

Sparse support recovery arises in many applications in communications an...
research
03/12/2022

Energy networks for state estimation with random sensors using sparse labels

State estimation is required whenever we deal with high-dimensional dyna...
research
06/17/2022

Design of Multi-model Linear Inferential Sensors with SVM-based Switching Logic

We study the problem of data-based design of multi-model linear inferent...
research
05/05/2020

Jointly Sparse Support Recovery via Deep Auto-encoder with Applications in MIMO-based Grant-Free Random Access for mMTC

In this paper, a data-driven approach is proposed to jointly design the ...
research
06/24/2021

Physics perception in sloshing scenes with guaranteed thermodynamic consistency

Physics perception very often faces the problem that only limited data o...

Please sign up or login with your details

Forgot password? Click here to reset