AutoEncoder for Interpolation

01/04/2021
by   Rahul Bhadani, et al.
0

In physical science, sensor data are collected over time to produce timeseries data. However, depending on the real-world condition and underlying physics of the sensor, data might be noisy. Besides, the limitation of sample-time on sensors may not allow collecting data over all the timepoints, may require some form of interpolation. Interpolation may not be smooth enough, fail to denoise data, and derivative operation on noisy sensor data may be poor that do not reveal any high order dynamics. In this article, we propose to use AutoEncoder to perform interpolation that also denoise data simultaneously. A brief example using a real-world is also provided.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/03/2017

Reconstruction of Missing Big Sensor Data

With ubiquitous sensors continuously monitoring and collecting large amo...
research
08/24/2023

Hydrogen jet diffusion modeling by using physics-informed graph neural network and sparsely-distributed sensor data

Efficient modeling of jet diffusion during accidental release is critica...
research
02/15/2022

Deep Convolutional Autoencoder for Assessment of Anomalies in Multi-stream Sensor Data

A fully convolutional autoencoder is developed for the detection of anom...
research
05/13/2019

Zoom To Learn, Learn To Zoom

This paper shows that when applying machine learning to digital zoom for...
research
10/06/2017

Intelligent Pothole Detection and Road Condition Assessment

Poor road conditions are a public nuisance, causing passenger discomfort...
research
06/12/2018

Drive2Vec: Multiscale State-Space Embedding of Vehicular Sensor Data

With automobiles becoming increasingly reliant on sensors to perform var...
research
11/18/2019

Zero-Interaction Security – Towards Sound Experimental Validation

Reproducibility and realistic datasets are crucial for advancing researc...

Please sign up or login with your details

Forgot password? Click here to reset