A deep learning model for mining object-energy correlations using social media image data

Matthew Dering; Chonghan Lee; Kenneth M. Hopkinson; Conrad S. Tucker

doi:10.1115/DETC201885417

A deep learning model for mining object-energy correlations using social media image data

Matthew Dering, Chonghan Lee, Kenneth M. Hopkinson, Conrad S. Tucker

School of Engineering Design and Innovation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The authors of this work present a method that mines big media data streams from large Social Media Networks in order to discover novel correlations between objects appearing in images and electricity utilization patterns. The hypothesis of this work is that there exist correlations between what users take pictures of, and electricity utilization patterns. This work employs a Convolutional Neural Network to detect objects in 578,232 images gathered from over 15,000,000 tweets sent in the San Diego area. These objects were considered in the context of concurrent power use, on a monthly and hourly basis. The results reveal both positive and negative correlations between power use and specific objects, such as lamps(.053 hourly), dogs(-.011 hourly), horses(.422 monthly) and motorcycles(-.415, monthly).

Original language	English (US)
Title of host publication	38th Computers and Information in Engineering Conference
Publisher	American Society of Mechanical Engineers (ASME)
ISBN (Electronic)	9780791851739
DOIs	https://doi.org/10.1115/DETC201885417
State	Published - 2018
Event	ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2018 - Quebec City, Canada Duration: Aug 26 2018 → Aug 29 2018

Publication series

Name	Proceedings of the ASME Design Engineering Technical Conference
Volume	1B-2018

Other

Other	ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2018
Country/Territory	Canada
City	Quebec City
Period	8/26/18 → 8/29/18

All Science Journal Classification (ASJC) codes

Mechanical Engineering
Computer Graphics and Computer-Aided Design
Computer Science Applications
Modeling and Simulation

Access to Document

10.1115/DETC201885417

Cite this

Dering, M., Lee, C., Hopkinson, K. M., & Tucker, C. S. (2018). A deep learning model for mining object-energy correlations using social media image data. In 38th Computers and Information in Engineering Conference (Proceedings of the ASME Design Engineering Technical Conference; Vol. 1B-2018). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC201885417

@inproceedings{68f9b9e520244c98aa322a9b22960400,

title = "A deep learning model for mining object-energy correlations using social media image data",

abstract = "The authors of this work present a method that mines big media data streams from large Social Media Networks in order to discover novel correlations between objects appearing in images and electricity utilization patterns. The hypothesis of this work is that there exist correlations between what users take pictures of, and electricity utilization patterns. This work employs a Convolutional Neural Network to detect objects in 578,232 images gathered from over 15,000,000 tweets sent in the San Diego area. These objects were considered in the context of concurrent power use, on a monthly and hourly basis. The results reveal both positive and negative correlations between power use and specific objects, such as lamps(.053 hourly), dogs(-.011 hourly), horses(.422 monthly) and motorcycles(-.415, monthly).",

author = "Matthew Dering and Chonghan Lee and Hopkinson, {Kenneth M.} and Tucker, {Conrad S.}",

note = "Publisher Copyright: Copyright {\textcopyright} 2018 ASME.; ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2018 ; Conference date: 26-08-2018 Through 29-08-2018",

year = "2018",

doi = "10.1115/DETC201885417",

language = "English (US)",

series = "Proceedings of the ASME Design Engineering Technical Conference",

publisher = "American Society of Mechanical Engineers (ASME)",

booktitle = "38th Computers and Information in Engineering Conference",

}

Dering, M, Lee, C, Hopkinson, KM & Tucker, CS 2018, A deep learning model for mining object-energy correlations using social media image data. in 38th Computers and Information in Engineering Conference. Proceedings of the ASME Design Engineering Technical Conference, vol. 1B-2018, American Society of Mechanical Engineers (ASME), ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2018, Quebec City, Canada, 8/26/18. https://doi.org/10.1115/DETC201885417

A deep learning model for mining object-energy correlations using social media image data. / Dering, Matthew; Lee, Chonghan; Hopkinson, Kenneth M. et al.
38th Computers and Information in Engineering Conference. American Society of Mechanical Engineers (ASME), 2018. (Proceedings of the ASME Design Engineering Technical Conference; Vol. 1B-2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - The authors of this work present a method that mines big media data streams from large Social Media Networks in order to discover novel correlations between objects appearing in images and electricity utilization patterns. The hypothesis of this work is that there exist correlations between what users take pictures of, and electricity utilization patterns. This work employs a Convolutional Neural Network to detect objects in 578,232 images gathered from over 15,000,000 tweets sent in the San Diego area. These objects were considered in the context of concurrent power use, on a monthly and hourly basis. The results reveal both positive and negative correlations between power use and specific objects, such as lamps(.053 hourly), dogs(-.011 hourly), horses(.422 monthly) and motorcycles(-.415, monthly).

AB - The authors of this work present a method that mines big media data streams from large Social Media Networks in order to discover novel correlations between objects appearing in images and electricity utilization patterns. The hypothesis of this work is that there exist correlations between what users take pictures of, and electricity utilization patterns. This work employs a Convolutional Neural Network to detect objects in 578,232 images gathered from over 15,000,000 tweets sent in the San Diego area. These objects were considered in the context of concurrent power use, on a monthly and hourly basis. The results reveal both positive and negative correlations between power use and specific objects, such as lamps(.053 hourly), dogs(-.011 hourly), horses(.422 monthly) and motorcycles(-.415, monthly).

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Y2 - 26 August 2018 through 29 August 2018

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A deep learning model for mining object-energy correlations using social media image data

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