Title

Knock! Knock! Who’s there? – Artificial Neural Network and Deep Learning Modeling

School Name

Governor's School for Science and Mathematics

Grade Level

12th Grade

Presentation Topic

Engineering

Presentation Type

Mentored

Written Paper Award

3rd Place

Abstract

Analyzing vibrational patterns, such as knocking, for identification can be used to help with safety and security. Applications of the identification process can be used in the Internet of Things industry and smart technology, such as smart homes. The objective of this research project was to use artificial neural networks to measure the vibrations of a door caused by a person knocking, and use that data to identify the knocker. One hundred sets of knock vibration data were collected from five test subjects and compared to ensure that this project was actually feasible. The experiment showed that although there was a lot of variation there was a distinct pattern in the overall knocks. A second program was written using artificial neural network technology to train the computer to learn the knock vibration patterns and to use this data to identify the person knocking. The results were able to find that knock vibrations can be used to identify a person, and that can lead to the prospect of using more sensitive vibrations to identify and detect humans.

Location

Neville 109

Start Date

4-14-2018 8:45 AM

Presentation Format

Oral and Written

COinS
 
Apr 14th, 8:45 AM

Knock! Knock! Who’s there? – Artificial Neural Network and Deep Learning Modeling

Neville 109

Analyzing vibrational patterns, such as knocking, for identification can be used to help with safety and security. Applications of the identification process can be used in the Internet of Things industry and smart technology, such as smart homes. The objective of this research project was to use artificial neural networks to measure the vibrations of a door caused by a person knocking, and use that data to identify the knocker. One hundred sets of knock vibration data were collected from five test subjects and compared to ensure that this project was actually feasible. The experiment showed that although there was a lot of variation there was a distinct pattern in the overall knocks. A second program was written using artificial neural network technology to train the computer to learn the knock vibration patterns and to use this data to identify the person knocking. The results were able to find that knock vibrations can be used to identify a person, and that can lead to the prospect of using more sensitive vibrations to identify and detect humans.