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Processing Data from Visual IoT Sensor for Fluid/Structure Interactions with Machine Learning

This project focuses on processing data from a visual IoT sensor to analyze fluid/structure interactions using machine learning algorithms. The team finalized the experiment set-up and collected data on fiber B3, U4, and U3. They analyzed effective length, droplet radius, and droplet velocity data, and found five potential regression algorithms to use. The project aims to address the long time it takes to collect and analyze data, by splitting the work between team members. The regression algorithms considered include Support-Vector-Regression, Multi-layer Perceptrons, Extreme-Learning Machines, Gaussian Process for Regression, and Discrete-Wavelet Transformation Algorithm. The team plans to finish up data collection, explore the use of particle filters, and begin implementing the regression algorithm next week.

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Processing Data from Visual IoT Sensor for Fluid/Structure Interactions with Machine Learning

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  1. Project 6: Processing data from a visual IoT sensor of fluid/structure interactions with machine learningREU Students: Pete Orkweha & Alexis DowningGraduate mentors: Nick Smith & SharareZehtabianFaculty mentor(s): Dr. Andrew Dickerson & Dr. Damla Turgut Week 2 (June 3 – June 7, 2019) Accomplishments: • Finalized experiment set-up • Collected data on fiber B3, U4, and U3 • Analyze effective length, droplet radius, and droplet velocity data. • Found 5 possible regression algorithms to use Problem & Solutions • Problem: Data collection and analyzation take a long time to complete. • Solution: We decided to split the work. I am in charge of data collection and Alexis is in charge of analyzing data.

  2. Finalized setup

  3. Project 6: Processing data from a visual IoT sensor of fluid/structure interactions with machine learningREU Students: Pete Orkweha & Alexis DowningGraduate mentors: Nick Smith & SharareZehtabianFaculty mentor(s): Dr. Andrew Dickerson & Dr. Damla Turgut Week 2 (June 3 – June 7, 2019) Regression Algorithms: • Support-Vector-Regression Algorithm (Classical model) • Commonly used in Engineering and Science field • Publicly available package • Multi-layer Perceptrons • Massively parallel artificial neural network • Works with non-linear problems • Extreme-Learning Machines • Fast learning algorithm based on Multi-layer Perceptrons

  4. Project 6: Processing data from a visual IoT sensor of fluid/structure interactions with machine learningREU Students: Pete Orkweha & Alexis DowningGraduate mentors: Nick Smith & SharareZehtabianFaculty mentor(s): Dr. Andrew Dickerson & Dr. Damla Turgut Week 2 (June 3 – June 7, 2019) Regression Algorithms: • Gaussian Process for Regression • Generic supervised-learning algorithm for regression • Discrete-Wavelet-Transformation Algorithm • Allow for pre-processing of input data to improve regression

  5. Project 6: Processing data from a visual IoT sensor of fluid/structure interactions with machine learningREU Students: Pete Orkweha & Alexis DowningGraduate mentors: Nick Smith & SharareZehtabianFaculty mentor(s): Dr. Andrew Dickerson & Dr. Damla Turgut Week 2 (June 3 – June 7, 2019) Plans for next week: • Finish up on data collection • Look into possible use of particle filter • Start looking into how to implement regression algorithm Video: https://youtu.be/qC_Lg6nYxAM

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