1. Project 6: Processing data from a visual IoT sensor of fluid/structure interactions with machine learning REU Students: Pete Orkweha & Alexis Downing Graduate mentors: Nick Smith & Sharare Zehtabian Faculty mentor(s): Dr. Andrew Dickerson & Dr. Damla Turgut
Week 4 (June 17 – June 21, 2019)
Accomplishments:
Data collection/analysis complete (240 random constant)
Explored Poly-regressor, Random Forest Regressor (RFR), and Multi-layer Perceptron (MLP) on a complete dataset
Literature research
Problem & Solutions
Problem: 𝑅 2 score doesn’t tell us everything. Need another way to score algorithms.
Solution: We used RE as another way to score algorithms.

2. Optimizing Hyper-Parameters
Random Search algorithm:
Within a specify range, use random combination of hyper-parameters on an algorithm and return a combination that gives the best score.
Grid Search algorithm
Perform an exhaustive search for best combination of hyper-parameters with a given range of parameters.
Results:
MLP:
Unoptimized score: 0.794
Optimized score: 0.905
RFR: no noticeable improvements

3. Varying Data set size Data size: 240 Starts at 3% (7 data)
Increment by 3%
K-fold = 5 folds

4. Machine learning algorithm result
Poly-Regression:
Each fold contains 47 data

5. Machine learning algorithm result
MLP:
Each fold contains 47 data

6. Machine learning algorithm result
RFR:
Each fold contains 47 data

7. Visualizing Algorithm Accuracy
Poly-Regression

8. Max. Deflection vs Effective Length
RFR algorithm
Poly-Regression
MLP algorithm

9. Max. Deflection vs Drop velocity
RFR algorithm
Poly-Regression
MLP algorithm

10. Max. Deflection vs Drop Diameter
RFR algorithm
Poly-Regression
MLP algorithm

11. Max. Deflection vs Moment of Inertia
RFR algorithm
Poly-Regression
MLP algorithm

12. Project 6: Processing data from a visual IoT sensor of fluid/structure interactions with machine learning REU Students: Pete Orkweha & Alexis Downing Graduate mentors: Nick Smith & Sharare Zehtabian Faculty mentor(s): Dr. Andrew Dickerson & Dr. Damla Turgut
Week 4 (June 17 – June 21, 2019)
Plans for next week:
Reducing features by combining features.
Continue literature research
Implement data pre-processing to improve algorithms
Find out the relationship between drop velocity and deflection on fiber B3