Application of Machine Learning Method to Quantitatively Evaluate the Droplet Size and Deposition Distribution of the UAV Spray Nozzle
Abstract
:Featured Application
Abstract
1. Introduction
2. Materials and Methods
2.1. Spray System
2.2. Equipment and Data Collection Methods
2.3. Machine Learning Methods
2.4. Performance Evaluation
3. Results
3.1. Evaluation of Droplet Size for TEEJET XR110015VS Nozzle
3.2. Evaluation of Droplet Size for Twin Nozzles Condition
3.3. Evaluation of Deposition Distribution for Twin Nozzles Condition
3.4. Droplet Size Prediction of Overlapping Area for Twin Nozzles Condition
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Calibration Set | Prediction Set | |||||||
---|---|---|---|---|---|---|---|---|
Groups | Sum | Range | Mean | S.D. | Sum | Range | Mean | S.D. |
A | 46 | 151.26–182.19 | 165.25 | 8.13 | 15 | 150.53–172.00 | 163.09 | 5.64 |
B | 72 | 154.20–183.00 | 170.99 | 7.08 | 24 | 156.23–182.30 | 170.79 | 7.06 |
C | 72 | 1.0829–3.2137 | 1.9144 | 0.4711 | 24 | 1.1818–3.1031 | 1.8962 | 0.4609 |
D | 66 | 154.20–183.82 | 171.15 | 7.49 | 22 | 156.23–182.74 | 170.95 | 7.44 |
Groups | Spray Height | |||
---|---|---|---|---|
Nozzle Spacing | 1 m | 1.5 m | 2 m | |
A | \ | 158.21 ± 5.30 µm c | 162.58 ± 5.24 µm b | 171.48 ± 5.65 µm a |
B | 0.5 m | 161.15 ± 4.96 µm d | 171.33 ± 6.85 µm c | 173.12 ± 7.02 µm b |
0.6 m | 163.03 ± 4.92 µm d | 172.98 ± 3.97 µm b | 178.55 ± 2.97 µm a | |
0.7 m | 167.87 ± 4.00 µm c | 168.71 ± 3.96 µm c | 178.48 ± 3.99 µm a | |
C | 0.5 m | 2.8265 ± 0.2591 mL/cm2 a | 1.9069 ± 0.1819 mL/cm2 c | 1.6924 ± 0.2573 mL/cm2 d |
0.6 m | 2.6962 ± 0.3879 mL/cm2 a | 1.8879 ± 0.2492 mL/cm2 d | 1.5997 ± 0.2267 mL/cm2 d | |
0.7 m | 2.1961 ± 0.2630 mL/cm2 b | 1.6391 ± 0.1728 mL/cm2 d | 1.5222 ± 0.1721 mL/cm2 d | |
D | 0.5 m | 161.15 ± 4.96 µm d | 171.33 ± 6.85 µm c | 172.17 ± 6.40 µm b |
0.6 m | 161.72 ± 4.76 µm d | 172.98 ± 3.97 µm b | 178.56 ± 2.97 µm a | |
0.7 m | 165.59 ± 3.91 µm d | 167.72 ± 3.63 µm c | 178.37 ± 3.46 µm a |
Model | Parameter [a] | Accuracy of Calibration Set | Accuracy of Prediction Set | ||
---|---|---|---|---|---|
RC2 | RMSEC (μm) | RP2 | RMSEP(μm) | ||
REGRESS | 3 | 0.9382 | 2.2011 | 0.9201 | 1.9761 |
LS-SVM | (24.169, 2.445 × 104) | 0.9378 | 2.2026 | 0.9201 | 1.8939 |
ELM | 9 | 0.7413 | 4.1362 | 0.6795 | 3.8519 |
RBFNN | 763 | 0.9395 | 1.9960 | 0.9253 | 1.9183 |
Model | Parameter [a] | Accuracy of Calibration Set | Accuracy of Prediction Set | ||
---|---|---|---|---|---|
RC2 | RMSEC (μm) | Rp2 | RMSEP(μm) | ||
REGRESS | 4 | 0.9206 | 1.9949 | 0.9204 | 2.0602 |
LS-SVM | (7.159, 1.490 × 106) | 0.9382 | 1.7634 | 0.9154 | 2.2450 |
ELM | 36 | 0.8727 | 2.5271 | 0.7602 | 3.5839 |
RBFNN | 130 | 0.8667 | 2.5843 | 0.7850 | 3.3721 |
Model | Parameter [a] | Accuracy of Calibration Set | Accuracy of Prediction Set | ||
---|---|---|---|---|---|
RC2 | RMSEC (mL/cm2) | Rp2 | RMSEP (mL/cm2) | ||
REGRESS | 3 | 0.8923 | 0.1546 | 0.7319 | 0.2525 |
LS-SVM | (2.664, 314.218) | 0.8926 | 0.1547 | 0.7646 | 0.2291 |
ELM | 18 | 0.7830 | 0.2194 | 0.6131 | 0.2919 |
RBFNN | 358 | 0.8806 | 0.1628 | 0.7994 | 0.2183 |
Model | Parameter [a] | Accuracy of Calibration Set | Accuracy of Prediction Set | ||
---|---|---|---|---|---|
RC2 | RMSEC (μm) | Rp2 | RMSEP(μm) | ||
LM-UGO | / | 0.7430 | 3.7959 | 0.6838 | 4.1984 |
LS-SVM | (0.599, 22.789) | 0.9116 | 2.1398 | 0.7714 | 3.5471 |
ELM | 36 | 0.8120 | 3.2470 | 0.7415 | 3.8389 |
RBFNN | 35 | 0.9567 | 1.5578 | 0.8616 | 3.2595 |
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Guo, H.; Zhou, J.; Liu, F.; He, Y.; Huang, H.; Wang, H. Application of Machine Learning Method to Quantitatively Evaluate the Droplet Size and Deposition Distribution of the UAV Spray Nozzle. Appl. Sci. 2020, 10, 1759. https://doi.org/10.3390/app10051759
Guo H, Zhou J, Liu F, He Y, Huang H, Wang H. Application of Machine Learning Method to Quantitatively Evaluate the Droplet Size and Deposition Distribution of the UAV Spray Nozzle. Applied Sciences. 2020; 10(5):1759. https://doi.org/10.3390/app10051759
Chicago/Turabian StyleGuo, Han, Jun Zhou, Fei Liu, Yong He, He Huang, and Hongyan Wang. 2020. "Application of Machine Learning Method to Quantitatively Evaluate the Droplet Size and Deposition Distribution of the UAV Spray Nozzle" Applied Sciences 10, no. 5: 1759. https://doi.org/10.3390/app10051759
APA StyleGuo, H., Zhou, J., Liu, F., He, Y., Huang, H., & Wang, H. (2020). Application of Machine Learning Method to Quantitatively Evaluate the Droplet Size and Deposition Distribution of the UAV Spray Nozzle. Applied Sciences, 10(5), 1759. https://doi.org/10.3390/app10051759