Using Machine Learning Algorithms for Accurate Received Optical Power Prediction of an FSO Link over a Maritime Environment
Abstract
:1. Introduction
2. Materials and Methods
2.1. Assessing Model Accuracy
2.2. Machine Learning Based FSO Research Background
2.3. Measurement Systems Overview
2.4. Methodology of Analysis
3. Results and Discussion
3.1. K-Nearest Neighbors Regression
3.2. Decision Trees
- Predictor space division into J distinct and non-overlapping regions, R1, R2, … RJ. The criterion to determine the optimal split point is to minimize the RSS given by,
- For every observation falling into a certain region, the prediction emerges from the response mean value based on the training observations that belong to the same region.
3.3. Random Forest
3.4. Gradient Boosting Regression
3.5. Artificial Neural Network
3.6. Model Comparison and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Parameter | Min Value | Mean Value | Max Value |
---|---|---|---|
RSSI | 187 | 420.4 | 517 |
p (hPa) | 987.7 | 1040.6 | 1015 |
T (°C) | 273.8 | 306 | 288.7 |
RH (%) | 22 | 63.5 | 94 |
DP (°C) | −5.5 | 11.3 | 24.6 |
WS (m/s) | 0 | 2.95 | 25.8 |
SF (W/m2) | 0 | 140.1 | 1149.5 |
ASTD (°C) | −11.1 | −1.39 | 10 |
Approach | R2 | RMSE | ||||
---|---|---|---|---|---|---|
Training | Validation | Test | Training | Validation | Test | |
Baseline | 0.36 | - | 0.05 | - | - | - |
KNN | 0.93 | - | 0.85 | 8.29 | - | 12.48 |
DT | 0.9764 | - | 0.91 | 4.9 | - | 9.71 |
RF | 0.994 | - | 0.947 | 2.7 | - | 7.37 |
GBR | - | - | 0.9417 | - | - | 7.71 |
ANN | 0.9496 | 0.9468 | 0.94867 | 10.06 | 10.19 | 10.17 |
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Lionis, A.; Peppas, K.; Nistazakis, H.E.; Tsigopoulos, A.; Cohn, K.; Zagouras, A. Using Machine Learning Algorithms for Accurate Received Optical Power Prediction of an FSO Link over a Maritime Environment. Photonics 2021, 8, 212. https://doi.org/10.3390/photonics8060212
Lionis A, Peppas K, Nistazakis HE, Tsigopoulos A, Cohn K, Zagouras A. Using Machine Learning Algorithms for Accurate Received Optical Power Prediction of an FSO Link over a Maritime Environment. Photonics. 2021; 8(6):212. https://doi.org/10.3390/photonics8060212
Chicago/Turabian StyleLionis, Antonios, Konstantinos Peppas, Hector E. Nistazakis, Andreas Tsigopoulos, Keith Cohn, and Athanassios Zagouras. 2021. "Using Machine Learning Algorithms for Accurate Received Optical Power Prediction of an FSO Link over a Maritime Environment" Photonics 8, no. 6: 212. https://doi.org/10.3390/photonics8060212
APA StyleLionis, A., Peppas, K., Nistazakis, H. E., Tsigopoulos, A., Cohn, K., & Zagouras, A. (2021). Using Machine Learning Algorithms for Accurate Received Optical Power Prediction of an FSO Link over a Maritime Environment. Photonics, 8(6), 212. https://doi.org/10.3390/photonics8060212