scholarly journals Numerical simulation and experimental demonstration of accurate machine learning aided IQ time-skew and power-imbalance identification for coherent transmitters

2019 ◽  
Vol 27 (26) ◽  
pp. 38367
Author(s):  
Xiaoxiao Dai ◽  
Xiang Li ◽  
Ming Luo ◽  
Shaohua Yu
Keyword(s):  
Machine Learning ◽  
Numerical Simulation ◽  
Power Imbalance ◽  
Experimental Demonstration

Thermal–Mechanical Coupling Evaluation of the Panel Performance of a Prefabricated Cabin-Type Substation Based on Machine Learning

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 93
Author(s):  
Xiangsheng Lei ◽  
Jinwu Ouyang ◽  
Yanfeng Wang ◽  
Xinghua Wang ◽  
Xiaofeng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Numerical Simulation ◽  
Fire Resistance ◽  
Bp Neural Network ◽  
Stress Test ◽  
Normal Operation ◽  
Model Parameters ◽  
Mechanical Coupling ◽  
The Impact

The panel performance of a prefabricated cabin-type substation under the impact of fires plays a vital role in the normal operation of the substation. However, current evaluations of the panel performance of substations under fire still focus on fire resistance tests, which seldom consider the relationship between fire behavior and the mechanical load of the panel under the impact of fires. Aiming at the complex and uncertain relationship between the thermal and mechanical performance of the substation panel under impact of fires, this paper proposes a machine learning method based on a BP neural network. First, the fire resistance test and the stress test of the panel is carried out, then a machine learning model is established based on the BP neural network. According to the collected data, the model parameters are obtained through a series of training and verification processes. Meanwhile, the correlation between the panel performance and fire resistance was obtained. Finally, related parameters are input into the thermal–mechanical coupling evaluation model for the substation panel performance to evaluate the fire resistance performance of the substation panel. To verify the correctness of the established model, numerical simulation of the fire test and stress test of the panel is conducted, and numerical simulation samples are predicted by the trained model. The results show that the prediction curve of neural network is closer to the real results compared with the numerical simulation, and the established model can accurately evaluate the thermal–mechanical coupling performance of the substation panel under fire.

scholarly journals Experimental Demonstration of a Machine Learning-Based in-band OSNR Estimator from Optical Spectra

2020 ◽  
Author(s):  
Fabiano Locatelli ◽  
Konstantinos Christodoulopoulos ◽  
Josep M. Fabrega ◽  
Michela Svaluto Moreolo ◽  
Laia Nadal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Spectra ◽  
Experimental Demonstration
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