Modulation Format Identification and Transmission Quality Monitoring for Link Establishment in Optical Network Using Machine Learning Techniques

2020 ◽  
Author(s):  
Jie Hong ◽  
Long Chen ◽  
Jiao Zhu ◽  
Wenhai Zhou ◽  
Bo Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Network ◽  
Quality Monitoring ◽  
Machine Learning Techniques ◽  
Modulation Format ◽  
Learning Techniques ◽  
Transmission Quality

scholarly journals Data-driven quality monitoring of bending processes in hairpin stator production using machine learning techniques

Procedia CIRP ◽  
2021 ◽  
Vol 103 ◽  
pp. 256-261
Author(s):  
Andreas Mayr ◽  
Philipp Röll ◽  
Daniel Winkle ◽  
Maximilian Enzmann ◽  
Benjamin Bickel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Monitoring ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Learning Techniques

Air Quality Monitoring Intelligent System Using Machine Learning Techniques

2018 ◽  
Author(s):  
Paul D. Rosero-Montalvo ◽  
Jorge A. Caraguay-Procel ◽  
Edgar D. Jaramillo ◽  
Jaime M. Michilena-Calderon ◽  
Ana C. Umaquinga-Criollo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Intelligent System ◽  
Quality Monitoring ◽  
Machine Learning Techniques ◽  
Air Quality Monitoring ◽  
Learning Techniques

scholarly journals Water Quality Monitoring and Prediction of Water Quality at College Premises using Internet of Things

2019 ◽  
Vol 8 (5S3) ◽  
pp. 53-57
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Random Forest ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Machine Learning Techniques ◽  
Learning Techniques

IoT is becoming more popular and effective tool for any real time application. It has been involved for various water quality monitoring system to maintain the water hygiene level. The main objective is to build a system that regularly monitors the water quality and manages the sustainability. This system deals with specific standards like low cost background and system efficiency when compared to other studies. In this paper, IoT based real time monitoring of water quality system is implemented along with Machine learning techniques such as J48, Multilayer Perceptron (MLP), and Random Forest. These machine learning techniques are compared based on the hyper-parameters and the results were obtained. The attributes such as pH, Dissolved Oxygen (DO), turbidity, conductivity obtained from the corresponding sensors are used to create a prediction model which classifies the quality of water. Measurement of water quality and reporting system is implemented by using Arduino controller, GSM/GPRS module for gathering data in real time. The collected data are then analyzed using WEKA interface which is a visualization tool used for the analysis of data and prediction modeling.The Random forest technique outperforms J48 and Multilayer perceptron by giving 98.89% of correctly classified instances.

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