Application of Machine Learning for Temperature Prediction in a Test Road in Alberta

2021 ◽  
Author(s):  
Mohamad Molavi Nojumi ◽  
Yunyan Huang ◽  
Leila Hashemian ◽  
Alireza Bayat
Keyword(s):  
Machine Learning ◽  
Temperature Prediction ◽  
Test Road

scholarly journals A Rail-Temperature-Prediction Model Based on Machine Learning: Warning of Train-Speed Restrictions Using Weather Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4606
Author(s):  
Sunguk Hong ◽  
Cheoljeong Park ◽  
Seongjin Cho
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Weather Forecasting ◽  
Thermal Environment ◽  
Management Plan ◽  
Temperature Prediction ◽  
Railway Systems ◽  
Train Speed ◽  
Speed Restrictions ◽  
Entire Network

Predicting the rail temperature of a railway system is important for establishing a rail management plan against railway derailment caused by orbital buckling. The rail temperature, which is directly responsible for track buckling, is closely related to air temperature, which continuously increases due to global warming effects. Moreover, railway systems are increasingly installed with continuous welded rails (CWRs) to reduce train vibration and noise. Unfortunately, CWRs are prone to buckling. This study develops a reliable and highly accurate novel model that can predict rail temperature using a machine learning method. To predict rail temperature over the entire network with high-prediction performance, the weather effect and solar effect features are used. These features originate from the analysis of the thermal environment around the rail. Precisely, the presented model has a higher performance for predicting high rail temperature than other models. As a convenient structural health-monitoring application, the train-speed-limit alarm-map (TSLAM) was also proposed, which visually maps the predicted rail-temperature deviations over the entire network for railway safety officers. Combined with TSLAM, our rail-temperature prediction model is expected to improve track safety and train timeliness.

Machine learning based temperature prediction of poly(N-isopropylacrylamide)-capped plasmonic nanoparticle solutions

2019 ◽  
Vol 21 (44) ◽  
pp. 24808-24819
Author(s):  
Sudaraka Mallawaarachchi ◽  
Yiyi Liu ◽  
San H. Thang ◽  
Wenlong Cheng ◽  
Malin Premaratne
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Solution Temperature ◽  
Thermosensitive Polymer ◽  
Temperature Prediction ◽  
Plasmonic Nanoparticle ◽  
Learning Techniques

Machine learning techniques can predict the solution temperature of thermosensitive polymer-capped nanoparticle solutions to within 1 °C of accuracy.

Ultra-high temperature ceramics melting temperature prediction via machine learning

2019 ◽  
Vol 45 (15) ◽  
pp. 18551-18555 ◽  
Author(s):  
Nan Qu ◽  
Yong Liu ◽  
Mingqing Liao ◽  
Zhonghong Lai ◽  
Fei Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Temperature ◽  
Melting Temperature ◽  
Temperature Prediction ◽  
Ultra High Temperature Ceramics ◽  
Ultra High Temperature

Comparison Of Rectal Temperature Prediction Models Utilizing Machine Learning

2017 ◽  
Vol 49 (5S) ◽  
pp. 454
Author(s):  
Luke N. Belval ◽  
Yuri Hosokawa ◽  
Lesley W. Vandermark ◽  
Rebecca L. Stearns ◽  
Lawrence E. Armstrong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rectal Temperature ◽  
Prediction Models ◽  
Temperature Prediction

scholarly journals Comparative Analysis on the Deployment of Machine Learning Algorithms in the Distributed Brillouin Optical Time Domain Analysis (BOTDA) Fiber Sensor

Photonics ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 79 ◽  
Author(s):  
Nur Dalilla Nordin ◽  
Mohd Saiful Dzulkefly Zan ◽  
Fairuz Abdullah
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Comparative Analysis ◽  
Time Domain ◽  
Prediction Accuracy ◽  
Processing Time ◽  
Time Domain Analysis ◽  
Fiber Sensor ◽  
Temperature Prediction ◽  
Optical Time

This paper demonstrates a comparative analysis of five machine learning (ML) algorithms for improving the signal processing time and temperature prediction accuracy in Brillouin optical time domain analysis (BOTDA) fiber sensor. The algorithms analyzed were generalized linear model (GLM), deep learning (DL), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM). In this proof-of-concept experiment, the performance of each algorithm was investigated by pairing Brillouin gain spectrum (BGS) with its corresponding temperature reading in the training dataset. It was found that all of the ML algorithms have significantly reduced the signal processing time to be between 3.5 and 655 times faster than the conventional Lorentzian curve fitting (LCF) method. Furthermore, the temperature prediction accuracy and temperature measurement precision made by some algorithms were comparable, and some were even better than the conventional LCF method. The results obtained from the experiments would provide some general idea in deploying ML algorithm for characterizing the Brillouin-based fiber sensor signals.

Human body skin temperature prediction based on machine learning

2020 ◽  
Author(s):  
Shin Morishima ◽  
Yingjie Xu ◽  
Akira Urashima ◽  
Tomoji Toriyama
Keyword(s):  
Machine Learning ◽  
Skin Temperature ◽  
Human Body ◽  
Temperature Prediction
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