Machine learning‐based approach for useful capacity prediction of second‐life batteries employing appropriate input selection

2021 ◽  
Author(s):  
Ankit Bhatt ◽  
Weerakorn Ongsakul ◽  
Nimal Madhu ◽  
Jai Govind Singh
Keyword(s):  
Machine Learning ◽  
Second Life ◽  
Input Selection ◽  
Capacity Prediction

scholarly journals Machine Learning Adaptive Computational Capacity Prediction for Dynamic Resource Management in C-RAN

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 89130-89142
Author(s):  
Rolando Guerra-Gomez ◽  
Silvia Ruiz-Boque ◽  
Mario Garcia-Lozano ◽  
Joan Olmos Bonafe
Keyword(s):  
Machine Learning ◽  
Resource Management ◽  
Computational Capacity ◽  
Dynamic Resource Management ◽  
Dynamic Resource ◽  
Capacity Prediction

scholarly journals An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design

Geosciences ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 504
Author(s):  
Josephine Morgenroth ◽  
Usman T. Khan ◽  
Matthew A. Perras
Keyword(s):  
Machine Learning ◽  
Engineering Design ◽  
Performance Metrics ◽  
Mining Industry ◽  
Learning Methods ◽  
Rock Engineering ◽  
Input Selection ◽  
Machine Learning Methods ◽  
And Performance ◽  
Rock Engineering Design

Machine learning methods for data processing are gaining momentum in many geoscience industries. This includes the mining industry, where machine learning is primarily being applied to autonomously driven vehicles such as haul trucks, and ore body and resource delineation. However, the development of machine learning applications in rock engineering literature is relatively recent, despite being widely used and generally accepted for decades in other risk assessment-type design areas, such as flood forecasting. Operating mines and underground infrastructure projects collect more instrumentation data than ever before, however, only a small fraction of the useful information is typically extracted for rock engineering design, and there is often insufficient time to investigate complex rock mass phenomena in detail. This paper presents a summary of current practice in rock engineering design, as well as a review of literature and methods at the intersection of machine learning and rock engineering. It identifies gaps, such as standards for architecture, input selection and performance metrics, and areas for future work. These gaps present an opportunity to define a framework for integrating machine learning into conventional rock engineering design methodologies to make them more rigorous and reliable in predicting probable underlying physical mechanics and phenomenon.

Hydro-power production capacity prediction based on machine learning regression techniques

2021 ◽  
Vol 222 ◽  
pp. 107012
Author(s):  
C. Condemi ◽  
D. Casillas-Pérez ◽  
L. Mastroeni ◽  
S. Jiménez-Fernández ◽  
S. Salcedo-Sanz
Keyword(s):  
Machine Learning ◽  
Production Capacity ◽  
Power Production ◽  
Hydro Power ◽  
Regression Techniques ◽  
Capacity Prediction

Machine-Learning for Li-Ion Battery Capacity Prediction in Manufacturing Process

2021 ◽  
Vol MA2021-02 (3) ◽  
pp. 427-427
Author(s):  
Mona Faraji Niri ◽  
Kailong Liu ◽  
Geanina Apachitei ◽  
Luis Roman-Ramirez ◽  
Michael Lain ◽  
...  
Keyword(s):  
Machine Learning ◽  
Manufacturing Process ◽  
Li Ion Battery ◽  
Battery Capacity ◽  
Li Ion ◽  
Capacity Prediction

scholarly journals Mutual Information Input Selector and Probabilistic Machine Learning Utilisation for Air Pollution Proxies

2019 ◽  
Vol 9 (20) ◽  
pp. 4475 ◽  
Author(s):  
Martha A. Zaidan ◽  
Lubna Dada ◽  
Mansour A. Alghamdi ◽  
Hisham Al-Jeelani ◽  
Heikki Lihavainen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Mutual Information ◽  
Air Pollutant ◽  
Bayesian Neural Network ◽  
Input Selection ◽  
Relevant Variables ◽  
Data Driven Approach ◽  
Probabilistic Machine Learning ◽  
The Cost

An air pollutant proxy is a mathematical model that estimates an unobserved air pollutant using other measured variables. The proxy is advantageous to fill missing data in a research campaign or to substitute a real measurement for minimising the cost as well as the operators involved (i.e., virtual sensor). In this paper, we present a generic concept of pollutant proxy development based on an optimised data-driven approach. We propose a mutual information concept to determine the interdependence of different variables and thus select the most correlated inputs. The most relevant variables are selected to be the best proxy inputs, where several metrics and data loss are also involved for guidance. The input selection method determines the used data for training pollutant proxies based on a probabilistic machine learning method. In particular, we use a Bayesian neural network that naturally prevents overfitting and provides confidence intervals around its output prediction. In this way, the prediction uncertainty could be assessed and evaluated. In order to demonstrate the effectiveness of our approach, we test it on an extensive air pollution database to estimate ozone concentration.

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