Thickness evaluation of AlO x barrier layers for encapsulation of flexible PV modules in industrial environments by normal reflectance and machine learning

2021 ◽  
Author(s):  
Enric Grau‐Luque ◽  
Maxim Guc ◽  
Ignacio Becerril‐Romero ◽  
Víctor Izquierdo‐Roca ◽  
Alejandro Pérez‐Rodríguez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Barrier Layers ◽  
Pv Modules ◽  
Industrial Environments ◽  
Thickness Evaluation

scholarly journals Performance Estimation Modeling via Machine Learning of an Agrophotovoltaic System in South Korea

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6724
Author(s):  
Sojung Kim ◽  
Sumin Kim
Keyword(s):  
Machine Learning ◽  
South Korea ◽  
Electricity Generation ◽  
Polynomial Regression ◽  
Agricultural Research ◽  
Evaluation Model ◽  
Performance Model ◽  
Performance Estimation ◽  
Temperate Climate ◽  
Pv Modules

The Agrophotovoltaic (APV) system is a novel concept in the field of Renewable Energy Systems. This system enables the generation of solar energy via photo-voltaic (PV) modules above crops, to mitigate harmful impact on food production. This study aims to develop a performance evaluation model for an APV system in a temperate climate region, such as South Korea. To this end, both traditional electricity generation models (solar radiation-based model and climate-based model) of PV modules and two major machine learning (ML) techniques (i.e., polynomial regression and deep learning) have been considered. Electricity generation data was collected via remote sensors installed in the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea. Moreover, economic analysis in terms of cost and benefit of the subject APV system was conducted to provide information about the return on investment to farmers and government agencies. As a result, farmers, agronomists, and agricultural engineers can easily estimate performance and profit of their APV systems via the proposed performance model.

scholarly journals Three-layer Approach to Detect Anomalies in Industrial Environments based on Machine Learning

2020 ◽  
Author(s):  
Daniel Gutierrez-Rojas ◽  
Mehar Ullah ◽  
Ioannis T. Christou ◽  
Gustavo Almeida ◽  
Pedro Nardelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Industrial Environments

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning
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