Photovoltaic Module Defects Classification Analysis Using ShuffleNet Architecture in Electroluminescence Images-Reference-Cited by-同舟云学术

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Photovoltaic Module Defects Classification Analysis Using ShuffleNet Architecture in Electroluminescence Images

Published:2023-08-15 Issue: Volume: Page:
ISSN:
Container-title:2023 9th International Conference on Computer and Communication Engineering (ICCCE)
language:
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Author:

Rozi Muhammad Waliuddin Faiz Mohamad¹,Shahbudin Shahrani²

Affiliation:

1. Universiti Teknologi MARA,School of Electrical Engineering, College of Engineering,Shah Alam,Selangor,Malaysia,40450

2. Universiti Teknologi MARA,Enabling Internet of Things Technologies Research Group School of Electrical Engineering, College of Engineering,Shah Alam,Selangor,Malaysia,40450

Funder

Universiti Teknologi MARA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10246011/10246027/10246047.pdf?arnumber=10246047

Reference25 articles.

1. Machine learning based photovoltaic energy prediction scheme by augmentation of on-site IoT data

2. Identifying defective solar cells in electroluminescence images using deep feature representations

3. Pre-trained Deep Convolutional Neural Network for Detecting Malaria on the Human Blood Smear Images

4. Hieroglyphs Language Translator using deep learning techniques (Scriba)

5. Deep Learning optimization in remote sensing image segmentation using dilated convolutions and ShuffleNet

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