Boost-Up Efficiency of Defective Solar Panel Detection With Pre-Trained Attention Recycling

Author:

Park YeongHyeon1ORCID,Kim Myung Jin2ORCID,Gim Uju2ORCID,Yi Juneho1ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea

2. SK Planet Company, Ltd., Seongnam, Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Systems Engineering

Reference50 articles.

1. A power case study for monocrystalline and polycrystalline solar panels in Bursa city, Turkey;ta?ç?o?lu;Int J Photoenergy,2016

2. The effect of monocrystalline and polycrystalline material structure on solar cell performance;hidayanti;Int J Adv Trends Comput Sci Eng,2020

3. Attention! Is Recycling Artificial Neural Network Effective for Maintaining Renewable Energy Efficiency?

4. Criticality of Cracks in PV Modules

5. Review of failures of photovoltaic modules;köntges;Int Energy Agency,2014

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1. Photovoltaic Cell Anomaly Detection Enabled by Scale Distribution Alignment Learning and Multiscale Linear Attention Framework;IEEE Internet of Things Journal;2024-08-15

2. Visual defect obfuscation based self-supervised anomaly detection;Scientific Reports;2024-08-14

3. Enhancing Defective Solar Panel Detection with Attention-Guided Statistical Features Using Pre-Trained Neural Networks;2024 IEEE International Conference on Big Data and Smart Computing (BigComp);2024-02-18

4. Comparative Analysis on Solar Panel Defect Detection Using Deep Learning Approaches;2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI);2023-12-21

5. Recycling for Recycling: RoI Cropping by Recycling a Pre-Trained Attention Mechanism for Accurate Classification of Recyclables;2023 IEEE International Conference on Smart Information Systems and Technologies (SIST);2023-05-04

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