A Portable Gas Pressure Control and Data Acquisition System using Regression Models

Yap, Xien Yin; Chia, Kim Seng; Tee, Kian Sek

doi:10.15676/ijeei.2021.13.1.14

Cited by 4 publications

(3 citation statements)

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“…As evident from Table 5, the proposed architecture resulted in only 4.67 million parameters. This would be considered lightweight compared to other architectures, such as Res-Net at 11.69 Million [20] and VGG with over 100 Million parameters [21]. Table 5 presents the internal architectural depth details for the proposed architecture.…”

Section: Proposed Architecturementioning

confidence: 99%

Automated Micro-Crack Detection within Photovoltaic Manufacturing Facility via Ground Modelling for a Regularized Convolutional Network

Animashaun,

Hussain

2023

Sensors

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The manufacturing of photovoltaic cells is a complex and intensive process involving the exposure of the cell surface to high temperature differentials and external pressure, which can lead to the development of surface defects, such as micro-cracks. Currently, domain experts manually inspect the cell surface to detect micro-cracks, a process that is subject to human bias, high error rates, fatigue, and labor costs. To overcome the need for domain experts, this research proposes modelling cell surfaces via representative augmentations grounded in production floor conditions. The modelled dataset is then used as input for a custom ‘lightweight’ convolutional neural network architecture for training a robust, noninvasive classifier, essentially presenting an automated micro-crack detector. In addition to data modelling, the proposed architecture is further regularized using several regularization strategies to enhance performance, achieving an overall F1-score of 85%.

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Section: Proposed Architecturementioning

confidence: 99%

Automated Micro-Crack Detection within Photovoltaic Manufacturing Facility via Ground Modelling for a Regularized Convolutional Network

Animashaun,

Hussain

2023

Sensors

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“…We will concentrate on analyzing the approach related to the creation of the deep learning models because we have an interest in the deep learning aspect. For comparison, the authors train the PV dataset on the MobileNet [74] and VGG-16 [75] architectures. The authors' decision to compare SGD and ADAM outcomes is commendable, rather than only using ADAM as the 'off-the-shelf' optimizer.…”

Section: Convolutional Network For Photovoltaicsmentioning

confidence: 99%

A Review on Defect Detection of Electroluminescence-Based Photovoltaic Cell Surface Images Using Computer Vision

Hussain

Al-Aqrabi

et al. 2023

Energies

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The past two decades have seen an increase in the deployment of photovoltaic installations as nations around the world try to play their part in dampening the impacts of global warming. The manufacturing of solar cells can be defined as a rigorous process starting with silicon extraction. The increase in demand has multiple implications for manual quality inspection. With automated inspection as the ultimate goal, researchers are actively experimenting with convolutional neural network architectures. This review presents an overview of the electroluminescence image-extraction process, conventional image-processing techniques deployed for solar cell defect detection, arising challenges, the present landscape shifting towards computer vision architectures, and emerging trends.

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“…Parameters (M) PV-CrackNet 7.01 VGG-19 [19] 143.67 ResNet-18 [20] 11.69 AlexNet [21] 61.0 GoogleNet [22] 13.0 To provide a more robust analysis, the precision, recall, and F1-score were also inspected. Precision was the highest at 98%, whilst the recall stood at a respective 96%.…”

Section: Architecturementioning

confidence: 99%

PV-CrackNet Architecture for Filter Induced Augmentation and Micro-Cracks Detection within a Photovoltaic Manufacturing Facility

2022

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Photovoltaic cell manufacturing is a rigorous process involving many stages where the cell surface is exposed to external pressure and temperature differentials. This provides fertile ground for micro-cracks to develop on the cell surface. At present, domain experts carry out a manual inspection of the cell surface to judge if any micro-cracks are present. This research looks to overcome the issue of cell data scarcity through the proposed filter-induced augmentations, thus providing developers with an effective, cost-free mechanism for generating representative data samples. Due to the abstract nature of the cell surfaces, the proposed augmentation strategy is effective in generating representative samples for better generalization. Furthermore, a custom architecture is developed that is computationally lightweight compared to state-of-the-art architectures, containing only 7.01 million learnable parameters while achieving an F1-score of 97%.

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A Portable Gas Pressure Control and Data Acquisition System using Regression Models

Cited by 4 publications

References 0 publications

Automated Micro-Crack Detection within Photovoltaic Manufacturing Facility via Ground Modelling for a Regularized Convolutional Network

Automated Micro-Crack Detection within Photovoltaic Manufacturing Facility via Ground Modelling for a Regularized Convolutional Network

A Review on Defect Detection of Electroluminescence-Based Photovoltaic Cell Surface Images Using Computer Vision

PV-CrackNet Architecture for Filter Induced Augmentation and Micro-Cracks Detection within a Photovoltaic Manufacturing Facility

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