Academic Journal
Android-Based Real-Time Classification of Electric Fire Short-Circuit Traces Using Lightweight Deep Learning Model.
| Τίτλος: | Android-Based Real-Time Classification of Electric Fire Short-Circuit Traces Using Lightweight Deep Learning Model. |
|---|---|
| Συγγραφείς: | Nazari, Mohammad Hadi, Bang, Junho |
| Πηγή: | Energies (19961073); Jul2026, Vol. 19 Issue 13, p3184, 21p |
| Θεματικοί όροι: | Convolutional neural networks, Fault diagnosis, Deep learning, Electric faults, Classification algorithms |
| Reviews & Products: | Android (Operating system) |
| Περίληψη: | This paper presents a lightweight deep learning framework for classifying electric fire short-circuit traces to enhance safety and fault diagnosis in electrical energy systems. Accurate differentiation between primary (PSCT) and secondary short-circuit traces (SSCT) is essential for identifying failure origins, yet conventional manual inspection is time-consuming and subjective. To address these limitations, we systematically evaluate three lightweight convolutional neural network (CNN) architectures MobileNetV2, MobileNetV3, and EfficientNet using transfer learning on a domain-specific image dataset. The models are assessed based on accuracy, loss, precision, recall, and F1-score. Experimental results show that EfficientNet achieves the highest classification accuracy, while MobileNetV3 demonstrates the lowest validation loss and superior generalization stability. Based on a performance–efficiency trade-off analysis, MobileNetV3 is deployed on an Android platform using TensorFlow Lite, enabling real-time, offline, and on-device inference. To the best of our knowledge, this is among the first studies to integrate lightweight CNN-based short-circuit trace classification with real-time mobile deployment for on-site energy system fault analysis. By bridging the gap between deep learning and field deployment, the proposed mobile system ensures low-latency execution and provides a rapid, reliable, and portable solution for improving operational safety in electrical fire investigations. [ABSTRACT FROM AUTHOR] |
| Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=19961073&ISBN=&volume=19&issue=13&date=20260701&spage=3184&pages=3184-3204&title=Energies (19961073)&atitle=Android-Based%20Real-Time%20Classification%20of%20Electric%20Fire%20Short-Circuit%20Traces%20Using%20Lightweight%20Deep%20Learning%20Model.&aulast=Nazari%2C%20Mohammad%20Hadi&id=DOI:10.3390/en19133184 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Android-Based Real-Time Classification of Electric Fire Short-Circuit Traces Using Lightweight Deep Learning Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nazari%2C+Mohammad+Hadi%22">Nazari, Mohammad Hadi</searchLink><br /><searchLink fieldCode="AR" term="%22Bang%2C+Junho%22">Bang, Junho</searchLink> – Name: TitleSource Label: Source Group: Src Data: Energies (19961073); Jul2026, Vol. 19 Issue 13, p3184, 21p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+faults%22">Electric faults</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink> – Name: SubjectProduct Label: Reviews & Products Group: Su Data: <searchLink fieldCode="PS" term="%22Android+%28Operating+system%29%22">Android (Operating system)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents a lightweight deep learning framework for classifying electric fire short-circuit traces to enhance safety and fault diagnosis in electrical energy systems. Accurate differentiation between primary (PSCT) and secondary short-circuit traces (SSCT) is essential for identifying failure origins, yet conventional manual inspection is time-consuming and subjective. To address these limitations, we systematically evaluate three lightweight convolutional neural network (CNN) architectures MobileNetV2, MobileNetV3, and EfficientNet using transfer learning on a domain-specific image dataset. The models are assessed based on accuracy, loss, precision, recall, and F1-score. Experimental results show that EfficientNet achieves the highest classification accuracy, while MobileNetV3 demonstrates the lowest validation loss and superior generalization stability. Based on a performance–efficiency trade-off analysis, MobileNetV3 is deployed on an Android platform using TensorFlow Lite, enabling real-time, offline, and on-device inference. To the best of our knowledge, this is among the first studies to integrate lightweight CNN-based short-circuit trace classification with real-time mobile deployment for on-site energy system fault analysis. By bridging the gap between deep learning and field deployment, the proposed mobile system ensures low-latency execution and provides a rapid, reliable, and portable solution for improving operational safety in electrical fire investigations. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19133184 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 3184 Subjects: – SubjectFull: Android (Operating system) Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Fault diagnosis Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Electric faults Type: general – SubjectFull: Classification algorithms Type: general Titles: – TitleFull: Android-Based Real-Time Classification of Electric Fire Short-Circuit Traces Using Lightweight Deep Learning Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nazari, Mohammad Hadi – PersonEntity: Name: NameFull: Bang, Junho IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 13 Titles: – TitleFull: Energies (19961073) Type: main |
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