The worldwide transition to electric vehicles (EVs) has resulted in a substantial rise in the quantity of end-of-life (EoL) EVs that need effective recycling methods. This study examined a method of transfer learning that uses features to classify valuable electrical components from end-of-life electric vehicles. The study made use of a dataset consisting of high-resolution photographs of different electronic control units (ECUs). The photos were processed using pre-trained InceptionV3 convolutional neural network (CNN) models to identify distinctive features. The performance of four classifiers, namely the Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Random Forest (RF), and Naive Bayes (NB), was tested using the collected features. The dataset was partitioned into training, validation, and test sets using a 70:15:15 stratified split to guarantee an equitable distribution of all classes. The InceptionV3-SVM pipeline achieved the highest performance, with training, validation, and test accuracies of 100%, 97%, and 97%, respectively. Other classifiers also demonstrated strong performance, with validation and test accuracies exceeding 94%. The high accuracy and generalization capabilities of the InceptionV3-SVM pipeline indicate its potential for practical deployment in sustainable manufacturing processes. This study provides a foundation for further research in the automated sorting and recovery of high-value electronic components from EVs, potentially extending to a broader range of electronic components and applications. The findings highlight the effectiveness of transfer learning techniques in enhancing the efficiency and accuracy of recycling operations in the automotive industry.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Feature-Based Transfer Learning for High-Value Component Recovery in Electric Vehicles: An InceptionV3 Model Evaluation


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Chen, Wei (Herausgeber:in) / PP Abdul Majeed, Anwar (Herausgeber:in) / Ping Tan, Andrew Huey (Herausgeber:in) / Zhang, Fan (Herausgeber:in) / Yan, Yuyao (Herausgeber:in) / Luo, Yang (Herausgeber:in) / Huang, Long (Herausgeber:in) / Liu, Chenguang (Herausgeber:in) / Zhu, Yuyi (Herausgeber:in) / Luo, Yang (Autor:in)

    Kongress:

    International Conference on Intelligent Manufacturing and Robotics ; 2024 ; Suzhou, China August 22, 2024 - August 23, 2024



    Erscheinungsdatum :

    01.04.2025


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Revolutionizing Alzheimer's Disease Prediction using InceptionV3 in Deep Learning

    Jansi, R. / Gowtham, Naraganti / Ramachandran, Siva et al. | IEEE | 2023



    Autism Detection using InceptionV3 and Security using Blockchain

    Annie Silviya, S H / Seetha, R / Raja, S et al. | IEEE | 2024


    CARBON FIBER COMPONENT BASED ELECTRIC VEHICLES

    HAZARAY SEAN MARIO / BEETZ RAMINO TROY | Europäisches Patentamt | 2023

    Freier Zugriff

    DETERMINING FEATURE POSES OF ELECTRIC VEHICLES TO AUTOMATICALLY CHARGE ELECTRIC VEHICLES

    MAUDERLI DAVID ANDRÉ / ALBIN RAJASINGHAM THIVAHARAN | Europäisches Patentamt | 2024

    Freier Zugriff