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    <journal-meta>
      <journal-id journal-id-type="ojs">JDF</journal-id>
      <journal-title-group>
        <journal-title xml:lang="en">Journal of Digital Frontier</journal-title>
      </journal-title-group>
      <publisher>
        <publisher-name>Digital Intelligence Press</publisher-name>
        <publisher-loc>
          <country>HK</country>
          <uri>https://dipscie.com/index.php/JDF/index</uri>
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      </publisher>
      <issn pub-type="epub">3135-7695</issn>
      <self-uri xlink:href="https://dipscie.com/index.php/JDF"/>
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    <article-meta>
      <article-id pub-id-type="publisher-id">40</article-id>
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          <subject>Articles</subject>
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      <title-group>
        <article-title xml:lang="en">&lt;bold&gt;Intelligent Defect Detection of Design Materials Based on Edge AI &lt;/bold&gt;</article-title>
      </title-group>
      <contrib-group content-type="author">
        <contrib>
          <name-alternatives>
            <name name-style="western" specific-use="primary">
              <surname>Du</surname>
              <given-names>Jiayu</given-names>
            </name>
          </name-alternatives>
          <email>16607443387@163.com</email>
          <xref ref-type="aff" rid="aff-1"/>
        </contrib>
        <contrib corresp="yes">
          <name-alternatives>
            <name name-style="western" specific-use="primary">
              <surname>Liu</surname>
              <given-names>Mengyu</given-names>
            </name>
          </name-alternatives>
          <email>daimou971231@naver.com</email>
        </contrib>
      </contrib-group>
      <aff id="aff-1">
        <institution content-type="orgname">Department of Art and Design, Beijing City University, Shunyi District, Beijing, China</institution>
      </aff>
      <pub-date date-type="pub" publication-format="epub">
        <day>22</day>
        <month>09</month>
        <year>2026</year>
      </pub-date>
      <fpage>27</fpage>
      <lpage>57</lpage>
      <pub-history>
        <event event-type="received">
          <event-desc>Received: <date date-type="received" iso-8601-date="2026-07-16T09:01:55+00:00"><day>16</day><month>7</month><year>2026</year></date></event-desc>
        </event>
      </pub-history>
      <permissions>
        <copyright-statement>Copyright (c) 2026 Jiayu Du, Mengyu Liu (Author)</copyright-statement>
        <copyright-year>2026</copyright-year>
        <copyright-holder>Jiayu Du, Mengyu Liu (Author)</copyright-holder>
        <license xlink:href="https://creativecommons.org/licenses/by/4.0">
          <license-p>&lt;a rel="license" href="https://creativecommons.org/licenses/by/4.0/"&gt;&lt;img alt="Creative Commons License" src="//i.creativecommons.org/l/by/4.0/88x31.png" /&gt;&lt;/a&gt;&lt;p&gt;This work is licensed under a &lt;a rel="license" href="https://creativecommons.org/licenses/by/4.0/"&gt;Creative Commons Attribution 4.0 International License&lt;/a&gt;.&lt;/p&gt;</license-p>
        </license>
      </permissions>
      <self-uri xlink:href="https://dipscie.com/index.php/JDF/article/view/10.67541_jdf2609"/>
      <kwd-group xml:lang="en">
        <kwd>Edge AI; Defect detection; Lightweight network; Multi-scale attention fusion; Adaptive inference</kwd>
      </kwd-group>
      <counts>
        <page-count count="31"/>
      </counts>
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    <ref-list>
      <ref id="R1">
        <mixed-citation>[1] Hassan, N. M., Hamdan, A., Shahin, F., Abdelmaksoud, R., &amp; Bitar, T. (2023). An artificial intelligent manufacturing process for high-quality low-cost production. International Journal of Quality &amp; Reliability Management, 40(7), 1777-1794. https://doi.org/10.1108/IJQRM-07-2022-0204</mixed-citation>
      </ref>
      <ref id="R2">
        <mixed-citation>[2] Zhao, T., Chen, G., Suraphee, S., Phoophiwfa, T., &amp; Busababodhin, P. (2025). A hybrid TCN-XGBoost model for agricultural product market price forecasting. PLoS One, 20(5), e0322496. https://doi.org/10.1371/journal.pone.0322496</mixed-citation>
      </ref>
      <ref id="R3">
        <mixed-citation>[3] Zhang, D., Zhou, S., Zheng, Y., &amp; Xu, X. (2025). Review on application of machine vision-based intelligent algorithms in gear defect detection. Processes, 13(10), 3370. https://doi.org/10.3390/pr13103370</mixed-citation>
      </ref>
      <ref id="R4">
        <mixed-citation>[4] Wang, X., Tang, Z., Guo, J., Meng, T., Wang, C., Wang, T., &amp; Jia, W. (2025). Empowering edge intelligence: A comprehensive survey on on-device AI models. ACM Computing Surveys, 57(9), 1-39. https://doi.org/10.48550/arXiv.2503.06027</mixed-citation>
      </ref>
      <ref id="R5">
        <mixed-citation>[5] Zhao, T., Chen, G., Pang, C., &amp; Busababodhin, P. (2025). Application and performance optimization of SLHS-TCN-XGBoost model in power demand forecasting. Comput. Model. Eng. Sci, 143(3), 2883-2917. http://dx.doi.org/10.32604/cmes.2025.066442</mixed-citation>
      </ref>
      <ref id="R6">
        <mixed-citation>[6] Shuvo, M. M. H., Islam, S. K., Cheng, J., &amp; Morshed, B. I. (2022). Efficient acceleration of deep learning inference on resource-constrained edge devices: A review. Proceedings of the IEEE, 111(1), 42-91. https://doi.org/10.1109/JPROC.2022.3226481</mixed-citation>
      </ref>
      <ref id="R7">
        <mixed-citation>[7] Wang, Z., Zhang, Z., Su, T., Ding, Z., &amp; Zhao, T. (2024, December). Research on supply chain network optimisation based on the CNNs-BiLSTM model. In 2024 International Conference on Information Technology, Comunication Ecosystem and Management (ITCEM) (pp. 197-202). IEEE. https://doi.org/10.1109/ITCEM65710.2024.00044</mixed-citation>
      </ref>
      <ref id="R8">
        <mixed-citation>[8] Abubakar, M., Che, Y., Zafar, A., Al-Khasawneh, M. A., &amp; Bhutta, M. S. (2025). Optimization of solar and wind power plants production through a parallel fusion approach with modified hybrid machine and deep learning models. Intelligent Data Analysis, 29(3), 808-830. https://doi.org/10.1177/1088467x241312592</mixed-citation>
      </ref>
      <ref id="R9">
        <mixed-citation>[9] Zhao, T., Chen, G., Pang, C., Li, L., &amp; Busababodhin, P. (2026). Forecasting global agricultural trade imbalances using a hybrid deep learning and gradient boosting framework. Discover Computing, 29(1), 443. https://doi.org/10.1007/s10791-026-10367-8</mixed-citation>
      </ref>
      <ref id="R10">
        <mixed-citation>[10] Dalal, S., Lilhore, U. K., Simaiya, S., Radulescu, M., &amp; Belascu, L. (2024). Improving efficiency and sustainability via supply chain optimization through CNNs and BiLSTM. Technological Forecasting and Social Change, 209, 123841. https://doi.org/10.1016/j.techfore.2024.123841</mixed-citation>
      </ref>
      <ref id="R11">
        <mixed-citation>[11] Liu, Y., Gong, M., Chen, G., &amp; Zhao, T. (2025, July). CNN-LSTM-based spatio-temporal prediction model for photovoltaic power generation. In Proceedings of the 2025 2nd International Conference on Big Data and Digital Management (pp. 570-575). https://doi.org/10.1145/3768801.3768892</mixed-citation>
      </ref>
      <ref id="R12">
        <mixed-citation>[12] Patil, S. P., Mallika, R. M., Sekhar, A. C., Josephson, P. J., &amp; Jacob, T. P. (2026, May). Hybrid CNN-BiLSTM Model for the Enhancement of Social Sustainability in Manufacturing Supply Chains. In 2026 International Conference on Intelligent and Sustainable Electronics &amp; Computing Technologies (INSECT) (pp. 1-6). IEEE. https://doi.org/10.1109/insect68872.2026.11663829</mixed-citation>
      </ref>
      <ref id="R13">
        <mixed-citation>[13] Peng, M., Hu, J., &amp; Zhao, T. (2026, March). Hybrid ARIMA-LSTM model for international trade logistics demand forecasting. In IET Conference Proceedings CP982 (Vol. 2026, No. 9, pp. 243-247). Stevenage, UK: The Institution of Engineering and Technology. https://doi.org/10.1049/icp.2026.2978</mixed-citation>
      </ref>
      <ref id="R14">
        <mixed-citation>[14] Wang, Y., &amp; Liang, X. (2025). Application of reinforcement learning methods combining graph neural networks and self-attention mechanisms in supply chain route optimization. Sensors, 25(3), 955. https://doi.org/10.3390/s25030955</mixed-citation>
      </ref>
      <ref id="R15">
        <mixed-citation>[15] Zhao, T., Chen, G., Pang, C., Seenoi, P., Papukdee, N., Busababodhin, P., &amp; Du, Y. (2025). Hybrid convolutional neural network-graph attention network-gradient boosting decision tree model for seismic impedance inversion prediction. J Seismic Explor, 34(5), 81-98. https://doi.org/10.36922/JSE025310051</mixed-citation>
      </ref>
      <ref id="R16">
        <mixed-citation>[16] Zhao, T., Chen, G., &amp; Busababodhin, P. (2026). An intelligent expert system for logistics disruption prediction and mitigation in global supply chains: Integrating graph-based risk inference with ensemble forecasting. Scientific Reports. https://doi.org/10.1038/s41598-026-61617-0</mixed-citation>
      </ref>
      <ref id="R17">
        <mixed-citation>[17] Qu, J., Qian, Z., &amp; Pei, Y. (2021). Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern. Energy, 232, 120996. https://doi.org/10.1016/j.energy.2021.120996</mixed-citation>
      </ref>
      <ref id="R18">
        <mixed-citation>[18] Chen, G., Zhao, T., Pang, C., &amp; Busababodhin, P. (2026). Integrated CNN–LSTM–XGBoost hybrid model predicts shale oil seismic attributes and global oil price trends. Scientific Reports, 16, 25588. https://doi.org/10.1038/s41598-026-55910-1</mixed-citation>
      </ref>
      <ref id="R19">
        <mixed-citation>[19] Ma, J., Huo, M., Han, J., Liu, Y., Lu, S., &amp; Yu, X. (2025). Integrated CNN‐LSTM for photovoltaic power prediction based on spatio‐temporal feature fusion. Engineering Reports, 7(1), e13088. https://doi.org/10.1002/eng2.13088</mixed-citation>
      </ref>
      <ref id="R20">
        <mixed-citation>[20] Zhao, T., Chen, G., Pang, C., Seenoi, P., Papukdee, N., &amp; Busababodhin, P. (2025). Time-lapse earthquake difference prediction based on physics-informed long short-term memory coupled with interpretability boosting. Journal of Seismic Exploration, 34(3), 25. https://doi.org/10.36922/JSE025310049</mixed-citation>
      </ref>
      <ref id="R21">
        <mixed-citation>[21] Zhang, X., Weng, Z., Zhu, P., Han, X., Zhu, J., &amp; Jiao, L. (2024). ESDINet: Efficient shallow-deep interaction network for semantic segmentation of high-resolution aerial images. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-15. https://doi.org/10.1109/tgrs.2024.3351437</mixed-citation>
      </ref>
      <ref id="R22">
        <mixed-citation>[22] Zhao, Y., Liu, Q., Su, H., Zhang, J., Ma, H., Zou, W., &amp; Liu, S. (2024). Attention-based multiscale feature fusion for efficient surface defect detection. IEEE Transactions on Instrumentation and Measurement, 73, 1-10. https://doi.org/10.1109/tim.2024.3372229</mixed-citation>
      </ref>
      <ref id="R23">
        <mixed-citation>[23] Su, J., Luo, Q., Yang, C., Gui, W., Silvén, O., &amp; Liu, L. (2024). PMSA-DyTr: Prior-modulated and semantic-aligned dynamic transformer for strip steel defect detection. IEEE Transactions on Industrial Informatics, 20(4), 6684-6695. https://doi.org/10.1109/tii.2023.3347747</mixed-citation>
      </ref>
      <ref id="R24">
        <mixed-citation>[24] Hamed, O., Bakkali, S., Blaschko, M., Moens, S., &amp; Van Landeghem, J. (2024, August). Multimodal adaptive inference for document image classification with anytime early exiting. In International Conference on Document Analysis and Recognition (pp. 270-286). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-70546-5_16</mixed-citation>
      </ref>
      <ref id="R25">
        <mixed-citation>[25] Zhong, H., Fu, D., Xiao, L., Zhao, F., Liu, J., Hu, Y., &amp; Wu, B. (2024). STFE-Net: A multi-stage approach to enhance statistical texture feature for defect detection on metal surfaces. Advanced engineering informatics, 61, 102437. https://doi.org/10.1016/j.aei.2024.102437</mixed-citation>
      </ref>
      <ref id="R26">
        <mixed-citation>[26] Xiang, Z., Jia, J., Zhou, K., Qian, M., &amp; Wu, W. (2025). Block-wise feature fusion for high-precision industrial surface defect detection. The Visual Computer, 41(11), 9277-9295. https://doi.org/10.1007/s00371-025-03927-4</mixed-citation>
      </ref>
      <ref id="R27">
        <mixed-citation>[27] Cai, X., Wang, Y., &amp; Zhang, L. (2022). Optimus: An operator fusion framework for deep neural networks. ACM Transactions on Embedded Computing Systems, 22(1), 1-26. https://doi.org/10.1145/3520142</mixed-citation>
      </ref>
      <ref id="R28">
        <mixed-citation>[28] Li, Y., Ren, Y., Wang, M., Zhang, Y., Zhang, X., &amp; Deng, J. (2026). Deployment method of deep object detection models integrating model pruning and data reuse for reconfigurable architectures: Y. Li et al. The Journal of Supercomputing, 82(12), 606. https://doi.org/10.1007/s11227-026-08749-2</mixed-citation>
      </ref>
      <ref id="R29">
        <mixed-citation>[29] Yao, G., Zhu, S., Zhang, L., &amp; Qi, M. (2024). HP-YOLOv8: high-precision small object detection algorithm for remote sensing images. Sensors, 24(15), 4858. https://doi.org/10.3390/s24154858</mixed-citation>
      </ref>
      <ref id="R30">
        <mixed-citation>[30] Wang, C., Zheng, B., &amp; Li, C. (2025). Efficient traffic sign recognition using YOLO for intelligent transport systems. Scientific reports, 15(1), 13657. https://doi.org/10.1038/s41598-025-98111-y</mixed-citation>
      </ref>
    </ref-list>
  </back>
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