{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T03:18:15Z","timestamp":1758079095902,"version":"3.44.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686196","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,16]]},"abstract":"<jats:p>In the domain of ship design and manufacturing, complex plates are widely used in ship structures, and the rapid retrieval of plate classifications is crucial for effective ship design management. This paper proposes a novel method for similar ship plate retrieval, which is based on semantic fuzzy classification. Commencing with the reconstruction of plate surface structures from point clouds, the method then proceeds to extract semantic features via multi-scale geometric feature extraction, feature line detection, and high-level semantic label extraction. To address the fuzzy boundaries between plate categories, the method constructs fuzzy category vectors to characterize the features of plates. Finally, by integrating fuzzy classification results with fine-grained geometric feature differences, the method achieves the retrieval of similar plates. Experimental results demonstrate that this approach significantly improves the efficiency and accuracy of similarity retrieval among complex ship plates, thereby providing robust support for efficient ship design, and holding important application value.<\/jats:p>","DOI":"10.3233\/faia250541","type":"book-chapter","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:19:55Z","timestamp":1758028795000},"source":"Crossref","is-referenced-by-count":0,"title":["A Similar Ship Plate Retrieval Method Based on Semantic Fuzzy Classification"],"prefix":"10.3233","author":[{"given":"Yuxin","family":"Zeng","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University"}]},{"given":"Shenyue","family":"Ni","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}]},{"given":"Yingjie","family":"Wu","sequence":"additional","affiliation":[{"name":"CSSC SEAGO SYSTEM TECHNOLOGY CO., LTD."}]},{"given":"Zhiye","family":"Xu","sequence":"additional","affiliation":[{"name":"CSSC SEAGO SYSTEM TECHNOLOGY CO., LTD."}]},{"given":"Bingqing","family":"Shen","sequence":"additional","affiliation":[{"name":"Shanghai International Studies University"}]},{"given":"Shenyuan","family":"Gu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}]},{"given":"Hongming","family":"Cai","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","New Trends in Intelligent Software Methodologies, Tools and Techniques"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250541","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:19:55Z","timestamp":1758028795000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250541"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"ISBN":["9781643686196"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250541","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,16]]}}}