<?xml version="1.0" encoding="UTF-8"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcq="http://purl.org/dc/terms/"><records count="1" morepages="false" start="1" end="1"><record rownumber="1"><dc:product_type>Journal Article</dc:product_type><dc:title>Explainable few-shot learning for online anomaly detection in ultrasonic metal welding with varying configurations</dc:title><dc:creator>Meng, Yuquan; Lu, Kuan-Chieh; Dong, Zhiqiao; Li, Shichen; Shao, Chenhui</dc:creator><dc:corporate_author/><dc:editor/><dc:description/><dc:publisher>Elsevier</dc:publisher><dc:date>2023-12-01</dc:date><dc:nsf_par_id>10484973</dc:nsf_par_id><dc:journal_name>Journal of Manufacturing Processes</dc:journal_name><dc:journal_volume>107</dc:journal_volume><dc:journal_issue>C</dc:journal_issue><dc:page_range_or_elocation>345 to 355</dc:page_range_or_elocation><dc:issn>1526-6125</dc:issn><dc:isbn/><dc:doi>https://doi.org/10.1016/j.jmapro.2023.10.047</dc:doi><dcq:identifierAwardId>1944345</dcq:identifierAwardId><dc:subject/><dc:version_number/><dc:location/><dc:rights/><dc:institution/><dc:sponsoring_org>National Science Foundation</dc:sponsoring_org></record></records></rdf:RDF>