{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T14:31:30Z","timestamp":1761921090069,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Xi\u2019an Key Laboratory of Intelligent Weapons, Natural Science Basic Research Project of Shaanxi Province","award":["2019220514SYS020CG042"],"award-info":[{"award-number":["2019220514SYS020CG042"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Honeycomb structure composites are taking an increasing proportion in aircraft manufacturing because of their high strength-to-weight ratio, good fatigue resistance, and low manufacturing cost. However, the hollow structure is very prone to liquid ingress. Here, we report a fast and automatic classification approach for water, alcohol, and oil filled in glass fiber reinforced polymer (GFRP) honeycomb structures through terahertz time-domain spectroscopy (THz-TDS). We propose an improved one-dimensional convolutional neural network (1D-CNN) model, and compared it with long short-term memory (LSTM) and ordinary 1D-CNN models, which are classification networks based on one dimension sequenced signals. The automated liquid classification results show that the LSTM model has the best performance for the time-domain signals, while the improved 1D-CNN model performed best for the frequency-domain signals.<\/jats:p>","DOI":"10.3390\/s23031149","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T05:06:14Z","timestamp":1674104774000},"page":"1149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Classification of Liquid Ingress in GFRP Honeycomb Based on One-Dimension Sequential Model Using THz-TDS"],"prefix":"10.3390","volume":"23","author":[{"given":"Xiaohui","family":"Xu","sequence":"first","affiliation":[{"name":"School of Armament Science and Technology, Xi\u2019an Technological University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjun","family":"Huo","sequence":"additional","affiliation":[{"name":"School of Armament Science and Technology, Xi\u2019an Technological University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, Xi\u2019an Technological University, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2824-2277","authenticated-orcid":false,"given":"Hongbin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Equipment Management and UAV Engineering, Air Force Engineering University, Xi\u2019an 710043, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"74850D","DOI":"10.1117\/12.830540","article-title":"Nondestructive terahertz imaging for aerospace applications","volume":"Volume 7485","author":"Petkie","year":"2009","journal-title":"Proceedings of the Millimetre Wave and Terahertz Sensors and Technology II"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s11465-018-0495-9","article-title":"Progress in terahertz nondestructive testing: A review","volume":"14","author":"Zhong","year":"2019","journal-title":"Front. 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