Multi-source domain open-set deep transfer adversarial network for operating performance assessment.

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Bibliographic Details
Title: Multi-source domain open-set deep transfer adversarial network for operating performance assessment.
Authors: Liu Y; College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China. Electronic address: liuyan@ise.neu.edu.cn., Fu L; College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China. Electronic address: 2410297@stu.neu.edu.cn., Xiong Y; Product R&D Department, Wuhan Xuanyuan Intelligent Driving Technology Co., Ltd, Wuhan, 430205, China. Electronic address: xiongyl@xy-idrive.com., Wang S; College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China. Electronic address: 2310329@stu.neu.edu.cn., Chu F; School of Information and Control Engineering, Underground Space Intelligent Control Engineering Research Center of the Ministry of Education, China University of Mining and Technology, Xuzhou, 221116, China. Electronic address: chufei@cumt.edu.cn., Bao C; Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, 110004, China. Electronic address: pw3zzy@sj-hospital.org., Wang F; College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China; State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China. Electronic address: wangfuli@ise.neu.edu.cn.
Source: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Sep; Vol. 201, pp. 108981. Date of Electronic Publication: 2026 Apr 11.
Publication Type: Journal Article
Journal Info: Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1879-2782
DOI:10.1016/j.neunet.2026.108981