001463291 000__ 04531cam\a22006497i\4500
001463291 001__ 1463291
001463291 003__ OCoLC
001463291 005__ 20230601003314.0
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001463291 007__ cr\cn\nnnunnun
001463291 008__ 230419s2023\\\\si\a\\\\ob\\\\000\0\eng\d
001463291 019__ $$a1376172088$$a1379548149
001463291 020__ $$a9789819909537$$qelectronic book
001463291 020__ $$a9819909538$$qelectronic book
001463291 020__ $$z9789819909520
001463291 020__ $$z981990952X
001463291 0247_ $$a10.1007/978-981-99-0953-7$$2doi
001463291 035__ $$aSP(OCoLC)1376422605
001463291 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX
001463291 049__ $$aISEA
001463291 050_4 $$aTF1460$$b.L58 2023
001463291 08204 $$a625.1$$223/eng/20230419
001463291 1001_ $$aLiu, Zhigang,$$eauthor.$$0(orcid)0000-0003-4154-5587$$1https://orcid.org/0000-0003-4154-5587
001463291 24510 $$aDeep learning-based detection of catenary support component defect and fault in high-speed railways /$$cZhigang Liu, Wenqiang Liu, Junping Zhong.
001463291 264_1 $$aSingapore :$$bSpringer,$$c2023.
001463291 300__ $$a1 online resource (xiii, 239 pages) :$$billustrations (some color).
001463291 336__ $$atext$$btxt$$2rdacontent
001463291 337__ $$acomputer$$bc$$2rdamedia
001463291 338__ $$aonline resource$$bcr$$2rdacarrier
001463291 4901_ $$aAdvances in high-speed rail technology,$$x2363-5029
001463291 504__ $$aIncludes bibliographical references.
001463291 5050_ $$aOverview of Catenary Detection of Electrified Railways -- Advance of Deep Learning -- Catenary Support Components and their Characteristics in High-speed Railways -- Preprocessing of Catenary Support Components' Images -- Positioning of Catenary Support Components -- Detection of Catenary Support Component Defect and Fault -- Detection of The parameters of Catenary Support Devices based on 3D Point Clouds.
001463291 506__ $$aAccess limited to authorized users.
001463291 520__ $$aThis book focuses on the deep learning technologies and their applications in the catenary detection of high-speed railways. As the only source of power for high-speed trains, the catenary's service performance directly affects the safe operation of high-speed railways. This book systematically shows the latest research results of catenary detection in high-speed railways, especially the detection of catenary support component defect and fault. Some methods or algorithms have been adopted in practical engineering. These methods or algorithms provide important references and help the researcher, scholar, and engineer on pantograph and catenary technology in high-speed railways. Unlike traditional detection methods of catenary support component based on image processing, some advanced methods in the deep learning field, including convolutional neural network, reinforcement learning, generative adversarial network, etc., are adopted and improved in this book. The main contents include the overview of catenary detection of electrified railways, the introduction of some advance of deep learning theories, catenary support components and their characteristics in high-speed railways, the image reprocessing of catenary support components, the positioning of catenary support components, the detection of defect and fault, the detection based on 3D point cloud, etc.
001463291 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 19, 2023).
001463291 650_0 $$aHigh speed trains.
001463291 650_0 $$aFault location (Engineering)
001463291 650_0 $$aDeep learning (Machine learning)
001463291 655_0 $$aElectronic books.
001463291 7001_ $$aLiu, Wenqiang,$$eauthor.$$0(orcid)0000-0002-0288-7149$$1https://orcid.org/0000-0002-0288-7149
001463291 7001_ $$aZhong, Junping,$$eauthor.$$0(orcid)0000-0003-3830-0722$$1https://orcid.org/0000-0003-3830-0722
001463291 77608 $$iPrint version: $$z981990952X$$z9789819909520$$w(OCoLC)1369601796
001463291 830_0 $$aAdvances in high-speed rail technology,$$x2363-5029
001463291 852__ $$bebk
001463291 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-0953-7$$zOnline Access$$91397441.1
001463291 909CO $$ooai:library.usi.edu:1463291$$pGLOBAL_SET
001463291 980__ $$aBIB
001463291 980__ $$aEBOOK
001463291 982__ $$aEbook
001463291 983__ $$aOnline
001463291 994__ $$a92$$bISE