001463742 000__ 07556cam\a22007337i\4500
001463742 001__ 1463742
001463742 003__ OCoLC
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001463742 019__ $$a1378008183$$a1378393544
001463742 020__ $$a9789819911455$$qelectronic book
001463742 020__ $$a9819911451$$qelectronic book
001463742 020__ $$z9789819911448
001463742 020__ $$z9819911443
001463742 0247_ $$a10.1007/978-981-99-1145-5$$2doi
001463742 035__ $$aSP(OCoLC)1378608776
001463742 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX
001463742 049__ $$aISEA
001463742 050_4 $$aTA1560$$b.I58 2022
001463742 08204 $$a006.6/93$$223/eng/20230509
001463742 1112_ $$aInternational Conference on 3D Imaging Technology$$n(4th :$$d2022)
001463742 24510 $$a3D imaging :$$bmultidimensional signal processing and deep learning.$$nVolume 2,$$pMultidimensional signals, video processing and applications /$$cedited by Srikanta Patnaik, Roumen Kountchev, Yonghang Tai, Roumiana Kountcheva.
001463742 24630 $$aMultidimensional signals, video processing and applications
001463742 264_1 $$aSingapore :$$bSpringer,$$c2023.
001463742 300__ $$a1 online resource (268 pages) :$$billustrations (black and white, and color).
001463742 336__ $$atext$$btxt$$2rdacontent
001463742 337__ $$acomputer$$bc$$2rdamedia
001463742 338__ $$aonline resource$$bcr$$2rdacarrier
001463742 4901_ $$aSmart innovation, systems and technologies ;$$vvolume 348
001463742 500__ $$aIncludes author index.
001463742 5050_ $$aIntro -- Preface -- Contents -- About the Editors -- 1 Prediction Based on Sentiment Analysis and Deep Learning -- 1.1 First Section -- 1.2 Benchmark Prediction -- 1.2.1 Capture Data of Stock Comments from www.guba.eastmoney.com [1] -- 1.2.2 Build a Model for False News Judgment -- 1.2.3 Test Stock Comments for False News -- 1.2.4 Build a Sentiment Classification Model for Stock Comment -- 1.2.5 Build an Index from the Analysis Results -- 1.2.6 Capture and Load Data -- 1.2.7 A Subsection Sample -- 1.3 Conclusion -- References -- 2 A Survey on Time Series Forecasting -- 2.1 Introduction
001463742 5058_ $$a2.2 Traditional Machine Learning-Based Method -- 2.2.1 Feature Extraction -- 2.2.2 Feature Selection -- 2.2.3 Model Training -- 2.2.4 Rolling Time Series Forecasting -- 2.3 Deep Learning-Based Method -- 2.3.1 RNN -- 2.3.2 LSTM -- 2.3.3 GRU -- 2.4 Experiment Results -- 2.4.1 Machine Learning Results -- 2.4.2 Deep Learning Results -- 2.5 Conclusion -- References -- 3 Research and Development of Visual Interactive Performance Test Methods and Equipment for Intelligent Cockpit -- 3.1 Introduction -- 3.2 System Overview -- 3.2.1 Visual Bionic Robot -- 3.2.2 Main Case of Bionic Robot
001463742 5058_ $$a3.2.3 Binocular High-Frame Camera -- 3.2.4 Software -- 3.3 Head Visual Tracking -- 3.3.1 Self-Stabilizing Function of the Head -- 3.3.2 High-Precision Servo Motor -- 3.3.3 Servo Encoder -- 3.3.4 Self-Stabilizing PID Algorithm for the Cradle Head -- 3.3.5 Precision Test of Self-Stabilizing Function of Head -- 3.4 System Effect -- 3.5 Conclusion -- References -- 4 Design and Validation of Automated Inspection System Based on 3D Laser Scanning of Rocket Segments -- 4.1 Introduction -- 4.2 3D Scanning Measurement Principle -- 4.2.1 Three-Dimensional Laser Scanning Equipment
001463742 5058_ $$a4.2.2 Lifting Platform System -- 4.2.3 Checking Standard Device -- 4.2.4 Measurement Software Design -- 4.3 Measurement System Accuracy Verification -- 4.3.1 Verification of Length Splicing Accuracy -- 4.3.2 Verification of Geometric Element Detection Accuracy -- 4.4 Conclusion -- 4.5 Discussion -- References -- 5 Research and Implementation of Electric Equipment Connectivity Data Analysis Model Based on Graph Database -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Research on the Method and Algorithm of Electric Data Modeling -- 5.3.1 Electric Data -- 5.3.2 Electric Data Modeling
001463742 5058_ $$a5.4 Implementation of Electric Data Model Based on Graph Database -- 5.4.1 Electric Data Relation Processing -- 5.4.2 Implementation and Construction of Power Data Model -- 5.4.3 Electric Equipment Connectivity Analysis Based on Power Grid Data Model -- 5.5 Application Results -- References -- 6 Improving CXR Self-Supervised Representation by Pretext Task and Cross-Domain Synthetic Data -- 6.1 Introduction -- 6.2 Related Works -- 6.2.1 Overview of CXR Classification -- 6.2.2 Self-Supervision and Contrastive Learning -- 6.2.3 Pretext Task and Data Augmentation -- 6.3 Problem Definition
001463742 506__ $$aAccess limited to authorized users.
001463742 520__ $$aThis book presents high-quality research in the field of 3D imaging technology. The fourth edition of International Conference on 3D Imaging Technology (3DDIT-MSP&DL) continues the good traditions already established by the first three editions of the conference to provide a wide scientific forum for researchers, academia and practitioners to exchange newest ideas and recent achievements in all aspects of image processing and analysis, together with their contemporary applications. The conference proceedings are published in 2 volumes. The main topics of the papers comprise famous trends as: 3D image representation, 3D image technology, 3D images and graphics, and computing and 3D information technology. In these proceedings, special attention is paid at the 3D tensor image representation, the 3D content generation technologies, big data analysis, and also deep learning, artificial intelligence, the 3D image analysis and video understanding, the 3D virtual and augmented reality, and many related areas. The first volume contains papers in 3D image processing, transforms and technologies. The second volume is about computing and information technologies, computer images and graphics and related applications. The two volumes of the book cover a wide area of the aspects of the contemporary multidimensional imaging and the related future trends from data acquisition to real-world applications based on various techniques and theoretical approaches.
001463742 588__ $$aDescription based on print version record.
001463742 650_0 $$aThree-dimensional imaging$$vCongresses.
001463742 650_0 $$aSignal processing$$vCongresses.
001463742 650_0 $$aDeep learning (Machine learning)$$vCongresses.
001463742 655_0 $$aElectronic books.
001463742 7001_ $$aPatnaik, Srikanta,$$eeditor.$$1https://isni.org/isni/000000004548568X
001463742 7001_ $$aKountchev, Roumen,$$eeditor.$$1https://isni.org/isni/000000036696012X
001463742 7001_ $$aTai, Yonghang,$$eeditor.
001463742 7001_ $$aKountcheva, Roumiana,$$eeditor.
001463742 77608 $$iPrint version:$$aInternational Conference on 3D Imaging Technology (4th), creator.$$t3D imaging : Volume 2, Multidimensional signals, video processing and applications.$$dSingapore : Springer, 2023$$z9789819911448$$w(OCoLC)1374596698
001463742 830_0 $$aSmart innovation, systems, and technologies ;$$v348.
001463742 852__ $$bebk
001463742 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-1145-5$$zOnline Access$$91397441.1
001463742 909CO $$ooai:library.usi.edu:1463742$$pGLOBAL_SET
001463742 980__ $$aBIB
001463742 980__ $$aEBOOK
001463742 982__ $$aEbook
001463742 983__ $$aOnline
001463742 994__ $$a92$$bISE