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000844047 019__ $$a1043863115
000844047 020__ $$a9783319905099$$q(electronic book)
000844047 020__ $$a3319905090$$q(electronic book)
000844047 020__ $$z9783319905082
000844047 020__ $$z3319905082
000844047 035__ $$aSP(OCoLC)on1043830580
000844047 035__ $$aSP(OCoLC)1043830580$$z(OCoLC)1043863115
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000844047 050_4 $$aTK7872.D48
000844047 08204 $$a005.74$$223
000844047 1112_ $$aIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (2000- )$$n(13th :$$d2017 :$$cTaegu, Korea)
000844047 24510 $$aMultisensor fusion and integration in the wake of big data, deep learning and cyber physical system :$$ban edition of the selected papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017) /$$cSukhan Lee, Hanseok Ko, Songhwai Oh, editors.
000844047 2463_ $$aMFI 2017
000844047 264_1 $$aCham, Switzerland :$$bSpringer,$$c2018.
000844047 300__ $$a1 online resource.
000844047 336__ $$atext$$btxt$$2rdacontent
000844047 337__ $$acomputer$$bc$$2rdamedia
000844047 338__ $$aonline resource$$bcr$$2rdacarrier
000844047 4901_ $$aLecture notes in electrical engineering,$$x1876-1100 ;$$vvolume 501
000844047 500__ $$aIncludes author index.
000844047 5050_ $$aIntro; Preface; Contents; Multi-sensor Fusion: Theory and Practice; Covariance Projection as a General Framework of Data Fusion and Outlier Removal; Abstract; 1 Introduction; 1.1 Problem Statement; 2 Proposed Approach; 3 Confidence Measure of Data Sources; 3.1 Inconsistency Detection and Exclusion; 3.2 Effect of Correlation on d Distance; 4 Simulation Results; 5 Conclusion; Acknowledgments; Appendix 1; Appendix 2; References; State Estimation in Networked Control Systems with Delayed and Lossy Acknowledgments; 1 Introduction; 2 Problem Formulation; 3 Derivation of the Proposed Estimator
000844047 5058_ $$a3.1 Modeling the NCS as a Markov Jump Linear System3.2 Estimator Design; 4 Evaluation; 5 Conclusions; References; Performance of State Estimation and Fusion with Elliptical Motion Constraints; 1 Introduction; 2 System Model; 2.1 Coordinated Turn (CT) Model; 2.2 Elliptical Constraint; 2.3 Generating Constrained States; 3 Projection-Based Constrained Estimation; 3.1 Direct Connection to Ellipse Center; 3.2 Shortest Distance to Unconstrained Estimate; 4 Fusion of Constrained Estimates; 4.1 Fusion Rules; 4.2 Fusion Rules with Constrained Estimates; 5 Constrained Fusion with Information Loss
000844047 5058_ $$a5.1 Simulation Setup5.2 Performance; 6 Conclusions; References; Relevance and Redundancy as Selection Techniques for Human-Autonomy Sensor Fusion; 1 Introduction; 2 Related Work; 3 Theory and Background; 3.1 Preliminaries; 3.2 Relevance; 3.3 Redundancy; 3.4 Relevance and Redundancy with Specific Fusion Algorithms; 4 Empirical Tests; 4.1 Redundancy; 4.2 Relevance; 4.3 Redundancy vs. Relevance; 5 Conclusions and Future Work; References; Classification of Reactor Facility Operational State Using SPRT Methods with Radiation Sensor Networks; 1 Introduction; 2 Detection Problem; 2.1 SPRT Detection
000844047 5058_ $$a2.2 Stack Intensity Estimation3 IRSS Experimental Results; 3.1 IRSS Datasets; 3.2 Experimental SPRTs; 3.3 Performance Comparison; 4 HFIR Experimental Results; 4.1 HFIR Datasets and Experimental SPRTs; 4.2 Performance Comparison; 5 Performance of IE SPRT Detection Method; 5.1 Single Location Measurements; 5.2 Network Measurements; 6 Conclusion; References; Improving Ego-Lane Detection by Incorporating Source Reliability; 1 Introduction; 2 Related Work; 2.1 Multi-source Fusion for Ego-Lane Detection; 2.2 Reliability in Fusion; 3 Concept of Reliability-Aware Ego-Lane Detection
000844047 5058_ $$a4 Reliability for Ego-Lane Detection4.1 Requirements; 4.2 Sensor-Independent Performance Measure; 5 Learning Reliabilities of Ego-Lane Estimations; 5.1 Learning Reliability Using Classifiers; 5.2 Training Data for the Classifiers; 5.3 Feature Selection; 5.4 Applying Classifiers Towards Learning Reliability; 6 Reliability-Aware Ego-Lane Fusion; 6.1 Dempster-Shafer Theory (DST):; 6.2 Other Fusion Approaches; 7 Experimental Evaluation; 7.1 Assessment of Reliability Estimation; 7.2 Assessment Information Fusion; 7.3 Exemplary Results; 8 Conclusion; References
000844047 506__ $$aAccess limited to authorized users.
000844047 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed July 13, 2018).
000844047 650_0 $$aMultisensor data fusion$$vCongresses.
000844047 650_0 $$aIntelligent control systems$$vCongresses.
000844047 650_0 $$aBig data$$vCongresses.
000844047 650_0 $$aCooperating objects (Computer systems)$$vCongresses.
000844047 7001_ $$aLee, Sukhan,$$eeditor.
000844047 7001_ $$aKo, Hanseok,$$eeditor.
000844047 7001_ $$aOh, Songhwai,$$eeditor.
000844047 77608 $$iPrint version: $$z3319905082$$z9783319905082$$w(OCoLC)1029652743
000844047 830_0 $$aLecture notes in electrical engineering ;$$vv. 501.
000844047 852__ $$bebk
000844047 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-90509-9$$zOnline Access$$91397441.1
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000844047 980__ $$aBIB
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