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220202s2022 ne o 000 0 eng d |
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|a 9780128210529
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|z 9780128210512 (pbk.)
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|a (OCoLC)1334128526
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|b .I58 2022eb
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|a 629.1
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|a UAMI
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|a IoT and spacecraft informatics /
|c edited by K.L. Yung, Andrew W. Ip, Fatos Xhafa, K.K. Tseng.
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|a Amsterdam :
|b Elsevier,
|c 2022.
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|a 1 online resource.
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|a text
|2 rdacontent
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|a computer
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|a online resource
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|a Aerospace engineering
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|a 1. Artificial intelligence approach for aerospace defect detection using single-shot multibox detector network in phased array ultrasonic<br>2. Classifying asteroid spectra by data-driven machine learning model<br>3. Recognition of target spacecraft based on shape features<br>4. Internet of things, an insight to digital twins and case studies<br>5. Subspace tracking for time-varying direction-of-arrival estimation with sensor arrays<br>6. An overview of optimization and resolution methods in satellite scheduling and spacecraft operation: description, modeling, and application<br>7. Colored Petri net modeling of the manufacturing processes of space instruments<br>8. Product performance model for product innovation, reliability and development in high-tech industries and a case study on the space instrument industry<br>9. Monocular simultaneous localization and mapping for a space rover application<br>10. Reliability and health management of spacecraft
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|a Description based on CIP data; resource not viewed.
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|a Front Cover -- IoT and Spacecraft Informatics -- Copyright Page -- Dedication -- Contents -- List of contributors -- About the editors -- Foreword -- Preface -- Acknowledgment -- 1 Artificial intelligence approach for aerospace defect detection using single-shot multibox detector network in phased arr... -- 1.1 Introduction -- 1.1.1 Ultrasonic inspection in aircraft -- 1.1.2 Autonomous inspection -- 1.2 Literature review -- 1.2.1 Composite material for the aerospace industry -- 1.2.2 Defects on composite materials -- 1.2.3 Defect inspection of composite materials -- 1.3 Defect detection algorithm -- 1.3.1 R-convolutional neural network -- 1.3.2 You only look once -- 1.3.3 Single-shot mulibox detector -- 1.3.4 Single-shot mulibox detector versus you only look once -- 1.3.5 Convolutional neural network-based object detection in nondestructive testing -- 1.4 Deployment of defect detection -- 1.4.1 Setting up of the deep learning environment -- 1.4.1.1 NVidia Tensorflow Object Detection API -- 1.4.1.2 TensorRT -- 1.4.1.3 OpenCV -- 1.4.2 Model training -- 1.4.3 Deployment in NVidia jetson TX2 -- 1.4.3.1 Program structure -- 1.4.3.2 OpenCV -- 1.4.3.3 MQTT -- 1.4.4 Validation -- 1.5 Implementation -- 1.5.1 Dataset preparation -- 1.5.2 Defect scanning -- 1.5.3 Image augmentation -- 1.5.4 Image annotation -- 1.6 Results -- 1.6.1 Loss -- 1.6.1.1 Classification loss and localization loss -- 1.6.1.2 Network configuration comparison and improvement -- 1.6.2 Validation of the defect detection system -- 1.6.2.1 Validation test sets -- 1.6.2.2 Manual labeling -- 1.6.2.3 Preliminary result of system and improvement -- 1.6.2.4 Automatic inspection -- 1.6.2.5 Comparison between automatic and manual inspection -- 1.7 Conclusions -- Acknowledgment -- References -- 2 Classifying asteroid spectra by data-driven machine learning model -- 2.1 Introduction.
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|a 2.1.1 Asteroid spectroscopic survey -- 2.1.2 Asteroid taxonomy -- 2.2 Related work -- 2.2.1 Notations used in this chapter -- 2.2.2 Low-dimensional feature learning for spectral data -- 2.2.3 Classifier models for spectral data classification -- 2.3 Neighboring discriminant component analysis: a data-driven machine learning model for asteroid spectra feature learning... -- 2.4 Experiments -- 2.4.1 Preprocessing for the asteroid spectral data -- 2.4.2 Experimental setup and results -- 2.4.3 Analysis for neighboring discriminant component analysi parameters -- 2.4.4 Analysis for extreme learning machine classifier parameters -- 2.5 Conclusion -- Acknowledgment -- Appendix A Reflectance spectra characteristics for some representative asteroids from different categories are used in this... -- References -- 3 Recognition of target spacecraft based on shape features -- 3.1 Introduction -- 3.1.1 Background -- 3.1.2 Related works -- 3.2 Artificial bee colony algorithm -- 3.3 Species-based artificial bee colony algorithm -- 3.3.1 Species -- 3.3.2 Species-based artificial bee colony algorithm -- 3.3.3 Benchmark test -- 3.4 The application of species-based artificial bee colony in circle detection -- 3.4.1 Representation of the circle -- 3.4.2 Assessment of circular accuracy -- 3.5 The application of species-based artificial bee colony in multicircle detection -- 3.5.1 Test experiments on drawn sketches -- 3.5.2 Detection for circular modules on noncooperative targets -- 3.5.3 Detection performance with noise -- 3.5.4 Detection performance under different light intensity -- 3.5.5 Detection performance during continuous flight -- 3.6 The application of species-based artificial bee colony in multitemplate matching -- 3.6.1 Multitemplate matching by species-based artificial bee colony -- 3.6.2 Multitemplate matching for blurred images.
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|a 3.6.3 Multitemplate matching for images with noises -- 3.7 Conclusions -- References -- 4 Internet of Things, a vision of digital twins and case studies -- 4.1 Introduction to internet of things -- 4.2 Components of internet of things -- 4.2.1 Sensor/devices -- 4.2.2 Connectivity -- 4.2.3 Data processing -- 4.2.4 User interface -- 4.3 Digital twin -- 4.4 Digital twin description in internet of things context -- 4.5 Multiagent system architecture -- 4.5.1 Dynamic real-life environment -- 4.5.2 Collaborative learning -- 4.6 The mathematical construct of a typical digital twin -- 4.7 Internet of things analytics -- 4.7.1 Case studies 1-internet of things devices for mobile link -- 4.7.2 Case study 2-intelligent internet of things -based system studying postmodulation factors -- 4.7.2.1 Radiotherapy treatment preparing system -- 4.7.2.2 Radiotherapy database administration system -- 4.7.2.3 Radiotherapy control system -- 4.7.3 Case studies 3-internet of things -based vertical plant wall for indoor climate control -- 4.8 Discussion -- 4.9 Conclusion -- References -- 5 Subspace tracking for time-varying direction-of-arrival estimation with sensor arrays -- 5.1 Introduction -- 5.1.1 Subspace tracking -- 5.1.2 Direction-of-arrival estimation -- 5.2 Subspace tracking algorithms -- 5.2.1 Signal model -- 5.2.2 Projection approximate subspace tracking -- 5.2.3 Modified projection approximate subspace tracking -- 5.2.4 Modified orthonormal projection approximate subspace tracking -- 5.2.5 Kalman filtering -- 5.2.6 Kalman filter with variable number of measurements based subspace tracking -- 5.3 Robust subspace tracking -- 5.3.1 Robust projection approximate subspace tracking -- 5.3.2 Robust Kalman filter with variable number of measuremen -- 5.4 Subspace-based direction-of-arrival tracking -- 5.5 Simulation results.
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|a 5.5.1 Subspace and direction-of-arrival tracking in Gaussian noise -- 5.5.2 Subspace and direction-of-arrival tracking in impulsive noise -- 5.6 Conclusions -- References -- 6 An overview of optimization and resolution methods in satellite scheduling and spacecraft operation: description, modelin... -- 6.1 Introduction -- 6.1.1 Background -- 6.1.2 Literature review and classification of scheduling problems -- 6.1.3 The scheduling problems -- 6.1.4 Integrating scheduling in the big data environment -- 6.2 Satellite scheduling problems -- 6.2.1 Satellite range scheduling -- 6.2.2 Satellite downlink scheduling -- 6.2.3 Satellite broadcast scheduling -- 6.2.4 Satellite scheduling data download -- 6.2.5 Satellite scheduling at large scale -- 6.2.6 Satellite scheduling at small scale -- 6.2.7 Multisatellite scheduling -- 6.2.8 Multisatellite, multistation TT & -- C scheduling -- 6.2.9 Ground station scheduling -- 6.2.10 Low-earth-orbit satellite scheduling -- 6.2.11 Computational complexity of satellites scheduling -- 6.2.12 Satellite deployment systems -- 6.3 Spacecraft optimization problems -- 6.4 Computational complexity resolution methods -- 6.4.1 Local search methods -- 6.4.1.1 Hill climbing -- 6.4.1.2 Simulated annealing -- 6.4.1.3 Tabu search method -- 6.4.1.4 Genetic algorithms -- 6.4.1.5 Two-stage heuristic -- 6.4.1.6 An Improved differential evolution algorithm -- 6.4.1.6.1 Symbol definition -- 6.4.1.7 Multisatellite task prescheduling algorithm based on conflict imaging probability -- 6.5 Future trend of algorithms and models and solutions of satellite scheduling problem -- 6.6 Benchmarking and simulation platforms -- 6.7 Conclusions and future work -- Acknowledgments -- References -- 7 Colored Petri net modeling of the manufacturing processes of space instruments -- 7.1 Introduction -- 7.1.1 Development of Petri net.
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|a 7.1.2 Classification of Petri net -- 7.1.2.1 Classical Petri net -- 7.1.2.2 Timed Petri net -- 7.1.2.3 Colored Petri net -- 7.1.2.4 Timed colored Petri net -- 7.1.2.5 Hierarchical Petri net -- 7.1.3 Petri net properties -- 7.1.3.1 Accessibility -- 7.1.3.2 Activity -- 7.1.3.3 Fairness -- 7.1.4 Modeling with TCPN -- 7.1.5 Application of Petri net -- 7.1.5.1 Modeling workflow -- 7.1.5.2 Supply chain -- 7.1.5.3 Flexible manufacturing system -- 7.1.5.4 Database system -- 7.1.6 Optimization tools -- 7.1.6.1 Random simulation with colored Petri net tool -- 7.1.6.2 Six sigma system -- 7.1.6.3 Critical time analysis -- 7.1.6.4 ECRS Method -- 7.2 Case study -- 7.2.1 Case modeling and simulation -- 7.2.1.1 Case description -- 7.2.1.2 Mapping workflow elements into colored Petri net -- Modeling process -- 7.2.2 Simulation result and analysis -- 7.2.2.1 Simulation result -- 7.2.2.2 Result analysis -- 7.2.3 Improvement strategy -- 7.2.3.1 Workflow structure -- 7.2.3.2 Assemble, rework, and inspection -- 7.2.3.3 Result comparison -- 7.3 Fault diagnosis of Rocket engine starting process -- 7.3.1 Online fault diagnosis method of observable Petri net -- 7.3.1.1 Observable Petri nets#x93;#x93;#x93;#x93; -- 7.3.1.2 Partial observable Petri net online fault diagnosis method -- 7.3.1.3 Partial observable Petri nets for LOX/CH4 expansion cycle engine analysis of fault diagnosis results -- 7.3.1.4 Example analysis and verification -- 7.3.1.5 Conclusion -- 7.4 Conclusion -- Acknowledgments -- References -- 8 Product performance model for product innovation, reliability and development in high-tech industries and a case study on... -- 8.1 Introduction -- 8.1.1 Project background -- 8.1.2 Project objectives -- 8.2 Literature review -- 8.2.1 Definition of innovation -- 8.2.2 Factors affecting innovations -- 8.2.3 Definition of product reliability -- 8.2.4 Factors affecting product reliability.
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| 590 |
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|a Knovel
|b ACADEMIC - Electronics & Semiconductors
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| 590 |
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|a Knovel
|b ACADEMIC - Aerospace & Radar Technology
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| 650 |
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|a Aerospace engineering
|x Technological innovations.
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| 650 |
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|a Internet of things.
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| 650 |
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|a Internet of things.
|2 fast
|0 (OCoLC)fst01894151
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| 700 |
1 |
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|a Yung, K. L.,
|e editor.
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| 700 |
1 |
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|a Ip, Andrew W. H.,
|e editor.
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| 700 |
1 |
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|a Xhafa, Fatos,
|e editor.
|1 https://isni.org/isni/0000000116244008.
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| 700 |
1 |
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|a Tseng, K. K.,
|e editor.
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| 776 |
0 |
8 |
|i Print version :
|z 9780128210512
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| 856 |
4 |
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|u https://appknovel.uam.elogim.com/kn/resources/kpITSI0001/toc
|z Texto completo
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| 938 |
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|a Askews and Holts Library Services
|b ASKH
|n AH36610968
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| 994 |
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|a 92
|b IZTAP
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