IoT and spacecraft informatics

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Bibliographic Details
Other Authors: Yung, K. L., (Editor), Ip, Andrew W. H., (Editor), Xhafa, Fatos, (Editor), Tseng, K. K., (Editor)
Format: eBook
Language: English
Published: Amsterdam : Elsevier, 2022.
Series: Aerospace engineering
Subjects:
Aerospace engineering > Technological innovations.
Internet of things.
elektronické knihy
electronic books
ISBN: 9780128210529
0128210524
9780128210512 (pbk.)
Physical Description: 1 online resource.

Cover

Table of contents

LEADER 11944cam a22004571i 4500
001 kn-on1334128526
003 OCoLC
005 20240717213016.0
006 m o d
007 cr cn|||||||||
008 220202s2022 ne o 000 0 eng d
040 |a UKMGB  |b eng  |e rda  |e pn  |c UKMGB  |d OCLCF  |d SFB  |d VLB  |d UKAHL  |d OCLCQ  |d OCLCO 
020 |a 9780128210529  |q (ePub ebook) : 
020 |a 0128210524 
020 |z 9780128210512 (pbk.) 
035 |a (OCoLC)1334128526 
245 0 0 |a IoT and spacecraft informatics /  |c edited by K.L. Yung, Andrew W. Ip, Fatos Xhafa, K.K. Tseng. 
264 1 |a Amsterdam :  |b Elsevier,  |c 2022. 
300 |a 1 online resource. 
336 |a text  |2 rdacontent 
337 |a computer  |2 rdamedia 
338 |a online resource  |2 rdacarrier 
490 0 |a Aerospace engineering 
500 |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 
505 0 |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. 
505 8 |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. 
505 8 |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. 
505 8 |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 &amp -- 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. 
505 8 |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. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Aerospace engineering  |x Technological innovations. 
650 0 |a Internet of things. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Yung, K. L.,  |e editor. 
700 1 |a Ip, Andrew W. H.,  |e editor. 
700 1 |a Xhafa, Fatos,  |e editor.  |1 https://isni.org/isni/0000000116244008. 
700 1 |a Tseng, K. K.,  |e editor. 
776 0 8 |i Print version :  |z 9780128210512 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpITSI0001/iot-and-spacecraft?kpromoter=marc  |y Full text 

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