| Description |
1 online resource |
| Series |
Aerospace engineering |
| Contents |
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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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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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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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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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 |
| Summary |
"IoT and Spacecraft Informatics provides the theory and applications of IoT systems in the design, development and operation of spacecraft. Sections present a high-level overview of IoT and introduce key concepts needed to successfully design IoT solutions, key technologies, protocols, and technical building blocks that combine into complete IoT solutions. The book features the latest advances, findings and state-of-the-art in research, case studies, development and implementation of IoT technologies for spacecraft and space systems. In addition, it concentrates on different aspects and techniques to achieve automatic control of spacecraft."-- Title details screen |
| Notes |
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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Description based on CIP data; resource not viewed |
| Subject |
Aerospace engineering -- Technological innovations
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Internet of things.
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Spacecraft.
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Internet of things
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| Form |
Electronic book
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| Author |
Yung, K. L., editor
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Ip, Andrew W. H., editor
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Xhafa, Fatos, editor
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Tseng, K. K., editor
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| ISBN |
9780128210529 |
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0128210524 |
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