IoT and spacecraft informatics /

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Yung, K. L. (Editor ), Ip, Andrew W. H. (Editor ), Xhafa, Fatos (Editor ), Tseng, K. K. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam : Elsevier, 2022.
Colección:Aerospace engineering
Temas:
Aerospace engineering > Technological innovations.
Internet of things.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.
  • 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.
  • 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.
  • 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.
  • 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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