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[null,"1.","Record Nr.",null,"UNISA996587859803316"]
[null,null,"Autore",null,"Blasch Erik"]
[null,null,"Titolo",null,"Dynamic Data Driven Applications Systems : 4th International Conference, DDDAS 2022, Cambridge, MA, USA, October 6-10, 2022, Proceedings"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"3-031-52670-8"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (434 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Series ; ; v.13984"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- Introduction to the DDDAS2022 Conference -- Introduction to the DDDAS2022 Conference Infosymbiotics/Dynamic Data Driven Applications Systems -- 1 DDAS Developments -- 2 DDDAS Concept Review -- 3 DDDAS2022 Conference - Proceedings Outline -- 4 DDDAS2022 - Main Track Plenary Presentations Overview -- 4.1 Space Systems -- 4.2 Aerospace Systems -- 4.3 Networked Systems and Security -- 4.4 Deep Learning Systems -- 4.5 Estimation and Tracking -- 5 Summary Remarks -- References -- Main-Track Plenary Presentations - Aerospace -- Generalized Multifidelity Active Learning for Gaussian-process-based Reliability Analysis -- 1 Introduction -- 2 Multifidelity Active Learning for Reliability Analysis -- 2.1 Multifidelity Active Learning Method Overview -- 2.2 Sample Location: EFF/AK-MCS/TIMSE -- 2.3 Information Source: Weighted Lookahead Information Gain -- 3 Numerical Experiment: Reliability Analysis of Acoustic Horn -- 4 Conclusions and Future Directions -- References -- Essential Properties of a Multimodal Hypersonic Object Detection and Tracking System -- 1 Introduction -- 2 Sensing Modalities for Hypersonic Detection -- 2.1 Radar -- 2.2 Thermal -- 3 Tracking -- 4 Iterative Sensor Operation in DDDAS System -- 5 Conclusion -- 6 Disclaimer -- References -- Dynamic Airspace Control via Spatial Network Morphing -- 1 Introduction -- 2 Background -- 3 Structured Airspace and Aircraft Trajectories -- 4 A DDDAS Approach to Multiple Aircraft Deconfliction -- 5 Experiments -- 6 Conclusion -- References -- On Formal Verification of Data-Driven Flight Awareness: Leveraging the "]
[{"$nulls":4},"Cramér-Rao Lower Bound of Stochastic Functional Time Series Models -- 1 Introduction -- 2 Functionally Pooled Time Series Models -- 2.1 CRLB Formulation for VFP-AR Models -- 3 Machine-Checked Proof of the CRLB Theorem."]
[{"$nulls":4},"4 Experimental Results and Discussion -- 5 Conclusions -- References -- Coupled Sensor Configuration and Path-Planning in a Multimodal Threat Field -- 1 Introduction -- 2 Problem Formulation -- 3 Multimodal Field Estimation -- 4 Coupled Sensor Configuration -- 5 Results and Discussion -- 5.1 Demonstrative Example -- 6 Conclusion -- References -- Main-Track Plenary Presentations - Space Systems -- Geometric Solution to Probabilistic Admissible Region Based Track Initialization -- 1 Introduction -- 2 Geometric Solution to Probabilistic Admissible Region -- 2.1 Probabilistic Admissible Region -- 2.2 G-PAR Algorithm -- 3 G-PAR for Short-Arc Radar Observations -- 3.1 Initialization: G-PAR MC Particle Cloud -- 4 G-PAR for Angles-Only, Short-Arc, Telescope Observation -- 4.1 Initialization: G-PAR MC Particle Cloud -- 5 Conclusion -- References -- Radar Cross-Section Modeling of Space Debris -- 1 Introduction -- 2 Mie Scattering Modeling Motivation -- 3 Bistatic Radar Characteristics and RCS -- 3.1 Forward Scatter Geometry -- 3.2 Radar Cross Section (RCS) -- 3.3 Velocity-Induced Doppler Shift -- 3.4 Rotation-Induced Micro-doppler Shift -- 4 Simulation Results -- 4.1 RCS vs. Silhouette Area -- 4.2 Translation (Velocity-Induced) Doppler Shift -- 4.3 Translation-Doppler-Shifted RCS -- 4.4 Micro-doppler (Rotation-Induced) Shift -- 4.5 Translation- and Rotation-Shifted RCS -- 5 Conclusions -- References -- High-Resolution Imaging Satellite Constellation -- 1 Introduction -- 2 Model of Satellite Constellation Imaging -- 3 Imaging Algorithm and Resolution -- 4 Simulation Results -- 5 Conclusions -- References -- Main-Track Plenary Presentations - Network Systems -- Reachability Analysis to Track Non-cooperative Satellite in Cislunar Regime -- 1 Introduction -- 2 Methodology -- 3 Results -- 4 Limits of the Method -- 5 Conclusions -- References."]
[{"$nulls":4},"Physics-Aware Machine Learning for Dynamic, Data-Driven Radar Target Recognition -- 1 Introduction -- 2 Physics-Based Models for μD Signature Synthesis -- 3 Adversarial Learning-Based μD Signature Synthesis -- 4 Physics-Aware GAN Design -- 5 Conclusion -- References -- DDDAS for Optimized Design and Management of 5G and Beyond 5G (6G) Networks -- 1 Introduction -- 2 Background of 5G and 6G Technologies -- 3 DDDAS for 5G Network Design and Optimization -- 3.1 Case Study I: Transmission of Multimedia Content to Mobile Wireless Users -- 3.2 Case Study II: Collaborative Microgrids -- 4 Conclusion -- References -- Plenary Presentations - Systems Support Methods -- DDDAS-Based Learning for Edge Computing at 5G and Beyond 5G -- 1 Introduction -- 2 Proposed Joint Optimization Approach -- 2.1 Task Scheduling and Offloading (TSO) -- 2.2 Microgrid Operational Planning -- 3 Simulation Results and Analysis -- 4 Conclusion -- References -- Monitoring and Secure Communications for Small Modular Reactors -- 1 Introduction -- 2 Model Development and Results -- 2.1 Liquid Sodium Vessel Monitoring Using LSTM Models -- 2.2 Secure Communications Using BB84 and AES Protocols -- 3 Conclusions -- References -- Data Augmentation of High-Rate Dynamic Testing via a Physics-Informed GAN Approach -- 1 Introduction -- 2 Theory Background -- 2.1 Dataset and Preprocessing -- 2.2 Convolutional Variational Autoencoders (CVAE) -- 2.3 FEM Based Conditional Generative Adversarial Networks (cGAN) -- 2.4 Support Vector Machine (SVM) -- 3 Validation of Damage Classifications -- 3.1 Convolutional Variational Autoencoder (CVAE) -- 3.2 FEM Based Conditional Generative "]
[{"$nulls":4},"Adversarial Network -- 3.3 Classification Results -- 4 Conclusions -- References -- Unsupervised Wave Physics-Informed Representation Learning for Guided Wavefield Reconstruction -- 1 Introduction -- 1.1 Overview."]
[{"$nulls":4},"2 Wave-Informed Regression Methodology -- 2.1 Mean Squared Error Loss -- 2.2 Wave-Informed Loss -- 2.3 Mode Number Loss -- 2.4 Wave-Informed Regression -- 2.5 Wave-Informed Algorithm -- 3 Simulation Setup -- 4 Results -- 5 Conclusions -- References -- Passive Radio Frequency-Based 3D Indoor Positioning System via Ensemble Learning -- 1 Introduction -- 2 Frequency-Adaptive PIPS -- 3 Experiment and Results -- 3.1 Experimental Setup -- 3.2 Results and Evaluation -- 4 Discussion -- 5 Conclusion -- References -- Plenary Presentations - Deep Learning -- Deep Learning Approach for Data and Computing Efficient Situational Assessment and Awareness in Human Assistance and Disaster Response and Battlefield Damage Assessment Applications -- 1 Introduction -- 2 Methodology -- 2.1 DDDAS -- 2.2 IP2CL -- 3 IP2CL Applications -- 3.1 IP2CL Applications in Semantic Segmentation -- 3.2 Patch-Based Damage Classification -- 4 Conclusion -- References -- SpecAL: Towards Active Learning for Semantic Segmentation of Hyperspectral Imagery -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Utilization -- 3.2 Neural Network Design -- 3.3 Acquisition Factor -- 4 Experiments and Results -- 4.1 Hyperparameters -- 4.2 Results -- 5 Conclusion -- References -- Multimodal IR and RF Based Sensor System for Real-Time Human Target Detection, Identification, and Geolocation -- 1 Introduction -- 2 System Architecture -- 2.1 RF Subsystem -- 2.2 IR Subsystem -- 2.3 Sensor System Integration -- 3 Experimental Results -- 4 Conclusion -- References -- Learning Interacting Dynamic Systems with Neural Ordinary Differential Equations -- 1 Introduction -- 2 Related Work -- 2.1 Neural Ordinary Differential Equations -- 2.2 Interacting Systems -- 3 Methodology -- 3.1 Problem Formulation and Notation -- 3.2 ISODE -- 4 Experiments -- 5 Conclusion -- References."]
[{"$nulls":4},"Relational Active Feature Elicitation for DDDAS -- 1 Introduction -- 2 Interactive Feature Elicitation for DDDAS -- 2.1 Preliminary Results -- 3 Conclusion -- References -- Explainable Human-in-the-Loop Dynamic Data-Driven Digital Twins -- 1 Introduction -- 2 Background and Related Work -- 3 Motivating Example -- 4 A Reference Architecture for Explainable Digital Twins Leveraging DDDAS Principles -- 4.1 Explainable Decisions for Human-in-the-Loop Digital Twins -- 5 Discussion -- 5.1 Trade-Off Analysis -- 5.2 Architecture Evaluation -- 6 Conclusion -- References -- Plenary Presentations - Tracking -- Transmission Censoring and Information Fusion for Communication-Efficient Distributed Nonlinear Filtering -- 1 Introduction -- 2 Jeffreys Divergence Based Sensor Censoring -- 2.1 Jeffreys Divergence -- 2.2 Evaluation of Jeffreys Divergence -- 3 Estimation of Posterior PDFs via GMM -- 4 Fusion of Gaussian Mixtures -- 5 Numerical Results for a Radar Tracking Example -- 5.1 System Model -- 5.2 Performance Evaluation in a Two-Radar Network -- 6 Conclusion -- References -- Distributed Estimation of the Pelagic Scattering Layer Using a Buoyancy Controlled Robotic System -- 1 Introduction -- 2 Preliminary -- 2.1 Perron-Frobenius Operator -- 2.2 Ensemble Kalman Filter -- 3 Estimation of the Scattering Layer -- 4 Numerical Results -- 5 Conclusion -- References -- Towards a Data-Driven Bilinear Koopman Operator for Controlled Nonlinear Systems and Sensitivity Analysis -- 1 Introduction -- 2 Continuous Bilinear System Identification with Specialized Input -- 3 Numerical Simulations -- 3.1 Hovering Helicopter -- 3.2 Controlled Duffing Oscillator -- 4 Conclusion -- References -- Main-Track Plenary Presentations - Security -- Tracking "]
[{"$nulls":4},"Dynamic Gaussian Density with a Theoretically Optimal Sliding Window Approach -- 1 Introduction -- 2 Problem Formulation and Algorithm."]
[{"$nulls":4},"3 Theoretical Result: Optimal Weight Sequence."]