Dynamic Data Driven Applications Systems
Description
This book constitutes the refereed proceedings of the 5th International Conference on Dynamic Data Driven Applications Systems, DDDAS/Infosymbiotics for Reliable AI 2024, held in New Brunswick, NJ, USA, during November 6-8, 2024.
The 43 full papers included in this book were carefully reviewed and selected from 52 submissions. By combining DDDAS and typical AI approaches, the papers address state-of-the-art efforts to create frameworks for enabling new and advanced Science and Technology capabilities to address challenges and create opportunities in important areas, spanning a wide set of areas, such as: materials and aerospace systems; communications networks; energy infrastructures; cyber-security; adverse environmental situations; societal dynamics; computer vision; robotics; laboratory automation; bio-informatics and pharmaceuticals design; and more.
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Content
.- Introduction to the Proceedings.
.- Introduction to the DDDAS2024 Conference Infosymbiotics/DDDAS and AI: Towards Reliable AI.
.- Plenary Papers.
.- Materials, Aerospace, and Geomechanics Systems Methods.
.- Online Fault Detection for Metal Additive Manufacturing with Data Driven Time Series Models.
.- Weight Decay Optimized Unsupervised Autoencoder Based Anomaly Detection in Uncontrolled Dynamic Structural Health Monitoring.
.- Novel Deep Learning Image Registration Techniques with Application to Microscopy Images of Metal Alloys.
.- A Probabilistic Machine Learning Pipeline Using Topological Descriptors for Real-Time State Estimation of High-Rate Dynamic Systems.
.- Information Fusion of Ultrasonic Waves and Low-Frequency Vibrations: Leveraging Probabilistic Machine Learning and Stochastic Time Series Models for Structural Awareness.
.- Earthen Embankment Monitoring using LiDAR data by Randomized Consensus of Topological Data Analysis.
.- Environmental Systems-Assessment/Response, DT Methods.
.- Large Language Models for Explainable Decisions in Dynamic Digital Twins.
.- DDDAS Probability Learning for Natural Disaster Change Detection.
.- Dynamic Data-Driven Digital Twin Testbed for Enhanced First Responder Training and Communication.
.- A Dynamic Data Driven Agent Based Model for Characterizing the Space Utilization of Asian Elephants in Response to Water Availability.
.- Adaptive Multi-stage Sensor Fusion under Neuro-symbolic Framework for The Multi-modal Ranging System in Adverse Weather Conditions.
.- Towards a Dynamic Data Driven Al Regional Weather Forecast Model.
.- Autonomous Uncrewed Aircraft for Mobile Operations in Severe Weather.
.- Autonomous Planning for Targeted Observation of Severe Weather.
.- Security Systems -Methods, infrastructures, applications.
.- Dynamic Data Driven Security Framework for Industrial Control Networks using Programmable Switches.
.- Security of RF Sensing and Imaging Systems in the Age of Digital Twins.
.- CCTV-Gun: Benchmarking Handgun Detection in CCTV Images.
.- D4: Dynamic Data-Driven Discovery of Adversarial Vehicle Maneuvers.
.- Data Poisoning: An Overlooked Threat to Power Grid Resilience.
.- GAN-Based Approach for Detecting Energy Deception Attacks in CPS.
.- Adversarial Attacks and Data-Driven Dynamic Outlier Detection Systems.
.- Utilizing Matrix Profile with the DDDAS Framework for Anomaly Detection in Nuclear Security.
.- Development of an Edge Resilient ML Ensemble to Tolerate ICS Adversarial Attacks.
.- Anomaly Detection Transformer: A Novel Approach for Time Series Analysis of Wearable Health Data.
.- A Spiral-Theoretic Approach for Trustworthy Al/ML in DDDAS.
.- Tracking Systems, Automation and Robotics.
.- Data-Driven Pixel Control: Challenges and Prospects.
.- Dynamic Data-Driven Approach for LEO PNT Selection of Satellites with Poorly Known Ephemerides.
.- Improving Physics-based Motion and Physical Parameter Estimations of a Tumbling, Non-cooperative Space Object Through DDDAS.
.- An Expected KLD Based Censoring Strategy for Target Tracking in Distributed Sensor Networks.
.- Reliable Al for UAVs Through Control/Perception Co-Design.
.- Constraint-Aware Diffusion Models for Trajectory Optimization.
.- Data-Driven Dynamics of Robot Locomotion on Granular Media.
.- Improving Physics-based Motion and Physical Parameter Estimations of a Tumbling, Non-cooperative Space Object Through DDDAS.
.- A Physics-Enhanced Deep Learning Model for Fast Prediction of the Behavior of a Forced Dynamic System.
.- Edge-to-Cloud Al-Assisted Augmented Reality for Robust and Real-time Assistance to Operators.
.- CAD Model Guided Semantic Segmentation for Radar Micro-UAV Signature Synthesis Across Different Clutter Environments Transformer: A Novel Approach for Time Series Analysis of Wearable Health Data.
.- Towards Reliable Neural Optimizers: A Permutation Equivariant Neural Approximation for Information Processing Applications.
.- Fast Topological Data Analysis Feature for Nonstationary Time Series.
.- Predictive Modeling of Application Runtime in Dragonfly Systems.
.- Adaptive Data Driven Network Slicing and Resource Blocks Assignment using Deep Reinforcement Learning.
.- Explainable Diffusion Model via Schroedinger Bridge in Multimodal Image Translation.
.- Using Mamba for Modeling Dynamical Systems in a Limited Data Scenario.
.- Application of a state space based neural network model for Uncertainty Propagation in dynamical systems.
.- From Positive to Negative: On the Role of Negative Data in Enhancing Generative Models for Engineering Constraint Satisfaction.