Dynamic Data Driven Applications Systems

5th International Conference, DDDAS/Infosymbiotics for Reliable AI 2024, New Brunswick, NJ, USA, November 6-8, 2024, Proceedings
Herausgegeben:Blasch, Erik; Darema, Frederica; Metaxas, Dimitris

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Lecture Notes in Computer Science 15514
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-94894-7
• Seitenzahl: 436
• Erscheinungstermin: 7. Juli 2025
• Englisch
• Abmessung: 235mm x 155mm
• ISBN-13: 9783031948947
• Artikelnr.: 74158914

Inhaltsangabe

.- 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.