Analytical Science for Autonomy Evaluation

Blasch, Erik; Pokines, Brett J.

doi:10.1109/naecon46414.2019.9057992

Cited by 8 publications

(3 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When solved, these equations produce the required output/simulation. Using the error minimization, the model verification results by comparing model predictions to system data not used in system modeling A graphical user interface is deployed on Google Cloud Platform (GCP) for easy access, data sharing, and performance evaluation [113]. Under the aircraft condition table management tab, users are able to scan the conditions of all the aircraft.…”

Section: Figure 8 -Cad Designs As Dt For Fusion Methodsmentioning

confidence: 99%

Digital twin meets information fusion: panel summary

Blasch,

Chen,

Chen

et al. 2024

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

View full text Add to dashboard Cite

Within the recent years, the concept of Digital Twins (DT) emerged to support digital engineering physical-systems design in the processing and analysis of device components, data flows, and networked systems. As an element of systems engineering, the DT serves as a method for life cycle management for operations and maintenance through monitoring, diagnostics, and prognostics. Key to DT methods is the use of distributed sensors to monitor the system and determine the functioning with the use of physical design information, such as a series of distributed edge sensors and the layout of a physical electrical grid. While industries like industrial manufacturing, electrical power, space systems, and healthcare maintenance have embraced DT; other groups are utilizing the concept for analysis. Given that a large number of sensors are used to gather data on the health of system, it is natural that data fusion, estimation theory, and signals processing support digital twin fusion (DTwF); but there are a variety of challenges such as big data, distribution fusion, and edge analytics. The panel will discuss areas in which data and information fusion techniques enhance DT applications.

show abstract

Section: Figure 8 -Cad Designs As Dt For Fusion Methodsmentioning

confidence: 99%

Digital twin meets information fusion: panel summary

Blasch,

Chen,

Chen

et al. 2024

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

View full text Add to dashboard Cite

show abstract

“…Many developments and AI principles have been developed from which research seeks to answer the questions posed, such a security [66]. There are many open discussions on how to best conduct AI T&E, especially for autonomy [67]. Among the many AI principles being explored, the goal is to build trustworthy AI systems [68].…”

Section: Ai Test and Evaluationmentioning

confidence: 99%

Human-machine cooperative AI decision-making with heterogeneous data

Blasch,

Bastian,

Aved

et al. 2023

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

View full text Add to dashboard Cite

Many techniques have been developed for sensor and information fusion, machine and deep learning, as well as data and machine analytics. Currently, many groups are exploring methods for human-machine teaming using saliency and heat maps, explainable and interpretable artificial intelligence, as well as user-defined interfaces. However, there is still a need for standard metrics for test and evaluation of systems utilizing artificial intelligence (AI), such as deep learning (DL), to support the AI principles. In this paper, we explore the elements associated with the opportunities and challenges emerging from designing, testing, and evaluating such future systems. The paper highlights the MAST (multi-attribute scorecard table), and more specifically the MAST criteria ―analysis of alternatives‖ by measuring the risk associated with an evidential DL-based decision. The concept of risk includes the probability of a decision as well as the severity of the choice, from which there is also a need for an uncertainty bound on the decision choice which the paper postulates a risk bound. Notional analysis for a cyber networked system is presented to guide to interactive process for test and evaluation to support the certification of AI systems as to the decision risk for a human-machine system that includes analysis from both the DL method and a user.

show abstract

“…These timescales and computational constraints must be coupled to allow for high-rate implementation that is robust, adaptable, and beneficial to the missions of interest. This grand challenge and possible methods to address these challenges are defined as (1) specifying the time scale, (2) determining the decision needs for performance assessment [9,10], (3) methods of statistical analysis (e.g., advances in deep learning [11]), (4) correspondence with materials response [12], and (5) effective methods of model correspondence with data collection [13])…”

Section: Introductionmentioning

confidence: 99%

High-Rate Structural Health Monitoring and Prognostics: An Overview

Dodson

Downey

Laflamme

et al. 2021

Conference Proceedings of the Society for Experimental Mechanics Series

Self Cite

View full text Add to dashboard Cite

Structural Health Monitoring (SHM) includes both static and highly dynamic engineering systems. With the advent of realtime sensing, edge-computing, and high-bandwidth computer memory, there is an ability to enable high-rate SHM (HR-SHM). The paper defines the technical area of high-rate structural health monitoring and prognostics and presents the HR-SHM technical grand challenges including: multi timescales of the problem, adequate sensor network and response, real-time assessment, and decision-making with quantified uncertainty and risk. Key issues to address in such challenges include the time duration of the event, time scales of the physics, multiple sources of uncertainty, as well as limited spatiotemporal constraints for hardware execution. The paper defines the high-rate time scale as 1 ms on the integrated paradigm including data acquisition, assessment execution, and decision-making. The spatial issues include the resolution of the area monitored, the communication distance, and the number of edge sensors. The temporal issue includes the sensor type (e.g., THz) as well as multiple sources of uncertainty. These constraints must be coupled to allow for high-rate implementation that is robust, adaptable, and beneficial to the missions of interest. To address the grand challenge, we propose physics-informed real-time fusion (PIRF) of high-speed dynamic data. Technologies such as machine learning and edge-computing can be further harnessed to enable structural and functional prognostics for high-rate dynamic systems. Quantification of uncertainty, both aleatory and epistemic, is necessary for real-time state estimation to be connected with the confidences to integrate risks into the decision-making.

show abstract

Analytical Science for Autonomy Evaluation

Cited by 8 publications

References 53 publications

Digital twin meets information fusion: panel summary

Digital twin meets information fusion: panel summary

Human-machine cooperative AI decision-making with heterogeneous data

High-Rate Structural Health Monitoring and Prognostics: An Overview

Contact Info

Product

Resources

About