Applies value-of-information decision analysis to quantify benefits of strain-based SHM versus traditional inspections for corrosion-induced thickness loss in ship hulls.
Managing engineering systems with large state and action spaces through deep reinforcement learning
4 Pith papers cite this work. Polarity classification is still indexing.
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Niching importance sampling yields a robust probability-of-failure estimator that avoids degeneracy on multi-modal performance functions by integrating evolutionary niching with importance sampling.
SAFT-GT is a new toolchain that bridges safety and security analysis for self-adaptive systems using Attack-Fault Tree generation and model combination, validated by a domain-expert user study.
Bayesian neural networks enable farm-wide virtual load monitoring by predicting structural loads on non-instrumented offshore wind turbines from a fleet-leader's data while quantifying prediction uncertainty.
citing papers explorer
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Value of Information-based assessment of strain-based thickness loss monitoring in ship hull structures
Applies value-of-information decision analysis to quantify benefits of strain-based SHM versus traditional inspections for corrosion-induced thickness loss in ship hulls.
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Niching Importance Sampling for Multi-modal Rare-event Simulation
Niching importance sampling yields a robust probability-of-failure estimator that avoids degeneracy on multi-modal performance functions by integrating evolutionary niching with importance sampling.
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Bridging Safety and Security in Complex Systems: A Model-Based Approach with SAFT-GT Toolchain
SAFT-GT is a new toolchain that bridges safety and security analysis for self-adaptive systems using Attack-Fault Tree generation and model combination, validated by a domain-expert user study.
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Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks
Bayesian neural networks enable farm-wide virtual load monitoring by predicting structural loads on non-instrumented offshore wind turbines from a fleet-leader's data while quantifying prediction uncertainty.