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Latent-KalmanNet: Learned Kalman filtering for tracking from high-dimensional signals
The Kalman filter (KF) is a widely used algorithm for tracking dynamic systems that are
captured by state space (SS) models. The need to fully describe an SS model limits its …
captured by state space (SS) models. The need to fully describe an SS model limits its …
Neural extended Kalman filters for learning and predicting dynamics of structural systems
Accurate structural response prediction forms a main driver for structural health monitoring
and control applications. This often requires the proposed model to adequately capture the …
and control applications. This often requires the proposed model to adequately capture the …
Differentiable factor graph optimization for learning smoothers
A recent line of work has shown that end-to-end optimization of Bayesian filters can be used
to learn state estimators for systems whose underlying models are difficult to hand-design or …
to learn state estimators for systems whose underlying models are difficult to hand-design or …
AI-Aided Kalman Filters
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal
processing. These methods are used for state estimation of dynamic systems by relying on …
processing. These methods are used for state estimation of dynamic systems by relying on …
Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision
Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving with
various open research questions. State-of-the-art approaches regress trajectories either in a …
various open research questions. State-of-the-art approaches regress trajectories either in a …
[HTML][HTML] Model-based Imitation Learning from Observation for input estimation in monitored systems
In the context of structural and industrial asset monitoring and twinning, the estimation of
unknown inputs–typically reflecting the loads acting onto a system–stands as a critical factor …
unknown inputs–typically reflecting the loads acting onto a system–stands as a critical factor …
Nonlinear Kalman Estimators for Low-Cost Bioprocess Monitoring with Unstructured Mechanistic Models
C Freitas Iglesias Junior - 2024 - ruor.uottawa.ca
Bioprocess monitoring is a critical component in the biopharmaceutical industry, essential
for ensuring the consistent production of high-quality biopharmaceutical products. Despite …
for ensuring the consistent production of high-quality biopharmaceutical products. Despite …
Degradation Vector Fields with Uncertainty Considerations
M Star - 2023 - espace.curtin.edu.au
The focus of this work is on capturing uncertainty in remaining useful life (RUL) estimates for
machinery and constructing some latent dynamics that aid in interpreting those results. This …
machinery and constructing some latent dynamics that aid in interpreting those results. This …
Deep State Space Models for Remaining Useful Life Estimation
Deep learning has been used to train neural networks to estimate the Remaining Useful Life
(RUL) of a machine given sensor signals from that machine. This has resulted in some …
(RUL) of a machine given sensor signals from that machine. This has resulted in some …
[PDF][PDF] Model-based Unknown Input Estimation via Partially Observable Markov Decision Processes
In the context of condition monitoring for structures and industrial assets, the estimation of
unknown inputs, usually referring to acting loads, is of salient importance for guaranteeing …
unknown inputs, usually referring to acting loads, is of salient importance for guaranteeing …