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Found 10 papers, 1 papers with code
Dynamic fault detection and diagnosis for alkaline water electrolyzer with variational Bayesian Sparse principal component analysis
Electrolytic hydrogen production serves as not only a vital source of green hydrogen but also a key strategy for addressing renewable energy consumption challenges.
Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning
A novel robust dynamic variational Bayesian dictionary learning (RDVDL) monitoring approach is proposed to improve the reliability and safety of AWE operation.
Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control
Variational inference is used by NSVB-MPC to assess the predictive accuracy and make the necessary corrections to quantify system uncertainty.
Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Perspectives
This paper provides insights into deep reinforcement learning (DRL) for process control from the perspective of transfer learning.
Chaotic Masking Protocol for Secure Communication and Attack Detection in Remote Estimation of Cyber-Physical Systems
In remote estimation of cyber-physical systems (CPSs), sensor measurements transmitted through network may be attacked by adversaries, leading to leakage risk of privacy (e. g., the system state), and/or failure of the remote estimator.
Decentralized Zeno-Free Event-Triggered Control For Multiple Networks Subject to Stochastic Network Delays and Poisson Pulsing Attacks
By designing the decentralized time-regularized (Zeno-free) event-triggered strategies for the state-feedback control law, this paper considers the stochastic stabilization of a class of networked control systems, where two sources of randomness exist in multiple decentralized networks that operate asynchronously and independently: the communication channels are constrained by the stochastic network delays and also by Poisson pulsing denial-of-service (Pp-DoS) attacks.
Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat Control
The Organic Rankine Cycle (ORC) is widely used in industrial waste heat recovery due to its simple structure and easy maintenance.
A perspective on Attitude Control Issues and Techniques
This paper reviews the attitude control problems for rigid-body systems, starting from the attitude representation for rigid body kinematics.
Stability Analysis for Stochastic Hybrid Inclusions
Basic sufficient conditions for stability/recurrence in probability are usually expressed based on different types of Lyapunov functions, including Lagrange/Lyapunov/Lyapunov-Forster functions respectively for Lagrange/Lyapunov/asymptotical stability in probability and Foster/Lagrange-Forster functions for recurrence, (weaker) relaxed Lyapunov-based sufficient conditions including Matrosov-Foster functions and the stochastic invariance principle, as well as Lyapunov-based necessary and sufficient conditions for asymptotical stability in probability or recurrence (i. e., converse theorems), etc.
Deep Domain Adversarial Adaptation for Photon-efficient Imaging
Photon-efficient imaging with the single-photon light detection and ranging (LiDAR) captures the three-dimensional (3D) structure of a scene by only a few detected signal photons per pixel.
