Search Results for author:
Found 8 papers, 0 papers with code
A study on fault diagnosis in nonlinear dynamic systems with uncertainties
It is demonstrated that the projection onto the manifold of uncertainty data, together with the correspondingly defined Bregman divergence, is also capable for fault detection.
Replay Attack Detection Based on Parity Space Method for Cyber-Physical Systems
The replay attack detection problem is studied from a new perspective based on parity space method in this paper.
Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems
The major efforts are made on the development of a control theoretic solution to the optimal fault detection problem, in which an analog concept to minimal sufficient statistic, the so-called lossless information compression, is introduced and proven for dynamic systems and fault detection specifications.
An alternative paradigm of fault diagnosis in dynamic systems: orthogonal projection-based methods
In this paper, we propose a new paradigm of fault diagnosis in dynamic systems as an alternative to the well-established observer-based framework.
Graph neural network-based fault diagnosis: a review
Graph neural network (GNN)-based fault diagnosis (FD) has received increasing attention in recent years, due to the fact that data coming from several application domains can be advantageously represented as graphs.
Application of the unified control and detection framework to detecting stealthy integrity cyber-attacks on feedback control systems
It is demonstrated, in the unified framework of control and detection, that all kernel attacks can be structurally detected when not only the observer-based residual, but also the control signal based residual signals are generated and used for the detection purpose.
Curriculum-based Deep Reinforcement Learning for Quantum Control
Deep reinforcement learning has been recognized as an efficient technique to design optimal strategies for different complex systems without prior knowledge of the control landscape.
Gradient Monitored Reinforcement Learning
The proposed method which we term as Gradient Monitoring(GM), is an approach to steer the learning in the weight parameters of a neural network based on the dynamic development and feedback from the training process itself.
