In this paper, hybrid models of Kalman filter and neural network for state estimation are
reviewed of their corresponding academic achievements, the creation of which is a …

Nowadays, neural networks have been widely applied in many fields such as pattern
recognition, signal and image processing and control theory. Over the past two decades or …

This paper is concerned with the secure estimation problem of the state of charge (SOC) for
Lithium-ion batteries subject to deception attacks. A second-order resistor-capacitor (RC) …

This paper studies the distributed H∞-consensus control problem for a class of multi-rate
multi-agent systems with fading measurements. A multi-rate sampling strategy is adopted to …

In this article, a two-stage deep offline-to-online transfer learning framework (DOTLF) is
proposed for long-distance pipeline leakage detection (PLD). At the offline training stage, a …

This paper is concerned with the secure estimation problem for the state of charge of Lithium-
ion batteries subject to malicious attacks during the data transmission from sensors to cloud …

In recent years, deep learning techniques have achieved significant progress in a variety of
tasks. However, existing techniques might not perform well in the presence of data scarcity …

This paper addresses the encoding–decoding-based fusion estimation problem for a class
of systems with signal relays and stochastic measurement delays. A set of Bernoulli …

This article is concerned with the secure state estimation problem for a networked system
with multirate measurements. The measurement data are transmitted to the estimator over a …

In this paper, an outlier-resistant sequential fusion problem is concerned for cyber-physical
systems with quantized measurements under denial-of-service attacks. The multi-sensor …