This paper is concerned with the secure distributed state estimation problem for large-scale interconnected systems (LISs) with sequential structure under false data injection (FDI) attacks. The considered FDI attacks may occur in multiple or all local measurements, as well as in communication between neighboring estimators. For the purpose of suppressing the impact of attacks in local measurements, a convex optimization problem with linear constraint is solved to determine the estimates of the non-fixed channel attacks. With the reconstructed local measurements, the secure distributed state estimator for each subsystem is designed based on local output and its neighboring information. Finally, an illustrative example of the maneuvering vehicles is employed to show the effectiveness of the proposed methods.

To improve a vehicle's anti-roll performance while consuming less energy, an active/passive dual-mode switched control method based on a hydraulically interconnected suspension (HIS) is proposed. This method has two key components as follows: modelling the “mechanical-liquid-gas” coupled dynamic system consisting of hydraulic cylinders, a hydraulic pump and accumulators; and robustly controlling a time-delayed system with uncertain model and lag time. In this study, the nonlinear characteristics of the underlying actuators were considered, and a four degrees-of-freedom (4DOF) roll-plane half-car model was employed. The backstepping algorithm was adopted to maintain the body in the desired posture. Subsequently, a modified Smith predictor and a Taylor expansion of lateral acceleration were applied simultaneously to compensate for the time delay. Finally, simulations with uncertain parameters and lag time were performed. The results indicate that the proposed method is robust in the case of an uncertain control system and can effectively improve the vehicle's anti-roll performance.

Electrocardiogram (ECG) signal plays a key role in the diagnosis of arrhythmia, which will pose a great threat to human health. As an effective feature extraction method, deep learning has shown excellent results in processing ECG signals. However, most of these methods neglect the cooperation between the multi-lead ECG series correlation and intra-series temporal patterns. In this work, a multi-domain collaborative analysis and decision approach is proposed, which makes the classification and diagnosis of arrhythmia more accurate. With this decision, we can realize the transition from the spatial domain to the spectral domain, and from the time domain to the frequency domain, and make it possible that ECG signals can be more clearly detected by convolution and sequential learning modules. Moreover, instead of the prior method, the self-attention mechanism is used to learn the relation matrix between the sequences automatically in this paper. We conduct extensive experiments on eight advanced models in the same field to demonstrate the effectiveness of our method.