Search

In networked control systems (NCSs), the network-induced delay, packet dropouts, noise and other constraints in the communication network will affect the optimal tracking performance (OTP) and even the system’s stability, which is also a problem that the NCSs approach must solve in practical applications. In this study, we primarily examine the OTP of NCSs that consider packet dropouts and nonzero mean additive white noise (AWN) constraints in communication networks. Based on the single-degree-of-freedom (SDOF) controller and the two-degree-of-freedom (TDOF) controller, respectively, using the coprime factorization and Youla parameterization approach, the explicit expressions of the OTP limitation of the NCSs under the constraints of nonzero mean noise and packet dropouts are obtained. The results reveal that the intrinsic features of the plant and the communication parameters of the network channel will affect the OTP of the NCSs. Finally, the correctness of the theoretical results is verified by the simulation of a multi-input and multioutput plant and an inverted pendulum system.

This article addresses the privacy-preserving bipartite consensus problem on signed networks. Traditional bipartite consensus protocols need agents to explicitly exchange initial state with their neighbors, which is undesirable when the state is private or contains some sensitive information. Therefore, in this article, a privacy-preserving bipartite consensus algorithm is proposed, and it aims to compute the accurate bipartite consensus value while preserving the initial state information of agents. The core idea of this algorithm is to exchange encrypted random values between cooperative agents before an information iterative update, and then agents construct false initial states based on these random values to replace the original initial states for transmission. A specific correctness analysis and privacy analysis are given subsequently. Finally, numerical examples are given to show the effectiveness of this algorithm and its superiority over some existing methods.

Gait recognition has emerged as a significant and promising solution for human identification. However, existing multisensor-based gait recognition studies have the problem of insufficiently utilizing the already extracted features during the feature fusion process. In this study, we proposed a novel method based on two-stream convolutional neural networks (Two-Stream CNNs) with progressive multisensor feature fusion for gait recognition. This method fuses gait data collected from inertial sensors and pressure sensors embedded in pressure insoles. A progressive feature fusion (PFF) module is designed to fully utilize the valuable information within the multistage gait features extracted by convolutional neural networks (CNNs). A feature enhancement channel attention (FECA) module is designed to highlight feature representations that emphasize the importance of crucial sensor data. Meanwhile, an attention-based feature fusion (ABFF) module is designed for more flexible feature fusion across different modalities and multistage gait features. The experimental results demonstrate that our method shows a higher recognition accuracy compared to the state-of-the-art studies, indicating the superiority and effectiveness of our method.