This study focuses on the coherence in a class of unbalanced networks, emphasizing the investigation of the impact of leader selection and network parameters on coherence. We discuss three different categories of consensus algorithms: leaderless algorithms of first and second orders, and first-order leader-follower algorithm. Firstly, we derive exact solutions for leaderless network coherence in the first and second orders. Secondly, we design various leader allocation schemes for the first-order leader-follower networks, obtaining expressions correlated with the network scale $N$, the double-ring cardinalities $p$ and $q$, and the number of leaders $k$. Thirdly, we conduct numerical simulations and robustness analyses to explore the impact of leader quantity and topology structure on network coherence. Specifically, the conclusions are as follows: (1) When $N$ decreases, an increase in $k$ demonstrates improved network coherence. (2) The smaller the difference between $p$ and $q$, the better the coherence. (3) The smaller the sum of $p$ and $q$, the stronger the robustness. (4) Uniform distribution of $k_{1}$ and $k_{2}$ leads to optimal coherence. Finally, the Laplacian energy and Kirchhoff index of the network are analyzed.

As the penetrated level of wind in power grids increases, the online system inertia becomes weak. Doubly-fed induction generator (DFIG)-based wind turbine generators (WTGs) are required to provide virtual inertia response to support system frequency. The present inertia control strategy with fixed control gain is not suitable and may cause stall of the DFIG-based WTG, as the virtual inertia response potential from the DFIG-based WTG is different with various wind speed conditions. This paper addresses a virtual inertia control method for the DFIG-based WTGs to improve the system frequency stability without causing stalling of the wind turbine for various wind speed conditions. The effectiveness of the proposed virtual inertia control method is investigated in a small system embedded with the DFIG-based WTG. Results demonstrate that the proposed virtual inertia strategy improves the frequency stability without causing the rotor speed security issue. Thus, the proposed control strategy can secure the dynamic system frequency security of power systems under the scenarios of full and partial loads, and, consequently, the proposed method provides a promising solution of ancillary services to power systems.

This study develops the space-time sampled-data control problem for memristor-based reaction-diffusion neural networks (MRDNNs) using a memory event-triggering scheme. Unlike traditional Markov switching models with fixed transition probabilities, the nonhomogeneous sojourn probabilities are capable of describing the switching behavior more accurately. By effectively leveraging historical transmitted packets, an innovative mode-dependent memory event-triggering scheme that incorporates nonhomogeneous sojourn probability information is introduced, optimizing communication resource utilization. A space-time sampled-data control law is designed by sampling both spatial and temporal domains, significantly reducing network communication resource consumption while achieving the desired system performance. The validity and superiority of the proposed space-time sampled-data control strategy are demonstrated through a simulation example.

Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual‐energy X‐ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time‐consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self‐built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present.