Synchronization control for stochastic neural networks with mixed time-varying delays.

Synchronization control of stochastic neural networks with time-varying discrete and continuous delays has been investigated. A novel control scheme is proposed using the Lyapunov functional method and linear matrix inequality (LMI) approach. Sufficient conditions have been derived to ensure the global asymptotical mean-square stability for the error system, and thus the drive system synchronizes with the response system. Also, the control gain matrix can be obtained. With these effective methods, synchronization can be achieved. Simulation results are presented to show the effectiveness of the theoretical results.

Graphene-based polymer composites in thermal management: materials, structures and applications.

Graphene, with its high thermal conductivity (k), excellent mechanical properties, and thermal stability, is an ideal filler for developing advanced high k and heat dissipation materials. However, creating graphene-based polymer nanocomposites (GPNs) with high k remains a significant challenge to meet the demand for efficient heat dissipation. Here, the effects of graphene material and structure on thermal properties are investigated from both microscopic and macroscopic perspectives. Initially, it briefly introduces the influence of graphene structural parameters on its intrinsic k, along with summarizing methods to adjust these parameters. Various techniques for establishing different thermal conductivity pathways at the macroscopic scale (including filler hybridization, 3D networks, horizontal alignment, and vertical alignment) are reviewed, along with their respective advantages and disadvantages. Furthermore, we discuss the applications of GPNs as thermal interface materials (TIMs), phase change materials (PCMs), and smart responsive thermal management materials in the field of thermal management. Finally, the current challenges and future perspectives of GPN research are discussed. This review offers researchers a comprehensive overview of recent advancements in GPNs for thermal management and guidance for developing the next generation of thermally conductive polymer composites.

Finite-Time Stability Control of Uncertain Nonlinear Systems With Self-Limiting Control Terms.

In this brief, we define a self-limiting control term, which has the function of guaranteeing the boundedness of variables. Then, we apply it to a finite-time stability control problem. For nonstrict feedback nonlinear systems, a finite-time adaptive control scheme, which contains a piecewise differentiable function, is proposed. This scheme can eliminate the singularity of derivative of a fractional exponential function. By adding a self-limiting term to the controller and the virtual control law of each subsystem, the boundedness of the overall system state is guaranteed. Then the unknown continuous functions are estimated by neural networks (NNs). The output of the closed-loop system tracks the desired trajectory, and the tracking error converges to a small neighborhood of the equilibrium point in finite time. The theoretical results are illustrated by a simulation example.

MagicCubePose, A more comprehensive 6D pose estimation network.

Most of the current mainstream 6D pose estimation methods use template or voting-based methods. Such methods are usually multi-stage or have multiple assumptions and post-correction, which will cause a certain degree of information redundancy and increase the computational cost, their real-time detection performance is poor. We point out that traditional path aggregation networks introduce new errors, therefore, we propose a loss function: MagicCubeLoss, a portable module: MagicCubeNet, and the corresponding 6D pose estimation model: MagicCubePose. MagicCubePose has good expansion performance and can build more efficient models for different calculation power and scenarios. Experiments show that our model has good real-time detection performance and the highest ADD(-S) accuracy.

Sufficient Condition for the Existence of the Compact Set in the RBF Neural Network Control.

In this brief, sufficient conditions are proposed for the existence of the compact sets in the neural network controls. First, we point out that the existence of the compact set in a classical neural network control scheme is unsolved and its result is incomplete. Next, as a simple case, we derive the sufficient condition of the existence of the compact set for the neural network control of first-order systems. Finally, we propose the sufficient condition of the existence of the compact set for the neural-network-based backstepping control of high-order nonlinear systems. The theoretic result is illustrated through a simulation example.