University of Electronic Science and Technology of China

About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Antenna (radio) & Dielectric. The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.

Natural van der Waals heterostructural single crystals with both magnetic and topological properties

Abstract: Heterostructures having both magnetism and topology are promising materials for the realization of exotic topological quantum states while challenging in synthesis and engineering. Here, we report natural magnetic van der Waals heterostructures of (MnBi2Te4)m(Bi2Te3)n that exhibit controllable magnetic properties while maintaining their topological surface states. The interlayer antiferromagnetic exchange coupling is gradually weakened as the separation of magnetic layers increases, and an anomalous Hall effect that is well coupled with magnetization and shows ferromagnetic hysteresis was observed below 5 K. The obtained homogeneous heterostructure with atomically sharp interface and intrinsic magnetic properties will be an ideal platform for studying the quantum anomalous Hall effect, axion insulator states, and the topological magnetoelectric effect.

A Joint Multi-Criteria Utility-Based Network Selection Approach for Vehicle-to-Infrastructure Networking

Abstract: The emerging technologies for connected vehicles have become hot topics. In addition, connected vehicle applications are generally found in heterogeneous wireless networks. In such a context, user terminals face the challenge of access network selection. The method of selecting the appropriate access network is quite important for connected vehicle applications. This paper jointly considers multiple decision factors to facilitate vehicle-to-infrastructure networking, where the energy efficiency of the networks is adopted as an important factor in the network selection process. To effectively characterize users’ preference and network performance, we exploit energy efficiency, signal intensity, network cost, delay, and bandwidth to establish utility functions. Then, these utility functions and multi-criteria utility theory are used to construct an energy-efficient network selection approach. We propose design strategies to establish a joint multi-criteria utility function for network selection. Then, we model network selection in connected vehicle applications as a multi-constraint optimization problem. Finally, a multi-criteria access selection algorithm is presented to solve the built model. Simulation results show that the proposed access network selection approach is feasible and effective.

Top Tension Control of a Flexible Marine Riser by Using Integral-Barrier Lyapunov Function

Abstract: This paper presents a boundary controller for a flexible marine riser to suppress the riser's vibration with a top tension constraint. The flexible marine riser is described by a distributed parameter system with a partial differential equation and four ordinary differential equations. The boundary controller is designed at the top boundary of the riser based on an integral-barrier Lyapunov function to suppress the riser's tension at top. Adaptive control is designed when the system parametric uncertainty exists. With the proposed robust adaptive boundary control, uniformed boundedness under the ocean disturbance can be achieved. Stability analysis of the closed-loop system is given using the Lyapunov stability theory. Simulation results illustrate the effectiveness of the proposed boundary controller with top tension constraint.

Heterogeneous Domain Adaptation Through Progressive Alignment

Abstract: In real-world transfer learning tasks, especially in cross-modal applications, the source domain and the target domain often have different features and distributions, which are well known as the heterogeneous domain adaptation (HDA) problem. Yet, existing HDA methods focus on either alleviating the feature discrepancy or mitigating the distribution divergence due to the challenges of HDA. In fact, optimizing one of them can reinforce the other. In this paper, we propose a novel HDA method that can optimize both feature discrepancy and distribution divergence in a unified objective function. Specifically, we present progressive alignment , which first learns a new transferable feature space by dictionary-sharing coding, and then aligns the distribution gaps on the new space. Different from previous HDA methods that are limited to specific scenarios, our approach can handle diverse features with arbitrary dimensions. Extensive experiments on various transfer learning tasks, such as image classification, text categorization, and text-to-image recognition, verify the superiority of our method against several state-of-the-art approaches.