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: Computer science & Antenna (radio). The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.

Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

Abstract: With the growing integration of distributed energy resources (DERs), flexible loads, and other emerging technologies, there are increasing complexities and uncertainties for modern power and energy systems. This brings great challenges to the operation and control. Besides, with the deployment of advanced sensor and smart meters, a large number of data are generated, which brings opportunities for novel data-driven methods to deal with complicated operation and control issues. Among them, reinforcement learning (RL) is one of the most widely promoted methods for control and optimization problems. This paper provides a comprehensive literature review of RL in terms of basic ideas, various types of algorithms, and their applications in power and energy systems. The challenges and further works are also discussed.

Significantly enhanced energy storage density for poly(vinylidene fluoride) composites by induced PDA-coated 0.5Ba(Zr0.2Ti0.8)O3–0.5(Ba0.7Ca0.3)TiO3 nanofibers

Abstract: In this study, high aspect ratio TiO2 nanofibers (TiO2 NFs), BaTiO3 nanofibers (BT NFs), CaCu3Ti4O12 nanofibers (CCTO NFs) and 0.5Ba(Zr0.2Ti0.8)O3–0.5(Ba0.7Ca0.3)TiO3 nanofibers (BZT-BCT NFs) were prepared via an electrospinning technique. The nanofibers have been modified with polydopamine (PDA), which exhibited excellent dispersion and good compatibility with the polymer matrix. The effects of the structure and morphology of the fillers on the dielectric properties, leakage current density and energy densities of the composites have been also discussed systematically. On comparing the five different poly(vinylidene fluoride) (PVDF) composites, we discovered that the BZT-BCT NFs/PVDF composite displayed low loss, small leakage current and excellent storage performance. On this basis, BZT-BCT NFs/PVDF composites with different volume contents were also fabricated. It can be found that the 7 vol% BZT-BCT NFs/PVDF nanocomposite possessed an excellent dielectric constant (e ∼ 17.6 at 100 Hz). Nevertheless, the 3 vol% BZT-BCT NFs/PVDF nanocomposite demonstrated higher energy storage density (Ue ∼ 7.86 J cm−3) and greater efficiency (η ∼ 58%) at 310 kV mm−1. This study may provide a new direction to enhance the energy density of inorganic/PVDF composites.

Backscatter-NOMA: A Symbiotic System of Cellular and Internet-of-Things Networks

Abstract: Non-orthogonal multiple access (NOMA) is envisioned as a key technology to enhance the spectrum efficiency for 5G cellular networks. Meanwhile, ambient backscatter communication is a promising solution to the Internet of Things (IoT), due to its high spectrum efficiency and power efficiency. In this paper, we are interested in a symbiotic system of cellular and IoT networks and propose a backscatter-NOMA system, which incorporates a downlink NOMA system with a backscatter device (BD). In the proposed system, the base station (BS) transmits information to two cellular users according to the NOMA protocol, while a BD transmits its information over the BS signals to one cellular user using the passive radio technology. In particular, if the BS only serves the cellular user that decodes BD information, the backscatter-NOMA system turns into a symbiotic radio (SR) system. We derive the expressions of the outage probabilities and the ergodic rates and analyze the corresponding diversity orders and slopes for both backscatter-NOMA and SR systems. Finally, we provide the numerical results to verify the theoretical analysis and demonstrate the interrelationship between the cellular networks and the IoT networks.

Local and Global Structure Preservation for Robust Unsupervised Spectral Feature Selection

Abstract: This paper proposes a new unsupervised spectral feature selection method to preserve both the local and global structure of the features as well as the samples. Specifically, our method uses the self-expressiveness of the features to represent each feature by other features for preserving the local structure of features, and a low-rank constraint on the weight matrix to preserve the global structure among samples as well as features. Our method also proposes to learn the graph matrix measuring the similarity of samples for preserving the local structure among samples. Furthermore, we propose a new optimization algorithm to the resulting objective function, which iteratively updates the graph matrix and the intrinsic space so that collaboratively improving each of them. Experimental analysis on 12 benchmark datasets showed that the proposed method outperformed the state-of-the-art feature selection methods in terms of classification performance.