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1,038 results on '"Jiang, Shaohua"'

1. Low-complexity Joint Phase Adjustment and Receive Beamforming for Directional Modulation Networks via IRS

Author

Dong, Rongen, Jiang, Shaohua, Hua, Xinhai, Teng, Yin, Shu, Feng, and Wang, Jiangzhou

Subjects
Computer Science - Information Theory, Electrical Engineering and Systems Science - Signal Processing
Abstract

Intelligent reflecting surface (IRS) is a revolutionary and low-cost technology for boosting the spectrum and energy efficiencies in future wireless communication network. In order to create controllable multipath transmission in the conventional line-of-sight (LOS) wireless communication environment, an IRS-aided directional modulation (DM) network is considered. In this paper, to improve the transmission security of the system and maximize the receive power sum (Max-RPS), two alternately optimizing schemes of jointly designing receive beamforming (RBF) vectors and IRS phase shift matrix (PSM) are proposed: Max-RPS using general alternating optimization (Max-RPS-GAO) algorithm and Max-RPS using zero-forcing (Max-RPS-ZF) algorithm. Simulation results show that, compared with the no-IRS-assisted scheme and the no-PSM optimization scheme, the proposed IRS-assisted Max-RPS-GAO method and Max-RPS-ZF method can significantly improve the secrecy rate (SR) performance of the DM system. Moreover, compared with the Max-RPS-GAO method, the proposed Max-RPS-ZF method has a faster convergence speed and a certain lower computational complexity.

Published
2022

48. SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check

Author

Cheng, Xingyi, Xu, Weidi, Chen, Kunlong, Jiang, Shaohua, Wang, Feng, Wang, Taifeng, Chu, Wei, and Qi, Yuan

Subjects
Computer Science - Computation and Language
Abstract

Chinese Spelling Check (CSC) is a task to detect and correct spelling errors in Chinese natural language. Existing methods have made attempts to incorporate the similarity knowledge between Chinese characters. However, they take the similarity knowledge as either an external input resource or just heuristic rules. This paper proposes to incorporate phonological and visual similarity knowledge into language models for CSC via a specialized graph convolutional network (SpellGCN). The model builds a graph over the characters, and SpellGCN is learned to map this graph into a set of inter-dependent character classifiers. These classifiers are applied to the representations extracted by another network, such as BERT, enabling the whole network to be end-to-end trainable. Experiments (The dataset and all code for this paper are available at https://github.com/ACL2020SpellGCN/SpellGCN) are conducted on three human-annotated datasets. Our method achieves superior performance against previous models by a large margin., Comment: Accepted by ACL2020

Published
2020

50. Effect of different proportions of CNTs/Fe3O4 hybrid filler on the morphological, electrical and electromagnetic interference shielding properties of poly(lactic acid) nanocomposites

Author

Wu Bozhen, Zhu Honghao, Yang Yuhao, Huang Jiang, Liu Tong, Kuang Tairong, Jiang Shaohua, Hejna Aleksander, and Liu Kunming

Subjects
multifunctional pla nanocomposites, cnts/fe3o4 hybrid filler, electrical conductivity, emi shielding, Polymers and polymer manufacture, TP1080-1185
Abstract

Due to the shortage of petroleum resources, poly(lactic acid) (PLA), a biodegradable polymer, has been widely considered as a replacement for traditional petroleum-based polymers. Therefore, multifunctional PLA composites have become increasingly popular. In this study, conductive carbon nanotubes (CNTs) and magnetic nano-Fe3O4 fillers were melt-blended with PLA. The impact of CNTs and nano-Fe3O4 composition on the electrical and electromagnetic interference (EMI) shielding properties of PLA nanocomposites was investigated in detail by adjusting the CNTs-to-nano-Fe3O4 ratio. When the hybrid filler content was fixed at 10 wt%, the electrical conductivity results indicated that the addition of single CNTs could effectively improve the conductivity of the nanocomposites, while nano-Fe3O4 contribution was hardly noted. A suitable ratio of electromagnetic hybrid fillers can yield excellent synergistic effects in EMI shielding properties. The nanocomposites containing CNTs and nano-Fe3O4 in a 50:50 ratio exhibited excellent electrical conductivity (90.6 S·m−1) and EMI shielding effectiveness (EMI SE ∼ 40.5 dB). This is primarily because CNTs provide good electrical conductivity, but the addition of magnetic nano-Fe3O4 provides additional interfacial polarization and eddy current losses caused by its dielectric and magnetic properties. These properties synergistically result in an impedance mismatch, dielectric loss, and polarization relaxation of the composite materials, improving the shielding properties against electromagnetic waves. Further, it was found that changing the ratio of electromagnetic hybrid fillers also affected electromagnetic wave absorption. When the ratio of CNT-to-nano-Fe3O4 was 25:75, the nanocomposites had an EMI SE of 24.6 dB, and the absorptivity could reach the maximum (40.3%). Thus, this study provides a valuable reference for preparing multifunctional polymer nanocomposites by constructing electromagnetic hybrid filler networks.

Published
2023
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