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880 results on '"Zhao, Zhonghua"'

4. Multi Agent Based High School Physics Network Course Automatic Generation System

Author

Chi, Haiquan, Zhao, Zhonghua, Jiang, Xiaojun, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Gui, Guan, editor, Li, Ying, editor, and Lin, Yun, editor

Published
2024
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5. High-temperature dielectric with excellent capacitive performance enabled by rationally designed traps in blends.

Author

Zhao, Zhonghua, Zhang, Shuo, Li, Mingru, Feng, Yang, Yang, Liuqing, and Li, Shengtao

Subjects
*DIELECTRICS, *ENERGY density, *POLYETHYLENEIMINE, *STRAY currents, *HIGH temperatures, *ELECTRONIC systems, *CONJUGATED polymers, *ENERGY storage
Abstract

Polymer dielectrics with excellent capacitive performance are urgently needed in advanced electrical and electronic systems. However, due to the dramatic increase in the conduction loss, the energy density and efficiency of polymers degrade severely at elevated temperatures, limiting their application in harsh environments up to 150 °C. Herein, an all-organic polyurea (PU)/polyetherimide (PEI) blend film is designed to prepare high-temperature polymer dielectric. It is found that carrier traps can be introduced by blending, and the hydrogen bond between PU and PEI increases the trap depth, leading to suppressed leakage current and enhanced breakdown strength, thus improving the energy storage performance. PU/30%PEI exhibits a high discharged energy density of ∼3.74 J/cm3 with an efficiency higher than 90% at 150 °C, which is 78% and 70% higher than pristine PU and PEI, respectively. This work provides a facile strategy to improve the energy storage performance of polymer dielectrics by introducing deep traps through blending. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Learning Social Image Embedding with Deep Multimodal Attention Networks

Author

Huang, Feiran, Zhang, Xiaoming, Li, Zhoujun, Mei, Tao, He, Yueying, and Zhao, Zhonghua

Subjects
Computer Science - Multimedia, Computer Science - Learning, Statistics - Machine Learning
Abstract

Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text description, and visual content), simply employing the embedding learnt from network structure or data content results in sub-optimal social image representation. In this paper, we propose a novel social image embedding approach called Deep Multimodal Attention Networks (DMAN), which employs a deep model to jointly embed multimodal contents and link information. Specifically, to effectively capture the correlations between multimodal contents, we propose a multimodal attention network to encode the fine-granularity relation between image regions and textual words. To leverage the network structure for embedding learning, a novel Siamese-Triplet neural network is proposed to model the links among images. With the joint deep model, the learnt embedding can capture both the multimodal contents and the nonlinear network information. Extensive experiments are conducted to investigate the effectiveness of our approach in the applications of multi-label classification and cross-modal search. Compared to state-of-the-art image embeddings, our proposed DMAN achieves significant improvement in the tasks of multi-label classification and cross-modal search.

Published
2017
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