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854 results on '"Wu, Zhenzhen"'

5. Generative Adversary Network Based on Cross-Modal Transformer for CT to MR Images Transformation

Author

Wu, Zhenzhen, Huang, Weijie, Cheng, Xingong, Wang, Hui, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Jing, Xingjian, editor, Ding, Hu, editor, Ji, Jinchen, editor, and Yurchenko, Daniil, editor

Published
2024
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15. First-principles prediction of one-dimensional conductive metallic organic polymers as ultrahigh energy density anode for lithium-ion batteries.

Author

Li, Mingli, Wu, Zhenzhen, Yang, Pan, Allen, Oscar J., Zhao, Di, Zhang, Lei, Zhang, Shanqing, and Wang, Yun

Subjects
*ENERGY density, *LITHIUM-ion batteries, *ELECTRIC batteries, *ANODES, *CHARGE transfer, *DENSITY functional theory, *POLYMERS, *SILICON nanowires, *ACTIVATION energy
Abstract

Metal–Organic Polymers (MOPs) have attracted growing attention for lithium-ion battery (LIB) applications due to their merits in orderly ionic transportation and robust structure stability in electrochemical reactions. However, they suffer from poor electronic conductivity. In this work, we apply first-principles density functional theory to explore the potential of three one-dimensional (1D) electrically conductive C6H2S4TM (TM = Fe, Co, and Ni) MOPs with the π–d conjugated coordination as anode materials for Li+ ions storage. Our theoretical results reveal that these 1D MOPs possess a superior theoretical capacity of over 748 mA h g−1. In particular, the 1D C6H2S4Ni MOP shows an exceptional theoretical specific capacity of 1110 mA h g−1 based on the three-electron transferring reaction, which significantly outperforms the traditional graphite-based anode material in LIBs. Moreover, the resonant charge transfer between Ni metal and ligand within the 1D C6H2S4Ni MOP reduces the diffusion energy barrier of the Li atoms when they migrate on the surface of the MOP. The ultrahigh theoretical specific capacity of the C6H2S4Ni MOP predicts that it can be a promising anode material for LIBs. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics

Author

Wang, Hongmei, Wu, Zhenzhen, Ma, Shuai, Lu, Songtao, Zhang, Han, Ding, Guoru, and Li, Shiyin

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

In this paper, we investigate deep learning (DL)-enabled signal demodulation methods and establish the first open dataset of real modulated signals for wireless communication systems. Specifically, we propose a flexible communication prototype platform for measuring real modulation dataset. Then, based on the measured dataset, two DL-based demodulators, called deep belief network (DBN)-support vector machine (SVM) demodulator and adaptive boosting (AdaBoost) based demodulator, are proposed. The proposed DBN-SVM based demodulator exploits the advantages of both DBN and SVM, i.e., the advantage of DBN as a feature extractor and SVM as a feature classifier. In DBN-SVM based demodulator, the received signals are normalized before being fed to the DBN network. Furthermore, an AdaBoost based demodulator is developed, which employs the $k$-Nearest Neighbor (KNN) as a weak classifier to form a strong combined classifier. Finally, experimental results indicate that the proposed DBN-SVM based demodulator and AdaBoost based demodulator are superior to the single classification method using DBN, SVM, and maximum likelihood (MLD) based demodulator.

Published
2019
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49. Fluorine‐Effect‐Enabled Photocatalytic 4‐Exo‐Trig Cyclization Cascade to Access Fluoroalkylated Cyclobutanes.

Author

Zhou, Yulu, Wu, Zhenzhen, Xu, Jinming, Zhang, Zuxiao, Zheng, Hanliang, and Zhu, Gangguo

Subjects
*RING formation (Chemistry), *CYCLOBUTANE, *DENSITY functional theory, *INTERMEDIATES (Chemistry), *BIOACTIVE compounds, *RADICALS (Chemistry)
Abstract

Cyclobutanes are popular structural units in bioactive compounds and versatile intermediates in synthetic chemistry, but their synthesis is challenging owing to high ring strain. In this study, a novel method for highly regio‐ and diastereoselective synthesis of fluoroalkylcyclobutanes bearing vicinal quaternary and tertiary stereocenters is realized by a photocatalytic 4‐exo‐trig cyclization cascade of thioalkynes or trifluoromethylalkenes. Density functional theory calculations reveal that a unique fluorine effect, arising from hyperconjugative π→σ*C‐F interactions, accounts for the regio‐reversed radical addition at the sterically hindered alkene carbon, which facilitates an unprecedented 4‐exo‐trig ring closure. This chemistry enables the direct and controllable construction of medicinally valuable quaternary‐carbon‐containing cyclobutanes from readily available raw materials, nicely complementing the existing methods. [ABSTRACT FROM AUTHOR]

Published
2024
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