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您的检索式：作者名="Dingchen Wang"
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| 1 | Dividing and setting strategy of improving calculation efficiency for needle electrode corona discharge with a large-scale space显示文摘Needle electrodes are widely used in the research of corona discharge.Aiming at the problem of needle electrode discharge in a large-scale space(tens or even hundreds of centimetres),a hybrid model was proposed in the previous work.However,its indis-pensable multiple iterations result in huge computation and further limit its wide appli-cations.Therefore,a strategy to improve the computing efficiency by setting initial values in different computational domains is put forward in this work.Three types of setting initial values(global constant,partition constant and partition exponent)are simulated and compared in detail.The calculation results show that the calculation efficiency can be increased by 1.4 times simply through setting initial values of charged particles as different constants for the divided subdomains,and further be improved by 4.3 times by setting those in the ionization region as an exponential distribution.The extension of the pro-posed strategy has also been discussed under various voltages,which shows that the results under other voltages can be quickly obtained based on the fitted coefficients under a specific voltage,and the improvement can reach up to 10 times.The accuracy of calculated results has been demonstrated by a needle-plate electrode device with a vertical distance of 25 cm.This research provides an effective strategy for improving the computing efficiency of corona discharge in a large-scale space,and implies the potential in optimizing the analyses of complex electrodes. | Dingchen Li Chuan Li Jiawei Li Pengyu Wang Zhi Liu Wendi Yang Ming Zhang Yong Yang Kexun Yu | 2022 | High Voltage2022，7，3： | 0 |
| 2 | Structural plasticity-based hydrogel optical Willshaw model for one-shot on-the-fly edge learning显示文摘Autonomous one-shot on-the-fly learning copes with the high privacy,small dataset,and in-stream data at the edge.Implementing such learning on digital hardware suffers from the well-known von-Neumann and scaling bottlenecks.The optical neural networks featuring large parallelism,low latency,and high efficiency offer a promising solution.However,ex-situ training of conventional optical networks,where optical path configuration and deep learning model optimization are separated,incurs hardware,energy and time overheads,and defeats the advantages in edge learning.Here,we introduced a bio-inspired material-algorithm co-design to construct a hydrogel-based optical Willshaw model(HOWM),manifesting Hebbian-rule-based structural plasticity for simultaneous optical path configuration and deep learning model optimization thanks to the underlying opto-chemical reactions.We first employed the HOWM as an all optical in-sensor AI processor for one-shot pattern classification,association and denoising.We then leveraged HOWM to function as a ternary content addressable memory(TCAM)of an optical memory augmented neural network(MANN)for one-shot learning the Omniglot dataset.The HOWM empowered one-shot on-the-fly edge learning leads to 1000boost of energy efficiency and 10boost of speed,which paves the way for the next-generation autonomous,efficient,and affordable smart edge systems. | Dingchen Wang Dingyao Liu Yinan Lin Anran Yuan Woyu Zhang Yaping Zhao Shaocong Wang Xi Chen Hegan Chen Yi Zhang Yang Jiang Shuhui Shi Kam Chi Loong Jia Chen Songrui Wei Qing Wang Hongyu Yu Renjing Xu Dashan Shang Han Zhang Shiming Zhang Zhongrui Wang | 2023 | InfoMat2023，5，4： | 0 |
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