L
Ling Zhang
Researcher at Missouri University of Science and Technology
Publications - 22
Citations - 148
Ling Zhang is an academic researcher from Missouri University of Science and Technology. The author has contributed to research in topics: Electromagnetic interference & Electromagnetic shielding. The author has an hindex of 6, co-authored 22 publications receiving 67 citations. Previous affiliations of Ling Zhang include Zhejiang University.
Papers
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Proceedings ArticleDOI
Decoupling Capacitor Selection Algorithm for PDN Based on Deep Reinforcement Learning
Ling Zhang,Zhongyang Zhang,Chenxi Huang,Han Deng,Hank Lin,Bin-Chyi Tseng,James L. Drewniak,Chulsoon Hwang +7 more
TL;DR: An inductance-based method is utilized to calculate the port priority fist, and afterwards deep reinforcement learning (DRL) with deep neural network (DNN) is applied to optimize the assignment of decaps on the prioritized locations.
Journal ArticleDOI
Sparse Emission Source Microscopy for Rapid Emission Source Imaging
Ling Zhang,Victor Khilkevich,Xiangyang Jiao,Xiao Li,Sukhjinder Toor,Alpesh Bhobe,Kyoungchoul Koo,David Pommerenke,James L. Drewniak +8 more
TL;DR: The feasibility of sparse sampling is mathematically proved, and it is shown that increasing number of scanning points increases the signal-to-noise ratio of reconstructed images, and a nearest neighbor interpolation method is applied in the real-time processing to estimate the radiated power through the scanning plane.
Proceedings ArticleDOI
An Enhanced Deep Reinforcement Learning Algorithm for Decoupling Capacitor Selection in Power Distribution Network Design
TL;DR: This paper presents an improved decap-selection algorithm based on deep reinforcement learning (DRL), which seeks the minimum number of decaps through a self-exploration training to satisfy a given target impedance, and demonstrates the feasibility of achieving decent performance with pre-trained knowledge for more complicated engineering tasks in the future.
Journal ArticleDOI
Analysis of the Effect on Image Quality of Different Scanning Point Selection Methods in Sparse ESM
TL;DR: The paper shows that sub-Nyquist is achievable and suggests uniform sampling is superior to nonuniform, in contrast to other reported uses of microwave imaging and care should be taken if the source reconstruction is based on uniform 2-D DFT.
Proceedings ArticleDOI
Solving Poisson's Equation using Deep Learning in Particle Simulation of PN Junction
TL;DR: The resulting I-V curve for the PN junction, using the deep learning solver presented in this work, shows a perfect match to the I-v curve obtained using the finite difference method, with the advantage of being 10 times faster at every time step.