Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning-Reference-Cited by-同舟云学术

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Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

Published:2019-10 Issue: Volume: Page:
ISSN:
Container-title:2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
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Author:

Hajilounezhad Taher,Oraibi Zakariya A.,Surya Ramakrishna,Bunyak Filiz,Maschmann Matthew R.,Calyam Prasad,Palaniappan Kannappan

Funder

National Science Foundation (NSF)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9170134/9174563/09316542.pdf?arnumber=9316542

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Creating semi-Quanta multi-layer synthetic CNT images using CycleGAN;2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR);2023-09-27

2. Deep Style Transfer for Generation of Photo-realistic Synthetic Images of CNT Forests;2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR);2023-09-27

3. CNT-NeRF: Carbon Nanotube Forest Depth Layer Decomposition in SEM Imagery using Generative Adversarial Networks;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW);2023-06

4. Self-supervised Orientation-Guided Deep Network for Segmentation of Carbon Nanotubes in SEM Imagery;Lecture Notes in Computer Science;2023

5. Prediction of the Near-Infrared Absorption Spectrum of Single-Walled Carbon Nanotubes Using a Bayesian Regularized Back Propagation Neural Network Model;C;2021-11-25

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