Pre-training on physical dynamics enhances indirect sensing in recurrent neural networks-Reference-Cited by-同舟云学术

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Pre-training on physical dynamics enhances indirect sensing in recurrent neural networks

Published:2022-11-27 Issue: Volume: Page:
ISSN:
Container-title:2022 International Symposium on Micro-NanoMehatronics and Human Science (MHS)
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Author:

Nishida Mitsuhiro¹,Sakurai Ryo¹,Wakao Yasumichi²,Nakajima Kohei³

Affiliation:

1. BridgestoneCorporation, Ogawahigashicho,KodairaCity,,DigitalEngineeringDepartment,Tokyo,Japan,187-8531

2. Bridgestone Corporation, Ogawahigashicho,KodairaCity,AdvancedMaterialDepartment,Tokyo,Japan,187-8531

3. The Universityo f Tokyo, Bunkyo-ku,InformationScienceandTechnology,Tokyo,Japan,113-8656

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10091921/10091944/10092140.pdf?arnumber=10092140

Reference33 articles.

1. Continuum-Body-Pose Estimation From Partial Sensor Information Using Recurrent Neural Networks

2. A Multitasking Flexible Sensor via Reservoir Computing

3. Object Recognition Using the Deformation Dynamics of Polyurethane Foam

4. Durable Pneumatic Artificial Muscles with Electric Conductivity for Reliable Physical Reservoir Computing

5. Flapping‐Wing Dynamics as a Natural Detector of Wind Direction

Cited by 1 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. On the Science of Soft Robots and Beyond;Journal of the Robotics Society of Japan;2024

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