Zobrazeno 1 - 10
of 140
pro vyhledávání: '"Yan, Wenzhong"'
We developed a new class of soft locomotive robots that can self-assemble into a preprogrammed configuration and vary their stiffness afterward in a highly integrated, compact body using contracting-cord particle jamming (CCPJ). We demonstrate this w
Externí odkaz:
http://arxiv.org/abs/2410.02974
Autor:
Yan, Wenzhong, Jones, Talmage, Jawetz, Christopher L., Lee, Ryan H., Hopkins, Jonathan B., Mehta, Ankur
Publikováno v:
Materials Horizons (2024)
Recent advances in active materials and fabrication techniques have enabled the production of cyclically self-deployable metamaterials with an expanded functionality space. However, designing metamaterials that possess continuously tunable mechanical
Externí odkaz:
http://arxiv.org/abs/2407.06362
Autor:
Gu, Hongyan, Yang, Chunxu, Magaki, Shino, Zarrin-Khameh, Neda, Lakis, Nelli S., Cobos, Inma, Khanlou, Negar, Zhang, Xinhai R., Assi, Jasmeet, Byers, Joshua T., Hamza, Ameer, Han, Karam, Meyer, Anders, Mirbaha, Hilda, Mohila, Carrie A., Stevens, Todd M., Stone, Sara L., Yan, Wenzhong, Haeri, Mohammad, Chen, Xiang 'Anthony'
As Artificial Intelligence (AI) making advancements in medical decision-making, there is a growing need to ensure doctors develop appropriate reliance on AI to avoid adverse outcomes. However, existing methods in enabling appropriate AI reliance migh
Externí odkaz:
http://arxiv.org/abs/2404.04485
In recent years, Graph neural networks (GNNs) have emerged as a prominent tool for classification tasks in machine learning. However, their application in regression tasks remains underexplored. To tap the potential of GNNs in regression, this paper
Externí odkaz:
http://arxiv.org/abs/2311.16856
Autor:
Gu, Hongyan, Yang, Chunxu, Haeri, Mohammad, Wang, Jing, Tang, Shirley, Yan, Wenzhong, He, Shujin, Williams, Christopher Kazu, Magaki, Shino, Chen, Xiang 'Anthony'
Artificial Intelligence (AI) brings advancements to support pathologists in navigating high-resolution tumor images to search for pathology patterns of interest. However, existing AI-assisted tools have not realized this promised potential due to a l
Externí odkaz:
http://arxiv.org/abs/2302.07309
Autor:
Mungekar, Mrunmayi, Ma, Leixin, Yan, Wenzhong, Kackar, Vishal, Shokrzadeh, Shyan, Jawed, M. Khalid
Fully soft bistable mechanisms have shown extensive applications ranging from soft robotics, wearable devices, and medical tools, to energy harvesting. However, the lack of design and fabrication methods that are easy and potentially scalable limits
Externí odkaz:
http://arxiv.org/abs/2301.09179
We introduce a new class of thin flexible structures that morph from a flat shape into prescribed 3D shapes without an external stimulus such as mechanical loads or heat. To achieve control over the target shape, two different concepts are coupled. F
Externí odkaz:
http://arxiv.org/abs/2301.06597
Autor:
Yan, Wenzhong, Mehta, Ankur
Publikováno v:
2022 IEEE 5th International Conference on Soft Robotics (RoboSoft)
Locomotive robots that do not rely on electronics and/or electromagnetic components will open up new perspectives and applications for robotics. However, these robots usually involve complicated and tedious fabrication processes, limiting their appli
Externí odkaz:
http://arxiv.org/abs/2202.03497
Autor:
Yan, Wenzhong, Mehta, Ankur
Publikováno v:
Soft Robotics, 2021
Origami-inspired robots are of particular interest given their potential for rapid and accessible design and fabrication of elegant designs and complex functionalities through cutting and folding of flexible 2D sheets or even strings, i.e.printable m
Externí odkaz:
http://arxiv.org/abs/2108.08449
Publikováno v:
Robotica, 2022
We propose a computational design tool to enable casual end-users to easily design, fabricate, and assemble flat-pack furniture with guaranteed manufacturability. Using our system, users select parameterized components from a library and constrain th
Externí odkaz:
http://arxiv.org/abs/2104.05052