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Haoqi Han

dblp:382/7526 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2024
0009-0003-5045-9311ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot manipulation · 100%
Computer graphics and multimedia
1 paper
Computational fabrication · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
micromanipulation
0.812024
Development of a 3-RRS Micromanipulator Based on Origami-Inspired Spherical Joint · ICRA 2024

Methods — techniques the papers use, named apart from their topics

spherical joint mechanism · 1.5origami-inspired design · 1.5
YearPublicationVenuePosition
2024 Development of a 3-RRS Micromanipulator Based on Origami-Inspired Spherical Joint
abstract
In recent years, micromanipulation technology has achieved extensive applications in industry and life science. Improving the precision and bandwidth of the micromanipulator and simultaneously reducing size, weight, and cost pose significant challenges to the existing micromanipulator design and fabrication methods. Here, we propose a 3-RRS micromanipulator with an origami-inspired spherical joint based on the PC-MEMS process, aiming for miniaturization and cost-effectiveness. The spherical joint allows rotations of 140° around the x-axis approximately, 140° around the y-axis approximately, and 20° around the z-axis approximately. The micromanipulator has weights of 0.8 g, dimensions of 16 mm × 16 mm × 22 mm, and workspace of 0.7 mm3. The end platform of the micromanipulator can be equipped with various effectors to accomplish different kinds of tasks. Experimental results validated its high precision and bandwidth, exhibiting its potential to perform intricate micromanipulation tasks.
Haoqi Han, Xiaoming Liu 0007, Hao Pang, Xiaoqing Tang, Dan Liu 0009, Qiang Huang 0002, Tatsuo Arai
ICRA1
2024 AbdomenAtlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking
Chongyu Qu, Xiaoxi Chen, Pedro R. A. S. Bassi, Yijia Shi, Yuxiang Lai, Qian Yu 0012, Huimin Xue, Yixiong Chen, Xiaorui Lin, Yutong Tang, Yining Cao, Haoqi Han, Tiezheng Zhang, Yujiu Ma, Alan L. Yuille, Zongwei Zhou
Medical Image Anal.13