Skip to main content
Xinyu Liu on Muck Rack

Xinyu Liu

Toronto
As seen in: Journal of the American Chemical Society, Medium, Color Research & Application, Love Lives, Nature, The Independent (UK), Wiley Online Library, Yahoo Canada, PLOS, PLOS ONE, ScienceDirect and
Percy Edward Hart Professor of @uoftmie, @UofT; also with @UofTRobotics and @bme_uoft. Group: liulab.mie.utoronto.ca.
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Xinyu

Contact Xinyu, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Bayesian optimization of ensemble learning algorithms in predicting local scouring around offshore monopiles

|
By Xinyu Liu, Nan Zhang Verified, Wenpeng Liu
| Taylor & Francis Online
Abstract Monopile foundations are widely used in marine engineering, but local scouring around the monopile can significantly reduce its bearing capacity. Accurately predicting the equilibrium scour depth is essential for the design of marine structures.
Open in Who Shared Issue with article?

An improved ResNet50 method for hazardous-region state recognition in CNC press brakes

|
By Xingtao Hu, Fan Li, Nan Wang, Sizhong Miao, Shanshan Peng, Pengpeng Zhou, Xinyu Liu, Chuanbing Wang
| Nature Verified
Abstract To address the open die region, frequent human-machine interaction, and insufficient active recognition capability of conventional protection methods during sheet-metal bending with CNC press brakes, this study develops an improved ResNet50 model for hazardous-region state recognition. A visual monitoring region is defined according to the overlap between the tool motion range and the workpiece placement region, and an on-site image dataset is constructed accordingly.
Open in Who Shared Issue with article?

Computer Science > Computer Vision and Pattern Recognition

|
By Tianxiang Nan, Xinyu Liu, Guisheng Zhang
| arxiv.org
Weakly supervised video anomaly detection relies solely on video-level labels for training, making it difficult to accurately localize anomalous events in complex scenes. In real-world videos, anomalous behaviors exhibit large variations in appearance and temporal duration, while scene appearance and action dynamics are often tightly entangled.
Open in Who Shared Issue with article?
See all 441 licensed articles (465 Total)