HU-FlowNet: A Method for Trajectory Detection in Dense Granular Flows Integrating the Hungarian Algorithm, U‑Net, and UnLiteFlowNet.

Yijian Xu, Liu Fu, Zhou Hu, Jiaxu Duan

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In one paragraph

Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

4 authors.

Yijian XuSchool of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou, Hubei 434023, China.ORCID https://orcid.org/0009-0008-3733-1184

Liu FuSchool of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou, Hubei 434023, China.ORCID https://orcid.org/0009-0001-8110-2134

Zhou HuSchool of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou, Hubei 434023, China.

Jiaxu DuanChina National Electric Apparatus Research Institute Co Ltd, Guangzhou, Guangdong 510300, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The accurate tracking of particle trajectories in dense granular flows is vital for optimizing industrial processes involving rotary drums. However, traditional tracking methods suffer from poor stability and low accuracy under complex conditions. To address these issues, this paper proposes HU-FlowNet, a trajectory detection method integrating U-Net for particle localization, UnLiteFlowNet for velocity field estimation, and the Hungarian algorithm for global position matching. Specifically, UnLiteFlowNet is used to predict particle positions at frame

Identifiers

PMID41244464

DOI10.1021/acsomega.5c09442

PMCPMC12612873

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.