SwePub
Sök i SwePub databas

  Extended search

Träfflista för sökning "WFRF:(Wu Pengcheng) "

Search: WFRF:(Wu Pengcheng)

  • Result 1-3 of 3
Sort/group result
   
EnumerationReferenceCoverFind
1.
  • Kristan, Matej, et al. (author)
  • The first visual object tracking segmentation VOTS2023 challenge results
  • 2023
  • In: 2023 IEEE/CVF International conference on computer vision workshops (ICCVW). - : Institute of Electrical and Electronics Engineers Inc.. - 9798350307443 - 9798350307450 ; , s. 1788-1810
  • Conference paper (peer-reviewed)abstract
    • The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitting. New performance measures and evaluation protocols have been created along with a new toolkit and an evaluation server. Results of the presented 47 trackers indicate that modern tracking frameworks are well-suited to deal with convergence of short-term and long-term tracking and that multiple and single target tracking can be considered a single problem. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available at the challenge website1
  •  
2.
  • Zhang, Pengcheng, et al. (author)
  • Geospatial learning for large-scale transport infrastructure depth prediction
  • 2024
  • In: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier. - 1569-8432 .- 1872-826X. ; 132
  • Journal article (peer-reviewed)abstract
    • Transportation infrastructure supports the smooth mobility of humans, commodities, and services. Pavement depth measures the quality of road infrastructure through representing the thickness of road surfaces, and influences various aspects of construction projects. However, accurately modeling and predicting pavement depth has been a critical challenge due to diverse and complex factors, such as weather dynamics, traffic patterns, maintenance interventions, and environmental fluctuations. This study develops a second-dimension spatial learning (SDSL) model that integrates geospatial models and machine learning for large-scale pavement depth prediction. SDSL models are implemented in pavement prediction for eight distinct regions in Western Australia, and they are validated using the observation of pavement depth through cross-validation. Results demonstrate that the proposed SDSL models can more accurately predict large-scale pavement depth than the existing first-dimension spatial learning (FDSL) models, with 17.3% to 37.6% increase of R2 values, 1.46% to 16.5% reduction of RMSE, 1.7% to 31.1% reduction of MAE and 21.0% reduction of prediction uncertainty. SDSL models enhance effective infrastructure management by accurately predicting pavement depth, essential for maintaining large-scale transportation infrastructure. The study significantly contributes to the efficient management of sustainable infrastructure assets, saving time and money. © 2024 The Authors
  •  
3.
  • Zhao, Yansong, et al. (author)
  • Single Cell RNA Expression Analysis Using Flow Cytometry Based on Specific Probe Ligation and Rolling Circle Amplification
  • 2020
  • In: ACS Sensors. - : American Chemical Society (ACS). - 2379-3694. ; 5:10, s. 3031-3036
  • Journal article (peer-reviewed)abstract
    • Conventional flow cytometry has been widely used for high-throughput single-cell gene expression analysis using specific antibody staining. However, this is limited by the availability of high-quality antibodies. We developed a novel flow cytometry RNA detection technique termed RCA-Flow for single-cell RNA expression analysis. We showed that it is able to analyze not only mRNAs but also microRNAs and circular RNAs that are otherwise difficult to analyze by other flow cytometry techniques. The versatility for high-throughput analysis of different types of RNA molecules makes our method possess great potential for both biomedical and clinical applications.
  •  
Skapa referenser, mejla, bekava och länka
  • Result 1-3 of 3

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Close

Copy and save the link in order to return to this view