Yifei Chen

ORCID: 0009-0009-2002-7909
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About
Contact & Profiles
Research Areas
  • Seismic Waves and Analysis
  • Bacteriophages and microbial interactions
  • Image Retrieval and Classification Techniques
  • Marine and fisheries research
  • Seismic Imaging and Inversion Techniques
  • Structural Health Monitoring Techniques
  • Advanced Statistical Methods and Models
  • earthquake and tectonic studies
  • Identification and Quantification in Food
  • Maritime Navigation and Safety
  • Fault Detection and Control Systems
  • Text and Document Classification Technologies
  • Advanced Image Processing Techniques
  • Image and Signal Denoising Methods
  • Brain Tumor Detection and Classification
  • Military Defense Systems Analysis

Jimei University
2025

Central China Normal University
2024

Nanjing University of Information Science and Technology
2024

University of New Brunswick
1983-1987

Abstract The identification of fishing vessel operations holds significant importance in addressing industry issues, such as overfishing and illegal, unreported unregulated (IUUF). Many countries utilise data from monitoring systems (VMSs) or automatic (AISs) to monitor activities. These include trajectories, headings speeds, among others. We aimed analyse the behaviours three types gear used by vessels (trawl, purse seine gill net) identify employed vessels. Therefore, a 1D CNN-LSTM...

10.1017/s037346332400033x article EN Journal of Navigation 2025-01-13

Undetected outliers may significantly influence the results and interpretation of high precision engineering surveys. The least-squares adjustment method smooths out effect makes their localization difficult. Low spatial redundancy, correlation among residuals, existence multiple compound problem This paper presents a strategy developed to overcome most these difficulties. Two cases are distinguished, based on whether variance factor σ0<2 is known or unknown. When known, rather than...

10.1139/tcs-1987-0036 article EN The Canadian Surveyor 1987-12-01

Alzheimer's Disease (AD) is a complex neurodegenerative disorder marked by memory loss, executive dysfunction, and personality changes. Early diagnosis challenging due to subtle symptoms varied presentations, often leading misdiagnosis with traditional unimodal diagnostic methods their limited scope. This study introduces an advanced multimodal classification model that integrates clinical, cognitive, neuroimaging, EEG data enhance accuracy. The incorporates feature tagger tabular coding...

10.48550/arxiv.2408.16343 preprint EN arXiv (Cornell University) 2024-08-29

10.1109/bibm62325.2024.10821906 article EN 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2024-12-03
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