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Yingzhen Lin

As seen in: Wiley Online Library, Frontiers in Marine Science, MDPI, International Journal of Climate Change Strategies and Management
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Articles

Self-supervised transfer learning for few-shot classification on marine plankton images

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By Yingzhen Lin, Zhenghui Feng, Feng Ling, Xuxiang Zhong
| Frontiers in Marine Science
ORIGINAL RESEARCH article Volume 12 - 2025 | https://doi.org/10.3389/fmars.2025.1729254 This article is part of the Research TopicUnderwater Visual Signal Processing in the Data-Driven EraView all 7 articles 1 Introduction Over the past three decades, in situ monitoring has generated vast amounts of data for marine plankton observation. Figure 1 shows images of four categories of marine plankton collected using this technique (Li et al., 2021).
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A new algorithm of boundary value problems based on improved wavelet basis and the reproducing kernel theory

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By Ruimin Zhang, Yingzhen Lin, Engineering Macau
| Wiley Online Library Verified
Corresponding Author Ruimin Zhang School of Computer Science and Engineering, Macau University of Science and Technology, Macau, China Zhuhai Campus, Beijing Institute of Technology, Zhuhai, Guangdong, China Correspondence Ruimin Zhang, School of Computer Science and Engineering, Macau University of Science and Technology, Macau, China. Email: mathrmzhang@126.com Communicated by: Q.
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An ε-Approximate Approach for Solving Variable-Order Fractional Differential Equations

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By Yahong Wang, Wenmin Wang, Liangcai Mei, Yingzhen Lin
| MDPI
Abstract : As a mathematical tool, variable-order (VO) fractional calculus (FC) was developed rapidly in the engineering field due to it better describing the anomalous diffusion problems in engineering; thus, the research of the solutions of VO fractional differential equations (FDEs) has become a hot topic for the FC community.
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