Congratulations to Wang Yinggang on successfully graduating!
Wang Yinggang graduated from Huaqiao University with a Bachelor’s degree in Electrical Engineering and Automation. In the autumn of 2020, he enrolled in the Software Engineering professional master’s program at the School of Computer Science, Shanghai University. After joining the research group, under the guidance of Professor Chen Qiaochuan, Professor Han Yuexing, and Professor Zhang Rui, he focused on studying curve information processing methods in scientific literature. With their careful guidance, he successfully completed the following research projects:
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Firstly, this paper explores the automatic extraction of curve information from curve coordinate data commonly found in current scientific literature, addressing the challenges and time-consuming nature of manual extraction. Curve images often exhibit diverse plotting styles, high density, and strong continuity, resulting in inaccurate curve extraction with different methods. This paper proposes an end-to-end curve extraction model based on dense network architecture to address issues such as cluttered and blurred curve lines generated by curve detection methods, aiming to improve the accuracy of curve information extraction. By incorporating adaptive dilated convolution modules to enlarge the receptive field and introducing progressive refinement pathways at each layer, with intermediate outputs being fed into subsequent refinement modules, the network performance is optimized through carefully designed loss function parameters. Additionally, a dedicated dataset for curve detection is constructed, and the improved model is trained to further enhance the ability of the network to extract curve edge information from curve images. The qualitative evaluation results further demonstrate the superiority of this method compared to others.
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Furthermore, to address the issues of excessive stacking of dense convolution modules leading to loss of channel feature information, a large number of trainable parameters, and longer training and inference times, this paper proposes a curve extraction network structure based on a dual efficient channel attention mechanism. This method utilizes Vgg as the backbone feature extraction network and employs a dual efficient channel attention mechanism to represent the weights of different channel features, better capturing the relationships between channels in the image and enhancing the ability of feature representation. Subsequently, an embedded stage feature fusion module is introduced to reduce feature loss and enhance feature expression. By fusing low-resolution and high-resolution features from different stages, the model gains a better understanding of the semantic information in the image, while significantly reducing the number of model parameters. Training and testing on a curve dataset demonstrate that the proposed method extracts curve structures with clear contours, well-defined hierarchy, and accurate localization. It effectively addresses the issue of blurred curve boundaries and achieves improved curve extraction accuracy with fewer parameters.
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Lastly, the complexity and implementation difficulty of curve data extraction algorithms often limit their application scope. Therefore, the development of user-friendly data extraction software can popularize and facilitate curve data extraction, further promoting its practical application. This paper focuses on the practical value of curve data extraction and develops a desktop data extraction software to facilitate the implementation of the algorithm. This software allows more people to benefit from the practical value of curve data extraction and promotes its widespread application in various fields.
After graduating, Wang Yinggang joined JD.com Retail Group and engaged in backend software development. During his time as a graduate student at Shanghai University, he worked hard to enhance his professional knowledge and research capabilities, and he benefited greatly from excellent mentors and supportive friends. With his original aspiration and a sense of mission, he will forge ahead and contribute his wisdom and strength to the development of JD.com. We believe that he will become an outstanding professional in the industry and continue to make remarkable achievements.
Essay: Research on curve information processing methods in scientific literature
