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Biography
Pei-Yu Chen is an emerging scholar in science education, currently pursuing a Ph.D. at the Department of Science Education, College of Science, National Taipei University of Education (NTUE), Taiwan .
Her research interests focus on integrating technology into science teaching, particularly through artificial intelligence (AI) and image recognition technologies. In a recent study, she explored how AI-driven robotic image recognition can enhance students' understanding of complex biological concepts, such as cell division, and improve their motivation to learn science .
Chen has co-authored articles in multidisciplinary open-access journals, contributing to the fields of machine learning and signal processing. Her work aims to bridge the gap between technological advancements and educational practices, promoting innovative approaches to science education.
At NTUE, she is part of a vibrant academic community dedicated to advancing science education. The university, established in 1895, is renowned for its commitment to teacher training and educational research .
Chen's contributions reflect a growing emphasis on the role of technology in education, positioning her as a valuable emerging voice in the field of science education research.
Research Interest
Dr. Pei-Yu Chen's research interests lie at the intersection of science education and technology integration. Her work primarily focuses on leveraging artificial intelligence (AI), image recognition, and robotics to enhance the learning experience in science education. She explores innovative ways to incorporate technology into teaching, particularly in complex biological topics like cell biology and genetics, to improve both understanding and student engagement. Dr. Chen is also interested in how emerging machine learning techniques can be utilized to develop educational tools that adapt to individual learning needs, promoting personalized learning environments. Through her research, she seeks to bridge the gap between advanced technological applications and pedagogical practices, striving to create an interactive and dynamic learning experience. Her work contributes to the evolving field of educational technology, aiming to shape the future of science education through technological advancements.
Open Access Policy refers to a set of principles and guidelines aimed at providing unrestricted access to scholarly research and literature. It promotes the free availability and unrestricted use of research outputs, enabling researchers, students, and the general public to access, read, download, and distribute scholarly articles without financial or legal barriers. In this response, I will provide you with an overview of the history and latest resolutions related to Open Access Policy.
byPei-Yu Chen, Chien-Chieh Huang and Yuan-Chen Liu
Semantic segmentation is the most significant deep learning technology. At present, automatic assisted driving (Autopilot) is widely used in real-time driving, but if there is a deviation in object detection in real vehicles, it can easily lead to misjudgment. Turning and even crashing can be quite dangerous. This paper seeks to propose a model for this problem to increase the accuracy of discrimination and improve security. It proposes a Convolutional Neural Network (CNN)+ Holistically-Nested Edge Detection (HED) combined with Spatial Pyr...amid Pooling (SPP). Traditionally, CNN is used to detect the shape of objects, and the edge may be ignored. Therefore, adding HED increases the robustness of the edge, and finally adds SPP to obtain modules of different sizes, and strengthen the detection of undetected objects. The research results are trained in the CityScapes street view data set. The accuracy of Class mIoU for small objects reaches 77.51%, and Category mIoU for large objects reaches 89.95%.
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