Arrow Research search

Author name cluster

Yi Yan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
1 author row

Possible papers

2

AAAI Conference 2025 Short Paper

LLM-based Online Prediction of Time-varying Graph Signals (Student Abstract)

  • Dayu Qin
  • Yi Yan
  • Ercan Engin Kuruoglu

In this paper, we propose a novel framework that leverages Large Language Models (LLMs) for predicting missing values in time-varying graph signals by exploiting spatial and temporal smoothness. We leverage the power of LLM to achieve a message-passing scheme. For each missing node, its neighbors and previous estimates are fed into and processed by LLM to infer the missing observations. Tested on the task of the online prediction of wind-speed graph signals, our model outperforms online graph filtering algorithms in terms of accuracy, demonstrating the potential of LLMs in effectively addressing partially observed signals in graphs.

PDF Details DOI

EAAI Journal 2024 Journal Article

Improved YOLOv8-GD deep learning model for defect detection in electroluminescence images of solar photovoltaic modules

  • Yukang Cao
  • Dandan Pang
  • Qianchuan Zhao
  • Yi Yan
  • Yongqing Jiang
  • Chongyi Tian
  • Fan Wang
  • Julin Li

Photovoltaic defect detection is an essential aspect of research on building-distributed photovoltaic systems. Existing photovoltaic defect detection models based on deep learning, such as YOLOv5 and YOLOv8, have significantly improved the accuracy of photovoltaic defect detection. However, these models are too large, and their feature extraction ability is insufficient, leading to low detection efficiency and inability to cope with the continuous evolution of defects. Therefore, this study proposes an accurate and lightweight YOLOv8 (You Only Look Once v8) GD algorithm. The algorithm is an improved version of YOLOv8, wherein DW-Conv (DepthWise-Conv) is applied to the YOLOv8 backbone network. Moreover, convolution is replaced with the GSConv (Group-shuffle Conv) and the BiFPN (bidirectional feature pyramid network) structure is added to the architecture. Several electroluminescent photovoltaic defect datasets are used to verify the effectiveness of the proposed method. The final experimental results show that the map@0. 5 and map@0. 5 ∼ 0. 95 of YOLOv8-GD are 92. 8% and 63. 1%, respectively, which are 4. 2% and 5. 7% higher than those of the original algorithm, respectively, and the model volume is reduced by 16. 7%. Thus, the proposed algorithm shows considerable potential in the field of photovoltaic defect detection.

Details DOI
v2026.09.13