Dong-Chang Lin
Papers
1
Total Citations
32
H-Index
1
About
Dong-Chang Lin is a leading researcher in visible light positioning (VLP) systems, where his work bridges machine learning and optical wireless communications. His most cited paper, "Positioning Unit Cell Model Duplication With Residual Concatenation Neural Network (RCNN) and Transfer Learning for Visible Light Positioning (VLP)" (2021, 32 citations), addresses a critical challenge in VLP: reducing training time and computational complexity without sacrificing accuracy. Lin pioneered the use of residual concatenation neural networks combined with transfer learning to enable efficient positioning across multiple unit cells, allowing models trained in one area to be adapted to others. This approach significantly diminishes the need for extensive retraining, making VLP systems more practical for real-world deployment. His contributions have been recognized for advancing the scalability of indoor positioning technologies, particularly in environments where traditional GPS fails. With a growing citation impact, Lin’s work continues to influence researchers developing intelligent, low-complexity localization systems for smart buildings, autonomous navigation, and IoT applications.
Research Focus
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Top Papers
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