Yiling Liu

Shanghai Jiao Tong University, Quanzhou Normal University

Papers

Total Citations

H-Index

About

Yiling Liu’s research bridges the frontiers of computer vision and environmental intelligence, with a focus on motion segmentation and AI-driven pollution control. In their 2019 work, “MotionRFCN: Motion Segmentation Using Consecutive Dense Depth Maps,” Liu introduced a novel deep-learning framework that leverages sequential depth data to improve motion segmentation accuracy—a critical task for autonomous systems and robotics. This foundational contribution has garnered 4 citations, reflecting its niche but growing influence in the field. More recently, Liu’s 2025 study, “Industrial pollution control based on artificial intelligence: A synergistic model using social network analysis and machine learning,” marks a bold interdisciplinary leap. Examining the interplay of articulated robots, environmental policy stringency, and foreign direct investment on PM2.5 levels across 12 developed nations from 1993 to 2023, Liu pioneered a hybrid model combining Social Network Analysis with machine learning. This work, with 3 citations, offers actionable insights for policymakers and industries seeking to balance technological adoption with environmental sustainability. Liu’s ability to synthesize complex datasets and translate them into practical frameworks underscores a career dedicated to solving real-world challenges—from robotic perception to global pollution mitigation.