Ling Li

Ling Li is affiliated with Old Dominion University in the United States and specializes in interdisciplinary research across engineering and computer science. Their scholarly work spans several subfields including computer vision and pattern recognition, civil and structural engineering, artificial intelligence, electrical and electronic engineering, and mechanical engineering.

The main academic fields covered in Ling Li's publications are engineering and computer science, with a significant focus on specific technical areas related to structural response and advanced image processing. The subfields of study contributing to their research profile include:

• Computer Vision and Pattern Recognition
• Civil and Structural Engineering
• Artificial Intelligence
• Electrical and Electronic Engineering
• Mechanical Engineering

Key research topics in Ling Li's work involve structural responses to dynamic loads, video surveillance and tracking methods, image enhancement techniques, infrastructure maintenance and monitoring, advanced neural network applications, structural health monitoring techniques, and advanced image processing techniques.

• Structural Response to Dynamic Loads
• Video Surveillance and Tracking Methods
• Image Enhancement Techniques
• Infrastructure Maintenance and Monitoring
• Advanced Neural Network Applications
• Structural Health Monitoring Techniques
• Advanced Image Processing Techniques

Ling Li's most frequent co-authors include Qilin Li, Hong Hao, Yanda Shao, Ruhua Wang, and Wensu Chen. The collaboration with these researchers has been consistent across numerous projects, indicating interdisciplinary teamwork within the fields of engineering and computer science.

• Qilin Li
• Hong Hao
• Yanda Shao
• Ruhua Wang
• Wensu Chen

Their research has been published in a variety of academic venues, with notable repeated contributions to arXiv (Cornell University), Engineering Structures, Computers & Structures, SSRN Electronic Journal, and Process Safety and Environmental Protection.

• arXiv (Cornell University)
• Engineering Structures
• Computers & Structures
• SSRN Electronic Journal
• Process Safety and Environmental Protection

Selected recent papers include:

• "Semantically Contrastive Learning for Low-Light Image Enhancement" (2022), Proceedings of the AAAI Conference on Artificial Intelligence
• "Prediction of BLEVE blast loading using CFD and artificial neural network" (2021), Process Safety and Environmental Protection
• "CholecTriplet2021: A benchmark challenge for surgical action triplet recognition" (2023), Medical Image Analysis
• "A comparative study on the most effective machine learning model for blast loading prediction: From GBDT to Transformer" (2022), Engineering Structures
• "A survey of blockchain with applications in maritime and shipping industry" (2020), Information Systems and e-Business Management