Papers
2
Total Citations
33
H-Index
2
About
Jihao Shi is a researcher at the forefront of environmental monitoring and industrial safety, specializing in underwater and pipeline gas leak detection. His work integrates autonomous robotics, advanced signal processing, and deep learning to address critical challenges in hazardous environment sensing. Shi’s most cited paper, “Underwater gas leak detection using an autonomous underwater vehicle (robotic fish)” (2022, 25 citations), introduces a bio-inspired robotic platform that mimics fish locomotion to navigate and detect gas leaks in aquatic settings—a novel approach that enhances maneuverability and reduces disturbance. His more recent contribution, “Optimized PSOMV-VMD combined with ConvFormer model: A novel gas pipeline leakage detection method based on low sensitivity acoustic signals” (2025, 8 citations), pioneers a hybrid method combining variational mode decomposition with a convolutional transformer architecture, enabling precise leak identification from faint acoustic signatures. This work pushes the boundaries of non-invasive monitoring, offering cost-effective solutions for aging infrastructure. With a growing citation record and a focus on real-world deployment, Shi’s research bridges robotics, acoustics, and AI, making him a rising voice in sustainable safety technologies.
Research Focus
Key Achievements
Top Papers
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