Publications from 2021 to 2026
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Active disturbance rejection control for flight test chamber system subject to multi-source disturbances: An extended state observer with dynamic compensation approach
Federated learning for spatio-temporal data mining: a survey
2High efficiency heterogeneous Fenton-like catalyst biochar modified CuS for the degradation of sulfisoxazole: Catalytic performance and mechanism
Homogeneous electrochemical sensor for sensitive detection of HBV DNA based on magnetic separation and CRISPR/Cas12a protein trans-cleavage.
A single near-infrared light-triggered nanoplatform based on upconversion nanoparticles for synergistic photodynamic/photothermal therapy.
Tunnel scanner: Geometry-informed synthetic point cloud generation and transfer learning for tunnel segmentation
Structural health monitoring of underground tunnels increasingly uses advanced sensing and data-driven methods. Laser-scanned 3D point clouds capture spatially rich measurements of segmental tunnel linings and require segmentation as a prerequisite for downstream analysis. Deep learning (DL) is effective for point-cloud segmentation, but scarce datasets and costly annotation limit practical use. This paper presents Tunnel Scanner , a high-fidelity simulator that synthesises realistic tunnel point clouds with automatic annotation. The plug-and-play module Hybrid Position–Normal Local Spatial Encoding embeds geometric priors into DL backbones and combines with transfer learning (TL) to exploit synthetic data for domain adaptation. Models trained only on synthetic data achieved 71.9% mean Intersection-over-Union (mIoU) and 86.6% Overall Accuracy (OA), and TL increased performance to at least 78.8% mIoU and 90.9% OA with limited real data. This paper highlights geometry-informed data synthesis as a viable augmentation approach for digital inspection and asset management of large-scale tunnels. • Develop a high-fidelity simulator to address the scarcity of tunnel point clouds. • Propose a plug-and-play module encoding geometric features into DL backbones. • Investigate transfer learning to enhance 3D Sim-to-Real domain adaptation. • The end-to-end framework yields ＋24% segmentation accuracy with limited real data.
Read moreEngineering oxygen vacancies and curled ultrathin architecture in ZnO nanosheets for efficient U(VI) removal under acidic conditions
Crystallization mechanism of hierarchical low silica X zeolite induced by chloride ions and its catalytic implications in low-temperature NH3-selective catalytic reduction.
8Recent advances in polyoxometalate-based functional materials for sustainable remediation of radionuclide and heavy metal pollution
Regulating nitrogen activation and proton supply to boost photocatalytic nitrogen fixation via Co-ZnO/ MIL-125(SH)2 S-scheme heterojunction
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