A5050672726- Image Processing Techniques and Applications
- Spectroscopy and Chemometric Analyses
- Advanced Image Processing Techniques
- Remote Sensing in Agriculture
- Advanced Vision and Imaging
- Seismic Imaging and Inversion Techniques
- Seismology and Earthquake Studies
- Adaptive Control of Nonlinear Systems
- Robotic Path Planning Algorithms
- Control and Dynamics of Mobile Robots
- Leaf Properties and Growth Measurement
National Engineering Research Center for Information Technology in Agriculture
2025
Beijing Academy of Agricultural and Forestry Sciences
2025
Ministry of Agriculture and Rural Affairs
2025
Shenzhen Polytechnic
2024
University of Vermont
2005
Abstract Leaf chlorophyll content (LCC) is a key indicator for assessing the growth of grapes. Hyperspectral techniques have been applied to LCC research. However, quantitative prediction grape using this technique remains challenging due baseline drift, spectral peak overlap, and ambiguity in sensitive range. To address these issues, two typical crop leaf hyperspectral data were collected reveal response characteristics standardization by variables (SNV) multiple far scattering correction...
This paper presents a new tracking method for mobile robot by combining predictive control and fuzzy logic control. Trajectory of autonomous robots usually has non-linear time-varying characteristics is often perturbed additive noise. To overcome the time delay caused slow response sensor, algorithm uses control, which predicts position orientation robot. In addition, used to deal with system. Experimental results demonstrate feasibility advantages this on trajectory
Multi-attributes classification has become a standard technique in seismic interpretation. In this paper, we apply transductive support vector machine (TSVM), which is one of semi-supervised methods, on attributes for reservoir characterization and hydrocarbon detection. order to the TSVM real attributes, an unlabelled samples selection strategy proposed reduce by prior knowledge improve efficiency. Our method exploits both precious well information soft obtain more reliable classifier....