We tried to address major error sources and assess their uncertainty: 1) 3D structure errors: .
The model parameters, disturbance inputs and model structure errors are unknown but bounded, and the parameter value can abruptly change.
3D structure errors: It has to be admitted that suppressed trees and irregular tree crowns are hard to detect from CHM.
The structure errors and kinematic pair errors of the rotary pairs are defined and identified by using the fixed and moving axes, and the kinematic model of the two-axis rotary table is deduced with those errors.
The influence on the reconstructed results increases with the increasing of the phantom structure errors.
We have explored the influence of the detection noises, optical property errors and phantom structure errors on the hybrid algorithm respectively.
To explore the influence of the phantom structure errors on the hybrid algorithm, we make some shifts to different components of the numerical phantom.
Individual student's misunderstanding can be diagnosed by using the synthesis map as a formative assessment tool and looking for errors in knowledge structure, errors in content, or omission of content (McClure et al., 1999).
Numerical simulations have been performed to validate this proposed algorithm and demonstrate its high performance in the multi-source situation even if the detection noises, optical property errors and phantom structure errors are involved in the forward imaging.
Besides, due to its tolerance of phantom structure errors and optical property errors, our algorithm can be probably used for the bioluminescent source reconstruction in the multi-modality imaging system.
