Proc. SPIE. 7658, 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Detector, Imager, Display, and Energy Conversion Technology
KEYWORDS: Mirrors, Photon counting, Backscatter, LIDAR, Signal attenuation, Aerosols, Clouds, Signal analyzers, Stray light, Signal detection
A co-axial transmission elastic-backscattered lidar aiming to detect the optical properties of the clouds is presented in
this paper. The modular co-axial design can guarantee the consistency of the transmitting part and the receiving part. In
practice a specific diaphragm is used to suppress the stray light of the primary mirror and background light to improve
SNR of the backscattered signal in the daytime. So the near ground signal must be corrected with the appropriate overlap
factor. A Licel transient recorder is used for data acquisition in analog and photon counting combined in one acquisition
system. With the 15 MHz sampling rate, the spatial resolution of 10 m can be attained. The control over the transient
recorder and the treatment of the data is performed on a PC. After getting the correctional backscattered signal, retrieving
and analyzing the extinction coefficient profile, the cloud base, cloud peak and related optical parameters of the clouds
can be confirmed. In order to testify the feasibility of our lidar, it was implemented with a Finland ceilometer Vaisala
simultaneously in May in 2008 in Hefei. Results show the lidar system is stable and the data is reliable.
The reasons why the coordinate measuring machine (CMM) dynamic error exists are complicate. And there are many elements which influence the error. So it is hard to build an accurate model. For the sake of attaining a model which not only avoided analyzing complex error sources and the interactions among them, but also solved the multiple colinearity among the variables. This paper adopted the Partial Least-Squares Regression (PLSR) to build model. The model takes 3D coordinates (X, Y, Z) and the moving velocity as the independent variable and takes the CMM dynamic error value as the dependent variable. The experimental results show that the model can be easily explained. At the same time the results show the magnitude and direction of the independent variable influencing the dependent variable.
The dynamic error sources of CMM were analyzed and the character of the dynamic error data was investigated in this
paper. Based on the character, the dynamic error model of CMM was built by using Bayesian statistical principle
combined with the standard quantity interposition method. The specific error model building procedures was deduced.
The CMM dynamic error separating and contrasting experimental devices were designed by using the laser
interferometer and the measuring block group. The theoretical analysis and the experiment result indicate that all
influences of the CMM dynamic error sources is considered in the model building method by using Bayesian statistical
principle combined with the standard values interposition method which meets the CMM working condition. The error
model accuracy reaches 2.4 μm and meets the CMM demand. The needed error data size is greatly reduced by using the
dynamic error model building method. The error separating principle by lapping-in the measuring block group is simple,
which is implemented easily and meets the timing dynamic error correcting needs of the ordinary CMM user.