Setting Up Railway Track Crosswise Roughness Spectral Using Time Series Analysis Method

In this paper, the method of time series analysis——the maximum entropy spectrum estimation is used in setting up the track crosswise roughness power spectrum density functions (call simple “the track spectra”), the track spectra are obtained by process the data measured from the ground. The results obtained in the way are prove to be superior to those obtained with the "FFT" methods.

Predicting Surface Roughness and Moisture of Bare Soils Using Multi- band Spectral Reflectance Under Field Conditions

Soil surface roughness, denoted by the root mean square height(RMSH), and soil moisture(SM) are critical factors that affect the accuracy of quantitative remote sensing research due to their combined influence on spectral reflectance(SR). In regards to this issue, three SM levels and four RMSH levels were artificially designed in this study; a total of 12 plots was used, each plot had a size of 3 m × 3 m. Eight spectral observations were conducted from 14 to 30 October 2017 to investigate the correlation between RMSH, SM, and SR. On this basis, 6 commonly used bands of optical satellite sensors were selected in this study, which are red(675 nm), green(555 nm), blue(485 nm), near infrared(845 nm), shortwave infrared 1(1600 nm), and shortwave infrared 2(2200 nm). A negative correlation was found between SR and RMSH, and between SR and SM. The bands with higher coefficient of determination R^2 values were selected for stepwise multiple nonlinear regression analysis. Four characterized bands(i.e., blue, green, near infrared, and shortwave infrared 2) were chosen as the independent variables to estimate SM with R^2 and root mean square error(RMSE) values equal to 0.62 and 2.6%, respectively. Similarly, the four bands(green, red, near infrared, and shortwave infrared 1) were used to estimate RMSH with R^2 and RMSE values equal to 0.48 and 0.69 cm, respectively. These results indicate that the method used is not only suitable for estimating SM but can also be extended to the prediction of RMSH. Finally, the evaluation approach presented in this paper highly restores the real situation of the natural farmland surface on the one hand, and obtains high precision values of SM and RMSH on the other. The method can be further applied to the prediction of farmland SM and RMSH based on satellite and unmanned aerial vehicle(UAV) optical imagery.

Investigation on the cleaning of KDP ultra-precision surface polished with micro water dissolution machining principle

A potassium dihydrogen phosphate(KDP) optical crystal was machined to an ultra-precision surface with water-in-oil(W/O) micro emulsion polishing fluid. The micro water dissolution principle utilized in the machining process is discussed, its planarization mechanism is illustrated, and an ultra-precision polished surface with 2.205 nm RMS roughness is obtained. However, a substantial quantity of residual contamination remained on the polished surface after machining. This can seriously impact the optical performance of the crystal, and so it must be removed. Fourier transform infrared(FTIR) spectroscopy was used to conduct an investigation into the composition of the surface residue, and the results showed that the residue was comprised of organic chemicals with hydrocarbon chains and aromatic ether, i.e., mostly the polishing fluid. The cleaning method and the principle on which the KDP ultra precision surface investigation is based are discussed in detail, and the cleaning experiments with selected KDP-compatible organic solvents were then performed. FTIR transmittance spectra measurement and microscopic observations were employed to assess the effects of the cleaning process on the surface of the KDP crystal. The results showed that toluene cleaning achieved the most desirable results. This cleaning method produced a surface roughness of 1.826 nm RMS, which allows the KDP crystal to be applied to subsequent engineering applications.