Lunar Penetrating Radar(LPR) has successfully been used to acquire a large amount of scientific data during its in-situ detection. The analysis of penetrating depth can help to determine whether the target is within t...Lunar Penetrating Radar(LPR) has successfully been used to acquire a large amount of scientific data during its in-situ detection. The analysis of penetrating depth can help to determine whether the target is within the effective detection range and contribute to distinguishing useful echoes from noise.First, this study introduces two traditional methods, both based on a radar transmission equation, to calculate the penetrating depth. The only difference between the two methods is that the first method adopts system calibration parameters given in the calibration report and the second one uses high-voltage-off radar data. However, some prior knowledge and assumptions are needed in the radar equation and the accuracy of assumptions will directly influence the final results. Therefore, a new method termed the Correlation Coefficient Method(CCM) is provided in this study, which is only based on radar data without any a priori assumptions. The CCM can obtain the penetrating depth according to the different correlation between reflected echoes and noise. To be exact, there is a strong correlation in the useful reflected echoes and a random correlation in the noise between adjacent data traces. In addition, this method can acquire a variable penetrating depth along the profile of the rover, but only one single depth value can be obtained from traditional methods. Through a simulation, the CCM has been verified as an effective method to obtain penetration depth. The comparisons and analysis of the calculation results of these three methods are also implemented in this study. Finally, results show that the ultimate penetrating depth of Channel 1 and the estimated penetrating depth of Channel 2 range from 136.9 m to 165.5 m(ε_r = 6.6) and from 13.0 m to 17.5 m(ε_r = 2.3), respectively.展开更多
Based on the factors impact strength model(FISM), we studied on calculation formulas of influence strength and key elements of FISM, and analyzed the turnover time of railway freight transportation of China. The resul...Based on the factors impact strength model(FISM), we studied on calculation formulas of influence strength and key elements of FISM, and analyzed the turnover time of railway freight transportation of China. The results show that wagon transfer time is the most critical factor among the three subjective factors of wagons turnover time. The FISM based analysis of wagon transfer time show that the wagon turnover time is significantly correlated with transit time with resorting. Among the seven factors of detention time of transit time with resorting, the time of waiting to departing, converging, and waiting to break-up are key factors, while the time of make-up, break-up, arrival and departure are general factors. We carried out one empirical research based on the data of Baoji East Railway Station in 2015. The results of empirical research and FISM are consistent completely.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 41403054)
文摘Lunar Penetrating Radar(LPR) has successfully been used to acquire a large amount of scientific data during its in-situ detection. The analysis of penetrating depth can help to determine whether the target is within the effective detection range and contribute to distinguishing useful echoes from noise.First, this study introduces two traditional methods, both based on a radar transmission equation, to calculate the penetrating depth. The only difference between the two methods is that the first method adopts system calibration parameters given in the calibration report and the second one uses high-voltage-off radar data. However, some prior knowledge and assumptions are needed in the radar equation and the accuracy of assumptions will directly influence the final results. Therefore, a new method termed the Correlation Coefficient Method(CCM) is provided in this study, which is only based on radar data without any a priori assumptions. The CCM can obtain the penetrating depth according to the different correlation between reflected echoes and noise. To be exact, there is a strong correlation in the useful reflected echoes and a random correlation in the noise between adjacent data traces. In addition, this method can acquire a variable penetrating depth along the profile of the rover, but only one single depth value can be obtained from traditional methods. Through a simulation, the CCM has been verified as an effective method to obtain penetration depth. The comparisons and analysis of the calculation results of these three methods are also implemented in this study. Finally, results show that the ultimate penetrating depth of Channel 1 and the estimated penetrating depth of Channel 2 range from 136.9 m to 165.5 m(ε_r = 6.6) and from 13.0 m to 17.5 m(ε_r = 2.3), respectively.
基金Funded by the Fundamental Research Funds for the Central Universities of China(No.26816WTD23)the National United Engineering Laboratory of Integrated and Intelligent Transportation of Southwest Jiaotong University,P.R.China(No.2682017ZT11)
文摘Based on the factors impact strength model(FISM), we studied on calculation formulas of influence strength and key elements of FISM, and analyzed the turnover time of railway freight transportation of China. The results show that wagon transfer time is the most critical factor among the three subjective factors of wagons turnover time. The FISM based analysis of wagon transfer time show that the wagon turnover time is significantly correlated with transit time with resorting. Among the seven factors of detention time of transit time with resorting, the time of waiting to departing, converging, and waiting to break-up are key factors, while the time of make-up, break-up, arrival and departure are general factors. We carried out one empirical research based on the data of Baoji East Railway Station in 2015. The results of empirical research and FISM are consistent completely.
