Economic indicators are a factor that affects air cargo volume. This study analyzes the different factors affecting air cargo volume by each Chinese cities according to the main characteristics. The purpose of this study is to help companies related to China, airlines, and other stakeholders predict and prepare for the fluctuations in air cargo volume and make optimal decisions. To this end, 20 economic data were used, and the entire data was reduced to 5 dimensions through factor analysis to build a dataset necessary and evaluated the influencing factors by multi regression. The result shows that Macro-Economic Indicators, Production/Service indicators are significant for every cities and Chinese manufacture/Customer indicators, Korean manufacture/Oil Price indicators, Trade/Current indicators are significant for each other city. All adjusted R2 values are high enough to explain our model and the result showed excellent performance in terms of analyzing the different factors which affects air cargo volume. If companies that are currently doing business with China can identify factors affecting China's cargo volume, they can be flexible in response to changes in plans such as plans to enter China, production plans and inventory management, and marketing strategies, which can be of great help in terms of corporate operations.

We performed the quantitative analysis using MATLAB for slice thickness, spatial resolution, and low contrast resolution with the 50 received images which have suitable judgement by scanning AAPM phantom using GE, PHILIPS, SIEMENS, and TOSHIBA. Slice thickness and spatial resolution were measured by using full width at half maximum(below FWHM). Spatial resolution was confirmed that FWHM interval more than 1.0 mm size. Low contrast resolution was measured that how close by using Roundness Index(below RI) until 6.4 mm size. The spatial resolution 9 images and low contrast resolution 10 images which have disapproved judgement were also had the quantitative analysis. The statistic analysis was judged to have statistically significant differences when p-value is less than 0.05 through SPSS statistics 19.0 and T-test (paired t-test). For slice thickness 5 mm, the average qualitative evaluation group shows 4.98 mm and quantitative evaluation group shows 5.02. For slice thickness 10 mm, the average qualitative evaluation group shows 9.98 mm and quantitative evaluation group shows 9.86. No significant differences (p=0.07, p=0.06) with 5 mm and 10 mm. It confirmed the interval for all FWHM until 1.75, 1.50, 1.25, 1.00 mm as approved images on spatial resolution ,and the average distance was 0.102 mm and the minimum distance 0.054 mm. For disapproved images, all interval was not showed under 1.00 mm. For low contrast resolution, spproved image’s RI value shows average 0.81 and minimum 0.77. The disapproved image’s RI average shows 0.70. Statiscally significant differences were presented (all p<0.05) following hole size.