The setting of values on door hinge mounting compensation for door assembly tolerance is a constant quality issue in vehicle production. Generally, heuristic methods are used in satisfying appropriate door gap and level difference, flushness to improve quality. However, these methods are influenced by the engineer's skills and working environment and result an increasement of development costs. In order to solve these problems, the system which suggests hinge mounting compensation value using CAE (Computer Aided Engineering) analysis is proposed in this study. A structural analysis model was constructed to predict the door gap and level difference, flushness through CAE based on CAD (Computer Aided Design) data. The deformations of 6-degrees of freedom which can occur in real vehicle doors was considered using a stiffness model which utilize an analysis model. The analysis model was verified using 3D scanning of real vehicle door hinge deformation. Then, system model which applying the structural analysis model suggested the final adjustment amount of the hinge mounting to obtain the target door gap and the level difference by inputting the measured value. The proposed system was validated using the simulation and showed a reliability in vehicle hinge mounting compensation process. This study suggests the possibility of using the CAE analysis for setting values of hinge mounting compensation in actual vehicle production.

In the international businesses human resource elements acquired in different countries might have different values in varied industries due to the different quality of education and experiences in the original countries. Using selection models to evaluate expected values in earnings equation of human resource elements such as education and experiences etc. acquired in sending countries, system equations are expanded to examine also the values of science and engineering degrees in technology jobs with selectivity bias correction. This paper used the US census survey data of 2015 on earnings, academic degrees, occupations etc. The US has long maintained the policy of accepting more STEM workers than any other countries and helped maintaining own technological leadership. Assuming per capita GDP gap between the sending country and the US downgrades immigrant human resource quality, it rarely affects occupational selection but depresses earnings on average by two or more years’ worth of education. Immigrant quality index in the sense of GDP gap appears to be a valid tool to assess the expected earnings of the worker with. Engineering degrees increase significantly the probability of selecting not only engineering jobs but also general management jobs, as well as increasing the expected earning additionally over nine years’worth of education. Getting a technology job is additionally worth about four years of education. Economics and business degrees are worth additionally almost six years of education but humanities degrees depress expected earnings. Since years after immigration does not very fast enhance earnings capacity, education level and English language ability might be more useful criteria to expect better future earnings by.

In international businesses human resource elements acquired in different countries might have different values in varied industries due to different quality of education and experiences in original countries. Using existing models to evaluate expected values of human resource elements such as education and experience setc. acquired in sending countries they are expanded to examine also the values of science and engineering degrees in technology jobs with selectivity bias correction. This paper used the US census survey data of 2015 on earnings, academic degrees etc. to contrast qualitative effects with quantitative effects of human resource elements compared to those in the native and/or white group.

Real-life time series characteristic data has significant amount of non-stationary components, especially periodic components in nature. Extracting such components has required many ad-hoc techniques with external parameters set by users in a case-by-case manner. In this study, we used Empirical Mode Decomposition Method from Hilbert-Huang Transform to extract them in a systematic manner with least number of ad-hoc parameters set by users. After the periodic components are removed, the remaining time-series data can be analyzed with traditional methods such as ARIMA model. Then we suggest a different way of setting control chart limits for characteristic data with periodic components in addition to ARIMA components.