• Analytical Science and Technology
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• v.14 no.2
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• pp.131-139
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• 2001

There is personal difference in the concentrations of trace elements in human hair according to human life or history suck as occupation, race, sex, age, food habit, social condition and so on. It is also found that the individual's deviation of elemental concentrations is reflecting the degree of environmental pollutants exposure to human body, intakes of food and metabolism. To compare the degree of accumulation in the hair tissue, human hair samples were collected from five positions of head and analyzed by non-destructive neutron activation analysis with and without washing according to IAEA's recommended method. Analytical quality control is performed using the certified reference material. The relative error of Cu, Cr, Na, Co, Mg, As, Se, Zn and those of Mn, Ca, Fe, Sr are within ${\pm}5%$ and ${\pm}10%$, respectively and the relative standard deviation of elements are within ${\pm}10%$. The deviations between the individuals and hair sampling positions were estimated. The deviation of individual was seven times more than that of positions. Under the defined condition, the difference and the correlation of elemental concentrations were compared with two different groups, office and factory workers. The result can be used as a fundamental data for human health and environment assessment.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.4
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• pp.299-308
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• 2007

Purpose: The aim of this study was to develop a bioinformatics software and to test it in serum samples of papillary thyroid cancer using mass spectrometry (SELDI-TOF-MS). Materials and Methods: Development of 'Protein analysis' software performing decision tree analysis was done by customizing C4.5. Sixty-one serum samples from 27 papillary thyroid cancer, 17 autoimmune thyroiditis, 17 controls were applied to 2 types of protein chips, CM10 (weak cation exchange) and IMAC3 (metal binding - Cu). Mass spectrometry was performed to reveal the protein expression profiles. Decision trees were generated using 'Protein analysis' software, and automatically detected biomarker candidates. Validation analysis was performed for CM10 chip by random sampling. Results: Decision tree software, which can perform training and validation from profiling data, was developed. For CM10 and IMAC3 chips, 23 of 113 and 8 of 41 protein peaks were significantly different among 3 groups (p<0.05), respectively. Decision tree correctly classified 3 groups with an error rate of 3.3% for CM10 and 2.0% for IMAC3, and 4 and 7 biomarker candidates were detected respectively. In 2 group comparisons, all cancer samples were correctly discriminated from non-cancer samples (error rate = 0%) for CM10 by single node and for IMAC3 by multiple nodes. Validation results from 5 test sets revealed SELDI-TOF-MS and decision tree correctly differentiated cancers from non-cancers (54/55, 98%), while predictability was moderate in 3 group classification (36/55, 65%). Conclusion: Our in-house software was able to successfully build decision trees and detect biomarker candidates, therefore it could be useful for biomarker discovery and clinical follow up of papillary thyroid cancer.

• Journal of Korea Entertainment Industry Association
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• v.13 no.1
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• pp.99-109
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• 2019

The study was conducted to investigate the activities of working people in the sports club. The subject of this study was to take samples of workers who participated in the physical education system using the convenience sampling method. Out of a total of 400 questionnaires, 387 were used for research purposes, except for invalid or error questionnaires. Factor analysis and reliability tests were performed using IBM SPSS statistics Ver 21.0. Frequency analysis was conducted to explore the general characteristics of the study participants. An independent sample t-test ANOVA were conducted to verify differences among groups according to demographic characteristics, and a correlation analysis was conducted to examine the relationship between variables. Regression was performed to verify the effect of variable factors. The results of the study are as follows. First, there was no difference in wellness and job satisfaction according to gender. Second, there was no difference in wellness and job satisfaction according to sport. Third, there was a significant difference intellectual wellness according to age. In particular, 40s and 50s were higher than 60s and over. Fourth, there was a significant difference in social wellness according to activity duration. In particular, 1~2 years were higher than 3 years or more. Finally, If you look at the impact of working people's wellness lifestyle sports club activities on job satisfaction, the professional wellness lifestyle club activities showed significant influence on job satisfaction.

• Economic and Environmental Geology
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• v.28 no.3
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• pp.199-211
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• 1995

The deep sea camera system could render it possible to obtain the detailed information of the nodule distribution, but difficult to estimate nodule abundance quantitatively. In order to estimate nodule abundance quantitatively from deep seabed photographs, the nodule abundance equation was derived from the box core data obtained in KODOS area(long.: $154^{\circ}{\sim}151^{\circ}W$, lat.: $9^{\circ}{\sim}12^{\circ}N$) during two survey cruises carried out in 1989 and 1990. The regression equation derived by considering extent of burial of nodule to Handa's equation compensates for the abundance error attributable to partial burial of some nodules by sediments. An average long axis and average extent of burial of nodules in photographed area are determined according to the surface textures of nodules, and nodule coverage is calculated by the image analysis method. Average nodule abundance estimated from seabed photographs by using the equation is approximately 92% of the actual average abundance in KODOS area. The measured sampling points by box core or free fall grab are in general very sparse and hence nodule abundance distribution should be interpolated and extrapolated from measured data to uncharacterized areas. The another goal of this study is to depict continuous distribution of nodule abundance in KODOS area by using PC-version of geostatistical model in which several stages are systematically proceeded. Geostatistics was used to analyse spatial structure and distribution of regionalized variable(nodule abundance) within sets of real data. In order to investigate the spatial structure of nodule abundance in KODOS area, experimental variograms were calculated and fitted to a spherical models in isotropy and anisotropy, respectively. The spherical structure models were used to map out distribution of the nodule abundance for isotropic and anisotropic models by using the kriging method. The result from anisotropic model is much more reliable than one of isotropic model. Distribution map of nodule abundance produced by PC-version of geostatistical model indicates that approximately 40% of KODOS area is considered to be promising area(nodule abundance > $5kg/m^2$) for mining in case of anisotropy.

• Journal of Service Research and Studies
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• v.10 no.2
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• pp.1-15
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• 2020

As big data become more important socially and economically in recent years, many problems have been derived from the indiscriminate application of big data. Big data are valuable because it can figure out the meaning of informative information hidden within the data. In particular, to predict customer behavior patterns and experiences, structured data that were extracted from Customer Relationship Management (CRM) or unstructured data that were extracted from Social Network Service(SNS) can be defined as a population to interpret the data, during which many errors can occur. However, those errors are usually overlooked. In addition to data analysis techniques, some data, which should be considered in the analysis, are not included in the population and thus do not show any meaningful patterns. Therefore, this study presents the measurement and interpretation of the data generated when the cause of error in the population setting is strong relationship and interaction between people or a person and an object. In other words, it will be shown that if the relationship and interaction are strong, it is important to include data collected from the perspective of user experience and ethnography in the population by comparing various cases of big data application, through which the meaning will be derived and the best direction will be suggested.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.51-66
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• 2022

SST (Sea Surface Temperature) is based on the atmosphere-ocean interaction, one of the most important mechanisms for the Earth system. Because it is a crucial oceanic and meteorological factor for understanding climate change, gap-free grid data at a specific spatial and temporal resolution is beneficial in SST studies. This paper examined the production of daily SST grid maps from 137 stations in 2020 through the ordinary kriging with variogram optimization and their accuracy assessment. The variogram optimization was achieved by WLS (Weighted Least Squares) method, and the blind tests for the interpolation accuracy assessment were conducted by an objective and spatially unbiased sampling scheme. The four-round blind tests showed a pretty high accuracy: a root mean square error between 0.995 and 1.035℃ and a correlation coefficient between 0.981 and 0.982. In terms of season, the accuracy in summer was a bit lower, presumably because of the abrupt change in SST affected by the typhoon. The accuracy was better in the far seas than in the near seas. West Sea showed better accuracy than East or South Sea. It is because the semi-enclosed sea in the near seas can have different physical characteristics. The seasonal and regional factors should be considered for accuracy improvement in future work, and the improved SST can be a member of the SST ensemble around South Korea.

• Journal of the Korean Geotechnical Society
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• v.38 no.3
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• pp.35-42
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• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.

• Journal of Korea Technology Innovation Society
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• v.14 no.2
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• pp.373-404
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• 2011

The purpose of research is to reveal the effect of innovation to enterprises' economic performance. The kind of this study has begun since 1960s and lively progressed then. The fmal theoretical result of the effect of innovation to the performance came positive in compare to the mixed results came out in empirical analysis. There are several reason why empirical results are different to the theoretical results. However the major factor is that of using imperfect statistics and inappropriateness of analysis method. This study used a population (1990~2008) provided from Korean Intellectual Property Office, KIPO for patent and also used a population (1990~2008) provided from Korea Investors Service, KIS for research and development. The contribution of this study is enormous statistical analysis. This study used principal component analysis made innovativeness index for appropriate index sampling, and made effort to minimize the error by using appropriate quantile regression for both to panel analysis and rapidly developed company analysis. Dividing the final results into two parts, the growth and the profit, the effect of technological innovation to the firm's growth is not significant to the panel analysis but heavily significant to the upper 10% of high growth firm. By classifying large company and small and medium enterprise, it is significant to upper 10% of high growth firm for large company and generally significant to small and medium enterprise. But for both lower 10% of low growth firms and 25% of low ranking firms are negatively effected, and for high growth firms larger than the medians are positively effected. Especially for upper 10% of high growth firms are mostly effected. It is more effective to the profitability than the growth. The effect to the profit for every enterprises are not significant, but effected significant to the larger enterprises than 25% of low ranking enterprises especially most effective to the upper 10% of high-profit enterprises. The analysis for the large company, it was significant and positively effected to the upper 10% of high profit enterprises and 25% of low ranking enterprises, but the negatively effected for the low-profit enterprises. For the small and medium enterprises, it is negatively effected for both 10% of low ranking enterprises and 25% of low ranking enterprises. However it is positively effective and significant for the high ranking enterprises than median, especially for those high growth firms. It is meaningful to recognize significancy by quantile, but more implicative result is to finding more effectiveness to the small and medium enterprises than to the large company.

• Tunnel and Underground Space
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• v.17 no.2 s.67
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• pp.139-151
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• 2007

Stereophotogrammetry is a method to extract information of an interested area by constructing a stereo-image from two or more photos. In this study, the stereophotogrammetry was adopted to obtain the joint orientation and trace length from a sampling window and its measurements were compared with the result by a clinocompass and measuring tape to evaluate the applicability of the stereophotogrammetry to rock joint survey. A commercial stereophotogrammetry program, ShapeMetriX 3D, was used for this purpose. Firstly, the accuracy of the measuring method using ShpaeMetrix 3D was evaluated by a model test. Secondly, joint orientations on a rock slope and tunnel were obtained by using ShapeMetriX 3D and compared with the measurement by a clinocompass. Finally. the effect of base-depth ratio in photographing was evaluated by comparing images with various base-depth ratios, and the usefulness of closed-up photographing on a rock exposure to increase the measurement accuracy was tested. The dip and dip direction of each model plane obtained by ShapeMetriX 3D showed an error ranged between $-5^{\circ}\;and\; 5^{\circ}$ on the basis of the results by the measuring tape. Base-depth ratio proved not to influence the analysis result by ShapeMetriX 3D if all the images were taken without any hidden area. The close-up photographing turned out useful to obtain the detailed images and therefore precise result when ShapeMetriX 3D was adopted.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.2 s.5
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• pp.129-140
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• 2004

In this paper, we conduct the research about optimal aggregation interval of travel time data on interrupted traffic flow and verify the reliability of AVI collected data by using car plate matching method in RTMS for systematic collection and analysis of link travel time data on interrupted traffic flow rural arterial. We perform Kolmosorov-Smirnov test on AVT collected sample data and on entire population data, and conclude that the sample data does not represent pure random sampling and hence includes sample collection error. We suggest that additional review is necessary to investigate the effectiveness of AVI collected sample data as link representative data. We also develop statistical model by applying two estimation techniques namely point estimation and interval estimation for calculating optimal aggregation interval. We have implemented our model and determine that point estimate is preferable over interval estimate for exactly selecting and deciding optimal aggregation interval. Our final conclusion is that 5-minute aggregation interval is optimal to estimate travel time in RTMS, as is currently being used our investigation is based on AVI data collected from Yang-ji to Yong-in $42^{nd}$ National road.