• Journal of Environmental Health Sciences
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• v.18 no.1
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• pp.22-33
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• 1992

In Iri industrial area, Heavy metals in ait of the factories, 10 metal and 8 non-metal factories, were examined for ten months(from Feb. to Nov, 1991). The methals in blood, urin and hair of 232 employees who have worked in the factories were also examined at the same time The results are summarized as follows 1. Heavy metals Pb, Cd and Mn in the metal factories were 0.031mg/m$^{3}$, 0.0065mg/m$^{3}$, and 0. 035mg/m$^{3}$ respectively, but 0.017mg/m$^{3}$, 0.021mg/m$^{3}$ and 0.014mg/m$^{3}$ in non-metal factories. 2. Heavy metals such as Pb, Cd and Mn in blood from employees in the metal factories were measured : 22.36$\mu$g/dl, 0.27$\mu$g/dl and 1.26$\mu$g/dl respectively, The values in the non-metal factories were 19.84$\mu$g/dl, 0.21$\mu$g/dl and 1.24$\mu$g/dl. 3. Heavy metals such as Pb, Cd and Mn in urine from employees in the metal factories were measured 32.94$\mu$g/l, 0.16$\mu$g/dl, and 1.60$\mu$g/dl respectively, whereas the values in the non-metal factories were 28.79$\mu$g/l, 0.13$\mu$g/dl and 1.35$\mu$g/l. 4. Heavy metals such as Pb, Cd and Mn in hair from employees in the metal factories were measured 8.92mg/kg, 0.33mg/kg and 3.71mg/kg respectively, but 8.14mg/kg, 0.31mg/kg and 3.26 rng/kg in the non-metal factories.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.4
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• pp.600-616
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• 2022

This study contemplates spatial context of startup in manufacturing, mainly analyzing job creation effects of time lag of startups in manufacturing at different technology levels. Using DID model, we found that each region including capital, non-capital and metropolitan area shows different job creation effects of time lag. In capial region, startup cohort in high R&D intensity manufacturing was found to show short-term job creation effects, but in non-capital region, long-term job creation effects was found with the one in medium-high R&D intensity manufacturing. In case of metropolitan area, we couldn't find much evidence of job creation effects that was statistically significant. The result of analysis implied that, in capital region, startup support policies, targeting at high R&D intensity manufacturing, ought to be focused on scale-up of startups that survived for a certain period. And non-capital area and some of metropolitan areas in non-capital region that have comparatively inferior infrastructure and brain-drain problems as well should focus on fostering startups in medium-high R&D intensity manufacturing in a long-term perspective and utilize their traditional manufacturing base.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.502-512
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• 2019

To examine the problem structure of balanced regional development policy and identify the causes of the gap between the capital and non-capital areas, this study examined the causal structure in terms of the concentration and distribution of functions in the metropolitan area and non-capital area and explored the policy leverage. As a research method, the causal map was drawn using System Dynamics techniques and policy leverage was derived through an exploration of the feedback structure. In particular, the causes of the problems in balanced regional development policies were approached by system accidents rather than by single-circuit accidents, and causality analysis was performed among the variables constituting balanced regional development policies based on system accidents. In particular, it singled out 31 variables, developed 13 feedback loops, and confirmed four major policy leverages, including relocation of the capital function, local decentralization policies, consistent development, and investment practices centered on the Non­Capital Area. Subsequently, a follow-up study by computer simulation is needed by modeling the structure of the system to identify the ripple effect of the policies of the Capital Area on the Non­Capital Area.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.34-41
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• 2018

This paper studied balanced regional development focused on employment in Korea, by analyzing regional disparity between regional and industrial employment. A Gini-coefficient decomposition method and Panel Granger causality test were conducted, using raw data of the Census on Establishments reported by the Statistics Korea. The regional and industrial disparity of employment, based on the Gini-coefficient decomposition method, have increased by year. However, the growth rates of disparity are on the decrease. Most of employment disparity occurred from regional disparity between SMA (Seoul Metropolitan Area) and Non-SMA. Industrial disparity are occurred mainly by the service industry. The amount of contribution to the whole disparity of inter/intra regional employment was differed by each industrial sector. Also the causal relationship between employment growth of manufacture and that of service industry was analyzed by Panel Granger causality test. In national level, the employment growth in manufacture industry has conduced that in service industry. On the other hand, in the Non-SMA, only the employment growth in manufacture industry has augmented that in service industry. In conclusion, to reduce employment disparity, the strategy for balanced regional development should be emphasized. Different strategies are needed across regions and industries. Basically creating new job in the Non-SMA is inevitable. In view of stable employment, manufacture industry is more desirable rather than service industry.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.1379-1382
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• 2009

The 3D display has been used in optical projection technology to connect twenty mini- projectors with seamless image tiling. In this way, we can improve the projected resolution by reducing each project screen and increase projected area by connect several mini-projectors. In this article, the illumination system uses the LED light source, non- telecentric structure and LCOS panel, and it's total length is less than 10 centimeter. It can build a seamless large display by tiling multiple projectors.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.345-352
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• 2004

This study analyzes interrelationship with air pollution quality and land use patterns using GIS. The relationship can be obtained via three steps: (1) making out air pollution map from air pollution information of study area, (2) dividing land use patterns into residential area, commercial area, industrial area, traffic concentrated area, and non-polluted area, and (3) spatial overlaying analysis of GIS. Moreover, through analyzing air pollution quality by land use patterns, pollution sources can be identified. The results also coincide with the characteristics of conventional air pollution finding. More detailed analyses using articulated on site air pollution quality measurement databases are needed to correctly identify the pollution sources through finding interrelationship with land use patterns and air pollution Quality using GIS. The developed method can help trace the path of pollution sources and plan urban land use projects.

• Journal of Environmental Health Sciences
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• v.38 no.6
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• pp.482-492
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• 2012

Objectives: The main purpose of this study is to produce background data which can be compared with data on vulnerable areas such as industrial complexes in Ulsan, SihwaBanwol, Gwangyang, Yeosu, Pohang, Cheongju and Daesan in Korea. Methods: This study was performed on 1,007 local residents in Gangneung using personal questionnaires and medical check-up. Environmental pollutants including heavy metals in blood and urine were analyzed and the results are as follows. Results: According to the results of medical check-up, 705 subjects were "Normal (A and B)", 232 subjects were "Disease doubtful (R1)" and 70 subjects were "High blood pressure or Diabetes doubtful (R2)". Regarding geometric mean concentration, blood lead was 1.57 ${\mu}g/dL$, urine cadmium was 0.82 ${\mu}g/g-cr$, urine mercury was 0.98 ${\mu}g/g-cr$ and urine arsenic was 15.78 ${\mu}g/g-cr$. In the analysis of 11 kinds of VOCs in blood, vinyl chloride, 1,3-butadiene and dichloroethylene were not detected, while the detection rate of other chemicals was above 70% except chloroform(49.7%) and trichloroethylene(19.0%). In analysis of 16 kinds of PAHs in blood, 10 kinds showed more than 80% in detection rate. Also, detection rate of 4 kinds of PCBs in blood ranged 52 to 78%. Conclusions: Compared with industrial compelxes, the concentration of blood lead was lower, while urine cadmium and mercury levels were similar. Also, urine arsenic ranged at a significant level. Further study is required to find the cause of regional differences in concentrations of environmental pollutants.

• Environmental Analysis Health and Toxicology
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• v.27
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• pp.12.1-12.8
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• 2012

Objectives: The aim of this study was to identify the health and environmental risk factors of air contaminants that influence environmental and respiratory diseases in Gyeongju, Pohang and Ulsan in South Korea, with a focus on volatile organic compounds (VOCs). Methods: Samples were collected by instantaneous negative pressure by opening the injection valve in the canister at a fixed height of 1 to 1.5 m. The sample that was condensed in $-150^{\circ}C$ was heated to $180^{\circ}C$ in sample pre-concentration trap using a 6-port switching valve and it was injected to a gas chromatography column. The injection quantity of samples was precisely controlled using an electronic flow controller equipped in the gas chromatography-mass spectrometer. Results: The quantity of the VOC emissions in the industrial area was 1.5 to 2 times higher than that in the non-industrial area. With regards to the aromatic hydrocarbons, toluene was detected at the highest level of 22.01 ppb in Ulsan, and chloroform was the halogenated hydrocarbons with the highest level of 10.19 ppb in Pohang. The emission of toluene was shown to be very important, as it accounted for more than 30% of the total aromatic hydrocarbon concentration. Conclusions: It was considered that benzene in terms of the cancer-causing grade standard, toluene in terms of the emission quantity, and chloroform and styrene in terms of their grades and emission quantities should be selected for priority measurement substances.

• Journal of Korean Society for Atmospheric Environment
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• v.16 no.2
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• pp.121-128
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• 2000

To identify the effects of air pollution on several physiological characteristics of Styrax japonica in Yeochon industrial comples soil properties photosynthetic capacity and chorophyll contents were investigated. All of these measurements were taken on Styrax japonica stand at 1. 3 and 7km from an industrial complex which is an air pollution source. In addition a Styrax japonica stand in Sang-ju city was selected as a control Each physiological measurement was in leaves of east-, west- south- and north-facing branches. The results obtained were as follows: 1) Al contents of soil in Yeochon industrial complex were higher than those of Sang-ju city a non-polluted area. higheral contents contributed to decrease in soil pH. 2) Chlorophyll contents of leaves at 1km from the industrial complex closest from the air pollution source were lower than those of 3 and 7km/ 3) Photosynthetic capacity of Styrax japonica at 1km from the air pollution source was lower than those at 3 and 7km.

• Journal of Industrial Technology
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• v.27 no.A
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• pp.95-102
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• 2007

The practice of business estimate flood discharge by rainfall-flow relation that is easy collection of observation data. The important factor is rainfall, coefficient of runoff, and drainage area for analysis of runoff-flow relation. The practice of business usually use probability rainfall that use a weighted average value after each observation post estimate probability of non-same time. It has more error than same time probability rainfall, and it can excess of estimation because it can't consider space distribution of rainfall. The study of result showed similar aspect with existing ARF but width of coefficient become smaller. And the comparison of peak flow did not different what used by ARF and same time probability rainfall(A group). But non-same time probability rainfall is bigger 25% more than another(B group). Between A group and B group of the difference increased with the lapse of time.