• 레미콘
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• no.7 s.60
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• pp.79-87
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• 1999

• 레미콘
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• no.4 s.51
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• pp.82-89
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• 1997

• 레미콘
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• no.10 s.61
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• pp.104-112
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• 1999

• The Optical Journal
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• v.5 no.2 s.24
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• pp.13-21
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• 1993

• 레미콘
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• no.1 s.58
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• pp.70-78
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• 1999

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2005.10a
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• pp.47-52
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• 2005

• Journal of the Korea Institute of Building Construction
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• v.19 no.4
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• pp.361-371
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• 2019

In 2017, the ratio of the number of victims and deaths in the construction industry was the highest with 25.2% and 29.6%, respectively. Especially, as safety accidents at construction sites continue to increase, the economic loss is greatly increased too. Therefore, in order to prevent safety accidents in the construction work, the safety risk assessment index by type of construction was developed, and the main results of this study are as follows. First, 17 factors related to safety accidents at construction sites were derived through survey and interview survey, and this study suggested 9 items(process, type of construction, progress rate, contract amount, number of floors, safety education, working days and weather) throughout the expert advisory meeting. Second, the risk assessment index for safety accidents was developed based on the ratio and intensity of safety accidents. Third, to verify the risk assessment model, the construction safety risk assessment index by type of construction was derived by surveying and analyzing the statistics of the construction accident. In addition, the risk strength was calculated by dividing human damage caused by construction safety accidents into those killed and injured. The risk assessment index based on the frequency and intensity of safety accidents by type of construction is expected to be utilized as basic data when assessing the risk of similar projects in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4B
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• pp.385-396
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• 2009

Non-household unit water use for each type of business are estimated in this study. The business types are subdivided into forty based on nine categories by the national industrial standard classification, such as office, commerce, public bathing, public water use etc. Correlation analysis and analysis of variance (ANOVA) are applied to obtain statistical characteristics between industrial water use data, surveyed in six cities including Nonsan, Seosan and the National Statistical Bureau and site area, employees number etc. for each detailed business area. As the proposed non-household unit water uses are compared with five surveyed data in USA, it is shown that almost of water uses per unit area are less than those in USA. Non-household unit water uses of 25% cumulative probability water use recommended as efficiency benchmarks among surveyed data in Korea are also less than those in USA. Especially, in the case of water use in school, the average and the range are similar results showing water use range between 0.4 and 6.2 ($l/m^2/day$) as liter per capita day per an unit area, also water use range between 11.9 to 64.0 (l/student/day) as liter per capita day per a person. From the result of correlation analysis with internal and exogenous affecting factors on non-household water use, it can be concluded that a unit area is most appropriate factor as a standard of non-household unit water use. In case of water use in educational business, the number of students including staffs is more correlated than site ares with water use for the settled water consumption tendency. Although the increase and decrease of educational institutes, retail/wholesale store and restaurants are shown remarkable by the temperature as a representative factor, low correlations are shown in water use fluctuation in lodging house and hospital.

• Environmental and Resource Economics Review
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• v.15 no.4
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• pp.761-782
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• 2006

Public concern about potential environmental risks of agricultural business for the livestock production and processing is increasing. However, due to differences in general industry structural characteristics, such as farm production and waste management practices, the effect of environmental policies may differ from species to species as well as across size categories. I hypothesize that additionally the Hog subsector may be more responsive to (or a greater driver of) a changing environmental policy environment than the beef cattle subsector. As a result, I expect to see more evidence of sensitivity in the environmental policy milieu from hog-operation stocking and location decisions than with the beef cattle industry. The written stringency may not effective, instead state's willingness to enforce has directed and regulated. However, in presence of rapid structural change, just like hog, industry location is affected by state regulation. The environmental compliance cost may be a small portion of industry total cost and fixed cost of beef industry makes for them to take into account environmental compliance for their decision location making. The special movements of flog industry have chance to minimize the cost of the operation and they willing to locate less stringent place.

• The Journal of the Convergence on Culture Technology
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• v.4 no.3
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• pp.235-240
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• 2018

ICT converged smart factory started from German Industry 4.0 has been the driving force for the $4^{th}$ Industrial Revolution and the center of manufacturing innovation for major industrial countries. It will be developed according to industry characteristics of each country. Korea is relatively later than other competing countries in the smart factory area. So, the government is establishing related policy and tendering all sorts of supports for smart factory mainly to the small and medium-sized enterprises to spread over the manufacturing industry. It is necessary for government to categorize among similar manufacturing industry and make them share digitalized production information mutually. It will be more effective method for securing global competitiveness than the uniform support. Also, large companies need to establish cloud based production forecasting system over similar industry and share it with other companies rather than expansion of individual smart factory. Mutual development in the manufacturing industry will be realized when the small and medium-sized enterprises and large companies take part in the cooperating ground of smart factory.