• Journal of Energy Engineering
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• v.12 no.3
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• pp.231-240
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• 2003

Reactivity of Char-CO$_2$ gasification of five coals for power generation was investigated with PTGA in the temperature range 850∼1000$^{\circ}C$ and the pressure range 0.5∼2.0 MPa. The effect of coal rank, initial char characteristics and pressure on the reaction rate was evaluated for five chars. The reactivity of low lank coal char was better than that of high rank coal char, and this could be explained with the initial pore structure and surface area of char. Meso/macro-pores of char seems to markedly affect char reactivity by way of providing channels for diffusion of reactant gas into the reactive surface area. For the range of tested pressure, the reaction rate is proportional to CO$_2$ partial pressure and the reaction order ranges from 0.4 to 0.7 for five chars. The effect of total pressure on the reaction rate was small, and kinetic parameters, based on the unreacted core model, were obtained for five chars.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.12 no.4
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• pp.253-265
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• 2014

Nuclear education & training is an important issue for sustainability of nuclear energy and the safety of the nuclear power plant. The purpose of this paper is to present policy initiatives for establishment of the national nuclear education & training system. It analyzed current status of nuclear manpower and nuclear education & training systems of Korean nuclear organizations and government strategic plans for nuclear manpower education & training. The features of the current nuclear education & training in Korea are institutional diversification and decentralization in Industry-University-Research system. However, linkages and cooperation systematically integrated between institutions are very weak. In addition, duplicated education & training programs and resource allocation, and the resultant inefficiency have been raised as a problem. Therefore, this paper proposed the national nuclear education & training system model as a macro policy initiatives and construction of control tower that manage and adjust overall nuclear education & training.

• Land and Housing Review
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• v.4 no.1
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• pp.43-54
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• 2013

This This paper investigates the variation of the factors to determinate housing price in Seoul metropolitan area after sub-prime financial crisis, in Korea, using a VAR model. The model includes housing price and housing rent (Jeonse) in Seoul metropolitan area from 1999 to 2011, and uses interest rate, real GDP, KOSPI, Producer Price Index and practices to impulse response and variance decomposition analysis to grasp the dynamic relation between a variable of macro economy and and a variable of housing price. Data is classified to 2 groups before and after the 3rd quater of 2008, when sub-prime crisis occurred; one is from the 1st quater of 1999 to the 3rd quater of 2008, and the other is from the 2nd quater of 1999 and the 4th quater of 2011. As a result, comparing before and after sub-prime crisis, housing price is more influenced by its own variation or Jeonse price's variation instead of interest rate and KOSPI. Both before and after sub-prime financial crisis, Jeonse price is also influenced by its own variation and housing price. While after sub-prime financial crisis, influences of Producer Price Index, KOSPI and interest rate were weakened, influence of real GDP is expanded. As housing price and housing rent are more influenced by real economy factors such as GDP, its own variation than before sub-prime financial crisis, the recent trend that the house prices is declined is difficult to be converted, considering domestic economic recession and uncertainty, continued by Europe financial crisis. In the future to activate the housing business, it ia necessary to promote purchasing power rather than relaxation of financial and supply regulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.6143-6150
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• 2012

The current society, namely information society is now moving to a specific topic which is SMART. In this sense, recently a variety of social areas including communications and SOC domains are moving fast to this topic. In Korea, The electric power area has been doing a pioneering job relatively successfully and the water supply area is just now taking the first step. The purpose of this research is to develop a technical Framework for Smart Water Grid Service. Related researches has been studied and the 4 constituting technical element areas were defined first. For each of the four areas, a framework modeling was fulfilled and as a result, a TRM(Technical Road Map) was suggested for each of the area. Finally, an Enterprise TRM covering all of the 4 areas was described. Furthermore, the currently suggested Framework model was compared to a related model and it was found that the integration of the models is desirable to wholly cover from Macro to Micro level applications and services. It is expected that the current approach contribute, more or less, to the smart implementation in the areas of water management.

• Journal of Korea Technology Innovation Society
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• v.12 no.4
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• pp.788-818
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• 2009

Renewable energy refers to solar energy, biomass energy, hydrogen energy, wind power, fuel cell, coal liquefaction and vaporization, marine energy, waste energy, and liquidity fuel made out of byproduct of geothermal heat, hydrogen and coal; it excludes energy based on coal, oil, nuclear energy and natural gas. Developed countries have recognized the importance of these energies and thus have set the mid to long term plans to develop and commercialize the technology and supported them with drastic political and financial measures. Considering the growing recognition to the field, it is necessary to analysis up-to-now achievement of the government's related projects, in the standards of type of renewable energy, management of sectional goals, and its commercialization. Korean government is chiefly following suit the USA and British policies of developing and distributing renewable energy. However, unlike Japan which is in the lead role in solar rays industry, it still lacks in state-directed support, participation of enterprises and social recognition. The research regarding renewable energy has mainly examinedthe state of supply of each technology and suitability of specific region for applying the technology. The evaluation shows that the research has been focused on supply and demand of renewable as well as general energy and solution for the enhancement of supply capacity in certain area. However, in-depth study for commercialization and the increase of capacity in industry followed by development of the technology is still inadequate. 'Cost-benefit model for each energy source' is used in analysis of technology development of renewable energy and quantitative and macro economical effects of its commercialization in order to foresee following expand in related industries and increase in added value. First, Investment on the renewable energy technology development is in direct proportion both to the product and growth, but product shows slightly higher index under the same amount of R&D investment than growth. It indicates that advance in technology greatly influences the final product, the energy growth. Moreover, while R&D investment on renewable energy product as well as the government funds included in the investment have proportionate influence on the renewable energy growth, private investment in the total amount invested has reciprocal influence. This statistic shows that research and development is mainly driven by government funds rather than private investment. Finally, while R&D investment on renewable energy growth affects proportionately, government funds and private investment shows no direct relations, which indicates that the effects of research and development on renewable energy do not affect government funds or private investment. All of the results signify that although it is important to have government policy in technology development and commercialization, private investment and active participation of enterprises are the key to the success in the industry.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.41-76
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• 2020

This study confirmed factors affecting smart factory technology acceptance through empirical analysis. It is a study on what factors have an important influence on the introduction of the smart factory, which is the core field of the 4th industry. I believe that there is academic and practical significance in the context of insufficient research on technology acceptance in the field of smart factories. This research was conducted based on the Unified Theory of Acceptance and Use of Technology (UTAUT), whose explanatory power has been proven in the study of the acceptance factors of information technology. In addition to the four independent variables of the UTAUT : Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, Government Assistance Expectancy, which is expected to be an important factor due to the characteristics of the smart factory, was added to the independent variable. And, in order to confirm the technical factors of smart factory technology acceptance, the Task Technology Fit(TTF) was added to empirically analyze the effect on Behavioral Intention. Trust is added as a parameter because the degree of trust in new technologies is expected to have a very important effect on the acceptance of technologies. Finally, empirical verification was conducted by adding Innovation Resistance to a research variable that plays a role as a moderator, based on previous studies that innovation by new information technology can inevitably cause refusal to users. For empirical analysis, an online questionnaire of random sampling method was conducted for incumbents of domestic small and medium-sized enterprises, and 309 copies of effective responses were used for empirical analysis. Amos 23.0 and Process macro 3.4 were used for statistical analysis. For accurate statistical analysis, the validity of Research Model and Measurement Variable were secured through confirmatory factor analysis. Accurate empirical analysis was conducted through appropriate statistical procedures and correct interpretation for causality verification, mediating effect verification, and moderating effect verification. Performance Expectancy, Social Influence, Government Assistance Expectancy, and Task Technology Fit had a positive (+) effect on smart factory technology acceptance. The magnitude of influence was found in the order of Government Assistance Expectancy(β=.487) > Task Technology Fit(β=.218) > Performance Expectancy(β=.205) > Social Influence(β=.204). Both the Task Characteristics and the Technology Characteristics were confirmed to have a positive (+) effect on Task Technology Fit. It was found that Task Characteristics(β=.559) had a greater effect on Task Technology Fit than Technology Characteristics(β=.328). In the mediating effect verification on Trust, a statistically significant mediating role of Trust was not identified between each of the six independent variables and the intention to introduce a smart factory. Through the verification of the moderating effect of Innovation Resistance, it was found that Innovation Resistance plays a positive (+) moderating role between Government Assistance Expectancy, and technology acceptance intention. In other words, the greater the Innovation Resistance, the greater the influence of the Government Assistance Expectancy on the intention to adopt the smart factory than the case where there is less Innovation Resistance. Based on this, academic and practical implications were presented.