• Environmental and Resource Economics Review
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• v.30 no.4
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• pp.627-648
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• 2021

The fine-dust season management system refers to the policy of implementing enhanced reduction measures in transportation, power, business and living sectors in winter, when fine dust levels are high. The fine dust season management system is a regulatory policy that causes social costs and transfers to various economic players. Equity is an important issue for the cost burden. Therefore, in this study, the cost of each power generator was analyzed using the coal power generation reduction amount of each power generator to verify that the cost of the power sector is evenly distributed. In particular, the effect of the fine dust season management system on coal power generation of power generators was analyzed by applying a synthetic control method that can identify the time-variable effect of the policy. It was confirmed that the fine dust season management system reduced volume of fuel and power generation in coal power plants, resulting in an increase in the cost of the power generation sector, even considering the effect of some power demand due to the COVID-19 crisis. However, it could be seen that these costs were not distributed equally among the generators, and that they were more costly to the specific generators.Social costs incurred by fine dust season management need to be improved so that stakeholders are equally burdened.

• Resources Recycling
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• v.31 no.5
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• pp.42-51
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• 2022

Aggregate resources in Korea are expected to run out owing to an increase in development demand and construction investment. Recycled concrete aggregates (RCA), extracted from waste concrete, have a lower quality than natural aggregates. However, RCA can produce concrete similar in quality to the normal concrete by aggregate pretreatment, use of admixtures, and quality control. RCA are most suitable for use in precast concrete products such as sidewalk blocks and revetment blocks. Herein, the feasibility of producing revetment blocks using recycled aggregate concrete (RAC), similar in quality to normal concrete, was analyzed. The amount of RCA was varied, and moderate high early strength cement and steam curing were used to produce the concrete test blocks. In the block test, the load resistance characteristics of the blocks were evaluated to determine optimal RAC and glass fiber reinforced polymer (GFRP) rebar compositions. Thus, the variable that reduced the cement content was determined at the same level as that of natural aggregate concrete by the control of steam curing. In the concrete block test, although this depends on the reinforcement ratio, the RAC block exhibited the same or better performance than a normal concrete block. Therefore, the low quality of RCA in RAC is no longer a problem when concrete mixing and curing are controlled and appropriate reinforcement is used.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1124-1128
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• 2021

As regulations of emissions from ships become more stringent, electric propulsion systems have been increasingly used to solve this problem in vessels ranging from large merchant ships to small and medium-sized ships. Methods for improving the efficiency of the electric propulsion system include the improvement of power sources; the use of a system linked to environmentally friendly power sources, such as batteries, fuel cells, and solar power; and the development of hardware and control methodology for rectifiers, power conversion devices, and propulsion motors. The method using a phase-shifting transformer with diodes has been widely used for rectification. Power semiconductor devices with grid connection to an environmentally friendly power source using DC distribution, a variable speed power source, and the application of small and medium-sized electric propulsion systems have been developed. Accordingly, the demand for active front-end (AFE) rectifiers is increasing. In this study, a method using a neural network rather than a conventional proportional-integral controller was proposed to control the AFE rectifier. Tested controller data were used to design a neural network controller trained through MATLAB/Simulink. The neural network controller was applied to a rectification system designed using PSIM software. The results indicated the effectiveness of improving the waveform and power factor DC output stage according to the load variation. The proposed system can be applied as a rectification system for small and medium-sized environmentally friendly ships.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.397-404
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• 2021

The web-based crowdfunding platform provides small-cap companies the opportunity to reduce financial risks and to reliably produce new products through pre-orders. Meanwhile, crowdfunding projects are also helping companies as a channel to test new products before mass production. Despite these advantages, from the point of view of businesses and consumers, it is true that web-based crowdfunding platforms have limitations in the retail environment. For example, the limited social elements of a web-based platform are somewhat in conflict with the basic characteristics of crowdfunding projects - which inevitably demand high social influences for the success. As such, understanding the mechanisms of social factors of crowdfunding platforms from the consumers' perspective is important. Therefore, in this study, we empirically tested the effect of social factors of crowdfunding platform on consumer participation and evaluation. Based on the Social Influence Theory and Social Presence Theory, we developed a conceptual framework where the social group size and social presence of other backers were the independent variables and the purchaser's intention to participate as the dependent variable. In the results, the size of the social group size and the perceived social presence have a significant positive effect on purchaser's participation intent. In addition, the social presence had a greater influence on the purchaser's intention to participate than the size of the sponsor's social group. We believe that our findings contribute to the extant literature by empirically demonstrating the valid effect of social factors of crowdfunding platforms on consumer evaluations.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1246-1246
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• 2001

The quality of meat is highly variable in many properties. This variability originates from both animal production and meat processing. At the pre-slaughter stage, animal factors such as breed, sex, age contribute to this variability. Environmental factors include feeding, rearing, transport and conditions just before slaughter (Hildrum et al., 1995). Meat can be presented in a variety of forms, each offering different opportunities for adulteration and contamination. This has imposed great pressure on the food manufacturing industry to guarantee the safety of meat. Tissue and muscle speciation of flesh foods, as well as speciation of animal derived by-products fed to all classes of domestic animals, are now perhaps the most important uncertainty which the food industry must resolve to allay consumer concern. Recently, there is a demand for rapid and low cost methods of direct quality measurements in both food and food ingredients (including high performance liquid chromatography (HPLC), thin layer chromatography (TLC), enzymatic and inmunological tests (e.g. ELISA test) and physical tests) to establish their authenticity and hence guarantee the quality of products manufactured for consumers (Holland et al., 1998). The use of Near Infrared Reflectance Spectroscopy (NIRS) for the rapid, precise and non-destructive analysis of a wide range of organic materials has been comprehensively documented (Osborne et at., 1993). Most of the established methods have involved the development of NIRS calibrations for the quantitative prediction of composition in meat (Ben-Gera and Norris, 1968; Lanza, 1983; Clark and Short, 1994). This was a rational strategy to pursue during the initial stages of its application, given the type of equipment available, the state of development of the emerging discipline of chemometrics and the overwhelming commercial interest in solving such problems (Downey, 1994). One of the advantages of NIRS technology is not only to assess chemical structures through the analysis of the molecular bonds in the near infrared spectrum, but also to build an optical model characteristic of the sample which behaves like the “finger print” of the sample. This opens the possibility of using spectra to determine complex attributes of organic structures, which are related to molecular chromophores, organoleptic scores and sensory characteristics (Hildrum et al., 1994, 1995; Park et al., 1998). In addition, the application of statistical packages like principal component or discriminant analysis provides the possibility to understand the optical properties of the sample and make a classification without the chemical information. The objectives of this present work were: (1) to examine two methods of sample presentation to the instrument (intact and minced) and (2) to explore the use of principal component analysis (PCA) and Soft Independent Modelling of class Analogy (SIMCA) to classify muscles by quality attributes. Seventy-eight (n: 78) beef muscles (m. longissimus dorsi) from Hereford breed of cattle were used. The samples were scanned in a NIRS monochromator instrument (NIR Systems 6500, Silver Spring, MD, USA) in reflectance mode (log 1/R). Both intact and minced presentation to the instrument were explored. Qualitative analysis of optical information through PCA and SIMCA analysis showed differences in muscles resulting from two different feeding systems.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.2
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• pp.168-176
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• 2023

The demand for measures to enhance the safety of maritime transportation is growing internationally, and coastal shipping companies are pursuing sustainability by establishing various maritime safety policies. Increased awareness of the safety management system and safety culture of coastal shipping companies will minimize accidents and increase corporate sustainability. This study empirically analyzed the impact of a safety management system (safety policy, risk management, and safety guarantee) on the corporate sustainability of coastal shipping companies through the mediation of safety culture. The survey questions for each variable were constructed based on previous research and a dministered to 204 employees of coastal shipping companies. Analysis founed that safety policy did not have a statistically significant effect on safety culture, but risk management and safety guarantees had a positive significant effect on safety culture, which in turn had a positive and significant effect on sustainability. Furthermore, we found that safety culture has a mediating effect on the relationship between risk management and safety guarantees and sustainability. Coastal shipping companies thus need to systematize and strengthen risk management and safety assurance to increase safety culture awareness, and increased safety culture awareness can also increase sustainability.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.5 no.5
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• pp.461-470
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• 2015

There has been a significant increase in the amount of research on reducing the lighting power consumption of buildings and also an increasing demand for technological development. Light shelf has been recognized as one of the most efficient solutions to this issue and related researches have been conducted, which have mostly focused on factors related to simple light shelves and are not suitable as an appropriate basis for the design of light shelves. Thus this study aims to establish the proper design basis for light shelves by evaluating the performance of shelves per reflection rate in indoor areas. Power consumption rate and indoor illumination intensity distribution of a testbed built based on actual living conditions were calculated for the performance evaluation, of which the results are as following: 1) Reduction of reflection rate of ceiling and walls caused average illumination intensity in summer, winter and median seasons, and evenness per reflection rate of indoor areas was found to be different in summer, winter and median seasons, making it a necessary consideration for designing light shelves. 2) Calculation of power consumption from lighting control showed that a high reflection rate of indoor areas may be suitable for power consumption reduction, and that reflection rates higher than 80% for ceilings and higher than 75% for walls in terms of the efficiency of researches on the indoor reflection rate and its application would be appropriate. This study is meaningful as the research focuses on light shelves based on considering indoor environmental factors. More studies will be required that consider a variety of factors.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• The Korean Journal of Franchise Management
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• v.3 no.1
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• pp.92-110
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• 2012

Due to the social problems including recent economic crisis and unemployment rate increase, the demand of business foundation market has been increased and, in the meantime, on the basis of the business foundation support policy of the government including youth foundation support policy and Small & Medium Business Administration foundation planning, business foundation market has been showing steady growth trend. With this enlargement of foundation market, as the accompanied increase of franchise market is expected, it is considered that the importance of more realistic and concrete research about franchise market be larger than before. This study considered brand image, main office support, foundation cost, information search activity as the advanced variable of effect on brand selection and established the cause of effect on brand selection by improving the existing advanced research, and its result is as follows. First, according to foundation business kind, age, sex, yes or no of marriage, there is some difference III the thought about brand image and foundation possibility. Second, Second, the most important factor of franchise contract intention is economical specificity. It is difficult to consider brand image, franchise support and information search activity as the property having a big effect on preliminary founder, and it was shown that the cost for franchise management(Consistency with initial foundation cost and self-capital, promotion cost, management fund, facility/equipment reinvestment, etc.) is an important property. Specially, it was shown that consistency with initial foundation cost and self-capital is the most important factor for preliminary founder.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.391-393
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• 2022

In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.