• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.563-568
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• 2023

The government's strengthening of the standards for mentally ill patients and expanding the scope of examinations to the entire nation reflects the changing times. According to the OECD's announcement (2021), the incidence of depression and anxiety has more than doubled since the prolonged COVID-19 pandemic in countries around the world, with Korea's prevalence ranking first. However, only 12.1% of those who have been diagnosed with mental disorders received counseling and treatment from experts. The difference between depression and simple depression is significant depending on whether it is medically treated or temporary, but it can be seen that the continuation of depression is depression. In order to reduce this depression, Kandinsky's work was visualized and created. In a study conducted by changing the playback speed of the produced Kandinsky image, beta and gamma values, which showed the largest deviation when compared to depressive patients and normal people, increased significantly when viewed at 90fps, which was most effective in relieving depression. Artistic creations are bound to be accepted differently depending on the individual's perspective, but it is hoped that research that can improve the phenomenon of individuals suffering from depression by integrating artificial intelligence and traditional mental health approaches will be further developed and widely used for treatment.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.542-549
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• 2023

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data. For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and qualitative feedback for process optimization.

• Journal of the Society of Disaster Information
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• v.20 no.4
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• pp.723-732
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• 2024

Purpose: It is necessary to analyze the rate of industrial accidents that occurred in facilities subject to safety inspection before and after the implementation of the safety inspection system to analyze whether the safety inspection system is effective in preventing disasters. Method: In order to analyze the serious industrial accidents that occurred in facilities subject to safety inspection from 2006~2022, the accident statistics data of the Ministry of Employment and Labor were reclassified and analyzed as follows. Result: As a result of re-validating the statistics before and after the implementation of the safety inspection system through the Walcoxon Code Ranking Test, the average rank (negative rank) was 8.10 for those whose scores decreased from the pre-introduction scores, and the average rank (positive rank) for those whose post-introduction scores increased from the pre-introduction scores was 3.33. This indicated that there were more cases of reduced disasters after the introduction than before the introduction. Conclusion: The average rate of fatal accidents occurred in the facilities inspected before the implementation of the safety inspection system was about 14.05%, and the average rate of fatal accidents after the implementation of the inspection system was about 10.72%, which showed that the safety inspection system is effective in preventing work-related accidents and fatalities.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.1
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• pp.55-73
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• 2024

Unicorn companies are attracting attention around the world as they are recognized for their high corporate value in a short period of time as an innovative business models. Their growth process presents good lessons for the startup ecosystem and have a positive impact on national economic development and job creation. However, previous studies related to unicorn companies are focused on 'event studies' and 'case studies' such as characteristics of founders, environmental factors, business models and success/failure cases of companies already recognized as unicorns rather than a multifaceted approach. The occurrence of unicorn companies and Macroscopic analysis of related factors is lacking. Against this background, this study are considering the characteristics of unicorns examined through previous research and the current status unicorns with a high proportion of technology companies, the purpose was to analyze the impact of the country's technological competitiveness, such as 'technology human resource index', 'R&D index', and 'technology infrastructure index', on the increase in unicorn companies. For statistical analysis, data published by various international organizations, the Bank of Korea, and Statistics Korea from 2017 to 2020 and unicorn company data compiled by CB Insights were used as panel data for 44 countries to be tested by multiple regression analysis. As a result of the study, it was confirmed that the number of science majors had a positive (+) effect on the increase of unicorn companies in the case of technology human resource index, and in the case of R&D index, the total amount of R&D investment had a positive (+) effect on the increase of unicorn companies, while the number of Triad Patents Families and the number of scientific and technological papers published had a negative (-) effect on the increase of unicorn companies. Finally, in the case of technology infrastructure index, it was confirmed that the number of the world's 500th-ranked universities had a positive (+) effect on the increase of unicorn companies. This study is the first to reveal the causal relationship between national technological competitiveness and unicorn company growth based on country-specific and time-series empirical data, which were insufficiently covered in previous studies. and compared to the UN's ranking of the global industrial competitiveness index and the OECD's total R&D investment by country, Korea is considered to have technological and growth potential, while the number of unicorn companies driving growth as leaders of the innovative economy is relatively small, so the research results can be used when establishing policies to discover and foster unicorn companies in the future.

• Journal of Korea Port Economic Association
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• v.38 no.2
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• pp.81-93
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• 2022

Since 2008, China's shipping industry has been in a slump, with shipbuilding orders falling sharply, and high-growth excess capacity has become increasingly apparent, leaving many firms with sharply reduced orders at risk of bankruptcy and shutdown. To ensure the development of the shipbuilding industry and enhance the international competitiveness of the shipbuilding industry, it is necessary to analyze the present situation of the shipbuilding industry and the financial situation of the shipbuilding enterprises. And analyzing the problems faced by enterprises from the perspective of capital structure is very meaningful to the shipbuilders with high capital operation. We are trying to analyze the determinants of capital structure of China's shipbuilding listed companies. 30 listed Chinese shipbuilding and listed companies have been designated as sample companies that can obtain financial statements for 13 consecutive years. They also divided 30 sample companies into shipbuilding, shipbuilding-related manufacturing, and shipbuilding-related transportation. Dependent variable is the debt level of the year, independent variable includes the debt level of the previous year, fixed asset ratio, profitability ratio, depreciation cost ratio and asset size. The regression model of the panel used to analyze determinants is capital structure. The results of the empirical analysis are as follows. First, a fixed-effect model for the entire entity showed that the debt-to-equity ratio and the size of the asset in the previous period had a positive effect on the debt-to-equity ratio in the current period. Second, the impact of the profitability ratio on the debt level in the prior term also supports the capital procurement ranking theory rather than the static counter-conflict theory. Third, it was shown that the ratio of the depreciation of the prior term, which replaces the non-liability tax effect, affects the debt-to-equity ratio in the current period.

• Journal of Venture Innovation
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• v.2 no.2
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• pp.95-115
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• 2019

In the context of today's business environment, not only is the nation or company's credit rating considered very important in our recent society, but it is also becoming important in international transactions. Likewise, at this point of time when the importance and reliability of credit evaluation are becoming important at home and abroad, this study analyzes financial ratios related to corporate profitability, safety, activity, financial growth, and profit growth to study the impact of financial indicators on enterprise value and corporate taxes on credit evaluation. To proceed with this, the financial ratio of 465 companies of KOSPI securities listed in 2017 was calculated and the impact of enterprise value and corporate taxes on credit evaluation was analyzed. Especially, this further study tried to derive a reliable and consistent conclusion by analyzing the financial data of KOSPI securities listed companies for eight years from 2011, which is the first year of K-IFRS introduction, to 2018. Research has shown that the significance levels among variables that show the profitability, safety, activity, financial growth, and profit growth of each financial ratio were significant at the 99% level, except for the profit growth. Validation of the research hypothesis found that while the profitability of KOSPI-listed companies significantly affects corporate value and income tax, indicators such as safety ratio and growth ratio do not significantly affect corporate value and income tax. Activity ratio resulted in significant effects on the value of enterprise value but not significant impacts on income taxes. In addition, it was found that the enterprise value has a significant effect on the company's credit and corporate income taxes, and that corporate income taxes also have a significant effect on the corporate credit evaluation, and this also shows that there is a mediating function of corporate tax. And as a result of further study, when looking at the financial ratio for eight years from 2011 to 2018, it was found that two variables, KARA and LTAX, are significant at a 1% significant level to KISC, whereas LEVE variables is not significant to KISC. The limitation of this study is that credit rating score and financial score cannot be said to be reliable indicators that investors in the capital market can normally obtain, compared to ranking criteria for corporate bonds or corporate bills directly related to capital procurement costs of enterprise. Above all, it is necessary to develop credit rating score and financial score reflecting financial indicators such as business cash flow or net assets market value and non-financial indicators such as industry growth potential or production efficiency.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.12
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• pp.2263-2273
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• 2000

Catalytic wet air oxidation of acetic acid over Mn-Ce based catalysts deposited on various supports ($SiO_2$, $TiO_2$, $ZrO_2$), $ZrSiO_4$, $ZrO_2(10wt%)/TiO_2$) have been carried out in high pressure microreactors. Also, promotional effects by small addition(O.5~1.0 wt%) of p-type semiconductors (CoO, $Ag_2O$, SnO) have been investigated. From the screening tests for initial activity ranking, both Mn(2.8)-Ce(7.2 wt%) and Ru(O.4)Mn(2.7)-Ce(6.9 wt%) supported on $TiO_2$ were selected as the promising reference candidates. In $Mn-Ce/TiO_2$ reference catalyst, addition of small amount of each p-type semiconductor (Co, Sn and Ag) resulted in activity promotional effect and the degree of the increase was in the following order: Co> Ag > Sn. Especially, $Mn-Ce/TiO_2$ promoted with 0.5 wt% Co gave the 2.6 folds activity increase compared to the reference case attributing to the surface area increase as well as synergy effect. In $Ru-Mn-Ce/TiO_2$ reference catalyst, only Co(1.0 wt%) promoted case showed a little reaction rate increase.

• Journal of The Korean Society of Grassland and Forage Science
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• v.25 no.3
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• pp.205-210
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• 2005

This study was conducted from March 21 to July 9 in 2004 at JeJu Island to investigate the effect of different particle sizes (0.3-0.5, 0.5-0.8, 0.8-1.0, 1.0-1.5 and 1.5-2.0mm) on creeping bentgrass vegetation. The results obtained were summarized as follows; plant height became shorter as particle size was increased from 0.3-0.5 to 1.5-2.0 n. Root length, Minolta SPAD-502 chlorophyll reading value, leave and root weight were directly proportional plant height response. Degree of land cover and density of creeping bentgrass decreased as the particle size was increased from 0.3-0.5 to 1.5-2.0nm, and degree land cover and density of weed increased. The number of weed species were increased as the sand particle size was increased. Then ranking of the dominant weeds were Portulaca oleracea, Trifolium repens and Cyperus amuricus (at 0.3-0.5 and 0.5-0.8mm particle size), Trifolium repens, Portulaca oleracea and Polygonum hydropiper (at 0.8-1.0mm particle size), Portulaca oleracea, Polygonum hydropiper and Poa annua (at 1.5-2.0mm particle size). Based on the these findings, the optimum sand particle size for growth of creeping bentgrass seems to be about 0.3-0.5m in volcanic ash soils of Jeju island.

• Journal of Life Science
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• v.31 no.12
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• pp.1079-1087
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• 2021

To analyze gut microbiota of livestock in Korea and compare taxonomic differences, we conducted 16S rRNA metagenomic analysis through next-generation sequencing. Fecal samples from broiler chickens, pigs, and cattle were collected from domestic feedlots randomly. α-diversity results showed that significant differences in estimated species richness estimates (Chao1 and ACE, Abundance-based coverage estimators) and species richness index (OUTs, Operational taxonomic units) were identified among the three groups. However, NPShannon, Shannon, and Simpson indices revealed that abundance and evenness of the species were statistically significant only for poultry (broiler chickens) and mammals (pigs and cattle). Firmicutes was the most predominant phylum in the three groups of fecal samples. Linear discriminant (LDA) effect size (LEfSe) analysis was conducted to reveal the ranking order of abundant taxa in each of the fecal samples. A size-effect over 2.0 on the logarithmic LDA score was used as a discriminative functional biomarker. As shown by the fecal analysis at the genus level, broiler chickens were characterized by the presence of Weissella and Lactobacillus, as well as pigs were characterized by the presence of provetella and cattele were characterized by the presence of Acinetobacter. A permutational multivariate analysis of variance (PERMANOVA) showed that differences of microbial clusters among three groups were significant at the confidence level. (p=0.001). This study provides basic data that could be useful in future research on microorganisms associated with performance growth, as well as in studies on the livestock gut microbiome to increase productivity in the domestic livestock industry.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.