• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.79-105
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• 2023

Due to unprecedented extreme weather events such as global warming and climate change, many parts of the world suffer from severe pain, and economic losses are also snowballing. In order to address these problems, 'The Paris Agreement' was signed in 2016, and an intergovernmental consultative body was formed to keep the average temperature rise of the Earth below 1.5℃. Korea also declared 'Carbon Neutrality in 2050' to prevent climate catastrophe. In particular, it was found that the increase in temperature caused by greenhouse gas emissions hurts the environment and society as a whole, as well as the export-dependent economy of Korea. In addition, as the diversification of transportation types is accelerating, the change in means of choice is also increasing. As the development paradigm in the low-growth era changes to urban regeneration, interest in idle railway sites is rising due to reduced demand for routes, improvement of alignment, and relocation of urban railways. Meanwhile, it is possible to partially achieve the solar power generation goal of 'Renewable Energy 3020' by utilizing already developed but idle railway sites and take advantage of being free from environmental damage and resident acceptance issues surrounding the location; but the actual use and plan for these solar power facilities are still lacking. Therefore, in this study, using the big data provided by the Korea National Railway and the Renewable Energy Cloud Platform, we develop an algorithm to discover and analyze suitable idle sites where solar power generation facilities can be installed and identify potentially applicable areas considering conditions desired by users. By searching and deriving these idle but relevant sites, it is intended to devise a plan to save enormous costs for facilities or expansion in the early stages of development. This study uses various cluster analyses to develop an optimal algorithm that can derive solar power plant locations on idle railway sites and, as a result, suggests 202 'actively recommended areas.' These results would help decision-makers make rational decisions from the viewpoint of simultaneously considering the economy and the environment.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.361-381
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• 2023

Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.

• Journal of the Daesoon Academy of Sciences
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• v.43
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• pp.67-102
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• 2022

The purpose of this study is to analyze the human rights found in the North Korean Constitution and their core problem by focusing on elements of human rights suggested by Daesoon Jinrihoe's doctrine of Haewon-sangsaeng (解冤相生 the Resolution of Grievances for Mutual Beneficence). Haewon-sangsaeng is seemingly the only natural law that could resolve human resentment lingering from the Mutual Contention of the Former World while leading humans work for the betterment of one another. Haewon-sangsaeng, as a natural law, includes the right to life, the right to autonomous decision-making, and duty to act according to human dignity (physical freedom, the freedom of conscience, freedom of religion, freedom of speech, freedom of press, etc.), the right to equal treatment in one's social environment, and the right to ensure the highest level of health through treatment. The North Korean Constitution does not have a character as an institutional device to guarantee natural human rights, the fundamental principle of the Constitution, and stipulates the right of revolutionary warriors to defend dictators and dictatorships. The right to life is specified so that an individual's life belongs to the life of the group according to their socio-political theory of life. Rights to freedom are stipulated to prioritize group interests over individual interests in accordance with the principle of collectivism. The right to equality and the right to health justify discrimination through class discrimination. The right to life provided to North Koreans is not guaranteed due to the death penalty system found within the North Korean Criminal Code and the Criminal Code Supplementary Provisions. The North Korean regime deprives North Koreans of their right to die with dignity through public executions. The North Korean regime places due process under the direction of the Korea Worker's Party, recognizes religion as superstition or opium, and the Korea Worker's Party acknowledge the freedoms of bodily autonomy, religion, media, or press. North Koreans are classified according to their status, and their rights to equality are not guaranteed because they are forced to live a pre-modern lifestyle according to the patriarchal order. In addition, health rights are not guaranteed due biased availability selection and accessibility in the medical field as well as the frequent shortages of free treatments.

• Journal of Consumption Culture
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• v.15 no.2
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• pp.143-169
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• 2012

Consumption is the most important cultural keyword in the modern society. This study tried an exploratory comparison of consumer culture of Korea, USA and Sweden in response to the needs on cultural comparison research perspective. Triandis's cultural dimensions were adopted to explore each country's cultural characteristics. A qualitative in-depth interviews were conducted to consumers who lived both in Korea and USA, or in Korea and Sweden, which enabled them to get familiar with each country's consumer culture. The research found that the culture is projected to the consumer culture in a micro domain. The individualism allowed consumers in USA and Sweden to be unconscious of other's eyes. But collectivism in Korea made Korean consumers locked in other's judgement. In contrast, in a macro domain of consumer culture such as donation and pro-environmental consumption, consumption practices were in a dissonance with their cultural orientation, where includes interaction with society and environment. In addition, in a post-materialistic society, symbolism of consumption goods gets weakened and experiential consumption evolves with a transition from mass consumption society to plural culture society. Lastly, consumer culture functions as a creative mechanism of new culture by consumer's reflexive planning, which is one of the clues of an autonomous consumer culture. This study tried to explore the consumer culture of Korea, USA and Sweden as an exploratory trial for the comparison of consumer cultures. To increase empirical consumer culture study, refined questionnaire item pool is to be extracted through various exploratory researches, which can be utilized commonly in various cultures. Moreover, an additional research is in need about a consumer culture in a macro domain and experiential consumer culture in a post-materialism society.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.69-88
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• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.311-328
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• 2022

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

• Korean Journal of Heritage: History & Science
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• v.56 no.2
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• pp.246-262
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• 2023

This study analyzed policy implementation factors focusing on how Donuimun, a demolished cultural heritage, was digitally restored and the policy implementation process of Donuimun Gate restoration. Through this, the characteristics of the implementation process of the digital Donuimun Gate restoration policy promoted by public-private multilateral collaboration were examined and implications were sought for how institutions with different interests solved problems and collaborated in the implementation process. The research method was focused on policy implementation factors including policy executive factors, policy content factors, policy resource factors, and policy environment factors, and the process was analyzed for each detailed component. Along with literature analysis, in-depth interviews were conducted with participants in policy implementation. As a result of the study, first, it was found in the policy executive factor that the quick decision-making leadership of the policy manager and the flexible attitude of the person in charge of the government agency had a positive effect on preventing conflicts between different interest groups. Second, in terms of policy content, establishing a common goal that everyone can accept and moving forward consistently gave trust and created synergy. Third, in the policy implementation resource factor, the importance of the budget was emphasized. Finally, as an environmental factor for policy implementation, the opening of 5G mobile communication for the first time along with the emergence of the Fourth Industrial Revolution at the time of policy implementation acted as a timely factor. The digital Donuimun Gate was the first case of restoring a lost cultural heritage with AR and VR, and received attention and support from the mass media and the public. This also shows that digital restoration can be a model case that can be a solution without conflicts with local residents where cultural heritages are located or conflicts between stakeholders in the preservation and restoration of real objects.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.4
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• pp.421-431
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• 2023

Sewer deterioration models can offer important information on prediction of future condition of the asset to decision makers in their implementing sewer pipe networks management program. In this study, Markov chain model was used to estimate sewer deterioration trend based on the historical structural condition assessment data obtained by CCTV inspection. The data used in this study were limited to Hume pipe with diameter of 450 mm and 600 mm in three sub-catchment areas in city A, which were collected by CCTV inspection projects performed in 1998-1999 and 2010-2011. As a result, it was found that sewers in sub-catchment area EM have deteriorated faster than those in other two sub-catchments. Various main defects were to generate in 29% of 450 mm sewers and 38% of 600 mm in 35 years after the installation, while serious failure in 62% of 450 mm sewers and 74% of 600 mm in 100 years after the installation in sub-catchment area EM. In sub-catchment area SN, main defects were to generate in 26% of 450 mm sewers and 35% of 600 mm in 35 years after the installation, while in sub-catchment area HK main defects were to generate in 27% of 450 mm sewers and 37% of 600 mm in 35 years after the installation. Larger sewer pipes of 600 mm were found to deteriorate faster than smaller sewer pipes of 450 mm by about 12 years. Assuming that the percentage of main defects generation could be set as 40% to estimate the life expectancy of the sewers, it was estimated as 60 years in sub-catchment area SN, 42 years in sub-catchment area EM, 59 years in sub-catchment area HK for 450 mm sewer pipes, respectively. For 600 mm sewer pipes, on the other hand, it was estimated as 43 years, 34 years, 39 years in sub-catchment areas SN, EM, and HK, respectively.

• Journal of Service Research and Studies
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• v.11 no.2
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• pp.37-66
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• 2021

This study aims to provide self-development opportunities to hair salons service workers through e-learning and provide the foundation of sustainable hair salons management by cultivating good talents to hair salons service business executives. In particular, the factors affecting e-learning achievement are identified according to learner characteristics to see whether these factors affect the satisfaction of e-learning learners and also affect the performance of management. The results of the study are summarized as follows. As a result of hypotheses testing on the relationship between e-learning learning environment and e-learning satisfaction, it was found that the higher the level of e-learning content quality is, the higher the satisfaction of e-learning is, the higher the satisfaction of e-learning is, and that the higher the quality level of the support infrastructure is, the higher the satisfaction of e-learning is. The results of the hypotheses testing on the moderating effect of learner factors showed that the influence of the quality of the support infrastructure on the e-learning satisfaction differs according to the level of the learner's goal consciousness. However, it was found that the influence of content quality on e-learning satisfaction according to the level of the learners goal awareness, the influence of content quality on e-learning satisfaction according to the level of the aggressiveness of the learners learning attitude, and the influence of the quality of the support infrastructure on the e-learning satisfaction according to the level of the aggressiveness of learners learning attitude were found to identically demonstrate no moderating effects. The results of hypotheses testing on the relationships among e-Learning performance show that the higher the satisfaction of e-learning was, the higher the customer orientation was, and the higher the satisfaction of e-learning was, the higher the contribution of management performance was, and the higher the customer orientation was, the higher the contribution of management performance was. The implications of this study are as follows. First, the actual path of realiting e-learning performance could be identified that is this study provided organizational decision makers involved in the hair salons service operations with practical guidance for the introduction and expansion of successful educational systems. Second, the e-learning environment derived from the theoretical background is different from the e-learning environment required by the learners.