• Journal of Service Research and Studies
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• v.13 no.1
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• pp.108-126
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• 2023

This study explored the sustainability of a blockchain-based cultural art performance video platform through the construction of Gyeonggi Art On, a new media art broadcasting station in Gyeonggi-do. In addition, the technical limitations of video content transaction using block chain, legal and institutional issues, and the protection of personal information and intellectual property rights were reviewed. As for the research method, participatory observation methods such as in-depth interviews with developers and operators and participation in meetings were conducted. The researcher participated in and observed the entire development process, including designing and developing blockchain nodes, smart contracts, APIs, UI/UX, and testing interworking between blockchain and content distribution services. Research Question 1: The results of the study on 'Which technology model is suitable for a blockchain-based performance video content distribution public platform?' are as follows. 1) The blockchain type suitable for the public platform for distribution of art performance video contents based on the blockchain is the private type that can be intervened only when the blockchain manager directly invites it. 2) In public platforms such as Gyeonggi ArtOn, among the copyright management model, which is an art based on NFT issuance, and the BC token and cloud-based content distribution model, the model that provides content to external demand organizations through API and uses K-token for fee settlement is suitable. 3) For public platform initial services such as Gyeonggi ArtOn, a closed blockchain that provides services only to users who have been granted the right to use content is suitable. Research question 2: What legal and institutional problems should be reviewed when operating a blockchain-based performance video distribution public platform? The results of the study are as follows. 1) Blockchain-based smart contracts have a party eligibility problem due to the nature of blockchain technology in which the identities of transaction parties may not be revealed. 2) When a security incident occurs in the block chain, it is difficult to recover the loss because it is unclear how to compensate or remedy the user's loss. 3) The concept of default cannot be applied to smart contracts, and even if the obligations under the smart contract have already been fulfilled, the possibility of incomplete performance must be reviewed.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.80-98
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• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

• Journal of Internet of Things and Convergence
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• v.10 no.3
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• pp.35-48
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• 2024

This study analyzed the value empathy of environmentally sustainable fashion products, encompassing environmental, economic, and social values, drawing from existing literature. We sought to verify the relationship between empathic value and the likability and purchase intention towards these products. To validate these relationships, we formulated research hypotheses and conducted an online survey targeting female college students residing in Guangzhou, Guangdong Province, China, who have experience purchasing environmentally sustainable fashion products. The survey was conducted from August 10th to August 20th, 2023, with a total distribution of 352 questionnaires. Among the collected responses, 313 valid responses were utilized for data analysis. The collected survey data underwent frequency analysis, exploratory factor analysis, reliability and validity analysis, correlation analysis, and multiple regression analysis using SPSS 26.0 software. The analysis yielded the following results. First, the empathy value of environmentally sustainable fashion products was classified into environmental protection values, economic values, and social values. Second, the economic and social values of environmentally sustainable fashion products were found to have a positive effect on favorability. Third, it was found that the environmental protection value and social value of environmentally sustainable fashion products had a positive effect on purchase intention. Fourth, it was found that Chinese female college students' favorability toward environmentally sustainable fashion products had a positive effect on their purchase intention. Based on these results, it is judged that companies need to emphasize the characteristics of products such as environmental protective value, economic value, and social value in order to promote consumers' purchase of environmentally sustainable fashion products. The purpose of this study is to help develop marketing strategies for environmentally sustainable fashion products by providing basic data, development ideas, and methods useful for environmentally sustainable fashion-related industries and companies by analyzing the relationship between empathy value, favorability, and purchase intention.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.89-101
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• 2017

Crime occurs differently based on not only place locations and building uses but also the characteristics of the people who use the place and the spatial structures of the buildings and locations. Therefore, if spatial big data, which contain spatial and regional properties, can be utilized, proper crime prevention measures can be enacted. Recently, with the advent of big data and the revolutionary intelligent information era, predictive policing has emerged as a new paradigm for police activities. Based on 7420 actual crime incidents occurring over three years in a typical provincial city, "J city," this study identified the areas in which crimes occurred and predicted risky areas. Spatial regression analysis was performed using spatial big data about only physical and environmental variables. Based on the results, using the street width, average number of building floors, building coverage ratio, the type of use of the first floor (Type II neighborhood living facility, commercial facility, pleasure use, or residential use), this study established a Crime Incident Prediction Model (CIPM) based on Bayesian probability theory. As a result, it was found that the model was suitable for crime prediction because the overlap analysis with the actual crime areas and the receiver operating characteristic curve (Roc curve), which evaluated the accuracy of the model, showed an area under the curve (AUC) value of 0.8. It was also found that a block where the commercial and entertainment facilities were concentrated, a block where the number of building floors is high, and a block where the commercial, entertainment, residential facilities are mixed are high-risk areas. This study provides a meaningful step forward to the development of a crime prediction model, unlike previous studies that explored the spatial distribution of crime and the factors influencing crime occurrence.

• Journal of the Korean Applied Science and Technology
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• v.35 no.4
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• pp.1120-1133
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• 2018

The purpose of this study is to validate the failure attribution factors affecting emotional changes after a failed service by local restaurant users, and the relapse effects of the perceived failure of a customer's brand relationship. In this study, the implications of this study can be divided into the null theory and the homogenous theory, in which the study of the relationship between individual belief that influences the null theory and the post-gender emotional response is minimal. The independence of the crash response (angerous VS compassion) has been equally validated as building a belief-gathering-emotion three-step model. First, emotional BRQ (intimate and love) has a reduction effect on controllable geeks, and behavioral BRQ (relative existence) has an extended effect on controllable geeks. From a management perspective, restaurant managers should be less aware of the repeatability of a customer's service failure and call for customer sympathy. Integratedly, restaurant managers must control the customer's perception of service failure and restore the impact of the customer's BRQ on emotional reactions. A variety of service recovery measures should be established and the cerumen should be controlled. In addition, since BRQs have different effects on anger and sympathy (extended VS), different service failure recovery plans should be presented depending on the characteristics of the customer BRQ. For example, measures such as monetary compensation or fair dealing, emotional distribution to close and loving customers, and persuasion of reciprocal benefits to interdependent customers should be developed according to circumstances. This study explored the effectiveness of the geeks after a service failure and has limitations that do not take into account the various regulatory factors in the BRQ-return-Empression process. Thus, in further studies, the effects of adjusting service failure strength should be considered and a more complete model should be built.

• Journal of The Korean Association For Science Education
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• v.41 no.1
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• pp.33-46
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• 2021

Inquiry occupies an important place in science education, and research related to inquiry is widely conducted. However, due to the inclusiveness of the concept of "exploration," each researcher perceives its meaning differently, and approaches may vary. In addition, criticisms have been raised that the results of classes using inquiry in science education do not guarantee meaningful changes to students. Therefore, this study attempts to identify the trend of SSCI-level research papers dealing with inquiry in science education over the past three years to confirm the current status and effectiveness of the inquiry. Researches used in the analysis are International Journal of Science Education, Journal of Research in Science Teaching, Research in Science Education, and Science Education, and limited to those that directly suggest "inquiry (enquiry)" as a keyword. Based on extracted 75 papers, the classification process was conducted, and an analysis frame was derived inductively by reflecting the subject and characteristics. Specific cases for each category were presented by dividing into three aspects: perception and perspective on inquiry, support and strategy for inquiry, and teacher professional development for inquiry. The results of examining the implications for scientific inquiry are as follows: First, rather than defining inquiry as an implicit proposition or presenting it as a step-by-step procedure, it was induced to grasp the meaning of inquiry more comprehensively and holistically. Second, as to whether the inquiry-based instruction is effective in all aspects of the cognitive, functional, and affective domains of science, the limitations are clearly presented, and the context-dependent and subject-specific properties and limitations of inquiry are emphasized. Third, uncertainty in science inquiry-based instruction can help learners to begin their inquiry and develop interest, but in the process of recognizing data and restructuring knowledge, explicit and specific guidance and scaffolding should be provided at an appropriate timing.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.4
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• pp.39-49
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• 2018

"Annals of The Joseon Dynasty" is a book recording the Joseon Dynasty's historical facts in an annalistic format. The King's amusement activities through "Annals of The Joseon Dynasty" which were established by the Ye-ak(禮樂) system were analyzed. The results are as follows. The king's amusement activities that were performed during the Joseon Dynasty period could be classified as state banquets, military banquets, and banquets for play. The analysis of the king's amusement activity was divided into five stages. The characteristic of [1 period : King Taejo~Sejo(Yejong)] was dominated the military banquets of the Goryeo Dynasty. Neo-Confucianism is the establishment of political and social turning of the ballast, considerations of military culture, culture, and Hoeryeyeon Jinpungjeong, a cloud of dust and elders banquets such as Giroyeon and Yangnoyeon on the nature of the party. A lasting ordinance was institutionalized[2 period : King Seongjong~Jungjong]. In the chopper and jeongyujaeran, Hong Kyung Rae led a royal amusement activities are stagnant, often produce isolated storage compute in the gloomy situation[3 period : King Injong~Hyeonjong]. Revival period is pride of the amusement activity through the culture of Joseon Dynasty royal culture [4 period : King Sukjong~Jeongjo]. The throne, crashed due to political power is an ebb of royal amusement activities, while also rapidly waning[5 period : King Seonjo~Seonjong]. During the early Joseon Dynasty, hunting took place around the forest area northeast of Hanyang and during King Seongjong's period, it took place closer to the capital city, while in Lord Yeonsan's period, it was expanded to a 39 kilometer radius area from the palace, and banquets such as various forms of entertainment of Cheoyongmu, and Flower-viewing. The Joseon kings who enjoyed hunting were King Sejong, Sejo, Seongjong, Yeonsan, and Jungjong. Most of hunting objects were tigers, bears, deer and roe deer, leopards, boars, their animals and falconry took, and the purpose of the hunting was to perform ancestral rites to the royal ancestry or the royal tombs. Lord Yeonsan's hunting activities had negative effects after King Jungjong the king's hunting activity decreased sharply. However, there were also positive aspects of Lord Yeonsan's Prohibition of cutting woods ect. In conclusion, the expansion of the King's garden(庭:courtyard${\rightarrow}$園:privacy garden${\rightarrow}$苑:king's garden${\rightarrow}$苑?:national hunting park) is evident which starts from formal and informal activities that took place in Oejo, Chijo, and Yeonjo, which went further to the separate and secret gardens, and then even further, thus setting the amusement activity area as a 39 kilometer radius range from Hanyang.