• Journal of Korean Society of Archives and Records Management
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• v.17 no.3
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• pp.115-138
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• 2017

Electronic record types are becoming diverse, and "composite electronic records," which are made up of various types of electronic records associated with functionality or user interaction that does not exist in current electronic document formats, are increasing. To ensure a continuous access to composite electronic records, metadata construction is a prerequisite for electronic records archiving. In this paper, we propose a metadata that can support archiving of composite electronic records associated with interactive functionality. The common elements were derived from an analysis of both domestic and international file format registry projects, and metadata elements related to functional requirements were identified from the analysis of the records on nursing education e-learning contents. We proposed the metadata elements for archiving composite electronic records, which consist of 25 high-level elements and 138 subelements.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.197-206
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• 2012

A exisiting recommedation system using association rules has the problem, such as delay of processing speed from a cause of frequent scanning a large data, scalability and accuracy as well. In this paper, using a Implicit method which is not used user's profile for rating, we propose the personalized recommendation system which is a new method using the FP-tree mining based on RFM. It is necessary for us to keep the analysis of RFM method and FP-tree mining to be able to reflect attributes of customers and items based on the whole customers' data and purchased data in order to find the items with high purchasability. The proposed makes frequent items and creates association rule by using the FP-tree mining based on RFM without occurrence of candidate set. We can recommend the items with efficiency, are used to generate the recommendable item according to the basic threshold for association rules with support, confidence and lift. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.113-125
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• 2019

Mobile social commerce is one of the fastest growing distribution channels in recent years. Therefore, it is important to understand customer's buying behavior in mobile social commerce in order to continuously grow in the competitive mobile social commerce market. To achieve the purpose of this study is to investigate how impulsive buying behaviors are applied in mobile shopping and how factors affect impulse purchasing in online shopping. In order to verify the hypothesis, we surveyed the customers who have experiences of using mobile social commerce and analyzed 280 valid data by Smart PLS 3.0. As a result, it was confirmed that consumers' innovation and purchasing experience influenced impulse purchase in mobile social commerce, and scarcity messages among information attributes affect impulse buying. Through this study, impulsive buying behavior which is a frequently analyzed variable in an online shopping context will be extended to the mobile shopping context. and it will provide practical implications for customer strategy establishment in mobile social commerce market.

• The Journal of Korean Association of Computer Education
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• v.21 no.6
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• pp.39-47
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• 2018

This paper dealt with investigating the number of minimal spanning ladders originated from ladder game and their properties as well as the related computational thinking aspects. The author modified the filtering techniques to enhance Mathematica project where a new type of graph was generated based on the algorithm using a generator of firstly found minimal spanning graph by repeatedly applying independent ladder operator to a subsequence of ladder sequence. The newly produced YC graphs had recursive and hierarchical graph structures and showed the properties of edge-symmetric. As the computational complexity increased the author divided the whole search space into the each floor of the newly generated minimal spanning graphs for the (5, 10) YC graph and the higher (6, 15) YC graph. It turned out that the computational thinking capabilities such as data visualization, abstraction, and parallel computing with Mathematica contributed to enumerating the new YC graphs in order to investigate their structures and properties.

• Journal of the Economic Geographical Society of Korea
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• v.22 no.2
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• pp.123-140
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• 2019

Empathy is the ability to feel, understand, and respond to the emotions of others from the standpoint of others. Recently, 'sympathy' has emerged as an important issue not only in emotional empathy in relation to individuals but also in 'social empathy', which sees sympathy as a basic principle for maintaining society. This study focuses on the issue of empathy as a new driving force for modern society and focused on 'urban scene' as a spatial application for cultural empathy in the city. Urban scenes approach the city as a space of consumption, classify it according to its inherent attributes, and analyze its characteristics. This study approaches the existing urban scene theory in terms of empathy. In addition, as a way to consume city images reflected on contents, a concrete example of 'contents tourism'is examined, and a 'contents scene' is proposed as a new urban scene and its meaning and possibility are presented.

• Journal of Environmental Impact Assessment
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• v.31 no.3
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• pp.161-172
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• 2022

This study attempts to understand urban evolution characteristics through suburban development cases considered as a contemporary urban issue. Methods are (1) Urban Expansion Intensity Index (UEII) for the 9 cities in the Korea (1980-2010) & 49 cities in the US, (2) Morphological Spatial Pattern Analysis(MSPA) and Entropy for the developed areas in the Chicago Metro (2019). Results are (1) a suburban development could be understood the universal characteristics, (2) the characteristics of the whole region might be appeared to be in a different direction from the characteristics of its sub-cities. Implications are (1) Suburban expansion can be understood as a functionally well served urban change phenomenon and it is important to focus on the functions of sub-level cities, (2) the urban evolutionary perspective makes a difference from the developmental growth perspective. The extensive empirical studies will be beneficial for better understating of urban evolution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1462-1468
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• 2022

Among the data used for the diagnosis of calf disease, feces play an important role in disease diagnosis. In the image of calf feces, the health status can be known by the shape, color, and texture. For the fecal image that can identify the health status, data of 207 normal calves and 158 calves with diarrhea were pre-processed according to fecal status and used. In this paper, images of fecal variables are detected among the collected calf data and images are trained by applying GLCM-CNN, which combines the properties of CNN and GLCM, on a dataset containing disease symptoms using convolutional network technology. There was a significant difference between CNN's 89.9% accuracy and GLCM-CNN, which showed 91.7% accuracy, and GLCM-CNN showed a high accuracy of 1.8%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.129-137
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• 2022

This study compared the sociodemographic characteristics and innovativeness of OPAL generation e-commerce users and non-users based on the innovation diffusion theory, and examined the effect of these personal characteristics on the availability of e-commerce use. The result showed that OPAL generation e-commerce users differed from non-users in gender, age, income level, education level, and functional, hedonistic, social and cognitive innovativeness. In addition, gender, age, educational level, functional and hedonistic innovativeness were found to be significant variables that determined the OPAL generation's use of e-commerce. Based on these results, we presented the theoretical and practical implications of this study.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.527-541
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• 2022

A company is a social group of many individuals that work together to obtain better results, and it is an organization that pursues common goals such as profit. As a result, forming networks among members, as well as individual communication abilities, is critical. The purpose of this research was to determine what factors influence the creation of employee advice relationships. Using the ERGM(Exponential Random Graph Model) approach, we looked at the network data of 44 individuals from consulting firms with offices in the United States and Europe. The significance of structural network factors like connectivity was first discovered. Second, the gender factor had the most significant main influence on the likelihood of adopting each other's advice. Third, geographical homogeneity resulted in higher link probabilities than major impacts of gender. This research looked at ways to make a company's network more efficient and active.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.27-38
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• 2023

In this paper, we propose a conversational AI agent based on continual learning that can continuously learn and grow with new data over time. A continual learning-based conversational AI agent consists of three main components: Task manager, User attribute extraction, and Auto-growing knowledge graph. When a task manager finds new data during a conversation with a user, it creates a new task with previously learned knowledge. The user attribute extraction model extracts the user's characteristics from the new task, and the auto-growing knowledge graph continuously learns the new external knowledge. Unlike the existing conversational AI agents that learned based on a limited dataset, our proposed method enables conversations based on continuous user attribute learning and knowledge learning. A conversational AI agent with continual learning technology can respond personally as conversations with users accumulate. And it can respond to new knowledge continuously. This paper validate the possibility of our proposed method through experiments on performance changes in dialogue generation models over time.