• 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 Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• The Journal of Society for e-Business Studies
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• v.25 no.2
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• pp.15-27
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• 2020

This study starts with the idea that the process of creating a Gantt Chart for schedule planning is similar to Tetris game with only a straight line. In Tetris games, the X axis is M machines and the Y axis is time. It is assumed that all types of orders can be worked without separation in all machines, but if the types of orders are different, setup cost will be incurred without delay. In this study, the game described above was named Gantris and the game environment was implemented. The AI-scheduling table through in-depth reinforcement learning compares the real-time scheduling table with the human-made game schedule. In the comparative study, the learning environment was studied in single order list learning environment and random order list learning environment. The two systems to be compared in this study are four machines (Machine)-two types of system (4M2T) and ten machines-six types of system (10M6T). As a performance indicator of the generated schedule, a weighted sum of setup cost, makespan and idle time in processing 100 orders were scheduled. As a result of the comparative study, in 4M2T system, regardless of the learning environment, the learned system generated schedule plan with better performance index than the experimenter. In the case of 10M6T system, the AI system generated a schedule of better performance indicators than the experimenter in a single learning environment, but showed a bad performance index than the experimenter in random learning environment. However, in comparing the number of job changes, the learning system showed better results than those of the 4M2T and 10M6T, showing excellent scheduling performance.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.77-97
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• 2019

The large amount of data that emerges from the initial connection environment of the Fourth Industrial Revolution is a major factor that distinguishes the Fourth Industrial Revolution from the existing production environment. This environment has two-sided features that allow it to produce data while using it. And the data produced so produces another value. Due to the massive scale of data, future information systems need to process more data in terms of quantities than existing information systems. In addition, in terms of quality, only a large amount of data, Ability is required. In a small-scale information system, it is possible for a person to accurately understand the system and obtain the necessary information, but in a variety of complex systems where it is difficult to understand the system accurately, it becomes increasingly difficult to acquire the desired information. In other words, more accurate processing of large amounts of data has become a basic condition for future information systems. This problem related to the efficient performance of the information system can be solved by building a semantic web which enables various information processing by expressing the collected data as an ontology that can be understood by not only people but also computers. For example, as in most other organizations, IT has been introduced in the military, and most of the work has been done through information systems. Currently, most of the work is done through information systems. As existing systems contain increasingly large amounts of data, efforts are needed to make the system easier to use through its data utilization. An ontology-based system has a large data semantic network through connection with other systems, and has a wide range of databases that can be utilized, and has the advantage of searching more precisely and quickly through relationships between predefined concepts. In this paper, we propose a defense ontology as a method for effective data management and decision support. In order to judge the applicability and effectiveness of the actual system, we reconstructed the existing air force munitions situation management system as an ontology based system. It is a system constructed to strengthen management and control of logistics situation of commanders and practitioners by providing real - time information on maintenance and distribution situation as it becomes difficult to use complicated logistics information system with large amount of data. Although it is a method to take pre-specified necessary information from the existing logistics system and display it as a web page, it is also difficult to confirm this system except for a few specified items in advance, and it is also time-consuming to extend the additional function if necessary And it is a system composed of category type without search function. Therefore, it has a disadvantage that it can be easily utilized only when the system is well known as in the existing system. The ontology-based logistics situation management system is designed to provide the intuitive visualization of the complex information of the existing logistics information system through the ontology. In order to construct the logistics situation management system through the ontology, And the useful functions such as performance - based logistics support contract management and component dictionary are further identified and included in the ontology. In order to confirm whether the constructed ontology can be used for decision support, it is necessary to implement a meaningful analysis function such as calculation of the utilization rate of the aircraft, inquiry about performance-based military contract. Especially, in contrast to building ontology database in ontology study in the past, in this study, time series data which change value according to time such as the state of aircraft by date are constructed by ontology, and through the constructed ontology, It is confirmed that it is possible to calculate the utilization rate based on various criteria as well as the computable utilization rate. In addition, the data related to performance-based logistics contracts introduced as a new maintenance method of aircraft and other munitions can be inquired into various contents, and it is easy to calculate performance indexes used in performance-based logistics contract through reasoning and functions. Of course, we propose a new performance index that complements the limitations of the currently applied performance indicators, and calculate it through the ontology, confirming the possibility of using the constructed ontology. Finally, it is possible to calculate the failure rate or reliability of each component, including MTBF data of the selected fault-tolerant item based on the actual part consumption performance. The reliability of the mission and the reliability of the system are calculated. In order to confirm the usability of the constructed ontology-based logistics situation management system, the proposed system through the Technology Acceptance Model (TAM), which is a representative model for measuring the acceptability of the technology, is more useful and convenient than the existing system.

• Proceedings of the Korean Association for Survey Research Conference
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• 2005.12a
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• pp.187-220
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• 2005

The purpose of this study is to analyze supporters' community formation motives/ Process/consumption experiences and community characteristics. For this purpose, this study collected the data using ethnographic interview. participant observation, documents and media reports. The results of this study show that supporters communities' formation and diffusion process were influenced by individual characteristics(e.g., personality, hobby and etc.), community characteristics(e.g.,team performance, star player, facilities and etc.) and external factors(ex: media movement etc.) and supporters have experienced various emotions such as intimacy. cohesion, pride and so on through various activities at on-line and off-line site. Community characteristics were classified into we-ness, rituals/traditions, moral responsibility. We found that we-ness influenced emotional dimensions such as joy, pleasure, fun and excitement. rituals and traditions made members feel passion. hope. love and vitality. and moral responsibility provided satisfaction. enthusiasm anxiety. regret and so on. Also, emotional attachment and brand loyalty were increased by these experiential aspects of community consumption.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.295-305
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• 2010

The occupational socialization process of security and secretary service goes through four stages of preparation, adaptation, conflicts, and maturity and dynamic and incessant changes. The preparation stage includes the preparation to become a security and secretary service, the importance of what to prepare, usefulness of college education, required courses, and certificates. The adaptation stage includes the percentage of bodyguard and secretary, systematic nature of work, stagnation of the job, abilities required for a security and secretary service, elements to work on, job satisfaction, information sources, professionalism of the job, and future of the job. In the conflicts stage includes conflicts at work, difficulty of security and secretary service, problem-solving efforts, advice and consultation, satisfaction with workload, job stress, perceptions of others for security and secretary service, experience of trying to get another job, and supplements. And the maturity stage includes the changes to the roles and capabilities of a security and secretary service, autonomy of business management, degree of others' recognition of one's abilities, methods to evaluate job performance, salary, social status and pride, and efforts for self-development.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.2
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• pp.397-409
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• 2014

According to the Personal Information Protection Act(PIPA) which is legislated in March 2011, the individual or company that handles personal information, called Personal information processor, should encrypt some kinds of personal information kept in his Database. For convenience sake we call it DB Encryption in this paper. Law enforcement and the implementation agency accordingly are being strengthen the supervision that the status of DB Encryption is being properly applied and implemented as the PIPA. However, the process of DB Encryption is very complicate and difficult as well as there are many factors to consider in reality. For example, there are so many considerations and requirements in the process of DB Encryption like pre-analysis and design, real application and test, etc.. And also there are surely points to be considered in related system components, business process and time and costs. Like this, although there are plenty of factors significantly associated with DB Encryption, yet more concrete and realistic validation entry seems somewhat lacking. In this paper, we propose a realistic DB Encryption Assurance Framework that it is acceptable and resonable in the performance of the PIPA duty (the aspect of the individual or company) and standard direction of inspection and verification of DB Encryption (the aspect of law enforcement).

• Journal of Convergence for Information Technology
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• v.9 no.1
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• pp.12-18
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• 2019

The purpose of this study is to analyze the effects of government's R&D investment outcome on the IT industry. The analysis of R&D investment outcome developed emphasizing qualitative outcome more than quantitative outcome. However, it is still leaning on technological outcome-centered methods, having relatively little interest in inputs that actually determine the outcome. Thus, this study intends to focus on the qualitative attributes of input resources. The results of the empirical analysis can be summarized as follows. In raising technological outcome and commercialization outcome of R&D investment, more funds per researcher and numbers of researchers and a longer development period had positive effects. However, a higher ratio of doctors had positive effects only on technological outcome (papers and patents), It is believed that leading to commercialization outcome needed a long period, but the period of task development was only an average of two years. On the contrary, collaboration had negative effects on technological process, which indicates that collaboration between two organizations having conflicting interests would lead to negative effects on the outcome. The results show that the qualitative attributes of input resources have significant effects on R&D investment outcome, and imply that it is necessary to emphasize the qualitative attributes from the input stage to promote government's R&D investment outcome in the future.

• Clean Technology
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• v.27 no.1
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• pp.33-38
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• 2021

Demand for lithium-based secondary batteries is greatly increasing with the explosive growth of related industries, such as mobile devices and electric vehicles. In Korea, there are several top-rated global lithium-ion battery manufacturers accounting for 40% of the global secondary battery business. Most discarded lithium secondary batteries are recycled as scrap to recover valuable metals, such as Nickel and Cobalt, but residual wastes are disposed of according to the residual lithium-ion concentration. Furthermore, there has not been an attempt on the possibility of water discharge system contamination due to the concentration of lithium ions, and the effluent water quality standards of public sewage treatment facilities are becoming stricter year after year. In this study, the as-received waste water generated from the cathode electrode coating process in the manufacturing of high-nickel-based NCM cathode material used for high-performance and long-term purposes was analyzed. We suggested a facile recycling process chart for waste water treatment. We revealed a correlation between lithium-ion concentration and pH effect according to the proposed waste water of each recycling process through analyzing standard water quality tests and daphnia ecological toxicity. We proposed a realistic waste water treatment plan for lithium electrode manufacturing plants via comparison with other industries' ecotoxicology.

• Management & Information Systems Review
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• v.30 no.2
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• pp.81-111
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• 2011

Relationship Marketing has been dealt with as an effective strategy to sustain customer loyalty in many previous researches. For relationship development, a customer's efforts are necessary as well as an organization's efforts. However, the role of customers for the development of the relationship with an organization has been dealt in few previous researches so far. Furthermore, whereas researchers understand the importance of consumers' motivation in the relationship, few researchers had paid attention. This research is based on the Self-Determination Theory (SDT) to explain the role of customer motivation in the process of relationship development and performance. We started by using SDT to confirm the psychological side of relationship development in customer aspects. Then, this paper verified the relationships among environmental factors(informative communication, perceived personalization), relationship motivation(identified motivation, internal motivation) and relational factors(affective commitment, relationship strength). It suggested that customer's roles in psychological parts be inevitable in developing the relationship and it acquired by such stimulations from service providers. In conclusion, this paper has several marketing implications on customer acquisition and retention. For service providers, they should recognize the fact that a customer's perception of self-determination factors can generate tangible and intangible performance in relationship development.