• Journal of Digital Convergence
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• v.18 no.1
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• pp.129-135
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• 2020

This study examined the consumer response of the current O2O service between service types. Previous studies mainly focused on the quality factor of O2O service or Technology Acceptance Model or extended TAM, This study is different from the fact that there are differences in factors that consumers consider important and consumer reactions by service type. It is also significant that we compared between representative food delivery services and car sharing services that are actively used among O2O services. As O2O service is closely related to consumer's life, this study chose three factors that the issue of personal information security and trust of intermediary platform companies, and finally subjective norm based on individuals and groups who are aware of new O2O service. To test hypotheses, data were collected and analyzed for 301 samples, focusing on delivery and car sharing service, As a result, the delivery service among the O2O services was more positive to attitude toward service in the consumer group with lower personal information security, trust in platform, and subjective norm than car sharing service. Based on these results, implications and future research directions were presented.

• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.67-81
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• 2006

While the existing Web focuses on the interface with human users based on HTML, the next generation Web will focus on the interaction among software agents by using XML and XML-based standards and technologies. The inference engine, which will serve as brains of software agents in the next generation Web, should thoroughly understand the Semantic Web, the standard language of the next generation Web. As abasis for the service, the W3C (World Wide Web Consortium) has recommended SWRL (Semantic Web Rule Language) which had been made by compounding OWL (Web Ontology Language) and RuleML (Rule Markup Language). In this research, we develop a backward chaining inference engine SMART-B (SeMantic web Agent Reasoning Tools -Backward chaining inference engine), which uses SWRL and OWL to represent rules and facts respectively. We analyze the requirements for the SWRL-based backward chaining inference and design analgorithm for the backward chaining inference which reflects the traditional backward chaining inference algorithm and the requirements of the next generation Semantic Web. We also implement the backward chaining inference engine and the administrative tools for fact and rule bases into Java components to insure the independence and portability among different platforms under the environment of Ubiquitous Computing.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.177-190
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• 2013

Recently, our society is seeing cultural value in a new light, perceiving it as an element that creates new values added. Reflecting this trend, the local governments in various levels focus on reviving the regional economies by enhancing cultural competencies and developing the culture and tourism industries. This research is conducted to perform the policy project 'administrative support plans to make Seongnam a hub of international culture and tourism,' which was proposed as part of the urban renewal strategy of the city. This study proposes the establishment of 'policy communities' to build the infrastructure for 'creative cultural content tourism' as a plan to accomplish the policy goal pursued by Seongnam. The focuses include what the policy communities are, how they work, and what the local government has to do with them for a successful implementation of proposed policies, by theoretically examining the composition variables of policy communities. In particular, the study concentrates on how to reconstruct the infrastructure for the culture content technology (CT) in the IT-based city and to transform itself into a city of "creative cultural content tourism". If the policy communities are formed under the proposed model in this paper, it is expected that they will become the viable alternatives in the field.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.653-660
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• 2013

In this research, students majoring in public health were tested with their interpersonal and emotional intelligence and its impact on psychological well being, studied by identifying the factors to promote and foster the students' emotions. The survey was conducted from May 15, 2003 for two weeks using only students majoring in Public Health. Factor analysis, emotional intelligence, emotion regulation, self-emotional awareness, emotions, taking advantage of others and emotional awareness was divided into four sub-factors. Emotional regulation and emotional advantage and recognition had a positive impact on interpersonal relationships, psychological wellbeing and emotional self-awareness. Emotional impact showed a positive advantage. On emotional intelligence, interpersonal factors that influence others and emotional awareness was highest in the psychological process. Students improve their interpersonal relationships and promote a more psychological well being as the emotion of anger and ability to set goals and to motivate efforts to strengthen the emotional awareness is more important. College students studying in Department of Health Services, as well as medical professionals provide better health care services for the sake of happiness that can inspire a systematic program development and is considered to require constant attention.

• Journal of Environmental Policy
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• v.4 no.1
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• pp.1-19
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• 2005

On January 1, 2005, the Special Act on Metropolitan Air Quality Improvement, known as the Blue Sky 21 Program, entered into force. This Special Act is aimed at reducing the annual average concentrations of $PM_{10}$ and $NO_2$ to $40{\mu}g/m^3$ and 22ppb, respectively, from their current levels of $76{\mu}g/m^3$ and 36ppb, within the next 10 years. While the main focus of the Act is to reduce $PM_{10}$ and $NO_2$ emissions, the government also anticipates that these reductions would lessen high ozone occurrences and improve visibility. However, reducing local PM emissions would not decrease $PM_{10}$ concentrations as much as expected since other significant sources, such as continental inflow and/or secondary formation from photochemical reactions, exist. This is corroborated by the fact that $PM_{10}$ in the greater Seoul Metropolitan area during the ozone season is mostly in the form of $PM_{2.5}$, which is secondarily formed on a regional scale. Furthermore, many modeling results indicate so-called '$NO_x$ disbenefits,' referring to the fostering of an increase in ozone concentrations from a decrease in $NO_x$ emissions, over an extensive area. In this paper, the current status of air quality in the Seoul Metropolitan area is reviewed, and several other issues, including the effect of long-range transport, photochemical characteristics, and tasks for successful implementation of the Special Act are discussed.

• Journal of Satellite, Information and Communications
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• v.9 no.2
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• pp.96-100
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• 2014

Recently, the management system of wastewater treatment facility has magnified due to the stringent regulations for the protection of the environment, and a sewage treatment plant efficiency and research of the car development are activated in large facilities or industrial park. however, the existing sewerage disposal system and specific water quality monitoring network reliability for real-time transmission of this building is insufficient. In this paper, we proposed a local wireless network design for sludge multi meter data collection and control for measuring the concentration of the sludge efficiently. Also, the collected data over the local wireless network to transmitted to the central monitoring system and accumulate the data in real time to calculate statistics is possible to monitor the status of the sewage treatment facilities. The proposed system uses a short-range wireless networks of IEEE 802.15.4 and configures an IEEE 802.11 network which can monitor real-time status in central system. Also, we install a sludge multimeter and communication network in sewage treatment facilities and confirm the usefulness of the proposed technique by demonstrating its effectiveness.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.302-313
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• 2021

As the domestic consulting industry matured, consultants were required to have insight into customer's business and consulting business. Gaining these insights requires deep understanding of the business domains and high degree of competencies. This study empirically analyzed the data collected through the survey in order to apply the ICMCI competence model to domestic consultants. As a result of the study, it was found that business competency and technical competency had a positive effect on project performance, but values & behavior competency were not statistically significant. On the other hand, it was found that only technical competency, values & behavior competency had a positive effect on social-relational competency. Through this study, it was confirmed that a deep understanding and perception of the consulting business is necessary to grow into a professional consultant, but there is a limit to generalizing the research results because the characteristics of the population cannot be sufficiently reflected with a small sample.

• Journal of Industrial Convergence
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• v.20 no.5
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• pp.83-92
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• 2022

As cases of social and ethical problems caused by artificial intelligence technology have occurred, artificial intelligence ethics are drawing attention along with social interest in the risks and side effects of artificial intelligence. Artificial intelligence ethics should not just be known and felt, but should be actionable and practiced. Therefore, this study proposes an artificial intelligence ethics education model to strengthen the practical ability of artificial intelligence ethics. The artificial intelligence ethics education model derived educational goals and problem-solving processes using artificial intelligence through existing research analysis, applied teaching and learning methods to strengthen practical skills, and compared and analyzed the existing artificial intelligence education model. The artificial intelligence ethics education model proposed in this paper aims to cultivate computing thinking skills and strengthen the practical ability of artificial intelligence ethics. To this end, the problem-solving process using artificial intelligence was presented in six stages, and artificial intelligence ethical factors reflecting the characteristics of artificial intelligence were derived and applied to the problem-solving process. In addition, it was designed to unconsciously check the ethical standards of artificial intelligence through preand post-evaluation of artificial intelligence ethics and apply learner-centered education and learning methods to make learners' ethical practices a habit. The artificial intelligence ethics education model developed through this study is expected to be artificial intelligence education that leads to practice by developing computing thinking skills.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.168-176
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• 2022

Purpose: This study was done to examine the effects of the SNS Gratitude Diary Program during the COVID-19 period on grateful disposition, job satisfaction and life satisfaction of medical institution workers. Methods: The study was a nonequivalent control group pre-post test design. The participants were 58 medical institution workers of which 28 were assigned to the experimental group and 30 to the control group. Data were collected from June 3 to August 10, 2020 and analyzed using SPSS Win 18.0 version program. The intervention was conducted 5 times a week for 8 weeks. Results: There were significant differences in reported grateful disposition between the two groups. However, the issue of job satisfaction and life satisfaction were not significant. Conclusion: The SNS Gratitude Diary Program using SNS during the COVID-19 period can be utilized as an effective intervention for grateful disposition. It will be a good solution to lead successful psychological health management.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.