• Journal of Digital Convergence
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• v.17 no.1
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• pp.141-148
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• 2019

The advancement of digital technology accelerates intelligence, convergence, and demands better change beyond traditional methods in all aspects of business models and technologies, infrastructure, processes, and platforms. Risk management is becoming more important because of various security risks, depending on the changing business environment and aligned to business goals is emerging from the existing information asset based risk management. For business aligned risk management, it is essential to understand the risk appetite for achieving business goals, which provides a basis for decision-making in subsequent risk management processes. In this paper, we propose a framework for analyzing the risk management framework, pre - existing risk analysis, and protection motivation theory that influences decisions on security risk management. To examine the practical feasibility of the developed risk appetite framework, we reviewed the applicability and significance of the proposed risk appetite framework through an advisory committee composed of security risk management specialists.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.461-475
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• 2021

Hidden Markov model (HMM) is a statistical model in which the system consists of two elements, hidden states and observable results. HMM has been actively used in various fields, especially for time series data in the financial sector, since it has a variety of mathematical structures. Based on the HMM theory, this research is intended to apply the domestic KOSPI200 stock index as well as the prediction of global stock indexes such as NIKKEI225, HSI, S&P500 and FTSE100. In addition, we would like to compare and examine the differences in results between the HMM and support vector regression (SVR), which is frequently used to predict the stock price, due to recent developments in the artificial intelligence sector.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.33-55
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• 2022

This study analyzed the comments and the replies on internet news related to the presidential election in order to verify whether online discussions are properly conducted. According to Habermas' public sphere theory, discussions is an effort among participants to reach a social consensus through the deliberations that are based on open communications. We propose that if such discussions properly take place through the act of writing in the Internet space, the comments and the replies will show a certain difference in terms of the structure and the content. To validate, this study analyzed more than 40,000 comments collected from Daum News portal site in Korea. The topic of the related news was the presidential election, because it is a topic of which people are highly interested in and that comments are actively running. The result of the t-test and topic modeling result show that all the hypotheses were supported thus we conclude that online discussions properly took places. This study also showed that online comments are not chaotic remarks that relieve people's stresses, but rather an outcome of the deliberation processes moving towards a social consensus.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.1-22
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• 2022

We investigate the dynamic relationships among digital piracy activities, TV ratings, and online buzz for Korean TV dramas using a panel vector autoregression model. Our main findings include 1) TV ratings are negatively affected by digital piracy activities but positively affected by google buzz, 2) digital piracy activities are negatively affected by TV ratings and social buzz, and 3) social buzz and google buzz are positively influenced by each other. While many empirical studies were conducted to reveal the effects of music or movie piracy, our understanding of drama piracy is limited. We provide empirical evidence of the dynamic relationships between drama piracy, TV ratings, and online buzz. Our findings show the presence of indirect piracy effects on TV ratings through online buzz. Further, we reveal that social buzz and google trends play different roles in promoting TV ratings and piracy activities. We discuss the implications of our findings for theory and practitioners.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.11-18
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• 2022

As the need for software development manpower increases, various educational programs appear and the popularity of bootcamp style education program for software development increases. However, despite the operations and forms of bootcamp education programs are completely different from the existing software development education programs, there is a lack of research in understanding bootcamp as a software education program. Therefore, this study tried to derive the core elements of the education program through a case study on bootcamp software developer education program. After conducting interviews of 7 members who have completed a series of bootcamp software developer education program X, seven characteristics of bootcamp-type software development education program were derived: intensive theory education, sense of growth and achievement, team project-based learning, community characteristics, peer pressure, stress and fatigue due to short-term learning, and contact-free specialty. Based on the derived characteristics, the advantages and improvements of bootcamp-type education were described, and the direction of the bootcamp-type education program for software developer was discussed.

• Journal of Korea Port Economic Association
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• v.38 no.2
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• pp.153-164
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• 2022

In this study, we estimate the quantile-regression framework of the shipping industry for the Capesize used ship, which is a typical raw material transportation from January 2000 to December 2021. This research aims two main contributions. First, we analyze the relationship between the Capesize used ship, which is a typical type in the raw material transportation market, and the freight market, for which mixed empirical analysis results are presented. Second, we present an empirical analysis model that considers the structural transformation proposed in the Hyunsok Kim and Myung-hee Chang(2020a) study in quantile-regression. In structural change investigations, the empirical results confirm that the quantile model is able to overcome the problems caused by non-stationarity in time series analysis. Then, the long-run relationship of the co-integration framework divided into long and short-run effects of exogenous variables, and this is extended to a prediction model subdivided by quantile. The results are the basis for extending the analysis based on the shipping theory to artificial intelligence and machine learning approaches.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.65-78
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• 2023

This study is based on place attachment theory, explores how to transform tourists' virtual experience into on-site tourism willingness through virtual attachment, And explores the moderating role of happiness in it. A real travel intention model is constructed for people who work in tourism or have the ability to travel alone. Analyzed through analysis tools such as SPSS and AMOS, We hope to explore if the virtual experience can be transformed into a willingness to travel on the ground, and how happiness moderates virtual attachment to virtual tourism experiences. The result shows, the relationship between Virtual Travel Experience and Virtual Attachment is moderated by Happiness, Virtual Attachment is positively related to Place Attachment (Place dependence & Place identity), Place Attachment is positively related to Real Travel Intention. The relationship between tourists and tourist destinations is explored in depth through this study to provide references and suggestions for tourism development.

• Education of Primary School Mathematics
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• v.26 no.2
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• pp.99-113
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• 2023

In this study, I explored the possibility of using ChatGPT math education. For this purpose, students' problem-solving outputs and conversation data between pre-service teachers and a student were selected as an analysis case. A case was analyzed using ChatGPT and compared with the results of mathematics education experts. The results that ChatGPT analyzed students' problem-solving strategies and mathematical thinking skills were similar to those of math education experts. ChatGPT was able to analyze teacher questions with evaluation criteria, and the results were similar to those of math education experts. ChatGPT could also respond with mathematical theory as a source of evaluation criteria. These results demonstrate the potential of ChatGPT to analyze students' thinking and teachers' practice in mathematics education. However, there are limitations in properly applying the evaluation criteria or providing inaccurate information, so the further review of the derived information is required.

• Steel and Composite Structures
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• v.46 no.3
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• pp.319-334
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• 2023

Localizing damages is an essential task to monitor the health of the structures since they may not be able to operate anymore. Among the damage detection techniques, non-destructive methods are considerably more preferred than destructive methods since damage can be located without affecting the structural integrity. However, these methods have several drawbacks in terms of detecting abilities, time consumption, cost, and hardware or software requirements. Employing artificial intelligence techniques could overcome such issues and could provide a powerful damage detection model if the technique is utilized correctly. In this study, the crack localization in flat and folded plate structures has been conducted by employing a Backpropagated Artificial Neural Network (BPANN). For this purpose, cracks with 18 different dimensions in thin, flat, and folded structures having 15⁰, 30⁰, 45⁰, and 60⁰ folding angle have been modeled and subjected to free vibration analysis by employing the Classical Plate Theory with Finite Element Method. A Four-nodded quadrilateral element having six degrees of freedom has been considered to represent those structures mathematically. The first ten natural frequencies have been obtained regarding healthy and cracked structures. To localize the crack, the ratios of the frequencies of the cracked flat and folded structures to those of healthy ones have been taken into account. Those ratios have been given to BPANN as the input variables, while the crack locations have been considered as the output variables. A total of 500 crack locations have been regarded within the dataset obtained from the results of the free vibration analysis. To build the best intelligent model, a feature search has been conducted for BAPNN regarding activation function, the number of hidden layers, and the number of hidden neurons. Regarding the analysis results, it is concluded that the BPANN is able to localize the cracks with an average accuracy of 95.12%.