• Smart Media Journal
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• v.11 no.6
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• pp.18-29
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• 2022

This study proposes an evaluation item model for the avatar expression process, satisfaction and self-identification for each expression element in order to investigate the expression attributes on the influence on the user in setting the avatar. The evaluation of avatar expression method was composed of self-identification and satisfaction by ease of expression and expression elements on the avatar production and expression process. To evaluate the proposed evaluation model, 17 people participated in the experiment. As a result, it was found that most of the participants had a high level of understanding of the production process, and the more diverse the expression elements, the higher the satisfaction with the avatar expression process. Hair shape and face shape were the most important expression factors, and clothes and overall harmony was the most concerned facotrs. Most of them tried to express themselves as realistically as possible. However, in self-identification, there was no significant correlation between expression and production process. In this study identify the decision-making factors that appear in the avatar expression process, the direction in which satisfaction can be formed, and the factors that affect self-identification. In addition, it will be a basic study on how avatars have a lasting influence on users in the future.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.339-346
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• 2022

Recently, the resource depletion and climate change problem caused by the massive usage of fossil fuels for electric power generation has become a critical issue worldwide. According to this issue, interest in renewable energy resources that can replace fossil fuels is increasing. Especially, photovoltaic power has gaining much attention because there is no risk of resource exhaustion compared to other energy resources and there are low restrictions on installation of photovoltaic system. In order to use the power generated by the photovoltaic system efficiently, a more accurate photovoltaic power forecasting model is required. So far, even though many machine learning and deep learning-based photovoltaic power forecasting models have been proposed, they showed limited success in terms of interpretability. Deep learning-based forecasting models have the disadvantage of being difficult to explain how the forecasting results are derived. To solve this problem, many studies are being conducted on explainable artificial intelligence technique. The reliability of the model can be secured if it is possible to interpret how the model derives the results. Also, the model can be improved to increase the forecasting accuracy based on the analysis results. Therefore, in this paper, we propose an explainable photovoltaic power forecasting scheme based on BiLSTM (Bidirectional Long Short-Term Memory) and SHAP (SHapley Additive exPlanations).

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.8
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• pp.249-258
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• 2022

Since the advent of Bitcoin, various virtual assets have been actively traded through virtual asset services of virtual asset exchanges. Recently, security accidents have frequently occurred in virtual asset exchanges, so the government is obligated to obtain information security management system (ISMS) certification to strengthen information protection of virtual asset exchanges, and 56 additional specialized items have been established. In this paper, we compared the domain importance of ISMS and CryptoCurrency Security Standard (CCSS) which is a set of requirements for all information systems that make use of cryptocurrencies, and analyzed the results after mapping them to gain insight into the characteristics of each certification system. Improvements for 4 items of High Level were derived by classifying the priorities for improvement items into 3 stages: High, Medium, and Low. These results can provide priority for virtual asset and information system security, support method and systematic decision-making on improvement of certified items, and contribute to vitalization of virtual asset transactions by enhancing the reliability and safety of virtual asset services.

• Journal of the Society of Disaster Information
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• v.19 no.1
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• pp.44-59
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• 2023

Purpose: In the event of mass casualties, triage must be done promptly and accurately so that as many patients as possible can be recovered and returned to the battlefield. However, medical personnel have received many tasks with less manpower, and the battlefield for classifying patients is too complex and uncertain. Therefore, we studied an artificial intelligence model that can assist and replace medical personnel on the battlefield. Method: The triage model is presented using reinforcement learning, a field of artificial intelligence. The learning of the model is conducted to find a policy that allows as many patients as possible to be treated, taking into account the condition of randomly set patients and the medical capability of the military hospital. Result: Whether the reinforcement learning model progressed well was confirmed through statistical graphs such as cumulative reward values. In addition, it was confirmed through the number of survivors whether the triage of the learned model was accurate. As a result of comparing the performance with the rule-based model, the reinforcement learning model was able to rescue 10% more patients than the rule-based model. Conclusion: Through this study, it was found that the triage model using reinforcement learning can be used as an alternative to assisting and replacing triage decision-making of medical personnel in the case of mass casualties.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.159-165
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• 2023

While the frequency of seismic occurrence has been increasing recently, the domestic seismic response system is weak, the objective of this research is to compare and analyze the seismic vulnerability of buildings using statistical analysis and machine learning techniques. As the result of using statistical technique, the prediction accuracy of the developed model through the optimal scaling method showed about 87%. As the result of using machine learning technique, because the accuracy of Random Forest method is 94% in case of Train Set, 76.7% in case of Test Set, which is the highest accuracy among the 4 analyzed methods, Random Forest method was finally chosen. Therefore, Random Forest method was derived as the final machine learning technique. Accordingly, the statistical analysis technique showed higher accuracy of about 87%, whereas the machine learning technique showed the accuracy of about 76.7%. As the final result, among the 22,296 analyzed building data, the seismic vulnerabilities of 1,627(0.1%) buildings are expected as more dangerous when the statistical analysis technique is used, 10,146(49%) buildings showed the same rate, and the remaining 10,523(50%) buildings are expected as more dangerous when the machine learning technique is used. As the comparison of the results of using advanced machine learning techniques in addition to the existing statistical analysis techniques, in spatial analysis decisions, it is hoped that this research results help to prepare more reliable seismic countermeasures.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1267-1273
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• 2022

Although the Korea shipbuilding industry has recently been receiving most of the orders for ships in the world, production processes are being disrupted due to a shortage of manpower at the production site. This is because the workers quit the shipyard as both work and wages were reduced due to the long slump in the shipbuilding industry. The main reason for the increase in orders was the large-scale orders for Qatar LNG carriers, and the situation in which the technical specifications required for ships are becoming more complex is also working to an advantage. Because the contract delivery time is of utmost importance for ships, the dock launch plan is the most important management item among the shipyard's major processes. The structure to be built in the dock may be a hull that has left the design work or a finished vessel, and in some cases, it is often at the level of some blocks of the hull. When launching, the hull is affected by the hogging or sagging moment due to the fluid force, and securing the safety of the structural strength of the block connection is of utmost importance. In a normal process, the connecting member launches after welding has been completed, but in actual shipbuilders, quick decision-making is needed on the conditions for securing structural safety to comply with the docking schedule. In this study, a detailed analysis method and applicability using a bending stress evaluation method and finite element analysis modelling were analyzed to rationally judge the above-mentioned problems from an engineering point of view. The main contents mentioned in the thesis can be used as good examples when conducting similar structural strength evaluations in the future.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.41-63
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• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.1-9
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• 2022

The objective of this study was to analyze the factors affecting the stages of change of exercise in middle-aged men who work. 170 middle-aged men who work surveyed, 40 to 59 years old, is residing, Chung-Buk and Chung-Nam province, who understand the purpose of this study and agree to participate in this study. This study data is analyzed by using frequency, percentage, standard deviation, t-test, 𝑥² test and Logistic regression analysis. The study show that the exercise self-efficacy(𝛽=.965, p=.003) and the perceived health status(𝛽=.805, p=.025) among middle aged men who work have an effect on the stages of change of exercise meaningfully. That is, the exercise self-efficacy of middle aged men who work who have exercise behavior is 2.6 times higher than middle aged men at work who don't have exercise behavior, and the perceived health status is 2.2 times higher. This study suggests that the development of better exercise practice for middle aged men who work should be aimed at promoting exercise self-efficacy and perceived health status, Based on this, it is necessary to find ways to operate exercise programs at the workplace and community level.

• The Mathematical Education
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• v.62 no.2
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• pp.289-302
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• 2023

In today's digital information society, student knowledge and skills to analyze big data and make informed decisions have become an important goal of school mathematics. Integrating big data statistical projects with digital technologies in high school <Artificial Intelligence> mathematics courses has the potential to provide students with a learning experience of high impact that can develop these essential skills. This paper proposes a set of guidelines for designing effective big data statistical project-based tasks and evaluates the tasks in the artificial intelligence mathematics textbook against these criteria. The proposed guidelines recommend that projects should: (1) align knowledge and skills with the national school mathematics curriculum; (2) use preprocessed massive datasets; (3) employ data scientists' problem-solving methods; (4) encourage decision-making; (5) leverage technological tools; and (6) promote collaborative learning. The findings indicate that few textbooks fully align with these guidelines, with most failing to incorporate elements corresponding to Guideline 2 in their project tasks. In addition, most tasks in the textbooks overlook or omit data preprocessing, either by using smaller datasets or by using big data without any form of preprocessing. This can potentially result in misconceptions among students regarding the nature of big data. Furthermore, this paper discusses the relevant mathematical knowledge and skills necessary for artificial intelligence, as well as the potential benefits and pedagogical considerations associated with integrating technology into big data tasks. This research sheds light on teaching mathematical concepts with machine learning algorithms and the effective use of technology tools in big data education.

• Journal of Korea Port Economic Association
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• v.38 no.4
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• pp.107-118
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• 2022

In this study, daily data from January 2002 to June 2022 were used to investigate the relationship between risk-return relationship and market fear, uncertainty, stock market, and maritime freight index for the crude oil market. For this study, the time varying EGARCH-M model was applied to the risk-return relationship, and the wavelet consistency model was used to analyze the relationship between market fear, uncertainty, stock market, and maritime freight index. The analysis results of this study are as follows. First, according to the results of the time-varying risk-return relationship, the crude oil market was found to be related to high returns and high risks. Second, the results of correlation and Granger causality test, it was found that there was a weak correlation between the risk-return relationship and VIX, EPU, S&P500, and BDI. In addition, it was found that there was no two-way causal relationship in the risk-return relationship with EPU and S&P500, but VIX and BDI were found to affect the risk-return relationship. Third, looking at the results of wavelet coherence, it was found that the degree of the risk-return relationship and the relationship between VIX, EPU, S&P500, and BDI was time-varying. In particular, it was found that the relationship between each other was high before and after the crisis period (financial crisis, COVID-19). And it was found to be highly associated with organs. In addition, the risk-return relationship was found to have a positive relationship with VIX and EPU, and a negative relationship with S&P500 and BDI. Therefore, market participants should be well aware of economic environmental changes when making decisions.