• Journal of Internet Computing and Services
• /
• v.21 no.2
• /
• pp.81-90
• /
• 2020

The purpose of this study is to study how to apply block-chain technology to prevent data forgery and alteration in the defense sector of AI(Artificial intelligence). AI is a technology for predicting big data by clustering or classifying it by applying various machine learning methodologies, and military powers including the U.S. have reached the completion stage of technology. If data-based AI's data forgery and modulation occurs, the processing process of the data, even if it is perfect, could be the biggest enemy risk factor, and the falsification and modification of the data can be too easy in the form of hacking. Unexpected attacks could occur if data used by weaponized AI is hacked and manipulated by North Korea. Therefore, a technology that prevents data from being falsified and altered is essential for the use of AI. It is expected that data forgery prevention will solve the problem by applying block-chain, a technology that does not damage data, unless more than half of the connected computers agree, even if a single computer is hacked by a distributed storage of encrypted data as a function of seawater.

• Journal of the Korea Convergence Society
• /
• v.13 no.4
• /
• pp.7-15
• /
• 2022

With the aim of an intelligent world in the age of individual customization through decentralization of information and technology, sharing/opening, and connection, we often see a tendency to cross expectations and concerns in the technological discourse and interest in artificial intelligence more than ever. Recently, it is easy to find claims by futurists that AI singularity will appear before and after 2045. Now, as part of preparations to create a paradigm of coexistence that coexists and prosper with AI in the coming age of artificial intelligence, a standard framework for setting up more correct AI ethics and regulations is required. This is because excluding the risk of omission of setting major guidelines and methods for evaluating reasonable and more reasonable guideline items and evaluation standards are increasingly becoming major research issues. In order to solve these research problems and at the same time to develop continuous experiences and learning effects on AI ethics and regulation setting, we collect guideline data on AI ethics and regulation of international organizations / countries / companies, and research and suggest ways to set up a standard framework (SF: Standard Framework) through a setting research model and text mining exploratory analysis. The results of this study can be contributed as basic prior research data for more advanced AI ethics and regulatory guidelines item setting and evaluation methods in the future.

• Journal of Korea Water Resources Association
• /
• v.55 no.spc1
• /
• pp.1177-1185
• /
• 2022

The water and sewage system is an infrastructure that provides safe and clean water to people. In particular, since the water and sewage pipelines are buried underground, it is very difficult to detect system defects. For this reason, the diagnosis of pipelines is limited to post-defect detection, such as system diagnosis based on the images taken after taking pictures and videos with cameras and drones inside the pipelines. Therefore, real-time detection technology of pipelines is required. Recently, pipeline diagnosis technology using advanced equipment and artificial intelligence techniques is being developed, but AI-based defect detection technology requires a variety of learning data because the types and numbers of defect data affect the detection performance. Therefore, in this study, various defect scenarios are implemented using 3D printing model to improve the detection performance when detecting defects in pipelines. Afterwards, the collected images are performed to pre-processing such as classification according to the degree of risk and labeling of objects, and real-time defect detection is performed. The proposed technique can provide real-time feedback in the pipeline defect detection process, and it would be minimizing the possibility of missing diagnoses and improve the existing water and sewerage pipe diagnosis processing capability.

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

• Journal of The Korean Association For Science Education
• /
• v.44 no.1
• /
• pp.57-75
• /
• 2024

This study aimed to investigate the current state and educational needs of risk education related to science and technology as perceived by secondary science teachers. A survey was conducted with a total of 366 secondary science teachers. The results are as follows. First, There were more teachers who had not provided education on risks arising from science and technology in terms of risk perception, risk assessment, and risk management than those who had not. Global warming was the most common risk taught by teachers, followed by earthquakes, artificial intelligence, and traffic accidents. Second, teachers recognized that they lacked understanding that the achievement standards of the 2022 revised science curriculum include risks that may occur due to science and technology, but they thought they were prepared to teach. Third, teachers recognized that their understanding of risk perception was higher than that of risk management and risk assessment. Fourth, the experience of teachers in training on risk was very limited, with fewer having training in risk assessment and risk management compared to risk perception. The most common training experienced was in laboratory safety. Fifth, teachers recognized that their capabilities for the 10 goals of risk education were not high. Middle school teachers or teachers majoring in integrated science education evaluated their capabilities relatively highly. Sixth, many teachers thought it was important to address risks in school science education. They prioritized 'information use', 'decision-making skills', and 'influence of mass media', in that order, for importance and called for urgent education in 'action skills', 'information use', and 'influence of risk perception'. Seventh, as a result of deriving the priorities of education needs for each of the 10 goals of risk education, 'action skills', 'influence of risk perception', and 'evaluate risk assessment' were ranked 1st, 2nd, and 3rd, respectively.

• Journal of Intelligence and Information Systems
• /
• v.21 no.3
• /
• pp.79-99
• /
• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of The Korean Association of Information Education
• /
• v.24 no.1
• /
• pp.21-28
• /
• 2020

Technological advancements such as artificial intelligence, robots, and big data are revolutionizing the entire society. In this paper, we analyzed preliminary teachers' daily internet/gaming/smartphone habit and the difference between preference factors in gender and diagnosis group in the situation of ethical dilemma in driverless cars. The result shows most of the male students are in high risk group of daily internet/gaming usage, and male students tend to be more immersed in games compared to female students, which negatively affects their daily lives. Students who have at least one of the daily internet/gaming/smartphone habits are more likely to be classified as high-risk group in all three of daily internet/gaming/smartphone habit. Fortunately, the students perceived themselves addicted and wanted change their habits. An analysis by a moral machine of these students tells that there is no significant difference in preference between male and female students and among diagnosis groups. However, specifically in the ethical dilemma of driverless cars, all the groups of male, female, normal, high-risk showed they have priority in pedestrians over drivers, a large number of people over small, and people who obey traffic rules over who do not. The tendency was pronounced in female group and high-risk students prioritized people who are older and in lower social status.

• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.34 no.1
• /
• pp.77-84
• /
• 2024

Objectives: Interventions promoting physical exercise and healthy habits in workplaces have been shown to be effective in reducing risk factors for metabolic syndrome. This study was conducted to examine the effects of an individualized conditioning exercise program of IT company office workers with or at higher risk of metabolic syndrome. Methods: A total of 444 IT company office workers with or at higher risk of metabolic syndrome participated in a 3-month conditioning exercise program. Body composition data using bioelectrical impedance analysis and cardiopulmonary data using cardiopulmonary exercise testing from 53 individuals (mean age: 34.8 ± 7.1 years, sex : 21% female, height : 170.4 ± 6.8 cm, weight : 75.2±12.2 kg, body mass index : 25.8±3.3 kg/m²) who have successfully completed pre-test, intervention, and post-test were analyzed. The 12 weeks intervention encompassed: (1) health counseling (2) supervised exercise(endurance-based, aerobic exercise, or circuit training once a week for 50 minutes at heart rate reserve(HRR) of 77-95%) (3) self-directed exercise and biweekly health screening checks. Results: The results indicated a significant decrease in body weight, body fat mass and body mass index, respectively. Moreover, VO₂peak, AT VO₂ and AT Time significantly improved, respectively. Resting blood pressure(SBP/DBP) showed positive changes but were not statistically significant. We observed the correlation between characteristics of participants and rate of changes in cardiopulmonary outcomes of participants, there are no significant correlation. These results indicate positive changes in body composition and cardiorespiratory fitness parameters following individualized conditioning exercise program. Conclusions: Individualized workplace exercise program for preventing metabolic syndrome can lead to improvements in body composition and cardiorespiratory fitness.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.5
• /
• pp.780-790
• /
• 2022

Quantitative risk levels must be presented by analyzing the causes and consequences of accidents and predicting the occurrence patterns of the accidents. For the analysis of marine accidents related to vessel traffic, research on the traffic such as collision risk analysis and navigational path finding has been mainly conducted. The analysis of the occurrence pattern of marine accidents has been presented according to the traditional statistical analysis. This study intends to present a marine accident prediction model using the statistics on marine accidents related to vessel traffic. Statistical data from 1998 to 2021, which can be accumulated by month and hourly data among the Korean domestic marine accidents, were converted into structured time series data. The predictive model was built using a long short-term memory network, which is a representative artificial intelligence model. As a result of verifying the performance of the proposed model through the validation data, the RMSEs were noted to be 52.5471 and 126.5893 in the initial neural network model, and as a result of the updated model with observed datasets, the RMSEs were improved to 31.3680 and 36.3967, respectively. Based on the proposed model, the occurrence pattern of marine accidents could be predicted by learning the features of various marine accidents. In further research, a quantitative presentation of the risk of marine accidents and the development of region-based hazard maps are required.

• Journal of The Korean Society of Integrative Medicine
• /
• v.10 no.4
• /
• pp.261-275
• /
• 2022

Purpose : According to the Korean Diabetes Association, the number of metabolic disease patients in Korea is approximately 14.97 million as of 2020;, the prevalence of diabetes among adults over 30 years old is 44.3 %, and the prevalence of diabetes in the elderly over 65 years old is 50.4%. These individuals exposed to the risk of complications. Therefore, the purpose of this study was to determine a distribution method for a customized fusion exercise marine healing program for metabolic disease patients. Methods : We have searched numerous papers concerning artificial intelligence (AI), virtual reality (VR), augmented reality (AR), and marine resources related to marine healing programs that can be introduced in marine healing centers for people with metabolic diseases Results : Through the production of various marine resources and evidence-based exercise programs, the provision of exercise programs using AI, and the development of exercise platforms using AR and VR, we were able to establish guidelines for how to operate marine healing programs at marine healing centers. Conclusion : Korea has much more diverse marine healing resources than other advanced countries in the marine healing industry. However, the development of these resources has only just begun. It is hoped that the studied marine healing program will be of great help to metabolic patients by creating contents that will be implemented in marine healing centers by using the cutting-edge technologies and various marine resources that Korea possesses.