• Journal of The Korean Institute of Defense Technology
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• v.5 no.1
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• pp.7-18
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• 2023

The military leader makes an operation plan to accomplish combat missions. The current doctrine for an operation planning requires the use of simple and clear procedures and methods that can be carried out with human effort under adverse conditions in the field. The work in the process of an operation planning can be said to be a series of decision-making, and the criteria for decision-making generally apply mission variables. However, detailed standards are not fixed as doctrine, but are creatively established and applied. However, for AI-based decision-making, it is necessary to formalize the criteria and the format used. This paper first aims to standardize various criteria and forms to present a method that can be used in a semi-automated assist system, and to seek a plan to artificialize it. To this end, mathematical models and decision-making methods established in the field of operations research were applied to improve efficiency.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.539-545
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• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.283-289
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• 2021

Many people with disabilities have shown interest in artificial intelligence speakers that serves as the main hub of the smart home. Therefore, the purpose of this study was to identify the intention of people with disabilities to use such speakers. The focus is on those with physical disabilities, a segment that accounts for the largest number of disability types. Based on the theoretical model of technology acceptance, the effect of perceived ease of use and perceived usefulness of artificial intelligence speakers by people with disabilities was analyzed using Structural Equation Modeling (SEM). Research has confirmed that the technology acceptance model is suitable for identifying the intention to use artificial intelligence speakers by people with disabilities, and specifically that the perceived ease of use has a significant impact on usefulness. Furthermore, the perceived ease of use for people with disabilities did not have a statistically significant effect on their intent to use whereas the perceived usefulness was shown to have a significant effect on the same. This study is meaningful as a foundation for developing customized artificial intelligence speaker services and improving the use of artificial intelligence speakers by people with disabilities.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.2
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• pp.55-67
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• 2023

This study aims to develop a model that can automatically identify the rooftop shape of apartment buildings using GIS and machine learning algorithms, and apply it to analyze the relationship between rooftop shape and characteristics of apartment complexes. A database of rooftop data for each building in an apartment complex was constructed using geospatial data, and individual buildings within each complex were classified into flat type, tower type, and mixed types using the random forest algorithm. In addition, the relationship between the proportion of rooftop shapes, development density, height, and other characteristics of apartment complexes was analyzed to propose the potential application of geospatial information in the real estate field. This study is expected to serve as a basic research on AI-based building type classification and to be utilized in various spatial and real estate analyses.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

• Journal of Nutrition and Health
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• v.50 no.1
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• pp.98-110
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• 2017

Purpose: The purpose of this study was to evaluate the dietary potassium intake, Na/K intake molar ratio, consumption of 18 food groups, and foods contributing to potassium intake of Korean adults as well as the relationships among quartile of potassium intake level and blood pressure, blood biochemical index. Methods: This study was conducted using the Korea National Health and Nutrition Examination Survey, 2007~2010. The total number of subjects was 20,291. All analyses were conducted using a survey weighting to account for the complex survey design. Results: Overall average intakes of potassium were 2,934.7, 3,070.6, 3,078.1, and 3,232.0 mg/day, and they significantly increased by year in Korean adults. The average dietary potassium intake was close to adequate intake (AI), whereas that of women was considerably lower than the AI. The Na/K intake molar ratio in males (2.89~3.23) was higher than in females (2.62~2.95). The major food groups contributing to potassium intake were vegetables, cereals, and fruits/meats. The two major foods contributing to potassium intake were polished rice and cabbage kimchi. The rankings of food source were as follows; polished rice > cabbage kimchi > potato > oriental melon > sweet potato > seaweed > radish > apple > black soybean. In 50~64 year old females, systolic blood pressure (SBP) significantly decreased (p < 0.01) and HDL-cholesterol significantly increased (p < 0.05) as potassium intake increased. Triglyceride (TG) was significantly higher in the other quartile of potassium intake level than in the first quartile (p < 0.05). Conclusion: In conclusion, our study suggests the need for an appropriate set of dietary reference intakes according to caloric intake by sex and age groups and for development of eating patterns to increase potassium intake and decrease sodium intake.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of Environmental Impact Assessment
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• v.33 no.4
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• pp.143-154
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• 2024

Heat waves caused by climate change are rapidly increasing health damage to vulnerable groups, and to prevent this, the national, regional, and local governments are establishing climate crisis adaptation policy. A representative climate crisis adaptation policy to reduce heat wave damage is to expand the number of cooling centers. Because it is highly effective in a short period of time, most metropolitan local governments, except Jeonbuk, include the project as an adaptation policy. However, the criteria for selecting a cooling centers are different depending on the budget and non-budget, so the utilization rate and effectiveness of the cooling centers are all different. Therefore, in this study, we developed logistic regression models that can predict and evaluate areas with a high probability of expanding cooling centers in order to implement adaptation policy in local governments. In Incheon Metropolitan City, which consists of various heat wave-vulnerable environments due to the coexistence of the old city and the new city, a logistic model was developed to predict areas where heat waves can be cooling centered by dividing it into Ganghwa·Ongjin-gun and other regions, taking into account socioeconomic and environmental differences. As a result of the study, the statistical model for the Ganghwa·Ogjin-gun region showed that the higher the ground surface temperature and the more and more the number of elderly people over 65 years old, the higher the possibility of location of cooling centers, and the prediction accuracy was about 80.93%. The developed logistic regression model can predict and evaluate areas with a high potential as cooling centers by considering regional environmental and social characteristics, and is expected to be used for priority selection and management when designating additional cooling centers in the future.

• Journal of agriculture & life science
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• v.46 no.4
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• pp.9-20
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• 2012

Eugenol is a volatile compound synthesized by eugenol synthase in various plants and belongs to phenylpropene compounds. However, characteristics of eugenol synthase in tomato has not been known. Therefore, we cloned a full length cDNA of a putative eugenol synthase from tomato 'Micro-Tom' using rapid amplification of cDNA ends (RACE) technique and named a clone SlEGS. Open reading frame of SlEGS was 921bp long and its deduced amino acid sequence was 307bp. The BLAST analysis indicated that SlEGS shared high similarity with PhEGS1 (67.1%) and CbEGS2 (69.4%). Amino acid composition of SlEGS was determined by CLC genomics workbench tool and 3D structure of SlEGS was constructed by homology modeling using Swiss-PDB viewer and validated using PROCHECK and ProSA-web tool. In addition, the physiochemical properties of SlEGS was evaluated using ExPASy's ProtParam tool. Molecular weight was 33.93kDa and isoelectric point was 5.85 showing acidic nature. Other properties such as extinction coefficient, instability index, aliphatic index, and grand average hydropathy was also analyzed.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.217-225
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• 2021

This study analyzed the perception formed by artificial intelligence users by converging technology readiness index and technology acceptance models and expanding them to models considering artificial intelligence ethics in order to find out the impact of technology acceptance and ethics. Independent variables include optimism, transparency, ethical awareness, user-centeredness, perceived usefulness and perceived ease of use as potential variables affected by independent variables, and defined the intention of use as potential variables as dependent variables. The survey results from an online and offline of men and women aged over 17 years old across the country (N=260) from September 5 to October 12, 2020 were used in the analysis. The findings, first, showed that optimism had a significant static effect on perceived usefulness and ease of use. Second, ethical awareness (transparency, ethical awareness, user-centeredness) did not have a significant impact on perceived usefulness and ease of use. Third, perceived usefulness and ease of use are finally found to have a significant static effect on the intention of use. Fourth, perceived usefulness has a relatively high influence over ease of use.