• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.864-881
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• 2021

Purpose: This study aims is to provide a total care solution preventing disaster based on Big Data and AI technology and to service safety considered by individual situations and various risk characteristics. The purpose is to suggest a method that customized comprehensive index services to prevent and respond to safety accidents for calculating the living safety index that quantitatively represent individual safety levels in relation to daily life safety. Method: In this study, we use method of mixing AHP(Analysis Hierarchy Process) and Likert Scale that extracted from consensus formation model of the expert group. We organize evaluation items that can evaluate life safety prevention services into risk indicators, vulnerability indicators, and prevention indicators. And We made up AHP hierarchical structure according to the AHP decision methodology and proposed a method to calculate relative weights between evaluation criteria through pairwise comparison of each level item. In addition, in consideration of the expansion of life safety prevention services in the future, the Likert scale is used instead of the AHP pair comparison and the weights between individual services are calculated. Result: We obtain result that is weights for life safety prevention services and reflected them in the individual risk index calculated through the artificial intelligence prediction model of life safety prevention services, so the comprehensive index was calculated. Conclusion: In order to apply the implemented model, a test environment consisting of a life safety prevention service app and platform was built, and the efficacy of the function was evaluated based on the user scenario. Through this, the life safety index presented in this study was confirmed to support the golden time for diagnosis, response and prevention of safety risks by comprehensively indication the user's current safety level.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1501-1512
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• 2024

Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.

• 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 the Computational Structural Engineering Institute of Korea
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• v.37 no.4
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• pp.225-232
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• 2024

This study focuses on the determination of the fracture mode in composite laminates using deep learning. With the increase in the use of laminated composites in numerous engineering applications, the insurance of their integrity and performance is of paramount importance. However, owing to the complex nature of these materials, the identification of fracture modes is often a tedious and time-consuming task that requires critical domain knowledge. Therefore, to alleviate these issues, this study aims to utilize modern artificial intelligence technology to automate the fractographic analysis of laminated composites. To accomplish this goal, scanning electron microscopy (SEM) images of fractured tensile test specimens are obtained from laminated composites to showcase various fracture modes. These SEM images are then categorized based on numerous fracture modes, including fiber breakage, fiber pull-out, mix-mode fracture, matrix brittle fracture, and matrix ductile fracture. Next, the collective data for all classes are divided into train, test, and validation datasets. Two state-of-the-art, deep learning-based pre-trained models, namely, DenseNet and GoogleNet, are trained to learn the discriminative features for each fracture mode. The DenseNet models shows training and testing accuracies of 94.01% and 75.49%, respectively, whereas those of the GoogleNet model are 84.55% and 54.48%, respectively. The trained deep learning models are then validated on unseen validation datasets. This validation demonstrates that the DenseNet model, owing to its deeper architecture, can extract high-quality features, resulting in 84.44% validation accuracy. This value is 36.84% higher than that of the GoogleNet model. Hence, these results affirm that the DenseNet model is effective in performing fractographic analyses of laminated composites by predicting fracture modes with high precision.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.31 no.1
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• pp.28-37
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• 2013

As a symbolic landscape of Seoul, Namsan has undergone not only physical changes but also changes in its human use and characteristics. At this point, research on Namsan, which holds diverse stories that have accumulated over a long period, as a cultural landscape is necessary. In particular, a concrete understanding of the characteristics of the mountain's use in the period of its initiation as a modern park is an important task in research on the history of urban parks. Consequently, the purpose of the present study lies in grasping the use of Namsan at the time of the establishment of Kyongsungbu Namsan Park Design Proposal in 1917 and examining the characteristics per space. The research process was based on the status of the park design plan. The primary source of information came from the analysis of historical newspaper articles. Additional materials including documents, old maps, photographs, postcard materials were also used. The period of the study was 1883 to 1917. This time was the initial period of Namsan Park soon after the opening up of Korea's ports to the world. The major spaces in which Namsan was used as a park encompassed Hanyang Park, Waeseongdae Park, Noin-jeong, Jangchung-dan, and remaining parts of Namsan in a natural state. When the main ways in which each space is used are examined based on the data analyzed, Namsan has been used for purposes including public events, accidents, religious worship, track and field days, field trips, and strolls. When the nature of each of the spaces is determined in terms of the characteristics of their use, these spaces were characterized as community parks, outdoor community spaces, indoor community spaces, sports arenas, and natural parks, among other things. The present study is significant in terms of research on the history of parks for confirming that Namsan in the initial period already served as a modern park for urban activities and grasping the specific urban activities that were engaged in on Namsan.

• The Journal of Korean Academic Society of Nursing Education
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• v.5 no.2
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• pp.177-190
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• 1999

This study has been conducted on the basis of the literature review of Nursing Education Research in China from 1990 through August 1998. Its purpose was to support the basic data of nursing education which is risen as major revolutionary of nursing in China and those for exchange of information between Korea-China nursing education. It is retrospective and descriptive research analyzing one hundred eighty articles published in The Journal of China Nursing. The results of the study were as follows. 1. Only 33.3% of the professors of Technical Nursing School who have played of major role of nursing education in China have carried out the study related to nursing education. Baccalaureate program professors have marked 22.2% of all studies, and diploma program professors have done 12.2% of all. Therefore, the professors of above the diploma program have done total 44.4%. It explains that the professors of baccalaureate and diploma programs have done more studies related to nursing education than those of Technical Nursing School. 2. In terms of the study design, most of the studies(38.8%) were case studies introducing the curriculum contents that were done at education institutions. And then, 28.5% were reviewing the articles, and 15.6% were descriptive studies. 3. In terms of the content of the study, 38.3% were relevant to education of Technical Nursing School, 15.0% were about baccalaureate education, and 10.4% is about diploma. 4. To analyze the specific contents of the studies ; a. In baccalaureate program, human resources (professor or teaching), course extension, lab, classes, teaching method, education philosophy, goal of education, evaluation method, and human resource development were included. b. In diploma program, teaching contents evaluation method, teaching method, and educational system were included c. In the technical school, there were qualification of professors , teaching method, evaluation method, opening the courses, teaching contents, goal of education and so on. d. Beyond these, there were practice guidance and appraisement, teaching method, and opening new courses which were not specially indicated as educational curriculum and score management as continuing education. What is above tell us that the study regarding development of university system has been progressed actively and widely. It has been for the effort of revolution which based on the China government force to reform of nursing education process during last 10 years. On the base of the result, we suggest the following questions and the alternatives. 1) Since most articles are case studies related to teaching methods and the others doesn't propose the research method. the study which is applied more exact research method is needed. 2) No study is regarding social change and health policy. Because University program, founded in 1983 is on the beginning point, the research about curriculum have to be taken as a top priority as well as to reflect social needs which are based on social changes and national health policy 3) Only one review article study tells nursing Human resource. To appear in large numbers in nursing manpower, avoid the present hospital nurses training system. Then, the study for manpower development which is able to accomplish in many fields has to be advanced. 4) Most studies did not have literature review processes, so it was impossible for researcher to know the past study tendency and there is no relation among studies as to same subject, the education about research method is needed.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.169-178
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• 2020

The reductions of troops/human resources, and improvement in combat power have made Korean Department of Defense actively adapt 4th Industrial Revolution technology (Artificial Intelligence, Big Data). The defense information system has been developed in various ways according to the task and the uniqueness of each military. In order to take full advantage of the 4th Industrial Revolution technology, it is necessary to improve the closed defense datamanagement system.However, the establishment and usage of data standards in all information systems for the utilization of defense big data and artificial intelligence has limitations due to security issues, business characteristics of each military, anddifficulty in standardizing large-scale systems. Based on the interworking requirements of each system, data sharing is limited through direct linkage through interoperability agreement between systems. In order to implement smart defense using the 4th Industrial Revolution technology, it is urgent to prepare a system that can share defense data and make good use of it. To technically support the defense, it is critical to develop Multi Repository Meta-Data Management (MRMM) that supports systematic standard management of defense data that manages enterprise standard and standard mapping for each system and promotes data interoperability through linkage between standards which obeys the Defense Interoperability Management Development Guidelines. We introduced MRMM, and implemented by using vocabulary similarity using machine learning and statistical approach. Based on MRMM, We expect to simplify the standardization integration of all military databases using artificial intelligence and bigdata. This will lead to huge reduction of defense budget while increasing combat power for implementing smart defense.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.37 no.3
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• pp.1-12
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• 2019

A bridge is a structure constructed on water or in the air for convenient passage. Compared to other buildings, the building materials and structures of bridge required unique functions to cross the space. It depends on the productivity of the building, the level of science and technology, and the ecological environment of the building site. Also, it has important relationship with functions such as politics, military, economy, and life. Most of the academic research on bridges is focused on research in the field of bridge-building technology, so the study on the landscape aesthetics and history of bridges is lacking. Against this backdrop, the study will be valuable as a accumulation of both countries' understanding of bridge types, history and culture, as well as technical and aesthetic data, by analyzing the bridges located within the palaces of Korea and China. The research method is to analyze the bridge through field survey and literature analysis.. First, the bridges of royal palace of Korea and China are to be classified quantitatively as physical shapes, landscapes, and decorations by comparing the materials, forms, landscapes, and decorative culture of bridges. Second, characteristics, common points, and differences are extracted by classifying bridges of both countries. Also, the results are discussed based on the physical environment or cultural background. This would be worth cross-referencing in the building technology and aesthetics of the two countries. For the first important characteristics of result, main materials of Korean and Chinese palaces are stone. However, the bridge in China's royal palaces is also focused on wood. Second, in terms of form, the bridges in the royal gardens of Korea and China are all based on the beam bridge. However, the specific form, ratio, style of the beam bridge, and airspace of arched bridge are very different. Third, most of the connection methods are focused on the over bridge. It values the convergence with the surrounding landscape. Due to the difference in the area and location of water, the bridge in the Korean palace is more focused on the convergence of the surrounding buildings and plants, while the bridge in the Chinese palace is more concerned about the harmony of hydration. Fourth, the decoration places importance on the artistry and aesthetics of both the bridges in Korea and China. There is a difference in style in the same type of decoration due to culture.

• Korean Journal of Food Science and Technology
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• v.37 no.4
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• pp.660-670
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• 2005

Standard procedure for development of food list was established based on food intake data of 2001 National Health and Nutrition Survey and 2002 Seasonal Nutrition Survey for Total Diet Study. Foods were sorted in descending order of mean intake, and 54 items within cumulative percentage of 80 were selected, followed by selection of 16 additional items with consumption frequency of 10% or higher. Based on higher consumption in certain seasons, regions, sexes, and age classes, 14 additional items were added. Additional 17 items with probable high contents of heavy metals or 23 items with probable high pesticide residues were added. Altogether, 101 and 107 individual food items were included for heavy metal and pesticide residue lists, accounting for 84.9 and 83.3% mean energy intakes of Korean population, respectively.