• 한국체육학회지인문사회과학편
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• v.58 no.1
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• pp.295-308
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• 2019

The purpose of the present study is to apply a health belief model to the serious perception of an increase in youth obesity, and to develop and validate a measurement tool for youth obesity prevention among this group who are experiencing increasing rates of obesity. The specific goals of this study are to 1) apply a health belief model to develop a measurement tool for obesity prevention among youth who are seeing rising rates of obesity, and 2) provide an integrated validation procedure and foundation for developing this measurement tool. A total of 1801 high school students[sample1: 902(male:464,female:438); sample2: 899(male:464,female:438)] were recruited and collected data from 12 high school in Seoul and Kyonggi area. For this study the analytic framework of unified validity was developed which can comprehensively reflect unified validity be Messick(1995), framework for conducting a strong program of construct validation by Benson(1998), the unified validity implementation method of Rasch model suggested by Wolfe and Smith(2007a, 2007b). Furthermore, after dividing the developed analytic framework into each stage(the substantive domain), the evidence of validity of Youth Obesity Prevention Scale(YOPS) applying Health Belief Model was systematically suggested. The YOPS suggested the evidence about the substantive domain of unified validity. The developed YOPS was consist of Susceptibility, Severity, Benefits, Barriers and Cues to Action. After 3 stage in substantive domain, the components of YOPS(5factors and 28items) satisfied the unidimensionality, and the 5 point Likert scale had the significant discrimination of the respondents' response.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1193-1201
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• 2020

This study aims to develop a regression model that estimates a low-flow index that can be applied to ungauged basins. A total of 30 midsized basins in South Korea use long-term runoff data provided by the National Integrated Water Management System (NIWMS) to calculate average low-flow, average minimum streamflow, and low-flow index duration and frequency. This information is used in the correlation analysis with 18 basin factors and 3 climate change factors to identify the basin area, average basin altitude, average basin slope, water system density, runoff curve number, annual evapotranspiration, and annual precipitation in the low-flow index regression model. This study evaluates the model's accuracy by using the root-mean-square error (RMSE) and the mean absolute error (MAE) for 10 ungauged, verified basins and compares them with the previous model's low-flow calculations to determine the effectiveness of the newly developed model. Comparative analysis indicates that the new regression model produces average low-flow, attributed to the consideration of varied basin and hydrologic factors during the new model's development.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.209-216
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• 2024

Real estate market prices are determined by various factors, including macroeconomic variables, as well as the influence of a variety of unstructured text data such as news articles and social media. News articles are a crucial factor in predicting real estate transaction prices as they reflect the economic sentiment of the public. This study utilizes sentiment analysis on news articles to generate a News Sentiment Index score, which is then seamlessly integrated into a real estate price prediction model. To calculate the sentiment index, the content of the articles is first summarized. Then, using AI, the summaries are categorized into positive, negative, and neutral sentiments, and a total score is calculated. This score is then applied to the real estate price prediction model. The models used for real estate price prediction include the Multi-head attention LSTM model and the Vector Auto Regression model. The LSTM prediction model, without applying the News Sentiment Index (NSI), showed Root Mean Square Error (RMSE) values of 0.60, 0.872, and 1.117 for the 1-month, 2-month, and 3-month forecasts, respectively. With the NSI applied, the RMSE values were reduced to 0.40, 0.724, and 1.03 for the same forecast periods. Similarly, the VAR prediction model without the NSI showed RMSE values of 1.6484, 0.6254, and 0.9220 for the 1-month, 2-month, and 3-month forecasts, respectively, while applying the NSI led to RMSE values of 1.1315, 0.3413, and 1.6227 for these periods. These results demonstrate the effectiveness of the proposed model in predicting apartment transaction price index and its ability to forecast real estate market price fluctuations that reflect socio-economic trends.

• Journal of Korean Society of Forest Science
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• v.99 no.6
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• pp.827-835
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• 2010

Reliable forest statistics provides important information to meet the UNFCCC. In this respect, the national forest inventory has played a crucial role to provide the reliable forest statistics for several decades. However, the previous forest statistics calculated by administrative district has not provided spatial information in a small scale. Thus, this study focused on developing models to estimate an explicit spatial distribution of forest growing stock. For this, first, stand volume model by stand types was developed using National Forest Inventory(NFI) data. Second, forest type map was integrated with this model. NFI data were used to calculate plot-level stand volume and basal area. The stand types of NFI plot including the species composition, age class, DBH class and crown density class are very crucial data to be connected with forest type map. Finally, polygonlevel stand volume map was developed with spatial uncertainty map. Average stand volume was estimated at 85.7 $m^3$/ha in the study area, and at 95% significance interval it was ranged from 79.7 $m^3$/ha to 91.8 $m^3$/ha.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.921-932
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• 2015

This study analyzed military personnel survivability in regards to offensive operations according to the scientific military training data of a reinforced infantry battalion. Scientific battle training was conducted at the Korea Combat Training Center (KCTC) training facility and utilized scientific military training equipment that included MILES and the main exercise control system. The training audience freely engaged an OPFOR who is an expert at tactics and weapon systems. It provides a statistical analysis of data in regards to state-of-the-art military training because the scientific battle training system saves and utilizes all training zone data for analysis and after action review as well as offers training control during the training period. The methodologies used the Cox PH modeling (which does not require parametric distribution assumptions) and decision tree modeling for survival data such as CART, GUIDE, and CTREE for richer and easier interpretation. The variables that violate the PH assumption were stratified and analyzed. Since the Cox PH model result was not easy to interpret the period of service, additional interpretation was attempted through univariate local regression. CART, GUIDE, and CTREE formed different tree models which allow for various interpretations.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.73-82
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• 2019

In order to build a smart factory, building a CPPS (Cyber Physical Product System) is an important system that must be accompanied. Through the CPPS, it is the reality of smart factories to move physical factories to a digital-based cyber world and to intelligently and autonomously monitor and control them. But The existing CPPS architectures present only an abstract modeling architecture, and the research that applied the OPC UA Framework (Open Platform Communication Unified Architecture), an international standard for data exchange in the smart factory, as the basic system of CPPS It was insufficient. Therefore, it is possible to implement CPPS that can include both cloud and IoT by collecting field data distributed by CPPS architecture applicable to actual factories and concentrating data processing in a centralized In this study, we implemented CPPS architecture through central OPC UA Server based on OPC UA conforming to central processing OPC UA Framework, and how CPPS logical process and data processing process are automatically generated through OPC UA modeling processing We have proposed the CPPS architecture including the model factory and implemented the model factory to study its performance and usability.

• Spatial Information Research
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• v.21 no.6
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• pp.23-32
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• 2013

Recently, there has been much interest in the converged platform that enables the harmonized collection, integration, exchange, presentation and analysis of various kinds of marine information by using the ICT means. Regarding such the converged platform, S-100 standards including the international hydrographic data model are being announced and various studies have been published based on the S-100 standards. However, the existing studies have presented simple solutions for only given problems on the converged service of the maritime information. They could not propose the design concept of the converged platform which makes it possible to provide the standardized and integrated services among the geospatial data, the real-time maritime data, and the next ENC. Therefore, we propose design and implementation details of the converged service platform for the geospatial and the maritime data based on the S-100, WMS, WMTS, WPS, SOS standards. The proposed platform has advantages of supporting both the S-57 and the S-101, supporting the converged services of heterogeneous geospatial data and ENC data, supporting the real-time services of sensor data such as weather, AIS, and CCTV, and supporting the development of various kinds of maritime systems such as ECDIS, ECS, VTS based on the WebApp service. Finally, we proved the effectiveness of our proposed platform through the actual implementation of the converged service of geospatial data, S-101 data, and KWeather data.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.143-150
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• 2018

Periodic and systemic disaster management has become more important than ever owing to the recent continuous occurrence of disasters, such as fires, earthquakes, and flooding. This management goes beyond simple disaster preparedness, which was introduced minimally under the existing legal system. For effective disaster management, facilities should be managed through regular maintenance on a daily basis, and in the case of an emergency, intuitive and accurate communication is essential regarding the situation and purpose. BIM manages the entire building property data using the effective 3D visualization model, so it can be used for various management purposes from design to facility maintenance. In this study, through an expert survey on the use of services in a BIM-based integrated disaster information system, the available areas of BIM data were organized in terms of facility information management, 3D visualization, and disaster control. Later, through the use service and DB definition within the BIM-based disaster integration information system, the main facilities monitoring and response services based on BIM and BIM-based spatial management service are proposed. Based on this study, it is hoped that the BIM-based application service functions within the system will be implemented to enable an effective system response.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.587-599
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• 2018

In building-integrated photovoltaic (BIPV) systems, power generation functions are integrated into building functions by installing solar modules in combination with building materials. While this integration appears to be attractive, a design method is needed to achieve maximum power generation. Previously, the influence of the design elements on power generation was analyzed by computer simulations and demonstration tools. On the other hand, problems remain due to the inaccuracy of power generation analysis and relationship analysis, and limited demonstration. To solve this problem, this paper proposed the use of an extended demonstration mock-up. The mock-up was designed and constructed by implementing the design elements of the module types, installation angles, and direction. The actual operation data for one year were analyzed to evaluate the effects of the design elements on power generation. These results can be used to determine the feasibility of future BIPV systems and the optimal selection of system design elements.