• Journal of Korean Home Economics Education Association
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• v.30 no.3
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• pp.67-90
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• 2018

The purpose of this study was to develop and apply the practical problem-based teaching·learning process plan for 'interacting with neighbors' of home economics subject. The plan consisting of 3 lessons has been developed and implemented according to the ADDIE model. Various activity materials (7 student's activity sheets, 3 reading texts, 1 homework sheet, 3 sets of ppt, 6 videos, and 3 teacher's reading texts) as well as questionnaire were developed for the 3-session lessons. The plans were implemented by the researcher to 204 freshmen, 8 classes, of C middle school in Seoul during september, 2017. The result, of students' lower level of actual participation in interacting with neighbors comparing to their interests in, supported the need of this study. Students were satisfied with the whole 3-lessons in the aspects such as beneficial usage of the contents in their daily life and in building the sense of community, as well as adequacy of materials and activities. Students also reported that they would highly aware to the importance of interacting with neighbors and to practice the contents learned from the lessons in daily life at community. They had an opportunity to reflect one's own attitude to neighbors and recommended to teach it to other schools, too. It can be concluded that the teaching·learning process plan for 'interacting with neighbors' would raise students' housing values living together and attain the overall objective and achievement standards of 2015 home economics middle school curriculum.

• Journal of Environmental Impact Assessment
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• v.27 no.2
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• pp.181-193
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• 2018

The present study was conducted to analyze the quality of the habitats of Abies koreana WILS. by using the InVEST model based on the analytic hierarchy process (AHP) technique and to evaluate the economic value by estimating the carbon fixation. Abies koreana WILS., an original biological species of South Korea, may be an essential element in establishing the national biological sovereignty in the future. The subjects of the present study were the national parks in Mt. Halla, Mt. Jiri, and Mt. Sobaek, which are the habitats of Abies koreana WILS. As suggested by previous studies as a limitation of the InVEST model, the utilization of the data from relevant international publications as the input data, due to the lack of the domestic input data, may decrease the accuracy of the modeling. Therefore, the AHP technique was applied for the input data. The modeling was performed with reference to the years of 1980, 1990, and 2000 for the scenario analysis. The result of the modeling showed that the habitat quality was changed most in the national park in Mt. Halla, as the habitat quality score was decreased from 0.96 in 1980 to 0.97 in 1990 and 0.94 in 2000. In the national part of Mt. Sobeak, the habitat quality was changed most in the sub-alpine zone, as the habitat quality score was decreased from 0.98 in 1980 and 0.98 in 1990 to 0.97 in 2000. The habitat quality was best conserved in the national part in Mt. Jiri, as the habitat quality score was 0.98 in 1980, 0.99 in 1990, and 0.99 in 2000. The estimated economic loss by the change of the habitat quality was 19,280,000 USD for Mt. Halla and 8,030,000 USD for Mt. Sobeak. In the present study, the habitat quality of the Abies koreana WILS, the original species of South Korea, was evaluated and the economic value of the ecological services provided by the habitats was estimated quantitatively. The result showed that the ecosystem service model may be used to qualitatively analyze the quality of a habitat located in a specific region and to estimate the economic value quantitatively. The objective evaluation of ecosystem services demonstrated in the present study may be applied to promote sustainable utilization of natural resources and conservation of the ecosystem by predicting the changes that may be caused by external factors including the development of preservation areas.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Economic and Environmental Geology
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• v.56 no.4
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• pp.435-445
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• 2023

A delta is a depositional landform that is formed when sediment transported by a river is deposited in a relatively low-energy environment, such as a lake, sea, or a main channel. Among these, a delta formed at the confluence of rivers has a great importance in river management and research because it has a significant impact on the hydraulic and sedimentological characteristics of the river. Recently, the equilibrium state of the confluence area has been disrupted by large-scale dredging and construction of levees in the Nakdong River. However, due to the natural recovery of the river, the confluence area is returning to its pre-dredging natural state through ongoing sedimentation. The time-series data show that the confluence delta has been steadily growing since the dredging, but once it reaches a certain size, it repeats growth and retreat, and the overall size does not change significantly. In this study, we developed a model to explain the sedimentation-erosion processes in the confluence area based on the assumption that the confluence delta reaches a dynamic equilibrium. The model is based on two fundamental principles: sedimentation due to supply from the tributary and erosion due to the main channel. The erosion coefficient that represents the Nakdong River confluence areas, was obtained using data from the tributaries of the Nakdong River. Sensitivity analyses were conducted using the developed model to understand how the confluence delta responds to changes in the sediment and water discharges of the tributary and the main channel, respectively. We then used annual average discharge of the Nakdong River's tributaries to predict the dynamic equilibrium positions of the confluence deltas. Finally, we conducted a simulation experiment on the development of the Gamcheon-Nakdong River delta using recorded daily discharge. The results showed that even though it is a simple model, it accurately predicted the dynamic equilibrium positions of the confluence deltas in the Nakdong River, including the areas where the delta had not formed, and those where the delta had already formed and predicted the trend of the response of the Gamcheon-Nakdong River delta. However, the actual retreat in the Gamcheon-Nakdong River delta was not captured fully due to errors and limitations in the simplification process. The insights through this study provide basic information on the sediment supply of the Nakdong River through the confluence areas, which can be implemented as a basic model for river maintenance and management.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.101-107
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• 2016

Due to the recent advancement in digital imaging technology, development of intervention equipment has become generalize. Video arbitration procedure is a process to insert a tiny catheter and a guide wire in the body, so in order to enhance the effectiveness and safety of this treatment, the high-quality of x-ray of image should be used. However, the increasing of radiation has become the problem. Therefore, the studies to improve the performance of x-ray detectors are being actively processed. Moreover, this intervention is based on the reference of the angiographic imaging and 3D medical image processing. In this paper, we propose a guidance system to support this intervention. Through this intervention, it can solve the problem of the existing 2D medical images based vessel that has a formation of cerebrovascular disease, and guide the real-time tracking and optimal route to the target lesion by intervention catheter and guide wire tool. As a result, the system was completely composed for medical image acquisition unit and image processing unit as well as a display device. The experimental environment, guide services which are provided by the proposed system Brain Phantom (complete intracranial model with aneurysms, ref H+N-S-A-010) was taken with x-ray and testing. To generate a reference image based on the Laplacian algorithm for the image processing which derived from the cerebral blood vessel model was applied to DICOM by Volume ray casting technique. $A^*$ algorithm was used to provide the catheter with a guide wire tracking path. Finally, the result does show the location of the catheter and guide wire providing in the proposed system especially, it is expected to provide a useful guide for future intervention service.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.6
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• pp.306-318
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• 2018

In order to evaluate the seismic capacity of massive vertical type breakwaters which have intensively been deployed along the coast of South Korea over the last two decades, we carry out the preliminary numerical simulation against the PoHang, GyeongJu, Hachinohe 1, Hachinohe 2, Ofunato, and artificial seismic waves based on the measured time series of ground acceleration. Numerical result shows that significant sliding can be resulted in once non-negligible portion of seismic energy is shifted toward the longer period during its propagation process toward the ground surface in a form of shear wave. It is well known that during these propagation process, shear waves due to the seismic activity would be amplified, and non-negligible portion of seismic energy be shifted toward the longer period. Among these, the shift of seismic energy toward the longer period is induced by the viscosity and internal friction intrinsic in the soil. On the other hand, the amplification of shear waves can be attributed to the fact that the shear modulus is getting smaller toward the ground surface following the descending effective stress toward the ground surface. And the weakened intensity of soil as the number of attacking shear waves are accumulated can also contribute these phenomenon (Das, 1993). In this rationale, we constitute the numerical model using the model by Hardin and Drnevich (1972) for the weakened shear modulus as shear waves go on, and shear wave equation, in the numerical integration of which $Newmark-{\beta}$ method and Modified Newton-Raphson method are evoked to take nonlinear stress-strain relationship into account. It is shown that the numerical model proposed in this study could duplicate the well known features of seismic shear waves such as that a great deal of probability mass is shifted toward the larger amplitude and longer period when shear waves propagate toward the ground surface.

• Journal of Korean Home Economics Education Association
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• v.32 no.2
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• pp.77-97
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• 2020

The purpose of this study was to develop and evaluate the sustainable housing teaching-learning process plan aimed to achieve the global SDGs through home economics class in middle school that is based on the ADDIE model. The overall objective of the plan was to contribute to cultivating students' sustainable housing values and to creating sustainable lifestyle through everyday practice. The plan consisting of 4 lessons contained various activity and visual resources(4 individual and 4 team activity sheets, 4 reading texts, 1 homework sheet and 1 evaluation sheet, and 7 videos) for students and (4 sets of ppt and 4 reading texts) for teachers. The theme and team activities of each lesson were related to 2~7 targets of 2~3 SDGs, in total 11 targets of 5 SDGs. The plan was implemented to 4 classes of 127 senior students at Y middle school in Cheongju city during the period from the 29^th of August to the 18^th of September, 2019. The results showed that students were very positive and highly satisfied with not only practical contents but also adequacy of resources and activities of the whole 4-lessons, so that they actively participated in the lessons more than usual and looked forward to learning more about it. They thoroughly enjoyed various team activities such as brain writing, mandal art, visual thinking, making UCC, and planning the sustainable village as well as writing a short reflective journal at the end of each lesson. Students also reported that they highly accomplished the goal of each lesson and the overall objective. It could be concluded that the teaching-learning process plan of 4-lessons could contribute to cultivating students' sustainable housing values and to creating sustainable lifestyle through practicing everyday life. It indicates that home economics is one of the major subjects to contribute to the attainment of global issue of SDGs for OECD education 2030 and to educate the practically acting global citizen.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Journal of Korea Technology Innovation Society
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• v.16 no.3
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• pp.650-671
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• 2013

Products elicit the consumer's purchasing behavior by satisfying their needs and are cognized as the combination of various needs attributes. Also R&D is referred as a series of technical development activities to meet the consumer's needs attributes. In particular, in the market-oriented R&D era, it could obtain the legitimacy by developing the R&D based on the needs attributes. In this study, we aimed to investigate the priority setting in R&D field, considering consumer's needs attributes. To be concrete, we tried to present the evolutional direction of desirable phased R&D according to 'the importance degree for consumers on the attributes (functions) of the certain products' and 'the urgency degree of technical quality to fulfill its needs'. To achieve this, we targeted SMART TV, the convergence product, which contains the uncertainty in terms of marketability and technological aspect, and analyzed the priority of the R&D in SMART TV field. Based on the result of the analysis, 4-steps product concept (ultra high definition TV, interactive TV, 3D/immersive TV, personalized TV) is derived by analyzing the evolutional direction of R&D in SMART TV field. This finding implies that the success possibilities of product could be enhanced during the process of the evolution of products that have multiple needs attributes, by pursuing the R&D which fulfills the needs attribute first required in the market. In addition, it provides a useful framework to design the R&D roadmap in an aspect of R&D strategy.

• Journal of KIBIM
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• v.1 no.2
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• pp.19-23
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• 2011

The planning stage of an architectural project has much more significant effects on the cost or outcome of the project than other stages of the project. In addition, the importance of architectural planning has been further increasing according to the recent trend of construction projects becoming larger in scale and more complex. In spite of this, the current situation is that the planning stage work is not being systematically managed. Accordingly, the purpose of this study was to develop a BIM-based simulation system for providing support during architectural planning stage such as spatial planning & review, cost review, project owner requirements management, etc. It is easy to review various alternatives using this system that allows not only the modeling of space object modeling but also the instantaneous review of spatial area & layout, cost, etc. based on object information. In addition, it can be used as a communication tool with the project owner as it provides the visualized information and quantitative data of the building model, and the information created through this system can be delivered to the following stage for usage. It is thought that using this system, the entire project work including the architectural planning stage can be supported and even contributing to the advancement of architectural process.