• Journal of Korean Society of Rural Planning
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• v.25 no.4
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• pp.77-86
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• 2019

The purpose of this study is to survey and analyze the operating state and characteristics of domestic and foreign green care, prioritize the facilities and programs for green care introduction through expert survey of importance, and thereby to provide a basic material for introducing green care in facilities in Rural Development Project districts, including domestic rural experience facilities. Domestic and foreign literature was analyzed in order to classify the necessary facilities programs, of green care, and the analysis results were modified and supplemented through Expert Delphi Survey. Based on the results, AHP based importance survey was conducted. In terms of the facilities and programs for green care introduction, necessary facilities (H/W) were categorized into four types (accommodation facility, resting facility, experience facility, therapy facility) and S/W programs into three types (learning experience type, therapy type, and care type). To verify the reliability of the AHP based importance survey, Consistency Index (C.I.) was analyzed. As a result, the C.I. value of nine respondents ranged from 0.000 to 0.083 so that the survey was found to have high consistency. The importance of S/W programs was 0.627, and that of H/W facilities was 0.373. For green care introduction, programs were found to be more important. Regarding the categories of necessary facilities, therapy facility had the highest value, or 0.348; experience facility 0.253; accommodation facility 0.211; resting facility 0.188. Therefore, therapy facility and experience facility were found to be important. In case of S/W programs, therapy type had the highest value, or 0.499, and learning experience type (0.255) and care type (0.246) were similarly important. Generally, the categories that had high importance values tended to show a remarkable difference in importance of their sub categories. In particular, facilities or therapy programs using natural ecology and forests were found to be highly important. In conclusion, it is required to actively review the introduction of active programs using resources, such as existing experience facilities and accommodation facilities and villages forests, and programs making the body and soul comfortable, such as natural ecology experience, Green Shower, and horticulture activity.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.113-120
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• 2021

As a result, the interaction between personal motivation and class type is not significant. On the other hand, the interaction between social motivation and class type is significant and overall online class increases engagement with educational issues. In particular, the group with low social motivation showed greater change than the group with high social motivation, so online education seems to be more effective in the group with low social motivation. It means that by stimulating students' social motivation rather than personal motivation, the effectiveness of online education can be enhanced, and it can lead to education outcomes - behavioral changes and attitudes of learners. In order to revitalize social motivation in the intensely personal space of online, it is necessary to activate social communication methods such as SNS, and development of interpersonal issues and learning materials would be more efficient. In order to derive more specific results, it is necessary to measure the level of prior knowledge and involvement of the participants in class, and to comprehensively investigate and analyze the state of learners before and after class through more variables. Finally, in order to increase the reliability of the research results, it is necessary to clearly prove it through the establishment of a structural model.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.391-398
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• 2021

Since the leased line is a structure that exclusively uses two connected areas for data transmission, a stable quality level and security are ensured, and despite the rapid increase in the number of switched lines, it is a line method that is continuously used a lot in companies. However, because the cost is relatively high, one of the important roles of the network operator in the enterprise is to maintain the optimal state by properly arranging and utilizing the resources of the network leased line. In other words, in order to properly support business service requirements, it is essential to properly manage bandwidth resources of leased lines from the viewpoint of data transmission, and properly predicting and managing leased line usage becomes a key factor. Therefore, in this study, various prediction models were applied and performance was evaluated based on the actual usage rate data of leased lines used in corporate networks. In general, the performance of each prediction was measured and compared by applying the smoothing model and ARIMA model, which are widely used as statistical methods, and the representative models of deep learning based on artificial neural networks, which are being studied a lot these days. In addition, based on the experimental results, we proposed the items to be considered in order for each model to achieve good performance for prediction from the viewpoint of effective operation of leased line resources.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1149-1161
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• 2021

With a trend of the utilization of computer vision for satellite images, cloud detection using deep learning also attracts attention recently. In this study, we conducted a U-Net cloud detection modeling using SPARCS (Spatial Procedures for Automated Removal of Cloud and Shadow) Cloud Dataset with the image data augmentation and carried out 10-fold cross-validation for an objective assessment of the model. Asthe result of the blind test for 1800 datasets with 512 by 512 pixels, relatively high performance with the accuracy of 0.821, the precision of 0.847, the recall of 0.821, the F1-score of 0.831, and the IoU (Intersection over Union) of 0.723. Although 14.5% of actual cloud shadows were misclassified as land, and 19.7% of actual clouds were misidentified as land, this can be overcome by increasing the quality and quantity of label datasets. Moreover, a state-of-the-art DeepLab V3+ model and the NAS (Neural Architecture Search) optimization technique can help the cloud detection for CAS500 (Compact Advanced Satellite 500) in South Korea.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.413-418
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• 2020

It is necessary to manage the prediction accuracy of the machine learning model to prevent the decrease in the performance of the grid network condition prediction model due to overfitting of the initial training data and to continuously utilize the prediction model in the field by maintaining the prediction accuracy. In this paper, we propose an automation technique for maintaining the performance of the model, which increases the accuracy and reliability of the prediction model by considering the characteristics of the power grid state data that constantly changes due to various factors, and enables quality maintenance at a level applicable to the field. The proposed technique modeled a series of tasks for maintaining the performance of the power grid condition prediction model through the application of the workflow management technology in the form of a workflow, and then automated it to make the work more efficient. In addition, the reliability of the performance result is secured by evaluating the performance of the prediction model taking into account both the degree of change in the statistical characteristics of the data and the level of generalization of the prediction, which has not been attempted in the existing technology. Through this, the accuracy of the prediction model is maintained at a certain level, and further new development of predictive models with excellent performance is possible. As a result, the proposed technique not only solves the problem of performance degradation of the predictive model, but also improves the field utilization of the condition prediction model in a complex power grid system.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.91-99
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• 2018

Sketch-based 3D object retrieval is a convenient way to search for various 3D data using human-drawn sketches as query. In this paper, we propose a new method of using Sketch CNN, Wasserstein CNN and Wasserstein center loss for sketch-based 3D object search. Specifically, Wasserstein center loss is a method of learning the center of each object category and reducing the Wasserstein distance between center and features of the same category. To do this, the proposed 3D object retrieval is performed as follows. Firstly, Wasserstein CNN extracts 2D images taken from various directions of 3D object using CNN, and extracts features of 3D data by computing the Wasserstein barycenters of features of each image. Secondly, the features of the sketch are extracted using a separate Sketch CNN. Finally, we learn the features of the extracted 3D object and the features of the sketch using the proposed Wasserstein center loss. In order to demonstrate the superiority of the proposed method, we evaluated two sets of benchmark data sets, SHREC 13 and SHREC 14, and the proposed method shows better performance in all conventional metrics compared to the state of the art methods.

• Journal of Biomedical Engineering Research
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• v.42 no.3
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• pp.71-79
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• 2021

Currently, the naked eyes-based diagnosis of bone metastases on CT images relies on qualitative assessment. For this reason, there is a great need for a state-of-the-art approach that can assess and follow-up the bone metastases with quantitative biomarker. Radiomics can be used as a biomarker for objective lesion assessment by extracting quantitative numerical values from digital medical images. In this study, therefore, we evaluated the clinical applicability of non-invasive and objective bone metastases computer-aided diagnosis using radiomics-based biomarkers in CT. We employed a total of 21 approaches consist of three-classifiers and seven-feature selection methods to predict bone metastases and select biomarkers. We extracted three-dimensional features from the CT that three groups consisted of osteoblastic, osteolytic, and normal-healthy vertebral bodies. For evaluation, we compared the prediction results of the classifiers with the medical staff's diagnosis results. As a result of the three-class-classification performance evaluation, we demonstrated that the combination of the random forest classifier and the sequential backward selection feature selection approach reached AUC of 0.74 on average. Moreover, we confirmed that 90-percentile, kurtosis, and energy were the features that contributed high in the classification of bone metastases in this approach. We expect that selected quantitative features will be helpful as biomarkers in improving the patient's survival and quality of life.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1579-1590
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• 2020

In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.543-552
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• 2022

When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage. In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data. XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.

• Education of Primary School Mathematics
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• v.25 no.4
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• pp.411-431
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• 2022

In this paper, implications for future curriculum compilation were sought by analyzing the process and results of compiling books for elementary school mathematics textbooks government published according to the 2015 revised curriculum. The 2015 revised elementary mathematics textbooks government published was operated with a systematic compilation system so that academia and school field experts across the country could demonstrate their expertise. As improvements in content, the unit and time to strengthen basic computational skills were increased, and the mathematical concept and principle introduction method and algorithm presentation method were improved, and the internal connection between contents was strengthened. The learning period was adjusted, such as moving and arranging contents that are difficult for students to understand to the upper semester or the upper grade. In the 1st and 2nd graders, the amount of reading was drastically reduced to suit the students' level of Korean, and sentences and vocabulary were improved, and instructions were briefly revised. As for editing and design improvements, illustrations of each unit's introduction and contextual pictures were presented in detail, and the characters in the textbook were consistently presented across all grades, giving children characters a role to actively participate in learning in the textbook. In the process of compiling, the media, the National Assembly, and civic groups raised opinions that sentences and vocabulary in first-year textbooks are more difficult than students' level of Hangeul education, that reducing textbooks makes it difficult for students to understand. Accordingly, efforts to improve textbook compilation and the results were viewed. Through the overall analysis as above, for future compilation of state-authored textbooks and certified textbooks, a plan to improve textbook compilation for students and teachers and a plan to operate compilation was proposed.