• Journal of the Economic Geographical Society of Korea
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• v.21 no.4
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• pp.289-302
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• 2018

As business activities of firms for maximizing profits has resulted in increases in economic, social and environmental problems in regional geographies, they have stimulated some social activities through enhancing their public image and justifying their conducts. In fact, firms have been given concerned with a decrease in profits due to the promotion of their social activities required by local communities. However, firms are able to create shared value resulted in added value creation centering on shareholder as well as stakeholder by expanding increases in economic and social values. Therefore, the main purpose of this research is to draw an index which is able to evaluate the mechanism of virtuous circle resulted from creating shared value(hereafter CSV). At the same time, it has attempted to analyze some limits to and real effects of CSV by the index. The paper has analyzed the CSV of certificated coffee by using the index of sustainability in the Vietnamese coffee industry. The introduction of certificated coffee showed positive effects in the order of environmental, economic and social indicators. However, the structure of closed decision-making in Vinacafe, which is a state-firm, has not able to secure enough economic profits for CSV, leading to some limits to the formation of virtuous circles in the Vietnamese certificated coffee industry.

• Korean Journal of Child Education & Care
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• v.19 no.1
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• pp.27-43
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• 2019

Objective: The purpose of this study is to investigate how the child care teachers experience about professional vision development through participating in video clubs with their peers while watching videos about their interactions with children in the classroom. Methods: We selected three child care teachers in a day care center in Seoul area and conducted the qualitative case study. Video clubs were designed to support the quality of teacher-child interaction by developing child-care teachers' professional vision. And the video clubs used the self-reflection process and cooperative self-reflection process as an important educational method. Results: Teachers were able to experience the change of attention in watching their interaction scene through the 4-time video club participation and to have opportunity for educational (knowledge based) reasoning. Particularly, through participation in the video club, the teacher could pay attention to teachers' intention, teachers' decision making process, and child's intention. In addition, through video club participation, the teachers experienced educational interpretation based on children's thinking and interest; and reasoning through reflective thinking about the results of teaching behavior. This change of professional vision was possible through mutual scaffolding through cooperative reflection among participating teachers. Conclusion/Implications: Based on the results of this study we discussed the importance of the professional vision development of the child care teachers and the effectiveness of the video club for supporting their professional vision development.

• Korean Journal of Cognitive Science
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• v.32 no.3
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• pp.117-139
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• 2021

The way of change detection in which presence of a different item is determined between memory and test arrays with a brief in-between time interval resembles how visual search is done considering that the different item is searched upon the onset of a test array being compared against the items in memory. According to the resemblance, the present study examined whether varying the probability of change occurrence in a visual short-term memory-based change detection task can influence the aspect of response-decision making (i.e., change prevalence effect). The simple-feature change detection task in the study consisted of a set of four colored boxes followed by another set of four colored boxes between which the participants determined presence or absence of a color change from one box to the other. The change prevalence was varied to 20, 50, or 80% in terms of change occurrences in total trials, and their change detection errors, detection sensitivity, and their subsequent RTs were analyzed. The analyses revealed that as the change prevalence increased, false alarms became more frequent while misses became less frequent, along with delayed correct-rejection responses. The observed change prevalence effect looks very similar to the target prevalence effect varying according to probability of target occurrence in visual search tasks, indicating that the background principles deriving these two effects may resemble each other.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.266-279
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• 2021

This study applied the e-PBL (e-Project-based learning) method for "Urban Forest Management" courses in the Department of Forest Science at S University to progress in university forest education. e-PBL effectively motivates self-directed learning, problem-solving, communication skills, and learners' responsibility by enabling them to choose, design, and perform their projects. Due to the COVID-19 pandemic in 2020, learners were encouraged to use online media to carry out projects and submit presentations for the campus forest. Learners' educational effects were subsequently investigated through a five-point Likert scale. This study discovered a positive effect on learners' motivation and interest (4.17) through e-PBL. Learners responded that e-PBL also helped their understanding regarding the subject (4.17). In addition, this study provided evidence that the e-PBL method was helpful in problem-solving (4.25), communication (4.33), and decision-making skills (4.21). According to learners' responses, there are positive indications that learners were satisfied with e-PBL. Learners responded that interactions and communications with team members could improve their understanding of the subject. Hence, there is scope for improving an efficient and successful e-PBL model suitable for university forest education by providing more efficient instructional time management, e-PBL method guidelines, and institutional support.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.156-172
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• 2020

The purpose of this study was to predict road surface temperature using high-resolution solar radiation data. The road surface temperature prediction model (RSTPM) was applied to predict road surface temperature; this model was developed based on the heat-balance method. In addition, using SOLWEIG (SOlar and LongWave Environmental Irradiance Geometry-model), the shadow patterns caused by the terrain effects were analyzed, and high-resolution solar radiation data with 10 m spatial resolution were calculated. To increase the accuracy of the shadow patterns and solar radiation, the day that was modeled had minimal effects from fog, clouds, and precipitation. As a result, shadow areas lasted for a long time at the entrance and exit of a tunnel, and in a high-altitude area. Furthermore, solar radiation clearly decreased in areas affected by shadows, which was reflected in the predicted road surface temperatures. It was confirmed that the road surface temperature should be high at topographically open points and relatively low at higher altitude points. The results of this study could be used to forecast the freezing of sections of road surfaces in winter, and to inform decision making by road managers and drivers.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.409-418
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• 2021

The land cover map is a very important data that is used as a basis for decision-making for land policy and environmental policy. The land cover map is mapped using remote sensing data, and the classification results may vary depending on the acquisition time of the data used even for the same area. In this study, to overcome the classification accuracy limit of single-period data, multi-series satellite images were used to learn the difference in the spectral reflectance characteristics of the land surface according to seasons on a U-Net model, one of the deep learning algorithms, to improve classification accuracy. In addition, the degree of improvement in classification accuracy is compared by comparing the accuracy of single-period data. Seoul, which consists of various land covers including 30% of green space and the Han River within the area, was set as the research target and quarterly Sentinel-2 satellite images for 2020 were aquired. The U-Net model was trained using the sub-class land cover map mapped by the Korean Ministry of Environment. As a result of learning and classifying the model into single-period, double-series, triple-series, and quadruple-series through the learned U-Net model, it showed an accuracy of 81%, 82% and 79%, which exceeds the standard for securing land cover classification accuracy of 75%, except for a single-period. Through this, it was confirmed that classification accuracy can be improved through multi-series classification.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.453-459
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• 2021

The system marginal price of electricity is the amount paid to all the generating units, which is an important decision-making factor for the construction and maintenance of an electrical power unit. In this paper, we suggest a long-term forecasting model for calculating the system marginal price based on prices of natural gas and oil. As most variables used in the analysis are nonstationary time series, the long run relationship among the variables should be examined by cointegration tests. The forecasting model is similar to an error correction model which consists of a long run cointegrating equation and another equation for short run dynamics. To mitigate the robustness issue arising from the relatively small data sample, this study employs various testing and estimating methods. Compared to previous studies, this paper considers multiple fuel prices in the forecasting model of system marginal price, and provides greater emphasis on the robustness of analysis. As none of the cointegrating relations associated with system marginal price, natural gas price and oil price are excluded, three error correction models are estimated. Considering the root mean squared error and mean absolute error, the model based on the cointegrating relation between system marginal price and natural gas price performs best in the out-of-sample forecast.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.146-153
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• 2022

Purpose: In the case of domestic port facilities, port structures that have been in use for a long time have many problems in terms of safety performance and functionality due to the enlargement of ships, increased frequency of use, and the effects of natural disasters due to climate change. A big data analysis method was studied to develop an approximate model that can predict the aging pattern of a port facility based on the maintenance history data of the port facility. Method: In this study, member-level maintenance history data for caisson-type quay walls were collected, defined as big data, and based on the data, a predictive approximation model was derived to estimate the aging pattern and deterioration of the facility at the project level. A state-based aging pattern prediction model generated through Gaussian process (GP) and linear interpolation (SLPT) techniques was proposed, and models suitable for big data utilization were compared and proposed through validation. Result: As a result of examining the suitability of the proposed method, the SLPT method has RMSE of 0.9215 and 0.0648, and the predictive model applied with the SLPT method is considered suitable. Conclusion: Through this study, it is expected that the study of predicting performance degradation of big data-based facilities will become an important system in decision-making regarding maintenance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.79-89
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• 2022

Images taken using drones have been applied to fields that require rapid decision-making as they can quickly construct high-quality 3D spatial information for small regions. To construct spatial information based on drone images, it is necessary to determine the relationship between images by extracting keypoints between adjacent drone images and performing image matching. Therefore, in this study, three study regions photographed using a drone were selected: a region where parking lots and a lake coexisted, a downtown region with buildings, and a field region of natural terrain, and the performance of AKAZE (Accelerated-KAZE), BRISK (Binary Robust Invariant Scalable Keypoints), KAZE, ORB (Oriented FAST and Rotated BRIEF), SIFT (Scale Invariant Feature Transform), and SURF (Speeded Up Robust Features) algorithms were analyzed. The performance of the keypoints extraction algorithms was compared with the distribution of extracted keypoints, distribution of matched points, processing time, and matching accuracy. In the region where the parking lot and lake coexist, the processing speed of the BRISK algorithm was fast, and the SURF algorithm showed excellent performance in the distribution of keypoints and matched points and matching accuracy. In the downtown region with buildings, the processing speed of the AKAZE algorithm was fast and the SURF algorithm showed excellent performance in the distribution of keypoints and matched points and matching accuracy. In the field region of natural terrain, the keypoints and matched points of the SURF algorithm were evenly distributed throughout the image taken by drone, but the AKAZE algorithm showed the highest matching accuracy and processing speed.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.