• Journal of The Korean Association For Science Education
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• v.43 no.2
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• pp.73-86
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• 2023

This study compared the characteristics of scientific emoticons made by general and science-gifted elementary students. To do this, fifth graders (n=53) at a general elementary school in Gyeonggi province and fifth graders (n=35) at a gifted science education institute in Seoul were selected. Scientific emoticons written by the students were compared according to the number and types. Analysis of the results reveal that the science-gifted students made more scientific emoticons than the general students for thirty minutes. In the comparison of the types of scientific emoticons, there were some similarities and significant differences between general students and science-gifted students. Overall, however, it was found that science-gifted students made more various types of scientific emoticons than general students in 'form' aspects (e.g., generative form of text, descriptive form of text, and expressive form of image) and 'information' aspects (e.g., emotion, construction level, excess of curriculum level, scientific disciplines, and use of scientific knowledge) of the texts and the images in the scientific emoticons. The scientific emoticons made by general and science-gifted elementary students included very few misconceptions. Educational implications of these findings are discussed.

• Korea Science and Art Forum
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• v.29
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• pp.279-292
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• 2017

This study was started by recognizing that visual storytelling(VST) is an important factor that determines the success of the work. The goal of this study is to analyze the VST study approaching from the narrative and visual dimension by analyzing the conflict intensity and VST factor. Therefore, in this paper, we analyzed the conflicts of the theater animation(4) that succeeded in the worldwide success and attempted the VST interpretation by approaching it technically. The results and contents of the study are as follows. Firstly, based on the narrative theory of Sung bong-Sun and Robert McKee, we classified the conflict scenes and found the kinds of conflicts. In addition, based on the 5B model, a total of 108 conflict shots were extracted. Secondly, through expert experiment, we found the conflict intensity of conflict shots. Thirdly, the visual elements of fifteen significant conflicts were extracted from internal and super individual conflicts. Fourth, as a result of the experiment, it was confirmed that the reliability of the visual elements in the inner and super personal conflicts was in the range of 100-83.33%, and the frequency of usage was found to be widely distributed in 5.88-70.59% and 5-70%. This means that the VST expression, which relied on the sense of the artist, can be engineered. Finally, I expect that it will be the basis of the development of the VST Tool which can predict the conflict expression of the work in the animation pre - production stage successfully.

• Journal of the Korean Society of Food Culture
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• v.25 no.5
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• pp.544-557
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• 2010

This study was performed with Korean adults to investigate Makgeolli drinking behavior, preference, and perception about Makgeolli and health, as well as Makgeolli complaints and prices. A survey questionnaire was formulated to obtain information on demographic variables, drinking habits, and perceptions about Makgeolli's function, price, and complaints. The questionnaire was distributed to 468 adults living in the capital area. The results are as follows. Makgeolli (16.1%) was third preferred, following Soju (45.1%) and beer (30.7%), and no significant differences were observed by gender and income, but the preference for Makgeolli increased with increasing age (p<0.001). According to the survey, the largest reason both genders drank Makgeolli was that it tastes good. Men preferred Makgeolli for its health effects and cheap price, while women preferred it for the atmosphere while drinking it. Also, older people and those with higher incomes preferred drinking Makgeolli for its health effect rather than its good taste (p<0.001 for each). No significant difference was observed by gender for the question "Do you think that Makgeolli has a health-promoting effect?" Overall, 51% of the subjects gave positive answers and only 5.9% gave negative answers. Significantly, older people and those with a higher income had a higher rate of answering positively to this question. Belching (45.1%) and headache (29.9%) were the most common symptoms among the side effects of drinking Makgeolli. No significant difference was observed by gender or income, but older people had a higher rate of belching and fewer headaches than younger people (p<0.001). Women had a significantly higher rate of perceiving that Makgeolli was cheap than men. Age and income differences did not influence price perception. To the question "What is the ideal price for high quality Makgeolli", 32.1% answered that the present rate (1,000 won) was ideal, and 59.4% answered that a price between 1,000 and 2,000 won was ideal. These results indicate that the high preference for Makgeolli is due to its good taste and health effects. However, belching and headache caused by drinking Makgeolli were the most common complaints and, thus, must be solved. Some opinions indicated that Makgeolli must eliminate its low-quality image, but, according to this survey, most subjects answered that the ideal price of higher-quality Makgeolli should be increased slightly, which would cause price resistance.

• Geomechanics and Engineering
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• v.34 no.4
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• pp.449-459
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• 2023

The design and development of underground nuclear waste repositories should cover the performance evaluation of the different components such as the construction materials because the long term stability will depend on their response to the surrounding conditions. In South Korea, Gyeonju bentonite has been proposed as a candidate to be used as buffer and backfilling material, especially in the form of blocks to speed up the construction process. In this study, various cylindrical samples were prepared with different dry density and water content, and their physical and mechanical properties were analyzed and correlated with X-ray CT observations. The main objective was to characterize the samples and establish correlations for non-destructive estimation of physical and mechanical properties through the utilization of X-ray CT images. The results showed that the Uniaxial Compression Strength and the P-wave velocity have an increasing relationship with the dry density. Also, a higher water content increased the values of the measure parameters, especially for the P-wave velocity. The X-ray CT analysis indicated a clear relation between the mean CT value and the dry density, Uniaxial Compression Strength, and P-wave velocity. The effect of the higher water content was also captured by the mean CT value. Also, the relationship between the mean CT value and the dry density was used to plot CT dry densities using CT images only. Moreover, the histograms also provided information about the samples heterogeneity through the histograms' full width at half maximum values. Finally, the particle size and heterogeneity were also analyzed using the Madogram function. This function identified small particles in uniform samples and large particles in some samples as a result of poor mixing during preparation. Also, the μ_max value correlated with the heterogeneity, and higher values represented samples with larger ranges of CT values or particle densities. These image-based tools have been shown to be useful on the non-destructive characterization of bentonite samples, and the establishment of correlations to obtain physical and mechanical parameters solely from CT images.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.143-156
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• 2023

To precisely and reliably analyze the contents of the satellite imagery, recognizing the clouds which are the obstacle to gathering the useful information is essential. In recent times, deep learning yielded satisfactory results in various tasks, so many studies using deep neural networks have been conducted to improve the performance of cloud detection. However, existing methods for cloud detection have the limitation on increasing the performance due to the adopting the network models for semantic image segmentation without modification. To tackle this problem, we introduced the multi-branch neural architecture search to find optimal network structure for cloud detection. Additionally, the proposed method adopts the soft intersection over union (IoU) as loss function to mitigate the disagreement between the loss function and the evaluation metric and uses the various data augmentation methods. The experiments are conducted using the cloud detection dataset acquired by Arirang-3/3A satellite imagery. The experimental results showed that the proposed network which are searched network architecture using cloud dataset is 4% higher than the existing network model which are searched network structure using urban street scenes with regard to the IoU. Also, the experimental results showed that the soft IoU exhibits the best performance on cloud detection among the various loss functions. When comparing the proposed method with the state-of-the-art (SOTA) models in the field of semantic segmentation, the proposed method showed better performance than the SOTA models with regard to the mean IoU and overall accuracy.

• Journal of Service Research and Studies
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• v.11 no.3
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• pp.98-116
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• 2021

With the advent of the era of the 4th industrial revolution, various factors such as economy, management, and culture are changing in modern society, unlike in the past. Among them, the main characteristic of management is the change from intangible goods to tangible goods, and companies are trying to pursue innovation such as introducing a new management method, converting from manufacturing to service, and expanding technology. However, with regard to human resources, which is becoming the most important for sustainable value creation in a changing era, efforts to enable practical innovation are lacking as they are still in a simple transition. Therefore, in this study, after recognizing the importance of human resources, we verified the relationship between the elements of the human resource in the service economy era according to organizational culture and organizational structure. The relationship between organizational culture and organizational structure by type was verified using the items of human resources, we verified the relationship between the elements of the human resource in the service economy era that were derived and verified in recent research. As a result, there were some significant differences in the image of human resources, we verified the relationship between the elements of the human resource by organizational culture and type of organization, but when the two factors were combined and interpreted, it was found that all of the human resources, we verified the relationship between the elements of the human resource in the service economy era were necessary. However, in order to overcome the limitation that the indicators of this study were limited, it is necessary to continue research through samples that consider various factors in the future and systematic classification by type of organization and industry by industry.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.217-228
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• 2023

Video instance segmentation is an intelligent visual task with high complexity because it not only requires object instance segmentation for each image frame constituting a video, but also requires accurate tracking of instances throughout the frame sequence of the video. In special, human instance segmentation in drama videos has an unique characteristic that requires accurate tracking of several main characters interacting in various places and times. Also, it is also characterized by a kind of the class imbalance problem because there is a significant difference between the frequency of main characters and that of supporting or auxiliary characters in drama videos. In this paper, we introduce a new human instance datatset called MHIS, which is built upon drama videos, Miseang, and then propose a novel video data augmentation method, CDVA, in order to overcome the data imbalance problem between character classes. Different from the previous video data augmentation methods, the proposed CDVA generates more realistic augmented videos by deciding the optimal location within the background clip for a target human instance to be inserted with taking rich spatio-temporal context embedded in videos into account. Therefore, the proposed augmentation method, CDVA, can improve the performance of a deep neural network model for video instance segmentation. Conducting both quantitative and qualitative experiments using the MHIS dataset, we prove the usefulness and effectiveness of the proposed video data augmentation method.

• Asia Marketing Journal
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• v.6 no.3
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• pp.59-81
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• 2004

Store loyalty is increasingly being recognized as a path to long-term business profitability. Customer contact employees deliver a service firm's promises and create an important image for the firm. A major purpose of this study is to investigate the effects of customer service and product value on store loyalty. In order to test research hypotheses, data were collected through surveys administered to 300 apparel store customers. Two hundred thirty nine usable data were used for the analysis. The findings of this research are as follows: First, a employee's voluntary service(EVS) has a positive impact on interpersonal r elationship, which then affects switching barrier and store loyalty. Second, a employee's regular service(ERS) has an influence on store satisfaction, which in turn affect store loyalty. Third, product value is shown to be a significant antecedent to store satisfaction, which have a direct effect on store loyalty. The study concludes with implications, contributions, and limitations of the research and the empirical findings of this research should be beneficial to marketing practitioners and retailing businessmen in developing effective marketing strategies.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.245-257
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• 2023

The representative crop in the Republic of Korea, rice, is cultivated over extensive areas every year, which resulting in reduced resistance to pests and diseases. One of the major rice diseases, rice blast disease, can lead to a significant decrease in yields when it occurs on a large scale, necessitating early detection and effective control of rice blast disease. Drone-based crop monitoring techniques are valuable for detecting abnormal growth, but frequent image capture for potential rice blast disease occurrences can consume significant labor and resources. The purpose of this study is to early detect rice blast disease using remote sensing data, such as drone and satellite images, along with weather data. Satellite images was helpful in identifying rice cultivation fields. Effective detection of paddy fields was achieved by utilizing vegetation and water indices. Subsequently, air temperature, relative humidity, and number of rainy days were used to calculate the risk of rice blast disease occurrence. An increase in the risk of disease occurrence implies a higher likelihood of disease development, and drone measurements perform at this time. Spectral reflectance changes in the red and near-infrared wavelength regions were observed at the locations where rice blast disease occurred. Clusters with low vegetation index values were observed at locations where rice blast disease occurred, and the time series data for drone images allowed for tracking the spread of the disease from these points. Finally, drone images captured before harvesting was used to generate spatial information on the incidence of rice blast disease in each field.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.73-80
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• 2023

The recent steep increase in the minimum hourly wage has increased the burden of labor costs, and the share of unmanned stores is increasing in the aftermath of COVID-19. As a result, theft crimes targeting unmanned stores are also increasing, and the "Just Walk Out" system is introduced to prevent such thefts, and LiDAR sensors, weight sensors, etc. are used or manually checked through continuous CCTV monitoring. However, the more expensive sensors are used, the higher the initial cost of operating the store and the higher the cost in many ways, and CCTV verification is difficult for managers to monitor around the clock and is limited in use. In this paper, we would like to propose an AI image processing fusion algorithm that can solve these sensors or human-dependent parts and detect customers who perform abnormal behaviors such as theft at low costs that can be used in unmanned stores and provide cloud-based notifications. In addition, this paper verifies the accuracy of each algorithm based on behavior pattern data collected from unmanned stores through motion capture using mediapipe, object detection using YOLO, and fusion algorithm and proves the performance of the convergence algorithm through various scenario designs.