• Health Policy and Management
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• v.34 no.2
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• pp.196-210
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• 2024

Background: In January 2018, revised elderly fixed outpatient copayment for the elderly were implemented. When people ages 65 years and older receive outpatient treatment at clinic-level medical institutions (clinic, dental clinic, Korean medicine clinic), with medical expenses exceeding 15,000 won but not exceeding 25,000 won, their copayment rates have decreased differentially from 30%. This study aimed to examine the changes of health utilization of elderly after revised elderly fixed outpatient copayment. Methods: We used Korea health panel data from 2016 to 2018. The time period is divided into before and after the revised elderly fixed outpatient copayment. We conducted Poisson segmented regression to estimate the changes in outpatient utilization and inpatient utilization and conducted segmented regression to estimate the changes in medical expenses. Results: Immediately after the revised policy, the number of clinic and Korean medicine outpatient visits of medical expenses under 15,000 won decreased. But the number of clinic outpatient visits in the range of 15,000 to 20,000 won and Korean medicine clinic in the range of 20,000 to 25,000 won increased. Copayment in outpatient temporarily decreased. The inpatient admission rates and total medical expenses temporarily decreased but increased again. Conclusion: We confirmed the temporary increase in outpatient utilization in the medical expense segment with reduced copayment rates. And a temporary decrease in medical expenses followed by an increase again. To reduce the burden of medical expense among elderly in the long run, efforts to establish chronic disease management policies aimed at preventing disease occurrence and deterioration in advance need to continue.

• Journal of Ginseng Culture
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• v.6
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• pp.135-151
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• 2024

In order to compare the ginseng consumption culture of Korean and Chinese college students, their purchase status of ginseng products, attitudes toward ginseng, and satisfaction with ginseng products were examined, and the purchase and recommendation intention of ginseng products was investigated. It targeted 267 Korean college students and 318 Chinese college students who had experience eating ginseng products. As a result of the survey, in the case of Korean college student consumers, interest in ginseng products increased compared to before COVID-19, and the intention to purchase and recommend ginseng products increased. In addition, the higher the satisfaction with ginseng, the higher the frequency of ginseng purchase experience, the higher the social benefit attitude toward ginseng, and the higher the age, the higher the intention to purchase and recommend ginseng products. Chinese college student consumers had higher parental purchases than Korea, higher positive intentions to purchase and recommend social and psychological benefits, and their 20s are already more interested and friendly than Korea. What Korean college students and Chinese college student consumers have in common is that interest in health, safety, and environment has increased since before COVID-19, and interest in ginseng-related products has changed in individual experiences, indicating that individual experiences are important and Chinese college student consumers are influenced by parents. In particular, COVID-19 is an opportunity to recognize the importance of health, which is important to those in their 20s, and is actually related to purchase intention. Focusing on these results, it seems that expansion to preferred products for college student consumers and differentiation of marketing strategies according to family influence and consumption culture should be made, and these new changes due to COVID-19 seem to be a timely opportunity. At a time when interest in health and safety has increased, strategic preparations are needed for the future consumersociety to respond to changesin product diversity and convergence, changes in marketing media to meet consumer consumption values, and changesin consumer family types, such assingle households.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• Horticultural Science & Technology
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• v.32 no.1
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• pp.53-59
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• 2014

This study aimed to determine an appropriate cooling timing in the root zone for lowering substrate temperature and its effect on physiological response of sweet pepper (Capsicum annum L. 'Orange glory') grown on coir substrate in summer, from the July 16 to October 15, 2012. Daily temperature of substrate, root activity, leaf water potential, first flowering date, and the number of fruits were measured by circulating cool water through a XL pipe in the root zone during either all day (all-day) or only night time (5 p.m. to 3 a.m.; night) from the July 23 to September 23, 2012. For comparison, no cooling (control) was also applied. Between the $23^{rd}$ of July and $31^{st}$ of August (hot temperature period), daily average temperatures in substrates were $25.6^{\circ}C$, $26.1^{\circ}C$, and $29.1^{\circ}C$ for the all-day and night treatment, and control respectively. About 1.8 to $5^{\circ}C$ lower substrate temperature was observed in both treatments compared to that of control. In sunny day ($600-700 W{\cdot}m^{-2}{\cdot}s^{-1}$), the highest temperature of substrate was measured between 4 p.m. and 5 p.m. under both the all-day and night treatments, whereas it was measured between 7 p.m. and 8 p.m. under the control. Substrate temperatures during the day (6 a.m. to 8 p.m.) and night (8 p.m. to 6 a.m.) differed depending on the treatments. During the day and night, averaged substrate temperature was lower about $3.3^{\circ}C$ and $4.0^{\circ}C$ for the all-day, and $2.1^{\circ}C$ and $3.4^{\circ}C$ for the night treatment, compared to that of control. In the all-day and night treatment, the TD [TD = temperature of (control)] was greater in bottom than that of other regions of the substrate. Between the day and night, no different TD values were observed under the all-day treatment, whereas under the night treatment there was difference with the greatest degree in the bottom of the substrate. During the hot temperature period, total numbers of days when substrate temperature was over $25^{\circ}C$ were 40, 23 and 27 days for the control, all-day, and night treatment, respectively, and the effect of lowering substrate temperature was therefore 42.5% and 32.5% for the all-day and night treatment, respectively, compared to that for the control. Root activity and leaf water potential of plants grown under the all-day treatment were significantly higher than those under the night treatment. The first flowering date in the all-day treatment was similar to that in the night treatment, but 4-5 day faster than in the control. Also, the number of fruits in both treatments was significantly higher than that in the control. However, there was no effect of root zone cooling on eliminating delay in fruiting caused by excessively higher air temperature (> $30^{\circ}C$), although the substrate temperature was reduced $18^{\circ}C$ to $5^{\circ}C$. These results suggest that the method of cooling root zone temperature need to be incorporated into the lowering growing temperature for growth and fruit set of health paprika.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.69-93
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• 2017

The development of ICT has leaded the diversification and changes of supplies and demands in markets. It also caused the creations of a variety of values which are differentiated from those in the existing market. Therefore, a new-type market is created, which can include multi-value chains which are from ICT-based created markets as well as the existing markets. We defined the platform as the new-type market. In the platform, the multi-value chains can be coexisted with multi-values. In true market, when a new-type value chain entered into an existing market, it is general that it can be conflicted with the existing value chain in the market. The conflicted problem among multi-value chains in a market is caused by the sharing of limited market resources like suppliers, consumers, services or products among the value chains. In other words, if there are multi-value chains in the platform, then it is possible to have conflictions, overlapping, creations or losses of values among the value chains. To solve the problem, we introduce coexistence factors to reduce the conflictions to reach market equilibrium in the platform. In the other hand, it is possible to lead the creations of differentiated values from the existing market and to augment the total market values in the platform. In the early era of ICT development, ICT was introduced for improvement of efficiency and effectiveness of the value chains in the existing market. However, according to the changed role of ICT from the supporter to the promotor of the market, ICT became to lead the variations of the value chains and creations of various values in the markets. For instance, Uber Taxi created a new value chain with ICT-based new-type service or products with new resources like new suppliers and consumers. When Uber and Traditional Taxi services are playing at the same time in Taxi service platform, it is possible to create values or make conflictions among values between the new and old value chains. In this research, like Uber and traditional taxi services, if there are conflictions among the multi-value chains, then it is necessary to minimize the conflictions in the platform for the coexistence of multi-value chains which can create the value-added values in the platform. So, it is important to predict and discuss the possible conflicted problems between new and old value chains. The confliction should be solved to reach market equilibrium with multi-value chains in the platform. That is, we discuss the possibility of the coexistence of multi-value chains in the platform which are comprised of a variety of suppliers and customers. To do this, especially we are focusing on the healthcare markets. Nowadays healthcare markets are popularized in global market as well as domestic. Therefore, there are a lot of and a variety of healthcare services like Traditional-, Tele-, or Intelligent- healthcare services and so on. It shows that there are multi-suppliers, -consumers and -services as components of each different value chain in the same platform. The platform can be shared by different values that are created or overlapped by confliction and loss of values in the value chains. In this research, as was said, we focused on the healthcare services to show if a platform can be shared by different value chains like traditional-, tele-healthcare and intelligent-healthcare services and products. Additionally, we try to show if it is possible to increase the value of each value chain as well as the total value of the platform. As the result, it is possible to increase of each value of each value chain as well as the total value in the platform. Finally, we propose a coexistence model to overcome such problems and showed the possibility of coexistence between the value chains through experimentation.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Korean Ophthalmic Optics Society
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• v.20 no.3
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• pp.319-324
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• 2015

Purpose: This study was designed to evaluate the changes of phoria and calculated AC/A ratio, and their recovery time points by watching 3D television (3D TV). Methods: 50 subjects (male 30, female 20) of 20s to 40s ages who can watch 3D, were measured phoria using a Howell phoria card at 3 m for distance and 40 cm for near. The phoria was evaluated before watching 3D TV and every 10 minutes from starting of watching 3D TV for 30 minutes, and every 5 minutes after finishing of watching 3D TV for 30 minutes again. Results: For the distance phoria during and after watching 3D TV, it was increased to more exophoria $-0.98{\pm}1.37{\Delta}$ (prism diopters) after 10 minutes from starting of 3D TV watching (p=0.063) and increased to more exophoria $-1.00{\pm}1.28{\Delta}$ after 30 minutes (p=0.024), and started to decrease after finishing of watching 3D TV and recovered to the level of before 3D TV watching ($-0.78{\pm}1.11{\Delta}$) after 20 minutes (p=0.32) with comparing to phoria of before watching 3D TV ($-0.80{\pm}1.12{\Delta}$). For the near phoria, it was also increased to more exophoria $-5.71{\pm}4.45{\Delta}$ after 10 minutes from starting of watching 3D TV (p=0.000) and $-6.58{\pm}4.36{\Delta}$ after 30 minutes (p=0.000), and started to decrease after finishing of watching 3D TV and recovered to the level of before watching 3D TV after 20 minutes ($-4.34{\pm}3.67{\Delta}$) (p=0.32) with comparing to the phoria of before watching 3D TV ($-4.36{\pm}3.66{\Delta}$). AC/A ratio was decreased from $4.92{\pm}1.17{\Delta}/D$ for before 3D TV watching to $4.11{\pm}1.50{\Delta}/D$ for after 30 minutes from starting of watching 3D TV (p=0.000), and increased after the end of watching 3D TV and recovered to the level of before 3D TV watching ($4.93{\pm}1.18{\Delta}/D$) after 25 minutes (p=0.598). Conclusions: During watching 3D TV at near, it showed a tendency of convergence insufficiency by decrease of calculated AC/A ratio as result that exophoria at near was higher increased than exophoria at distance. However, the increased exophoria at both near and distance was recovered to the level of base line after 25 minutes from the end of watching 3D TV. Through this study, it seems to need rational proposals of advice for watching 3D TV.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.867-879
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• 2020

As the era of space technology utilization is approaching, the launch of CAS (Compact Advanced Satellite) 500-1/2 satellites is scheduled during 2021 for acquisition of high-resolution images. Accordingly, the increase of image usability and processing efficiency has been emphasized as key design concepts of the CAS 500-1/2 ground station. In this regard, "CAS 500-1/2 Image Acquisition and Utilization Technology Development" project has been carried out to develop core technologies and processing systems for CAS 500-1/2 data collecting, processing, managing and distributing. In this paper, we introduce the results of the above project. We developed an operation system to generate precision images automatically with GCP (Ground Control Point) chip DB (Database) and DEM (Digital Elevation Model) DB over the entire Korean peninsula. We also developed the system to produce ortho-rectified images indexed to 1:5,000 map grids, and hence set a foundation for ARD (Analysis Ready Data)system. In addition, we linked various application software to the operation system and systematically produce mosaic images, DSM (Digital Surface Model)/DTM (Digital Terrain Model), spatial feature thematic map, and change detection thematic map. The major contribution of the developed system and technologies includes that precision images are to be automatically generated using GCP chip DB for the first time in Korea and the various utilization product technologies incorporated into the operation system of a satellite ground station. The developed operation system has been installed on Korea Land Observation Satellite Information Center of the NGII (National Geographic Information Institute). We expect the system to contribute greatly to the center's work and provide a standard for future ground station systems of earth observation satellites.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• The Korean Journal of Nuclear Medicine Technology
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• v.21 no.1
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• pp.54-59
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• 2017

Purpose The present study aimed at assessing the effectiveness of the respiratory gating method used in the flow mode and additional localized respiratory-gated imaging, which differs from the step and go method. Materials and Methods Respiratory gated imaging was performed in the flow mode to twenty patients with lung cancer (10 patients with stable signals and 10 patients with unstable signals), who underwent PET/CT scanning of the torso using Biograph mCT Flow PET/CT at Bundang Seoul University Hospital from June 2016 to September 2016. Additional images of the lungs were obtained by using the respiratory gating method. SUVmax, SUVmean, and Tumor Volume ($cm^3$) of non-gating images, gating images, and additional lung gating images were found with Syngo,bia (Siemens, Germany). A paired t-test was performed with GraphPad Prism6, and changes in the width of the amplitude range were compared between the two types of gating images. Results The following results were obtained from all patients when the respiratory gating method was applied: $SUV_{max}=9.43{\pm}3.93$, $SUV_{mean}=1.77{\pm}0.89$, and $Tumor\;Volume=4.17{\pm}2.41$ for the non-gating images, $SUV_{max}=10.08{\pm}4.07$, $SUV_{mean}=1.75{\pm}0.81$, and $Tumor\;Volume=3.56{\pm}2.11$ for the gating images, and $SUV_{max}=10.86{\pm}4.36$, $SUV_{mean}=1.77{\pm}0.85$, $Tumor\;Volume=3.36{\pm}1.98$ for the additional lung gating images. No statistically significant difference in the values of $SUV_{mean}$ was found between the non-gating and gating images, and between the gating and lung gating images (P>0.05). A significant difference in the values of $SUV_{max}$ and Tumor Volume were found between the aforementioned groups (P<0.05). The width of the amplitude range was smaller for lung gating images than gating images for 12 from 20 patients (3 patients with stable signals, 9 patients with unstable signals). Conclusion In PET/CT scanning using the respiratory gating method in the flow mode, any lesion movements caused by respiration were adjusted; therefore, more accurate measurements of $SUV_{max}$, and Tumor Volume could be obtained from the gating images than the non-gating images in this study. In addition, the width of the amplitude range decreased according to the stability of respiration to a more significant degree in the additional lung gating images than the gating images. We found that gating images provide information that is more useful for diagnosis than the one provided by non-gating images. For patients with irregular signals, it may be helpful to perform localized scanning additionally if time allows.