• Korean Journal of Heritage: History & Science
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• v.54 no.2
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• pp.136-153
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• 2021

The restitution of cultural heritage located abroad has been going on for 107 years, starting with the return of the Jigwangguksa Tower to Beopcheonsaji Temple in Wonju after it was taken during the Japanese occupation in 1915. The Overseas Cultural Heritage Foundation, established in 2012, has laid the foundation for retrieval, preservation, restoration, and exchange of cultural heritage through research cooperation and the purchasing of cultural heritage items. The pace of the collection of cultural heritage objects and the locating of others has increased every year since its establishment, and the number of returned, rather than recovered, cultural heritage items has also increased. The present study aimed to complete a value inquiry of the cultural relics of Waryongmae (臥龍梅) and a restitution of cultural heritage as the main focuses. The process of recovering relics from Waryongmae has been recorded in the book The Cultural Property Returned into Our Arms, published by the above-mentioned foundation. This record was revised and supplemented to try and raise its cultural value by adding elaborate storytelling to the process of recovering the Waryongmae that grew in the courtyard of Changdeokgung Palace. The cultural value of Waryongmae is that it is unique. The Waryongmae is the first living cultural heritage, and therefore has cultural value due to its uniqueness. Second, the Waryongmae has unique cultural value due to its restitution and return to Korea twice, once in 1992, and another time in 1999. The first restitution was special in that it was featured by the Japanese media, and the second was special in that it was intensively reported by the Korean media. Third, 42 Waryongmae cultural content types were explored, including nineteen visual contents, eleven interactive contents, and twelve skate contents.

• Journal of the Korean earth science society
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• v.41 no.6
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• pp.617-629
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• 2020

Sea surface temperature (SST), which plays an important role in climate change and global environmental change, can be divided into skin sea surface temperature (SSST) observed by satellite infrared sensors and the bulk temperature of sea water (BSST) measured by instruments. As sea surface temperature products distributed by many overseas institutions represent temperatures at different depths, it is essential to understand the relationship between the SSST and the BSST. In this study, we constructed an observation system of infrared radiometer onboard a marine research vessel for the first time in Korea to measure the SSST. The calibration coefficients were prepared by performing the calibration procedure of the radiometer device in the laboratory prior to the shipborne observation. A series of processes were applied to calculate the temperature of the layer of radiance emitted from the sea surface as well as that from the sky. The differences in skin-bulk temperatures were investigated quantitatively and the characteristics of the vertical structure of temperatures in the upper ocean were understood through comparison with Himawari-8 geostationary satellite SSTs. Comparison of the skin-bulk temperature differences illustrated overall differences of about 0.76℃ at Jangmok port in the southern coast and the offshore region of the eastern coast of the Korean Peninsula from 21 April to May 6, 2020. In addition, the root-mean-square error of the skin-bulk temperature differences showed daily variation from 0.6℃ to 0.9℃, with the largest difference of 0.83-0.89℃ at 1-3 KST during the daytime and the smallest difference of 0.59℃ at 15 KST. The bias also revealed clear diurnal variation at a range of 0.47-0.75℃. The difference between the observed skin sea surface temperature and the satellite sea surface temperature showed a mean square error of approximately 0.74℃ and a bias of 0.37℃. The analysis of this study confirmed the difference in the skin-bulk temperatures according to the observation depth. This suggests that further ocean shipborne infrared radiometer observations should be carried out continuously in the offshore regions to understand diurnal variation as well as seasonal variations of the skin-bulk SSTs and their relations to potential causes.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1449-1463
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• 2020

As recent global warming and climate changes become more serious, the importance of CO2 absorption by forests is increasing to cope with the greenhouse gas issues. According to the UN Framework Convention on Climate Change, it is required to calculate national CO2 absorptions at the local level in a more scientific and rigorous manner. This paper presents the gridded expansion of forest flux observations and mapping of daily CO2 absorption by the forests in Korea using numerical weather prediction data and satellite images. To consider the sensitive daily changes of plant photosynthesis, we built a machine learning model to retrieve the daily RACA (reference amount of CO2 absorption) by referring to the climax forest in Gwangneung and adopted the NIFoS (National Institute of Forest Science) lookup table for the CO2 absorption by forest type and age to produce the daily AACA (actual amount of CO2 absorption) raster data with the spatial variation of the forests in Korea. In the experiment for the 1,095 days between Jan 1, 2013 and Dec 31, 2015, our RACA retrieval model showed high accuracy with a correlation coefficient of 0.948. To achieve the tier 3 daily statistics for AACA, long-term and detailed forest surveying should be combined with the model in the future.

• Journal of Nutrition and Health
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• v.54 no.4
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• pp.398-411
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• 2021

Purpose: The objective of this study was to investigate food and nutrition information utilization practices of adults aged between 20 and 30 years to provide the basic data for developing customized content. Methods: Statistical analyses were performed using the SPSS program (ver. 24.0) for the 𝛘²-test, t-test, one-way analysis of variance, and Duncan's multiple range test. Results: Of the 570 subjects surveyed, 45.4% were men, 54.6% were women, 66.3% were in their 20s, 33.7% were in their 30s, 41.4% were single-person households, and 58.6% lived with their families. On average, 14.2% of televisions (TVs), 26.0% of personal computers (PCs), and 63.7% of smartphones were used for more than three hours per day. 30.9% of respondents searched for food and nutrition information more than once a week. 70.0% of the respondents had then applied the information in real life and 54.7% of the respondents said they would share information with others. Information retrieval rate was in the order of 'restaurant (64.8%)', 'diet (57.5%)', and 'food recipes (55.7%)'. Overall satisfaction with food and nutrition information averaged 3.33 on a five-point scale. Satisfaction score was in the order of 'enough description and easy to understand (3.43)', 'matching title and content (3.35)', and 'providing new and novel information (3.22)'. Satisfaction scores were significantly higher in the group that searched for information (p < 0.001), the group that used the retrieved information in real life (p < 0.001), and the group that conveyed this information to others (p < 0.001). Conclusion: To improve information user satisfaction, it is necessary to provide customized information that fits the characteristics of information users. For this purpose, it is necessary to continuously conduct surveys and satisfaction evaluations for each target group.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• 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 Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

• Korean Journal of Occupational Health Nursing
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• v.3
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• pp.54-70
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• 1993

According as the automation of clerical work(OA ; Office Automation) develops, the use of VDT(Visual or Video Display Terminal) is increasing suddenly. But, in proportion to the spread of office automation(OA tendency), the self-conciousness syptom attendant upon the work is appearing also (Kim, Jung Tae, Lee, Young Ook, 1990). The apparatuses of office enable the clerical workers to be convenient and perform mass businesses. But, they are increasing the opportunity to be exposed to VDT syndrom, techno stress, computer terminal disease, pain by muscle strain(RSI), bradycausia of noise nature, and electromagnetic waves, etc. which are referred to as the new type of occupational diseases to the workers. It is the real situation that the workers to use VDT is complaining of the physical inconvenience sense in the recent newspaper and literature, it is the point of time that the sydrome to come from VDT use and computer terminal disease, etc. must be classified into the occupational disease(Lee, Kwang Young 1990, Lee, Kyoo Hak 1990, Lee, Won Ho 1991, Lee, Si Young 1991, Lee, Joon 1991, Choi, Young Tae 1991, Heo, Seung Ho 1989). In addition, it is the real situation that the scientifitic study result about the scope that electromagnetic waves has influence on the human body has not been suggested yet, and criticism on the stable exposure permission standard about electromagnetic waves to be emitted from VDT and on the problem in the health about electromagnetic waves is continuing. (IEEE Spectrum, 1990). In addition according to the experience of nursery business of industry field, it is the real situation that the patients who consult complaining of physical and mental inconvenience sence, among the users of apparatus of office automation, are reaching 10% of the patients coming to doctor's room. Therefore, it is necessary to confirm the self-consciousness symptom that the clerical workers complain of multilaterally with the actual state examination about the use of the apparatuses of offices automaton. Thus, this study was tried as th basic data for the cosultation and education for the maintenance and furtherance of the health of workers as the nurse of industry field, by confirming the contents of self-consciousness symptom attendant upon the use of the apparatus for office outomation making the financial institution in which the spparatus for office automation in most frequently used as the subject, and by examining whether there is the difference according to the subject of study, the data were collected, by using the questionnaire method, making 200 workers who consented to the study participation as the subject, among the persons who have spent over 3 months since they used the apparatuses for office automation and didn't receive the treatment in hospital due to the clerical disease for recent 3 years. The period of data collection was from Oct. 9, 1991 to Oct. 12. As for the measurement instrument about the complaint if self-consciousness symptom attendant upon the use of apparatuses fo office automation, the question item on the complaint symptom of health problem attendant upon the treatment of VDT that Kim(1991) developed and on CMI health problem and the question items on the fatigue degree due to industry were used by previous examination to 25 persons. Collected data were analyzed with the statistical method such as percentage, arithmetic mean, Person correlation coeffient, Kai square verfication, t-test, ANOVA, etc. by using SPSS/PC+ program, and the result is as follows : 1. The self-consciousness symptom that the clerical workers complained of most frequetly appeared high in 'My eyes are tired'(99.4%), 'I feel fatigue and weariness'(99.4%), 'I feel that my head is heavy5(90.0%), 'eyesight fell'(88.8%), 'I have a stiff neck'(88.8%), 'I fell pain in the shoulder'(85.0%), 'I feel cold and painful in the eyes'(76.9%), 'I feel the dry sense of eyeball'(76.2%), 'My nerves are edgy, and I an fretful, (75.6%), 'I feel pain in the waist'(73.2%) and 'I fell pain in the back'(72.8%). It emerged that the subject use the apparatuses for office automation complained of self-consciousness symptoms related to visual symptoms and musculoskeletal symptoms. 2. As for the general feature of examination subjects, the result to see the distribution by classifying into sex, age, school career, use career of apparatuses for office automation, skillfulness degree of the use of apparatus for office automation, use hours of the apparatuses for office automation per 1 day, type of business of the apparatus for office automation, rest hours during the use of apparatus for office automation, satifaction degree of business of office automation, and work circumstance, etc. emerged as follows : As for the sex of subjects, the distribution showed that men were 58.8% and women were 41.3%, Age was average 26.9. As the distribution of school career, the distribution showed that4below the graduation of high school' was 58.8%, 'graduation from junior college-university' was 35.0%, and 'over graduate school' was 6.3%. In the question to ask the existence or non-existence of experience of health consultation in connection with the work of office automation, the response that I had the consultation exprience and I feel the necessity emergerd as 90.1% And, the case that the subject who didn't wear the glasses or lens before using the OA apparatus wear glasses or lens after using OA apparatus emerged as 28.3% of whole. As for the existence or non-existence of use career of OA apparatus, the case under 3 years was highest as 52. 7%. As for the skillfulnness degree about the use of apparatus for office automation, most of them are skillful with the fact that 'common' was 44.4%, 'skill' was 42.5%, and 'unskillful' was 13.1% As for the use average hours of the apparatus for office automation per 1 day, the distribution showed that the case under 3-6 hours was 33.1%, the case under 6-9 hours was 28.1%, the case under 3 hours was 30.6%, and the case over 9 hours was 8.1% Main OA business and the use hours for 1 day showed in the order of keeping and retrieval, business of information transmission(162min), business of information transmission(79.3 min), business of document framing(55.5 min), and business of duplication and printing(25.4min). as for the rest during the use of apparatus for affice automation, that I take rest occasion demands the major portion, but that I take after completing the work emerged as 33.8%. Though the subiness gets to be convenient by the use of the apparatus for of office automation, respondents who showed the dissatisfaction about the present OA business emergd high as 78.1%. The work circumstances of each office was good with the fact that the temperature of office was 21.8, noise was average 42.7db, and the illumination was average 364.4 lx, in the light of ANSi/HFS 100 Standard. 3. Sight syptom, musculoskeletal symptom, skin and other symptoms showed the significant difference according to the extent of skillfulness of the apparatus for office automation. All the symptoms exept skin symptom showed the difference according to the use hours of the apparatus for office automation. All the question items exept the sytoms of digestive organs and the rest hours during the apparatus for office automation showed the signicant difference. The question item which showed the signicant difference from the satisfaction degree of present OA business showed the significant difference from all the question item classified into 6 groups. But, age and school career didn't significant difference from the complaint of any self-consciousness symptoms.