• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2027-2034
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• 2021

Using multi-temporal KOMPSAT-3/3A high-resolution satellite images, the Normalized Difference Vegetation Index (NDVI) for the area around the Fukushima daiichi nuclear power plant was determined, and the pattern of vegetation changes was analyzed. To calculate the NDVI, surface reflectance from the KOMPSAT-3/3A satellite image was used. Satellite images from four years were used, and the zones where the images overlap was designated as the area of interest (AOI) for the study, and by setting a profile passing through highly vegetated area as a data analysis method, the changes by year were examined. In addition, random points were extracted within the AOI and displayed as a box plot to quantitatively indicate change of NDVI distribution pattern. The main results of this study showed that the NDVI in 2014 was low within AOI in the vicinity of the nuclear power plant, but vegetated area continued to expand until 2021. These results were also confirmed in the change monitoring results shown in a profile or box plot. In disaster areas where access is restricted, such as the Fukushima nuclear power plant area, where it is difficult to collect field data, obtaining land cover classification products with high accuracy using satellite images is challenging, so it is appropriate to analyze them using primary outputs such as vegetation indices obtained from high-resolution satellite imagery. It is necessary to establish an international cooperation system for jointly utilizing satellite images. Meanwhile, to periodically monitor environmental changes in neighboring countries that may affect the Korean peninsula, it is necessary to establish utilization models and systems using high-resolution satellite images.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.161-170
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• 2019

Multi-product mass production small and medium enterprise factories have a wide variety of products and a large number of products, wasting manpower and expenses for inventory management. In addition, there is no way to check the status of inventory in real time, and it is suffering economic damage due to excess inventory and shortage of stock. There are many ways to build a real-time data collection environment, but most of them are difficult to afford for small and medium-sized companies. Therefore, smart factories of small and medium enterprises are faced with difficult reality and it is hard to find appropriate countermeasures. In this paper, we implemented the contents of extension of existing inventory management method through character extraction on label with barcode and QR code, which are widely adopted as current product management technology, and evaluated the effect. Technically, through preprocessing using OpenCV for automatic recognition and classification of stock labels and barcodes, which is a method for managing input and output of existing products through computer image processing, and OCR (Optical Character Recognition) function of Google vision API. And it is designed to recognize the barcode through Zbar. We propose a method to manage inventory by real-time image recognition through Raspberry Pi without using expensive equipment.

• The Korean Journal of Franchise Management
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• v.10 no.2
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• pp.33-47
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• 2019

Purpose - The purpose of this study is to classify and analyze existing studies from various angles through systematic literature review of how human resources development has been researched in the domestic franchise business. These studies are intended to suggest the direction in which human resource development research should be conducted in the future in the franchise business. Research design, data, and methodology - This study is based on systematic literature review methodology. It has gone through the process of subject language setting, literature search routing, search term selection, literature selection, literature classification and literature analysis. The systematic literature review identified 59 peer-reviewed dissertations and scientific journal publications on the subject of HRD in Korea franchise business. Result - This study analyzed by research methods, research industries, research population and dependent variable using the systematic review process. The literature studied in the 2000s mainly led to research on education and training of franchise employees in beauty franchise business. In the literature studied since 2010, human resources development was mainly studied in the supervisor in the restaurant franchise business, and in the study of competence rather than education and training. According to the research methods, statistical methods were mostly relatively simple, such as t-test or one-way distribution analysis until the 2000s, and after 2010, in-depth and structural studies using multiple return analysis, structural method analysis, path analysis, multi-dimensional scale analysis, AHP, etc were conducted. When classified by study dependant, early research until the 2000s focused on the study of education and training, which is an independent variable, on the satisfaction of education programs, job satisfaction, and immersion. On the other hand, studies conducted since 2010 have produced more complex results using various medium variants, and those related to management performance and relationship performance have been mainly studied, rather than the satisfaction of the education itself. Conclusions - While the domestic franchise business is expanding in terms of quantity, such as the number of franchises and franchises, the development in terms of quality for the joint growth of franchises and franchisees is still lacking. In order for the franchisee to continue to grow with each other, the franchisee must identify and develop their current performance or expected capabilities through capacity modeling at various targets and levels.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.153-161
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• 2021

The exterior color of the building reflects its style. With the advancement of the 'Village Art' project in South Korea, many villages have gradually developed into tourist attractions. Therefore, it is also important to distinguish between residents' lifestyles and tourist needs through architectural color design. This article first investigates the literature and analyzes the use function of the building and the role of the exterior color in the villages. Secondly, the four types of buildings in Huinnyeoul Culture Village: residential, commercial, leisure, and public toilets are investigated as the research objects. Analyze through the KSCA color measurement analysis system and questionnaire survey, and propose a color plan for the exterior of the building. Research results: First, the main color area of the outer wall of the residential building should be the largest, with a tendency of high brightness and low saturation. Second, small buildings with high chroma in the recreation area can be used as visual guidance.Third, the buildings in the commercial area are mainly of high brightness, high chroma, and multi-hue. Fourth, the main color of the bathroom is the main color of the residential area and the leisure area, which can provide a sense of visual stability.

• Journal of Family Relations
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• v.21 no.1
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• pp.143-160
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• 2016

Objectives: The purpose of this study is to empirically verify the difference between traditional gender role attitude and realistic dual earner attitude. Most preceding studies analyzed gender role attitude by summated rating scale while few studies have been conducted on the difference between traditional gender role attitude and dual earner attitude. Method: By using the 9th Korea Welfare Panel Data (year 2014), this study extracted households of married couples and with children in working age (18 through 64 years of age) who are the main subjects in work-family relationship. First, this study classified traditional group and modern group in their gender role attitude and passive group and active group in their dual earner attitude and by crossing these attitudes, drew out 4 gender role/dual earner attitudes. For analysis, this study investigated frequency of attitude types through descriptive statistics. By conducting ANOVA, study verified if there are significant differences in the satisfaction with family relationship depending on the types of gender role/dual earner attitudes. Lastly, study examined the effect of socio-economic status and demographic characteristics on the types of gender role/dual earner attitudes by conducting multi-nominal logistic regression analysis. Results: As the result of analysis, first, half of respondents belonged to 'equal-role-oriented type' who has modern gender role attitude and active dual earner attitude. However, there were not a few 'dual-earner-avoiding type' who has modern gender role attitude but passive dual earner attitude and 'incongruous attitude type' who has traditional gender role attitude but active dual earner attitude. Next, 'incongruous attitude type' had significantly low level of satisfaction with overall family life, relationship with spouse and relationship with children than other attitude types. Lastly, those whose incomes belong to poor strata, those who are high school graduates and lower, males and those who are over the age of 60 had significantly higher probability of belonging to 'incongruous attitude type.' Conclusion: The results of analysis demonstrate that the difference between gender role attitude and dual earner attitude is an important matter and since 'incongruous attitude type' exhibits low level of satisfaction with family relationship, scrupulous policy approaches are required for those who have high possibility of belonging to 'incongruous attitude type.'

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1605-1613
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• 2020

The tidal channel is a coastal sedimentary terrain that plays the most important role in the formation and development of tidal flats, and is considered a very important index for understanding and distribution of tidal flat sedimentation/erosion terrain. The purpose of this study is to understand the changes in tidal channels by a period after the opening of the floodgate of the seawall in the reclaimed land of Sihwa Lake using KOMPSAT high-resolution multispectral satellite image data and to evaluate the applicability and efficiency of high-resolution satellite images. KOMPSAT 2 and 3 images were used for extraction of the tidal channels' lineaments in 2009, 2014, and 2019 and were applied to supervised classification method based on Principal Component Analysis (PCA), Artificial Neural Net (ANN), Matched Filtering (MF), and Spectral Angle Mapper (SAM) and band ratio techniques using Normalized Difference Water Index (NDWI) and MF/SAM. For verification, a numerical map of the National Geographic Information Service and Landsat 7 ETM+ image data were utilized. As a result, KOMPSAT data showed great agreement with the verification data compared to the Landsat 7 images for detecting a direction and distribution pattern of the tidal channels. However, it has been confirmed that there will be limitations in identifying the distribution of tidal channels' density and providing meaningful information related to the development of the sedimentary process. This research is expected to present the possibility of utilizing KOMPSAT image-based high-resolution remote exploration as a way of responding to domestic intertidal environmental issues, and to be used as basic research for providing multi-platform-image-based convergent thematic maps and topics.

• Journal of Korea Water Resources Association
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• v.54 no.8
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• pp.567-576
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• 2021

Climate change threatens the security of domestic water resources in South Korea. To overcome the potential water shortage, various approaches are being studied by alterning the operation of dams or by integrated operation of multiple dams and reservoirs. However, most of the related researches were developed and applied for multi-purpose dams, and few studies were conducted for the hydropower dams. The main purpose of the hydropower dam is to generate electric energy; however, the potential water shortage due to prolonged droughts brings the idea to supply water from the hydropower dam in the basin. To that end, it is required to estimate the water supply ability of the hydropower dams. In this study, we proposed a methodology to classify the hydropower dam into a "storage-type" and "run-of-river type" dam. The proposed approach was demonstrated using the hydropower dams located in North-han River basin. The results of this study are expected to contribute for further analysis of the hydropower dams, such as evaluation of water supply capacity and drought mitigation purpose operation of the hydropower dams.

• 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.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.6
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• pp.175-181
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• 2022

The condition of infrastructure deteriorates as the service life increases. Since most infrastructure in South Korea were intensively built during the period of economic growth, the proportion of outdated infrastructure is rapidly increasing now. Aging of such infrastructure can lead to safety accidents and even human casualties. To prevent these issues in advance, periodic and accurate inspection is essential. For this reason, the need for research to detect various types of damage using computer vision and deep learning is increasingly required in the field of remotely controlled or autonomous inspection. To this end, this study proposed a neural network structure that can detect concrete damage by classifying it into three types. In particular, the proposed neural network can detect them more accurately through a hierarchical learning technique. This neural network was trained with 2,026 damage images and tested with 508 damage images. As a result, we completed an algorithm with average mean intersection over union of 67.04% and F1 score of 52.65%. It is expected that the proposed damage detection algorithm could apply to accurate facility condition diagnosis in the near future.

• Korean Journal of Construction Engineering and Management
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• v.23 no.3
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• pp.45-55
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• 2022

BIM models allow building spaces to be instantiated and recognized as unique objects independently of model elements. These instantiated spaces provide the required semantics that can be leveraged for building code checking, energy analysis, and evacuation route analysis. However, theses spaces or rooms need to be designated manually, which in practice, lead to errors and omissions. Thus, most BIM models today does not guarantee the semantic integrity of space designations, limiting their potential applicability. Recent studies have explored ways to automate space allocation in BIM models using artificial intelligence algorithms, but they are limited in their scope and relatively low classification accuracy. This study explored the use of Graph Convolutional Networks, an algorithm exclusively tailored for graph data structures. The goal was to utilize not only geometry information but also the semantic relational data between spaces and elements in the BIM model. Results of the study confirmed that the accuracy was improved by about 8% compared to algorithms that only used geometric distinctions of the individual spaces.