• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1332-1344
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• 2004

Upon development of information super-highway and multimedia-related technoiogies in recent years, more efficient technologies to transmit, store and retrieve the multimedia data are required. Among such technologies, firstly, it is common that the semantic-based image retrieval is annotated separately in order to give certain meanings to the image data and the low-level property information that include information about color, texture, and shape Despite the fact that the semantic-based information retrieval has been made by utilizing such vocabulary dictionary as the key words that given, however it brings about a problem that has not yet freed from the limit of the existing keyword-based text information retrieval. The second problem is that it reveals a decreased retrieval performance in the content-based image retrieval system, and is difficult to separate the object from the image that has complex background, and also is difficult to extract an area due to excessive division of those regions. Further, it is difficult to separate the objects from the image that possesses multiple objects in complex scene. To solve the problems, in this paper, I established a content-based retrieval system that can be processed in 5 different steps. The most critical process of those 5 steps is that among RGB images, the one that has the largest and the smallest background are to be extracted. Particularly. I propose the method that extracts the subject as well as the background by using an Image, which has the largest background. Also, to solve the second problem, I propose the method in which multiple objects are separated using RGB channel selection techniques having optimized the excessive division of area by utilizing Watermerge's threshold value with the object separation using the method of RGB channels separation. The tests proved that the methods proposed by me were superior to the existing methods in terms of retrieval performances insomuch as to replace those methods that developed for the purpose of retrieving those complex objects that used to be difficult to retrieve up until now.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.29-40
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• 2005

According as base of digital hologram has been magnified, discussion of compression technology is expected as a international standard which defines the compression technique of 3D image and video has been progressed in form of 3DAV which is a part of MPEG. As we can identify in case of 3DAV, the coding technique has high possibility to be formed into the hybrid type which is a merged, refined, or mixid with the various previous technique. Therefore, we wish to present the relationship between various image/video coding techniques and digital hologram In this paper, we propose an efficient coding method of digital hologram using standard compression tools for video and image. At first, we convert fringe patterns into video data using a principle of CGH(Computer Generated Hologram), and then encode it. In this research, we propose a compression algorithm is made up of various method such as pre-processing for transform, local segmentation with global information of object image, frequency transform for coding, scanning to make fringe to video stream, classification of coefficients, and hybrid video coding. Finally the proposed hybrid compression algorithm is all of these methods. The tool for still image coding is JPEG2000, and the toots for video coding include various international compression algorithm such as MPEG-2, MPEG-4, and H.264 and various lossless compression algorithm. The proposed algorithm illustrated that it have better properties for reconstruction than the previous researches on far greater compression rate above from four times to eight times as much. Therefore we expect that the proposed technique for digital hologram coding is to be a good preceding research.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.6 s.165
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• pp.974-984
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• 2007

The purpose of this study was to find differences in evaluation criteria and to find differences in preferred images based on benefits segmented groups of jeans products consumers. Male and female Korean university students participated in the study. Quota sampling method was used to collect the data based on gender and a residential area of the respondents. Data from 492 questionnaires were used in the analysis. Factor analysis, Cronbach's alpha coefficient, cluster analysis, one-way ANOVA, and post-hoc test were conducted. As a result, respondents who seek multi-benefits considered aesthetic criteria(e.g., color, style, design, fit) and quality performance criteria(e.g., durability, ease of care, contractibility, flexibility) more importantly when evaluating and purchasing jeans products. Respondents who seek brand name considered extrinsic criteria(e.g., brand reputation, status symbol, country of origin, fashionability) more importantly than respondents who seek economic efciency. Respondents who seek multi-benefits such as attractiveness, fashion, individuality, and utility tend to prefer all the images: individual image, active image, sexual image, sophisticated image, and simple image when wearing jeans products. Respondents who seek fashion are likely to prefer individual image, and respondents who seek brand name more prefer both individual image and polished image. Mean while, respondents who seek economical efficiency less prefer sexual image and polished image.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.83-98
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• 2021

Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.57-75
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• 2019

In this paper, we conducted a study focusing on the innovation direction of the documentary model on the Internet of Things industry, which is the most actively industrialized among the core technologies of the 4th Industrial Revolution. Policy, economic, social, and technical issues were derived using PEST analysis for global trend analysis. It also presented future prospects for the Internet of Things industry of ICT-related global research institutes such as Gartner and International Data Corporation. Global research institutes predicted that competition in network technologies will be an issue for industrial Internet (IIoST) and IoT (Internet of Things) based on infrastructure and platforms. As a result of the PEST analysis, developed countries are pushing policies to respond to the fourth industrial revolution through cooperation of private (business/ research institutes) led by the government. It was also in the process of expanding related R&D budgets and establishing related policies in South Korea. On the economic side, the growth tax of the related industries (based on the aggregate value of the market) and the performance of the entity were reviewed. The growth of industries related to the fourth industrial revolution in advanced countries overseas was found to be faster than other industries, while in Korea, the growth of the "technical hardware and equipment" and "communication service" sectors was relatively low among industries related to the fourth industrial revolution. On the social side, it is expected to cause enormous ripple effects across society, largely due to changes in technology and industrial structure, changes in employment structure, changes in job volume, etc. On the technical side, changes were taking place in each industry, representing the health and medical sectors and manufacturing sectors, which were rapidly changing as they merged with the technology of the Fourth Industrial Revolution. In this paper, various management methodologies for innovation of existing business model were reviewed to cope with rapidly changing industrial environment due to the fourth industrial revolution. In addition, four criteria were established to select a management model to cope with the new business environment: 'Applicability', 'Agility', 'Diversity' and 'Connectivity'. The expert survey results in an AHP analysis showing that Business Model Canvas is best suited for business model innovation methodology. The results showed very high importance, 42.5 percent in terms of "Applicability", 48.1 percent in terms of "Agility", 47.6 percent in terms of "diversity" and 42.9 percent in terms of "connectivity." Thus, it was selected as a model that could be diversely applied according to the industrial ecology and paradigm shift. Business Model Canvas is a relatively recent management strategy that identifies the value of a business model through a nine-block approach as a methodology for business model innovation. It identifies the value of a business model through nine block approaches and covers the four key areas of business: customer, order, infrastructure, and business feasibility analysis. In the paper, the expansion and application direction of the nine blocks were presented from the perspective of the IoT company (ICT). In conclusion, the discussion of which Business Model Canvas models will be applied in the ICT convergence industry is described. Based on the nine blocks, if appropriate applications are carried out to suit the characteristics of the target company, various applications are possible, such as integration and removal of five blocks, seven blocks and so on, and segmentation of blocks that fit the characteristics. Future research needs to develop customized business innovation methodologies for Internet of Things companies, or those that are performing Internet-based services. In addition, in this study, the Business Model Canvas model was derived from expert opinion as a useful tool for innovation. For the expansion and demonstration of the research, a study on the usability of presenting detailed implementation strategies, such as various model application cases and application models for actual companies, is needed.

• Knowledge Management Research
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• v.23 no.4
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• pp.91-112
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• 2022

In order to improve the quality of consumption in senior generation, it is necessary to build trust in the digital consumption environment. However, there are limitations from previous researches on consumption in the digital environment that has mainly focused on Millennial and Z generations. Therefore, this study aims to examine the antecedents of digital consumption trust and to explore the market segments of senior generation created by the dynamics of these antecedents. In addition, in order to provide practical implications, we investigate the difference in the level of perceived digital consumption trust using segmentation. This study, therefore, used 2021 Korea's consumer life index data conducted by the Korea Consumer Agency for general consumers, and only consumer data of 60s and older are extracted for the analysis. As a result, we confirm from the main results that the three antecedents, consumer competency, perceived corporate responsibility, and local community's problem-solving environment, are significant in building the digital consumption trust in the senior generation. It is academically significant in this aspect to look at approaches to improve senior customers' trust in digital consumption circumstances by dividing generations because generations exhibit varying levels of understanding of online consumption or digital consumption conditions. It is academically significant in this aspect to look at approaches to improve senior customers' trust in digital consumption circumstances by dividing generations because generations exhibit varying levels of understanding of online consumption or digital consumption conditions. Additionally, it is proposed as a practical implication that it should be managed so that significant improvements of customers, businesses, and regional public institutions are developed in order to allow the senior consumers to prepare trusted digital consumption circumstances.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Korea journal of population studies
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• v.23 no.2
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• pp.79-112
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• 2000

This study was focused on the contrasting pattern of part-time employment between Japan and Taiwan where the environments are similar in terms of expanding service industries and increasing flexibility of labor. In Japan, the expansion of part-time employment and its feminization have occurred, whereas they have not at all in Taiwan. The purpose of this study was to examine the reasons behind this phenomena, and to explore what relations they might have with the supply of women\`s labor in each country. Data analysis showed the following results. First, when the phenomena of part-time employment in Japan and Taiwan are summarized as \`active\` and \`inactive\` models, the difference could be explained by a structure-oriented approach rather than an individual-oriented approach. In other words, the difference between the two countries is mainly because of the structural characteristics of the labor market. a combination of capitalism and patriarchy, and an effect of state welfare and family policies rather than a \`voluntaristic choice\` due tn household work and child rearing. In light of this. the labor market segmentation and flexibility of labor theory in particular provided a useful frame for explanation. Second, with regard to the supply of women\`s labor, the difference between Japan and Taiwan could be found in the structure of the labor market and in family response strategies. The large corporation-oriented and strictly divided labor market structure in Japan activated part-time employment and its feminization, whereas, the small family-oriented businesses and less divided labor market in Taiwan supported the continuity of full-time employment of married women. There was also a room for informal employment in Taiwan which made part-time employment unnecessary. This study showed that even within similar environments of expanding service industry and pursuing flexibility of labor different measures and adaptations were possible. The case of Taiwan in particular, showed the significance of an informal labor market which was a part of industrialization process and a strategy of producing various products through a subcontracting network.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.6
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• pp.894-902
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• 2016

The purpose of this study was to analyze the attributes considered as important by parents in the selection of laver for infants through conjoint analysis techniques. A total of 917 questionnaires were distributed in January 2016, of which 211 were completed (23.0%). Statistical data analyses were performed using SPSS/Win 21.0 for descriptive statistics and conjoint analysis. The conjoint design was applied to evaluate the hypothetical laver for infants. According to the analysis of attributes and levels of laver for infants, the relative importance of each attribute was follows: seasoning (26.55%), flavor (19.33%), texture (18.75%), oil (15.15%), size (10.61%), and certification (9.61%). The results of the conjoint analysis indicate that parents raising infants preferred laver with the characteristics of non-seasoning, general flavor, softness, half-size, organic certification, and perilla oil. The most preferred laver for infants gained a 53.7% potential market share from choice simulation compared with laver being sold. Using utility and relative importance, the laver market for infants was classified into two segments. As a result of market segmentation, parents of cluster 1 preferred the laver model being sold (soy seasoning) while parents of cluster 2 preferred the optimized laver model (non-seasoning).

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.377-391
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• 2018

In order to efficiently monitor red tide over a wide range, the need for red tide detection using remote sensing is increasing. However, the previous studies focus on the development of red tide detection algorithm for ocean colour sensor. In this study, we propose the use of multi-sensor to improve the inaccuracy for red tide detection and remote sensing data in coastal areas with high turbidity, which are pointed out as limitations of satellite-based red tide monitoring. The study area were selected based on the red tide information provided by National Institute of Fisheries Science, and spatial fusion and spectral-based fusion were attempted using GOCI image as ocean colour sensor and Landsat OLI image as terrestrial sensor. Through spatial fusion of the two images, both the red tide of the coastal area and the outer sea areas, where the quality of Landsat OLI image was low, which were impossible to observe in GOCI images, showed improved detection results. As a result of spectral-based fusion performed by feature-level and rawdata-level, there was no significant difference in red tide distribution patterns derived from the two methods. However, in the feature-level method, the red tide area tends to overestimated as spatial resolution of the image low. As a result of pixel segmentation by linear spectral unmixing method, the difference in the red tide area was found to increase as the number of pixels with low red tide ratio increased. For rawdata-level, Gram-Schmidt sharpening method estimated a somewhat larger area than PC spectral sharpening method, but no significant difference was observed. In this study, it is shown that coastal red tide with high turbidity as well as outer sea areas can be detected through spatial fusion of ocean colour and terrestrial sensor. Also, by presenting various spectral-based fusion methods, more accurate red tide area estimation method is suggested. It is expected that this result will provide more precise detection of red tide around the Korean peninsula and accurate red tide area information needed to determine countermeasure to effectively control red tide.