• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.143-174
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• 2023

With the development of deep learning technologies, Artificial Intelligence powered Optical Character Recognition (AI-OCR) has evolved to read multiple languages from various forms of images accurately. For the financial industry, where a large number of diverse documents are processed through manpower, the potential for using AI-OCR is great. In this study, we present a configuration and a design of an AI-OCR modality for use in the financial industry and discuss the platform construction with application cases. Since the use of financial domain data is prohibited under the Personal Information Protection Act, we developed a deep learning-based data generation approach and used it to train the AI-OCR models. The AI-OCR models are trained for image preprocessing, text recognition, and language processing and are configured as a microservice architected platform to process a broad variety of documents. We have demonstrated the AI-OCR platform by applying it to financial domain tasks of document sorting, document verification, and typing assistance The demonstrations confirm the increasing work efficiency and conveniences.

• Korean journal of applied entomology
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• v.61 no.3
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• pp.399-408
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• 2022

There has been much debate on the morphometric divergence between the recently identified Apis cerana koreana and Apis cerana honey bees. The aim of this study was to obtain phenotypic information that can be used to compare A. c. koreana data with other A. cerana subspecies data from open resources and determine breeding results on the basis of morphometric traits. To differentiate A. c. koreana, we investigated 22 classic morphological characteristics; royal jelly secretion; and the weight of workers, queens, and drones of A. c. koreana bred in Korea. To define the selection results, we used the geometric morphometric method. The artificially selected A. c. koreana secreted significantly more royal jelly (1.18 times) than the naturally selected A. c. koreana, which positively influenced the health of the colonies. These honey bees were identified more clearly with the geometric morphometric method than with the classic morphometric method, which is traditionally used to determine the subspecies. Large trends were noted for A. c. koreana on the basis of our results and literature from the 1980s regarding A. cerana sizes in Korea (tarsal index, length of forewing, and cubital index were measured). The cluster analysis revealed the proximity of A. c. koreana, A. cerana in China, and A. c. indica on the basis of eight classic characters, which, perhaps, relay the origin of the honey bees. The results of this study defined the morphometric responses of A. c. koreana honey bees to geographic isolation, climate change, and selection, which are important to identify, protect, and preserve honey bee stock in Korea.

• Korean Journal of Plant Resources
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• v.36 no.4
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• pp.369-380
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• 2023

Developing and breeding improved legume-based food resources require collecting useful genetic traits with heritability even though requiring some time-consuming, costly, and labor intensive. We attempted to infer heritability of nine genetic traits-days to flowering, days to maturity, period from flowering to maturity, the number of seeds per pod, 100-seeds weight, and four contents such as crude protein, crude oil, crude fiber, and dietary fiber-using 455 homologous chloroplast gene sets of six species of legumes. Correlation analysis between genetic trait differences and phylogenetic distance of homologous gene sets revealed that days to flowering, the number of seeds per pod, and crude oil content were influenced by genetic factors rather than environmental factors by 62.86%, 69.45%, 57.14% of correlated genes (P-value ≤ 0.05) and days to maturity showed intermediate genetic effects by 62.42% (P-value ≤ 0.1). The period from flowering to maturity and 100-seeds weight showed different results compared to those of some previous studies, which may be attributed to highly complicated internal (epistatic or additive gene effects) and external effects (cultural environment and human behaviors). Despite being slightly unexpected, our results and method can widely contribute to analyze heritability by including genetic information on mitochondria, nuclear genome, and single nucleotide polymorphisms.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4B
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• pp.385-396
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• 2009

Non-household unit water use for each type of business are estimated in this study. The business types are subdivided into forty based on nine categories by the national industrial standard classification, such as office, commerce, public bathing, public water use etc. Correlation analysis and analysis of variance (ANOVA) are applied to obtain statistical characteristics between industrial water use data, surveyed in six cities including Nonsan, Seosan and the National Statistical Bureau and site area, employees number etc. for each detailed business area. As the proposed non-household unit water uses are compared with five surveyed data in USA, it is shown that almost of water uses per unit area are less than those in USA. Non-household unit water uses of 25% cumulative probability water use recommended as efficiency benchmarks among surveyed data in Korea are also less than those in USA. Especially, in the case of water use in school, the average and the range are similar results showing water use range between 0.4 and 6.2 ($l/m^2/day$) as liter per capita day per an unit area, also water use range between 11.9 to 64.0 (l/student/day) as liter per capita day per a person. From the result of correlation analysis with internal and exogenous affecting factors on non-household water use, it can be concluded that a unit area is most appropriate factor as a standard of non-household unit water use. In case of water use in educational business, the number of students including staffs is more correlated than site ares with water use for the settled water consumption tendency. Although the increase and decrease of educational institutes, retail/wholesale store and restaurants are shown remarkable by the temperature as a representative factor, low correlations are shown in water use fluctuation in lodging house and hospital.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.353-365
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• 2023

The effects of climate change on green infrastructure and environmental media remain uncertain and context-specific despite numerous climate projections globally. In this study, the extreme weather conditions in seven major cities in South Korea were characterized through statistical analysis of 20-year daily meteorological data extracted fro m the Korea Meteorological Administration (KMA). Additionally, the impacts of extreme weather on Nature-based Solutions (NbS) were determined through a comprehensive review. The results of the statistical analysis and comprehensive review revealed the studied cities are potentially vulnerable to varying extreme weather conditions, depending on geographic location, surface imperviousness, and local weather patterns. Temperature extremes were seen as potential threats to the resilience of NbS in Seoul, as both the highest maximum and lowest minimum temperatures were observed in the mentioned city. Moreover, extreme values for precipitation and maximum wind speed were observed in cities from the southern part of South Korea, particularly Busan, Ulsan, and Jeju. It was also found that extremely low temperatures induce the most impact on the resilience of NbS and environmental media. Extremely cold conditions were identified to reduce the pollutant removal efficiency of biochar, sand, gravel, and woodchip, as well as the nutrient uptake capabilities of constructed wetlands (CWs). In response to the negative impacts of extreme weather on the effectiveness of NbS, several adaptation strategies, such as the addition of shading and insulation systems, were also identified in this study. The results of this study are seen as beneficial to improving the resilience of NbS in South Korea and other locations with similar climate characteristics.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.

• Journal of Venture Innovation
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• v.6 no.4
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• pp.23-40
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• 2023

This study examines the customer value factors affecting the intention to continue using the mobile app of department stores, which are traditional offline retailers, in the retail industry that is rapidly digitalizing and becoming mobile. This study clarifies multidimensional customer value in three dimensions; functional, convenience, and social. Functional value refers to the integrated channel, and consistent customer experience provided between channels in the omnichannel retail environment, while convenience value is the convenience of saving time and effort save while customers use a mobile app. Social value refers to the improvement of social approval or social self-concept occurring due to the use of products or services related to green marketing within the mobile app of the department store. The influence of each on the dependent variable, the mobile app's continuous use intention, was analyzed by using the three dimensions of customer value as independent variables. Data was collected from customers who have a history of using the mobile app of Shinsegae Department Store in Korea, and a confirmatory analysis was conducted using Smart PLS 4.0. The analysis results showed that all three dimensions of customer value; functional value, convenience value, and social value, had a positive (+) influence on customers' intention to continue using the mobile app, and the influence of functional value had the greatest impact. As functional value appears to be the most important influencing factor due to the omnichannel retail trend by advancement of technology, it suggests that it is important for department stores, and offline retailers, to provide integrated channels. This provides insights into the direction of customer-centered strategy formulation for activating department store mobile apps and suggests basic analytical data for customized services and marketing activities that department stores can effectively meet the changing expectations and demands of customers through new mobile channels rather than existing offline channels.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1097-1109
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• 2023

Urban is an area where small-scale changes to individual buildings occur frequently. An existing urban building database requires periodic updating to increase its usability. However, there are limitations in data collection for building changes over a wide urban. In this study, we check the possibility of detecting building changes and updating a building database by using satellite images that can capture a wide urban region by a single image. For this purpose, building areas in a satellite image are first extracted by projecting 3D coordinates of building corners available in a building database onto the image. Building areas are then divided into roof and facade areas. By comparing textures of the roof areas projected, building changes such as height change or building removal can be detected. New height values are estimated by adjusting building heights until projected roofs align to actual roofs observed in the image. If the projected image appeared in the image while no building is observed, it corresponds to a demolished building. By checking buildings in the original image whose roofs and facades areas are not projected, new buildings are identified. Based on these results, the building database is updated by the three categories of height update, building deletion, or new building creation. This method was tested with a KOMPSAT-3A image over Incheon Metropolitan City and Incheon building database available in public. Building change detection and building database update was carried out. Updated building corners were then projected to another KOMPSAT-3 image. It was confirmed that building areas projected by updated building information agreed with actual buildings in the image very well. Through this study, the possibility of semi-automatic building change detection and building database update based on single satellite image was confirmed. In the future, follow-up research is needed on technology to enhance computational automation of the proposed method.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.17-32
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• 2023

In recent years, the need for social ventures that aim to grow while solving social problems through the efficiency and effectiveness of commercial organizations in the market has increased, while there is a limit to how much the government and the public can do to solve social problems. Against this background, the number of social venture startups is increasing in the domestic startup ecosystem, and interest in impact investors, which are investors in social ventures, is also increasing. Therefore, this research utilized judgment analysis technology to objectively analyze the validity and weight of judgment information based on the cognitive process and decision-making environment in the investment decision-making of impact investors. We proceeded with the research by constructing three classifications; first, investment priorities at the initial investment stage for financial benefit and return on investment as an investor, second, the political skills of the entrepreneurs (teams) for the social impact and ripple power, and social venture coexistence and solidarity, third, the social mission of a social venture that meets the purpose of an impact investment fund. As a result of this research, first of all, the investment decision-making priorities of impact investors are the expertise of the entrepreneur (team), the potential rate of return when the entrepreneur (team) succeeds, and the social mission of the entrepreneur (team). Second, impact investors do not have a uniform understanding of the investment decision-making factors, and the factors that determine investment decisions are different, and there are differences in the degree of the weighting. Third, among the various investment decision-making factors of impact investment, "entrepreneur's (team's) networking ability", "entrepreneur's (team's) social insight", "entrepreneur's (team's) interpersonal influence" was relatively lower than the other four factors. The practical contribution through this research is to help social ventures understand the investment determinant factors of impact investors in the process of financing, and impact investors can be expected to improve the quality of investment decision-making by referring to the judgment cases and analysis of impact investors. The academic contribution is that it empirically investigated the investment priorities and weighting differences of impact investors.

• Research in Plant Disease
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• v.30 no.2
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• pp.103-113
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• 2024

Since 2009, the Korean Society of Plant Pathology has established the Committee on Common Names of Plant Disease to systematically review and determine plant disease names and related terminologies. The committee published the 6th edition of the List of Plant Diseases in Korea (LPDK) in 2022, and the list has been made publicly accessible online. The online database has significantly enhanced user accessibility, expedited update processes, and improved interoperability with other databases. As a result, the 6.1 edition of the list was released by online LPDK in 2023, detailing new disease names added over the preceding year and revisions to existing names. Subsequently, in 2024, the 6.2 edition was published, encompassing 6,765 diseases caused by 2,503 pathogen taxa across 1,432 host species. The public release of the online database has, however, introduced several challenges and tasks. Addressing these issues necessitates the development of modern, standardized nomenclature guidelines and a robust system for the registration of new disease names. Open communication and collaboration among the diverse members of the Korean Society of Plant Pathology are required to ensure the reliability of the LPDK.