• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.1
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• pp.21-35
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• 2021

Based on social capital theory, this study investigated the influence of the start-up founder's social competence on the start-up's access to funding sources and performance through the mediating role of the type of social network. This study aimed to examine two types of social networks empirically (i.e., personal networks and business networks) as social capital in analyzing the effect of the founder's social competence and social capital on the method of accessing funding sources. A self-report questionnaire was administered to 252 South Korean start-up founders whose businesses are based in South Korea. Path analysis and mediation effect analysis were performed by structural equation modeling(SEM) using STATA 16.1. This study examined the full mediating effect of the founder's social competence on his/her personal and business networks, respectively, and how the effect leads to different methods to approach funding sources. This is the first study in South Korea to analyze empirically how social competence has contrasting effects on personal and business networks as well as how each type of network varies in its influence on the method founders use to attract investment. This study is also significant in that it proposed a new methodology by utilizing the position generator as the measure of personal and business networks to analyze social networks in detail. The analyses of 252 survey data collected over a period of six months will be a valuable resource that may provide researchers, founders, investors, and other stakeholders in the start-up ecosystem with meaningful implications.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.6
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• pp.35-48
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• 2021

In Suwon, the overall hygiene of the city is threatened by the emergence of the rook(Corvus fugilegus) in the city. Rooks began to appear in November of 2016 and has continued to appear from November to March every year. In order to eradicate or to prepare an alternative habitat for rooks, this study aimed to identify the preferred habitat and specific environmental variables. Therefore, in this work, we aim to understand the predicted distribution of rooks in Suwon City with citizen science and through MaxENT, the most widely utilized habitat modeling using citizen science to analyze the preferred habitat of harmful tides appearing in urban areas. In this study, seven environmental variables were chosen: biotope group complex, building floor, vegetation, euclidean distance from farmland, euclidean distance from streetlamp, and euclidean distance from pole and DEM. Among the estimated models, after the time period of sunrise (08:00~18:00) the contribution percentage were as following: euclidean distance from arable land(39.2%), DEM(25.5%), euclidean distance from streetlamp(22.3%), euclidean distance from pole(7.1%), biotope group complex(4.9%), building floor(1%), vegetation(0%). In the time period after sunset(18:00~08:00) the contribution percentage were as following: biotope group complex(437.4%), euclidean distance from pole(26.8%), DEM(13.4%), euclidean distance from streetlamp(11.8%), euclidean distance from farmland(7.9%), building floor(1.4%), vegetation(1.3%).

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1623-1631
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• 2022

Since oil spills can be a significant threat to the marine ecosystem, it is necessary to obtain information on the current contamination status quickly to minimize the damage. Satellite-based detection of marine oil spills has the advantage of spatiotemporal coverage because it can monitor a wide area compared to aircraft. Due to the recent development of computer vision and deep learning, marine oil spill detection can also be facilitated by deep learning. Unlike the existing studies based on Synthetic Aperture Radar (SAR) images, we conducted a deep learning modeling using PlanetScope optical satellite images. The blind test of the DeepLabV3+ model for oil spill detection showed the performance statistics with an accuracy of 0.885, a precision of 0.888, a recall of 0.886, an F1-score of 0.883, and a Mean Intersection over Union (mIOU) of 0.793.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.spc
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• pp.37-43
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• 2022

Post-spill monitoring of hazardous noxious substances accidents is essential in the event of a spillage of significant quantities of pollutants and for the management of the marine environment resulting from the long-term effects of the persistent toxic substances. The accidental introduction of a sinker into the marine environment can create harmful anaerobic conditions in the benthic ecosystem and spread over the seafloor by the topography and currents. Through case studies, most post-spill monitoring includes modeling, remote sensing, and chemical analyses of the sediment and benthic organisms. The monitoring also evaluates the effectiveness of restoration and recovery activities and assesses damages and compensation.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.3
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• pp.111-121
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• 2013

Accurate prediction of sea water temperature has been emphasized to make precise local weather forecast and to understand change of ecosystem. The Yellow Sea, which has turbid water and strong tidal current, is an unique shallow marginal sea. It is essential to include the effects of the turbidity and the strong tidal mixing for the realistic simulation of temperature distribution in the Yellow Sea. Evaluation of ocean circulation model response to vertical mixing scheme and turbidity is primary objective of this study. Three-dimensional ocean circulation model(Regional Ocean Modeling System) was used to perform numerical simulations. Mellor- Yamada level 2.5 closure (M-Y) and K-Profile Parameterization (KPP) scheme were selected for vertical mixing parameterization in this study. Effect of Jerlov water type 1, 3 and 5 was also evaluated. The simulated temperature distribution was compared with the observed data by National Fisheries Research and Development Institute to estimate model's response to turbidity and vertical mixing schemes in the Yellow Sea. Simulations with M-Y vertical mixing scheme produced relatively stronger vertical mixing and warmer bottom temperature than the observation. KPP scheme produced weaker vertical mixing and did not well reproduce tidal mixing front along the coast. However, KPP scheme keeps bottom temperature closer to the observation. Consequently, numerical ocean circulation simulations with M-Y vertical mixing scheme tends to produce well mixed vertical temperature structure and that with KPP vertical mixing scheme tends to make stratified vertical temperature structure. When Jerlov water type is higher, sea surface temperature is high and sea bottom temperature is low because downward shortwave radiation is almost absorbed near the sea surface.

• Journal of Environmental Impact Assessment
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• v.24 no.1
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• pp.16-34
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• 2015

Impervious covers(IC) are artificial structures, such as driveways, sidewalks, building's roofs, and parking lots, through which water cannot infiltrate into the soil. IC is an environmental concern because the pavement materials seal the soil surface, decreasing rainwater infiltration and natural groundwater recharge, and consequently disturb the hydrological cycle in a watershed. Increase of IC in a watershed can cause more frequent flooding, higher flood peaks, groundwater drawdown, dry river, and decline of water quality and ecosystem health. There has been an increased public interest in the institutional adoption of LID(Low Impact Development) and GI(Green Infrastructure) techniques to address the adverse impact of IC. The objectives of this study were to construct the modeling site for a samll urban watershed with the Storm Water Management Model(SWMM), and to evaluate the effect of various LID techniques on the control of rainfall runoff processes and non-point pollutant load. The model was calibrated and validated using the field data collected during two flood events on July 17 and August 11, 2009, respectively, and applied to a complex area, where is consist of apartments, school, roads, park, etc. The LID techniques applied to the impervious area were decentralized rainwater management measures such as pervious cover and green roof. The results showed that the increase of perviousness land cover through LID applications decreases the runoff volume and pollutants loading during flood events. In particular, applications of pervious pavement for parking lots and sidewalk, green roof, and their combinations reduced the total volume of runoff by 15~61 % and non-point pollutant loads by TSS 22~72 %, BOD 23~71 %, COD 22~71 %, TN 15~79 %, TP 9~64 % in the study site.

• Journal of Wetlands Research
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• v.16 no.4
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• pp.303-318
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• 2014

Baseflow gives a significant contribution to stream function in the regions where climatic characteristics are seasonally distinct. In this regard, variable baseflow can make it difficult to maintain a stable water supply, as well as causing disruption to the stream ecosystem. Changes in land use can affect both the direct flow and baseflow of a stream, and consequently, most other components of the hydrologic cycle. Baseflow estimation depends on the observed streamflow in gauge watersheds, but accurate predictions of streamflow through modeling can be useful in determining baseflow data for ungauged watersheds. Accordingly, the objectives of this study are to 1) improve predictions of SWAT by applying the alpha factor estimated using RECESS for calibration; 2) estimate baseflow in an ungauged watershed using the WHAT system; and 3) evaluate the effects of changes in land use on baseflow characteristics. These objectives were implemented in the Gapcheon watershed, as an ungauged watershed in South Korea. The results show that the alpha factor estimated using RECESS in SWAT calibration improves the prediction for streamflow, and, in particular, recessions in the baseflow. Also, the changes in land use in the Gapcheon watershed leads to no significant difference in annual baseflow between comparable periods, regardless of precipitation, but does lead to differences in the seasonal characteristics observed for the temporal distribution of baseflow. Therefore, the Guem River, into which the stream from the Gapcheon watershed flows, requires strategic seasonal variability predictions of baseflow due to changes in land use within the region.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.5
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• pp.79-96
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• 2021

Green infrastructure planning represents landscape planning measures to reduce particulate matter. This study aimed to derive factors that may be used in planning green infrastructure for particulate matter reduction using text mining techniques. A range of analyses were carried out by focusing on keywords such as 'particulate matter reduction plan' and 'green infrastructure planning elements'. The analyses included Term Frequency-Inverse Document Frequency (TF-IDF) analysis, centrality analysis, related word analysis, and topic modeling analysis. These analyses were carried out via text mining by collecting information on previous related research, policy reports, and laws. Initially, TF-IDF analysis results were used to classify major keywords relating to particulate matter and green infrastructure into three groups: (1) environmental issues (e.g., particulate matter, environment, carbon, and atmosphere), target spaces (e.g., urban, park, and local green space), and application methods (e.g., analysis, planning, evaluation, development, ecological aspect, policy management, technology, and resilience). Second, the centrality analysis results were found to be similar to those of TF-IDF; it was confirmed that the central connectors to the major keywords were 'Green New Deal' and 'Vacant land'. The results from the analysis of related words verified that planning green infrastructure for particulate matter reduction required planning forests and ventilation corridors. Additionally, moisture must be considered for microclimate control. It was also confirmed that utilizing vacant space, establishing mixed forests, introducing particulate matter reduction technology, and understanding the system may be important for the effective planning of green infrastructure. Topic analysis was used to classify the planning elements of green infrastructure based on ecological, technological, and social functions. The planning elements of ecological function were classified into morphological (e.g., urban forest, green space, wall greening) and functional aspects (e.g., climate control, carbon storage and absorption, provision of habitats, and biodiversity for wildlife). The planning elements of technical function were classified into various themes, including the disaster prevention functions of green infrastructure, buffer effects, stormwater management, water purification, and energy reduction. The planning elements of the social function were classified into themes such as community function, improving the health of users, and scenery improvement. These results suggest that green infrastructure planning for particulate matter reduction requires approaches related to key concepts, such as resilience and sustainability. In particular, there is a need to apply green infrastructure planning elements in order to reduce exposure to particulate matter.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.6-7
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• 2021

Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.3
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• pp.53-68
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• 2024

Generative AI is receiving a lot of attention around the world, and ways to effectively utilize it in the business environment are being explored. In particular, since the public release of the ChatGPT service, which applies the GPT-3.5 model, a large language model developed by OpenAI, it has attracted more attention and has had a significant impact on the entire industry. This study focuses on the emergence of Generative AI, especially ChatGPT, which applies OpenAI's GPT-3.5 model, to investigate its impact on the startup industry and compare the changes that occurred before and after its emergence. This study aims to shed light on the actual application and impact of generative AI in the business environment by examining in detail how generative AI is being used in the startup industry and analyzing the impact of ChatGPT's emergence on the industry. To this end, we collected company information of generative AI-related startups that appeared before and after the ChatGPT announcement and analyzed changes in industry, business content, and investment information. Through keyword analysis, topic modeling, and network analysis, we identified trends in the startup industry and how the introduction of generative AI has revolutionized the startup industry. As a result of the study, we found that the number of startups related to Generative AI has increased since the emergence of ChatGPT, and in particular, the total and average amount of funding for Generative AI-related startups has increased significantly. We also found that various industries are attempting to apply Generative AI technology, and the development of services and products such as enterprise applications and SaaS using Generative AI has been actively promoted, influencing the emergence of new business models. The findings of this study confirm the impact of Generative AI on the startup industry and contribute to our understanding of how the emergence of this innovative new technology can change the business ecosystem.