• 한국벤처창업학회:학술대회논문집
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• 2023.11a
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• pp.85-89
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• 2023

최근 급변하는 세계 경제 환경에서 창업기업은 혁신, 고용 및 경제 활력의 핵심 동력으로 등장했다. 그러나 초기 창업 기업이 성장하는 여정에는 많은 어려움이 따르며, 이 과정에서 엔젤투자자의 역할이 필수적이다. 초기 창업 시장의 촉매제 역할을 하는 엔젤투자자는 필요한 자본을 제공할 뿐만 아니라 경영 노하우 및 네트워크를 통해 창업기업이 성공할 수 있도록 지원한다. 그러나 최근 데이터에 따르면 신생 기업 중 엔젤 투자의 혜택을 받는 기업은 1.4%에 불과한 것으로 나타나 창업기업의 잠재력과 지원 사이에는 분명한 공백이 존재한다. 이 연구는 지속가능한 경제 성장을 위해 창업 생태계의 중요한 요소인 엔젤투자자의 위험회피 성향을 이해함으로써 이들의 투자 행동, 의사결정 프로세스에 대한 통찰력을 얻을 수 있다. 위험회피가 엔젤투자에 미치는 영향을 알아보고 엔젤투자가 활성화 될 수 있는 방법을 제안하고자 한다. 본 연구를 통해 위험회피 성향이 엔젤투자에 대한 관심도와 실제 투자 활동 간의 관계를 추정해보고자 한다. 위험회피 성향이 엔젤투자자에 어떻게 영향을 미치는지 탐색하는 것에 중점을 두고, 엔젤투자자의 행동과 관심사에 영향을 미치는 다양한 요인들을 포함하여 진행한다. 연구 표본은 서울 및 경기 지역의 일반 성인과 엔젤투자자로 설문조사를 통해 필요한 데이터를 수집한다. 이 연구는 위험회피 성향이 엔젤투자 결정에 어떻게 반영되는지에 대한 깊은 이해를 제공하고, 엔젤투자 활성화를 위한 정책 및 전략을 수립하는데 중요한 통찰력을 제공할 것이다.

• Journal of KIISE
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• v.43 no.6
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• pp.613-620
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• 2016

This paper presents a MOHA(Many-Task Computing on Hadoop) framework which aims to effectively apply the Many-Task Computing(MTC) technologies originally developed for high-performance processing of many tasks, to the existing Big Data processing platform Hadoop. We present basic concepts, motivation, preliminary results of PoC based on distributed message queue, and future research directions of MOHA. MTC applications may have relatively low I/O requirements per task. However, a very large number of tasks should be efficiently processed with potentially heavy inter-communications based on files. Therefore, MTC applications can show another pattern of data-intensive workloads compared to existing Hadoop applications, typically based on relatively large data block sizes. Through an effective convergence of MTC and Big Data technologies, we can introduce a new MOHA framework which can support the large-scale scientific applications along with the Hadoop ecosystem, which is evolving into a multi-application platform.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.4
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• pp.153-167
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• 2007

Dokdo research has been worked in various fields. However, the continuous accumulation and systematic management of Dokdo research data on marine science haven't been made. In particular, a systematic database system hasn't been established for the research data on marine environment and ecosystem in Dokdo and its surrounding sea. Therefore, GIS database construction on a spatial basis is required for the systematic management and efficient use of Dokdo marine research data, and a marine data model on a GIS basis is needed on the design stage to build the database. In this study, we collected previous observed marine data, and classified them as three groups, such as a framework data group on a GIS basis, a research data group and a thematic data group, according to the data types and characteristics. Moreover, the attributes of each research data were designed to be connected to GIS framework data. The result of the study to build an integrated GIS data model may be useful for developing a management system for marine research data observed in other sea as well as Dokdo.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.15-25
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• 2024

For effective analysis of animal ecosystems, technology that can automatically identify the current status of animal habitats is crucial. Specifically, animal sound classification, which identifies species based on their sounds, is gaining great attention where video-based discrimination is impractical. Traditional studies have relied on a single deep learning model to classify animal sounds. However, sounds collected in outdoor settings often include substantial background noise, complicating the task for a single model. In addition, data imbalance among species may lead to biased model training. To address these challenges, in this paper, we propose an animal sound classification scheme that combines predictions from multiple models using Focal Loss, which adjusts penalties based on class data volume. Experiments on public datasets have demonstrated that our scheme can improve recall by up to 22.6% compared to an average of single models.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.721-732
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• 2024

Species identification of zooplankton is the most basic process in understanding the marine ecosystem and studying global warming. In this study, we propose an convolutional neural network model that can classify females and males of three zooplankton at the species level. First, training data including morphological features is constructed based on microscopic images acquired by researchers. In constructing training data, a data argumentation method that preserves morphological feature information of the target species is applied. Next, we propose a convolutional neural network model in which features can be learned from the constructed learning data. The proposed model minimized the information loss of training image in consideration of high resolution and minimized the number of learning parameters by using the global average polling layer instead of the fully connected layer. In addition, in order to present the generality of the proposed model, the performance was presented based on newly acquired data. Finally, through the visualization of the features extracted from the model, the key features of the classification model were presented.

• Korean Journal of Environment and Ecology
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• v.37 no.3
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• pp.221-231
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• 2023

Great tit (Parus major) is a bioindicator species that can measure environmental changes in urban ecosystems and plays an important role in maintaining health as a representative insectivorous bird. Researchers have utilized artificial nest box surveys to understand the reproductive ecology of the Paridae family of birds, including the Great tits, but it is difficult to conduct a macroscopic study due to spatial and temporal limitations. This study designed and applied a citizen-participatory monitoring of artificial nest boxes project to transcend the limitations of expert-centered monitoring methods. The Suwon Front Yard Bird Monitoring Team installed artificial nest boxes in green spaces in Suwon, Gyeonggi Province and observed the reproductive ecology of the Paridae family through the participation of voluntary citizen surveyors. Participants were recruited through an online survey from February 9 to February 22, 2021, and they directly performed from installation to observation of artificial next boxes from February 23 to August 31, 2021. Online education was provided to the volunteers for the entire monitoring process to lower the entry barrier for non-expert citizen surveyors and collect consistent data, and observation records were collected through a mobile app. A total of 98 citizen surveyors participated in the citizen-participatory monitoring of artificial nest boxes project, and 175 (84.95%) of the 256 distributed artificial nest boxes were installed in green spaces in Suwon City. Among the installed artificial nest boxes, the results of the citizen science project were confirmed for 173 (83.98%), excluding two boxes with position coordinate generation errors. A total of 987 artificial nest box observation records were collected from citizen surveyors, with a minimum of one time, a maximum of 26 times, and an average of 5.71±4.37 times. The number of observations of artificial birdhouses per month was 70 times (7.09%) in February, 444 times (44.98%) in March, 284 times (28.77%) in April, 133 times (13.48%) in May, 46 times (4.66%) in June, 6 times (0.61%) in July, and 4 times (0.41%) in August. Birds using the artificial nest boxes were observed in 57 (32.95%) of the 173 installed artificial nest boxes, and they included Great tit (Parus major) using 12 boxes (21.05%), Varied Tit (Parus varius) using 7 boxes (12.28%), and unidentified birds using 38 boxes (66.67%). This study is the first to consider citizen participation in the monitoring of artificial nest boxes, a survey method for the reproductive ecology of the Paridae family, including Great tits, and it can be utilized as basic data for the design of ecological monitoring combined with citizen science in the future.

• Korean Journal of Ecology and Environment
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• v.46 no.2
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• pp.145-153
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• 2013

It is a global trend that the water management policy is shifting from a water quality-oriented assessment to the aquatic ecosystem-based assessment. The majority of aquatic ecosystem assessment systems were developed solely based on physicochemical factors (e.g., water quality and bed structure) and a limited number of organisms (e.g., plankton and benthic organisms). Only a few systems use plants for a health assessment, although plants are sensitive indicators reflecting long-term disturbances and alterations in water regimes. The development of an assessment system is underway to evaluate and manage lakes as ecosystem units in the Korean Ministry of Environment. We reviewed the existing multivariate health assessment methods of other leading countries, and discussed their applicability to Korean lakes. The application of multivariate assessment methods is costly and time consuming, in addition to the correlation problem among variables. However, a single variable is not available at this moment, and the multivariate method is an appropriate system due to its multidimensional evaluation and cumulative data generation. We, therefore, discussed multivariate assessment methods in three steps: selecting metrics, scoring metrics and assessing indices. In the step of selecting metrics, the best available metrics are species-related variables, such as composition and abundance, as well as richness and diversity. Indicator species, such as sensitive species, are the most frequently used in other countries, but their system of classification in Korea is not yet complete. In terms of scoring metrics, the lack of reference lakes with little anthropogenic impact make this step difficult, and therefore, the use of relative scores among the investigated lakes is a suitable alternative. Overall, in spite of several limitations, the development of a plant-based multivariate assessment method in Korea is possible using mostly field research data. Later, it could be improved based on qualitative metrics on plant species, and with the emergence of further survey data.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.131-143
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• 2022

Focusing on FinTech keywords, this study is analyzing newspaper articles and Twitter data by using text mining methodology in order to understand trends in the industry of domestic digital financial service. In the growth of FinTech lifecycle, the frequency analysis has been performed by four important points: Mobile Payment Service, Internet Primary Bank, Data 3 Act, MyData Businesses. Utilizing frequency analysis, which combines the keywords 'China', 'USA', and 'Future' with the 'FinTech', has been predicting the FinTech industry regarding of the current and future position. Next, sentiment analysis was conducted on Twitter to quantify consumers' expectations and concerns about FinTech services. Therefore, this study is able to share meaningful perspective in that it presented strategic directions that the government and companies can use to understanding future FinTech market by combining frequency analysis and sentiment analysis.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1139-1149
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• 2017

Measuring tree's volume is very important input data of various environmental analysis modeling However, It's difficult to use economical and equipment to measure a fragmented small green space in the city. In addition, Trees are sensitive to seasons, so we need new and easier equipment and quantification methods for measuring trees than lidar for high frequency monitoring. In particular, the tree's size in a city affect management costs, ecosystem services, safety, and so need to be managed and informed on the individual tree-based. In this study, we aim to acquire image data with UAV(Unmanned Aerial Vehicle), which can be operated at low cost and frequently, and quickly and easily quantify a single tree using SfM-MVS(Structure from Motion-Multi View Stereo), and we evaluate the impact of reducing number of images on the point density of point clouds generated from SfM-MVS and the quantification of single trees. Also, We used the Watertight model to estimate the volume of a single tree and to shape it into a 3D structure and compare it with the quantification results of 3 different type of 3D models. The results of the analysis show that UAV, SfM-MVS and solid model can quantify and shape a single tree with low cost and high time resolution easily. This study is only for a single tree, Therefore, in order to apply it to a larger scale, it is necessary to follow up research to develop it, such as convergence with various spatial information data, improvement of quantification technique and flight plan for enlarging green space.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.1
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• pp.47-62
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• 2012

To propagate clinical disease management service, there should be built a ecosystem where service developers, service providers, device suppliers closely cooperate for u-Health platform. However, most u-Health platform is difficult to build an effective ecosystem due to the lack of secure and effective PHR(Personal Health Record) management, the lack of personalized and intelligent service, difficulties of N-screen service. To solve these problems we suggest the CDMP(Chronic Disease Management Platform) architecture. The CDMP is a software platform that provides the core functions to develop the chronic disease management services and performs a hub function for the link and integration rbetween various services and systems. CDMP is SOA based platform that enables a provision of reusability, expansibility and it provides open API where everybody can share information, contents and services easily. CDMP supports the multi platform system foN-screen service and the self management functions via SNS. In this paper, we design and implement the CDMP including PHR service based on hybrid data model for privacy preservation. Experiment results prove the effectiveness of hybrid model-based PHR service.