• 유통과학연구
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• 제17권8호
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• pp.25-34
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• 2019

Purpose - Investigate the association of internal and external factors of consumers and analysing the role of moderating comparative marketing aspects, especially the part of YouTuber and celebgram in influencing purchase decisions. Apart from that, it provides an overview of the pattern of purchase decision making in forming Millennials and Y generation consumer culture Research design, data, and methodology - This study uses a quantitative research approach with descriptive, predictive, and prospective data analysis on 300 eligible Millennials and Y aged 20-35 years who are bachelor-educated. Data collection using online surveys with final statistical analysis using the Partial Least Square (PLS) approach Results - All hypothesis are declared accepted, indirect testing the dominant internal consumer factors have a positive and significant effect on consumers' purchase decisions. Through testing Moderating, aspect marketing comparative is also authoritative able to moderate internal consumer factors towards purchase decision making. Conclusions - Digital technology is changing the paradigm and perceptions of the millennials and Y generations in terms of behaving as a generation of technology connoisseurs who also influence and shape the culture of that generation and the generations to come in the future.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 1992년도 수공학연구발표회논문집
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• pp.443-447
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• 1992

This study describes the strategy for advanced decision support system (ADSS) development for integrated management of water resources and quality in reservoir systems. The developed ADSS consists of database that contain hydrologic data, observed operational data, and data to support specific reservoir operations simulation, optimization models, and water quality models. The optimization model, mass balance simulation model and water quality models are used in a general prototype ADSS, menu driven controlling framework that assists the user to specify and evaluate the alternative operational scenarios at one time. These alternative scenarios are evaluated by the models and the results are compared through the use of a graphical based display system. This graphical based system uses an icon based schematic representation of the system to organize the presentation of the results. The ADSS includes the ability to use monthly or weekly time periods of analysis for the models and it can use monthly historical or stochastically generated inflows.

• 전자통신동향분석
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• 제34권3호
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• pp.106-113
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• 2019

Mobile traffic is one of the most important indexes of the growth of the mobile communications market, and it has a close relationship with subscribers' service usage patterns, frequency demand and supply, network management, and information communication policy. The purpose of this paper is to understand mobile data usage in Korea and to suggest the optimal steps for establishing the frequency supply and demand system by researching the traffic trends that reflect the characteristics of radio resources in the mobile communications field. To achieve this goal, attempts were made to increase the possibility of policy use by analyzing and forecasting mobile traffic trends, and to improve the accuracy of the research through the verification of the existing prediction results. The paper ends with a discussion of the necessity of a frequency management system based on data science.

• 정보과학회 논문지
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• 제44권5호
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• pp.449-459
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• 2017

DDS(Data Distribution Service)는 유연성, 확장성, 실시간 통신 환경을 지원하는 통신 미들웨어이다. 본 논문에서는 DDS 미들웨어의 성능을 향상시키기 위한 방법들을 제안한다. DDS 미들웨어 내부 동작과 관련된 세부 이벤트를 정의하고, 이벤트 구동형 구조에 적용하기 위해 하나의 DDS 메시지를 의미 있는 서브메시지 단위로 분해함으로써 처리 복잡도를 낮출 수 있다. 제안하는 히스토리캐시 관리 기법은 DDS의 특성 상 상태접근과 임의접근이 빈번하게 발생한다는 사실을 이용한다. 제안한 방법들을 본 연구팀이 개발한 EchoDDS에 적용하여 성능을 향상시켰다.

• Smart Structures and Systems
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• 제30권1호
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• pp.35-47
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• 2022

Data-driven engineering is crucial for information delivery between design, fabrication, assembly, and maintenance of prefabricated structures. Design for manufacturing and assembly (DfMA) is a critical methodology for prefabricated bridge structures. In this study, a novel concept of digital engineering model that combined existing knowledge of DfMA with object-oriented parametric modeling technologies was developed. Three-dimensional (3D) geometry models and their data models for each phase of a construction project were defined for information delivery. Digital design models were used for conceptual design, including aesthetic consideration and possible variation during fabrication and assembly. The seismic performance of a bridge pier was evaluated by linking the design parameters to the calculated moment-curvature curves. Control parameters were selected to consider the tolerance control and revision of the digital models. Digitalized fabrication of the prefabricated members was realized using the digital fabrication model with G-code for a concrete printer or a robot. The fabrication error was evaluated and the design digital models were updated. The revised fabrication models were used in the preassembly simulation to guarantee constructability. For the maintenance of the bridge, the as-built information was defined for the prefabricated bridge piers. The results of this process revealed that data-driven information delivery is crucial for lifecycle management of prefabricated bridge piers.

• 한국지진공학회논문집
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• 제27권6호
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• pp.231-236
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• 2023

The importance of Structural Health Monitoring (SHM) in the industry is increasing due to various loads, such as earthquakes and wind, having a significant impact on the performance of structures and equipment. Estimating responses is crucial for the effective health management of these assets. However, using numerous sensors in facilities and equipment for response estimation causes economic challenges. Additionally, it could require a response from locations where sensors cannot be attached. Digital twin technology has garnered significant attention in the industry to address these challenges. This paper constructs a digital twin system utilizing the Long Short-Term Memory (LSTM) model to estimate responses in a pipe system under simultaneous seismic load and arbitrary loads. The performance of the data-driven digital twin system was verified through a comparative analysis of experimental data, demonstrating that the constructed digital twin system successfully estimated the responses.

• International Journal of Advanced Culture Technology
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• 제12권2호
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• pp.167-179
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• 2024

This study addresses the challenge of objectively evaluating the performance of early-stage startups amidst limited information and uncertainty. Focusing on companies selected by TIPS, a leading private sector-driven startup support policy in Korea, the research develops a new indicator to assess technological efficiency. By analyzing various input and output variables collected from Crunchbase and KIND (Korea Investor's Network for Disclosure System) databases, including technology use metrics, patents, and Crunchbase rankings, the study derives technological efficiency for TIPS-selected startups. A prediction model is then developed utilizing machine learning techniques such as Random Forest and boosting (XGBoost) to classify startups into efficiency percentiles (10th, 30th, and 50th). The results indicate that prediction accuracy improves with higher percentiles based on the technical efficiency index, providing valuable insights for evaluating and predicting startup performance in early markets characterized by information scarcity and uncertainty. Future research directions should focus on assessing growth potential and sustainability using the developed classification and prediction models, aiding investors in making data-driven investment decisions and contributing to the development of the early startup ecosystem.

• Journal of Information Technology Applications and Management
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• 제16권4호
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• pp.223-244
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• 2009

Defense R&D is an essential investment for the national security. Recently our nation has also begun to initiate a number of defense R&D projects. As a lot of fund and resources are allocated to these projects, we need to identify which projects to initiate and then how to manage these projects well. Though there have been a number of studies on R&D projects in commercial sector, there are only a few studies in defense R&D sector. Moreover, these existing defense R&D studies mainly deal with the former issues, which are occurring at the stage of project planing. We are more concerned with project management issues, such as how to manage projects that had already been evaluated to undertake at the planning stage. Specifically our study aims to identify project management factors leading to the success of defense R&D projects. Results of the empirical analysis indicate that management support, user-driven requirements management, and project planning capability are key elements for project performance.

• Journal of Multimedia Information System
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• 제9권2호
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• pp.171-176
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• 2022

Today, it is imperative to educate students on how to best prepare themselves for the new data driven era of the future. Undergraduate education plays an important role in providing students with more Data Science opportunities and expanding the supply of Data Science talent. This paper surveys and analyzes the curricula of Data Science-related bachelor's degree programs in the United States. The 'required' and 'elective' courses in a curriculum for obtaining a B.S. degree were evaluated by course weight to indicate its necessity. As a result, it was possible to find out which courses were important in Data Science programs and which areas were emphasized for B.S. degrees in Data Science. We found that courses belong to the Data Science area, such as data management, data visualization, and data modeling, were more required for Data Science B.S. degrees in the United States.

• 한국전자거래학회지
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• 제27권2호
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• pp.37-64
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• 2022

인공지능의 사회적수용도가 증가하면서 머신러닝 기법을 기업에 적용하는 사례가 증가하고 있다. 머신러닝 기법의 선정에는 주로 정확성이나 해석 가능성 등 기술적 요인이 주로 기준이 되어왔다. 그러나 머신러닝 채택의 성공은 개발부서, 사용부서, 리더십과 조직문화 등 경영관리 요인도 영향을 주기도 한다. 아쉽게도 기술적 요인과 경영관리적 요인이 함께 고려된 머신러닝 선정의 성공 요인을 이해하는 통합 연구가 거의 존재하지 않는다. 이에 본 논문의 목적은 기업 내 머신러닝 선정을 이해하기 위해 John Rice의 algorithm selection process model과 task-technology fit, 그리고 IS Success Model 이론을 결합한 기술-경영관리 통합 모형을제안하고 실증적 분석을 하는 것이다. 머신러닝을 도입한 국내 기업 240곳을 대상으로 설문 분석을 실시한 결과 알고리즘 품질과 데이터 품질이 높을수록 문제-알고리즘 적합성에 높게 영향을 주는 것으로 나타났으며, 문제-알고리즘 적합성은 조직의 생산성과 혁신성에도 유의한 영향을 미치는 것으로 검증되었다. 또한 외주화와 경영진 지원이 머신러닝 시스템 품질에 긍정적인 영향을 미치고, 데이터 중심 경영 및 동기화와 같은 조직문화 요인은 활용성과에 높은 영향을 미치는 것으로 확인되었다.