• 아태비즈니스연구
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• 제13권1호
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• pp.37-52
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• 2022

Purpose - The objective of this study is to investigate what motivates global FDI inflows in the different economic development level and to clarify the FDI motivation type in the level of qualitative economic growth. Design/methodology/approach - Major macroscopic social·economic factors induced FDI inflows were analyzed using fixed-effect panel regression with 30-year panel data of 28 countries from 1985 to 2014. For analysis in the stage of economic growth, two category of developed and developing countries was used. And to analyze FDI motivation type in the level of qualitative economic growth, 4 shares of GDP; consumption·government·investment expenditure and export, was used as explanatory variable. Findings - In developed country, TFP(total factor productivity) and GDP have a great influence on FDI inflows, and consumption and labor compensation have a slight effect. This result indicates that the market seeking-driven, horizontal type investment is shown along with efficiency seeking investment. In developing country, human capital and TFP is shown to have greater impact on FDI inflows and labor compensation, exports, investment and government expenditures also have impacts. Thus it has confirmed that not only efficiency-seeking vertical investment for using low cost well educated laborer, but also government-driven economic growth and export policies could affect the FDI inflows. Research implications or Originality - The FDI investment decision making of multinational companies is decided by their own purpose. But, in the concept of as follows; 1) FDI is a long-term capital flowing for maximization of economic utility with limited global resource, 2) Thus FDI could be affected by macro socio·economic factors of host country. 3) Also such macro factors is different by each economic growth qualitative level. Therefore macro socio·economic factors of each country could be affected by the qualitative level of their own economic growth. To attract FDI inflows, it is desirable to implement differentiated incentive policies in the qualitative level of economic growth. Furthermore in developing countries it is recommended to implement government driven economic growth policies as follows; fostering well educated human resources, improving technology productivity in the relative lower cost labor market compared to developed countries and boosting international export volume.

• 한국안전학회지
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• 제32권3호
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• pp.83-89
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• 2017

A new technology has provided the nation, industry, society, and people with innovative and useful functions. National economy and society has been improved through this technology innovation. Despite the benefit of technology innovation, however, since technology society was sufficiently mature, the unintended side effect and negative impact of new technology on society and human beings has been highlighted. Thus, it is important to investigate a risk of new technology for the future society. Recently, the risks of the new technology are being suggested through a large amount of social data such as news articles and report contents. These data can be used as effective sources for quantitatively and systematically forecasting social risks of new technology. In this respect, this paper aims to propose a data-driven process for forecasting and assessing social risks of future new technology using the text mining, 4M(Man, Machine, Media, and Management) framework, and analytic hierarchy process (AHP). First, social risk factors are forecasted based on social risk keywords extracted by the text mining of documents containing social risk information of new technology. Second, the social risk keywords are classified into the 4M causes to identify the degree of risk causes. Finally, the AHP is applied to assess impact of social risk factors and 4M causes based on social risk keywords. The proposed approach is helpful for technology engineers, safety managers, and policy makers to consider social risks of new technology and their impact.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권3호
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• pp.1362-1376
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• 2016

A cloud computing system can be characterized by the provision of resources in the form of services to third parties on a leased, usage-based basis, as well as the private infrastructures maintained and utilized by individual organizations. To attain the desired reliability and energy efficiency in a cloud data center, trade-offs need to be carried out between system performance and power consumption. Resolving these conflicting goals is often the major challenge encountered in the design of optimization strategies for cloud data centers. The work presented in this paper is directed towards the development of an Energy-efficient and Performance-aware Cloud System equipped with strategies for dynamic switching of optimization approach. Moreover, a platform is also provided for the deployment of a Wind Farm CMS (Condition Monitoring System) which allows ubiquitous access. Due to the geographically-dispersed nature of wind farms, the CMS can take advantage of the cloud's highly scalable architecture in order to keep a reliable and efficient operation capable of handling multiple simultaneous users and huge amount of monitoring data. Using the proposed cloud architecture, a Wind Farm CMS is deployed in a virtual platform to monitor and evaluate the aging conditions of the turbine's major components in concurrent, yet isolated working environments.

• 응용통계연구
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• 제18권3호
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• pp.555-565
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• 2005

고객세분화는 기업이 관계하고 있는 고객을 이해하고 그 이해를 바탕으로 효과적인 고객관리를 수행하기 위해 필수적인 요소인데 데이터마이닝이 기업의 정보관리영역에 적극적으로 활용되면서 보다 과학적이고 최적화된 형태로 개발되고 있다. 본 연구에서는 신용카드고객 의 카드사용행태에 근거하여 각 고객을 서로 유사한 사용행태를 보이는 고객군으로 세분화하는 과정을 소개하였다. 고객이 실제로 신용카드를 사용하면서 발생시킨 거래정보에만 의존하여 고객세분화를 개발하였으며 이는 마케팅의 관점에서 상당히 의미있는 내용이 될 수 있다. 고객세분화의 개발을 위하여 데이터마이닝기법인 k-평균 군집방법과 최장연결법에 의한 계보적 군집방법을 단계적으로 활용하는 이단계 군집방법을 이용하였다.

• 한국응용과학기술학회지
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• 제40권2호
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• pp.210-222
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• 2023

본 연구는 K-뷰티브랜드의 CSR동기가 CSR 진정성과 브랜드 신뢰에 주는 영향과 CSR 진정성과 브랜드 신뢰가 브랜드 지지에 주는 영향을 확인하고자 한·중 소비자 데이터를 이용해 비교연구하여 진행하였다. 수집된 데이터 중 392부를 검증에 사용하였고 2단계 접근법과 최우도 측정법을 활용해 분석하였다. 연구 결과, 자기본위적 동기를 제외한 가치지향적, 이해관계자지향, 전략적 동기는 CSR 진정성과 브랜드 신뢰에 긍정적 영향을 주는 것으로 나타났다. 또한 CSR 진정성은 브랜드 신뢰에 긍정적 영향을 주었고, 이는 브랜드를 지지하는 것으로 나타났다. 한·중 소비자를 비교한 결과, 두 나라 모두 CSR동기가 진정성과 브랜드 신뢰에 영향을 주는 것으로 나타났으나 중국소비자가 한국소비자보다 더 많은 부분에서 높게 나타난 결과를 보였는데 이러한 결과는 K-뷰티 브랜드의 중국시장 확장을 위해서는 중국소비자를 이해하고 그들이 추구하는 CSR동기 전략을 강화해야 한다는 시사점을 가진다.

• 대한원격탐사학회지
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• 제30권3호
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• pp.375-381
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• 2014

Soil moisture is an essential satellite-driven variable for understanding hydrologic, pedologic and geomorphic processes. The European Space Agency (ESA) has endorsed soil moisture as one of Climate Change Initiates (CCI) and had merged multi-satellites over 30 years. The $0.25^{\circ}$ coarse resolution soil moisture satellite data showed correlations with variables of a water stress index, Temperature-Vegetation Dryness Index (TVDI), from a stepwise regression analysis. The ancillary data from TVDI, Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from MODIS were inputted to a multi-regression analysis for estimating the surface soil moisture. The estimated soil moisture was validated with in-situ soil moisture data from April, 2012 to March, 2013 at Andong observation sites in South Korea. The soil moisture estimated using satellite-based LST and NDVI showed a good agreement with the observed ground data that this approach is plausible to define spatial distribution of surface soil moisture.

• 유통과학연구
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• 제19권4호
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• pp.5-13
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• 2021

Purpose: The sporting field is one of the most lucrative industries that most producers would want to share and drive-in sales towards its direction. The purpose of the present study is to evaluate how sports psychology has become a useful discipline in enhancing consumer purchase intentions. Research design, data, and methodology: This study employs a qualitative coding method to analyze and interpret the data obtained with a PRISMA declaration for analytical purposes. Using Web QDA (Qualitative Data Analysis) online tools, the current study coded the data obtained. Results: According to the prior studies, marketers should go the extra mile of looking for what sports customers are looking for. They understand that one way to increase the customers' willingness to purchase their products is by looking into the specific things that the customers look for and enjoy in sports. Conclusions: After all, the present study concludes that most marketers need to apply the concepts of sports psychology to understand consumer purchase intentions in particular retail stores. Consumers are likely to be influenced by their peers or groups to make decisions driven towards purchasing given sports apparel and the retail store to purchase a product.

• Smart Structures and Systems
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• 제29권1호
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• Structural Engineering and Mechanics
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• 제85권5호
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• pp.635-644
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• 2023

The design of honeycomb sandwich structures is often challenging because these structures can be tailored from a variety of possible cores and face sheets configurations, therefore, the design of sandwich structures is characterized as a time-consuming and complex task. A data-driven computational approach that integrates the analytical method and Artificial Neural Network (ANN) is developed by the authors to rapidly predict the design of sandwich structures for a targeted maximum structural deflection. The elaborated ANN reverse design approach is applied to obtain the thickness of the sandwich core, the thickness of the laminated face sheets, and safety factors for composite sandwich structure. The required data for building ANN model were obtained using the governing equations of sandwich components in conjunction with the Monte Carlo Method. Then, the functional relationship between the input and output features was created using the neural network Backpropagation (BP) algorithm. The input variables were the dimensions of the sandwich structure, the applied load, the core density, and the maximum deflection, which was the reverse input given by the designer. The outstanding performance of reverse ANN model revealed through a low value of mean square error (MSE) together with the coefficient of determination (R2) close to the unity. Furthermore, the output of the model was in good agreement with the analytical solution with a maximum error 4.7%. The combination of reverse concept and ANN may provide a potentially novel approach in designing of sandwich structures. The main added value of this study is the elaboration of a reverse ANN model, which provides a low computational technique as well as savestime in the design or redesign of sandwich structures compared to analytical and finite element approaches.

• 한국임상약학회지
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• 제26권3호
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• pp.245-253
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• 2016

Objective: This study was conducted to evaluate payer-driven medication adherence intervention program from the patient's and counselor's perspectives. Methods: Target patients for intervention were selected by retrospective adherence measures based on national health insurance claims data for hypertension, diabetes and hyperlipidemia. As a serial intervention for higher risk groups of medication non-adherence, initial direct mailing, the first direct telephone call and the second direct call or a home visit were followed. Interview approach to qualitative inquiry was used to evaluate intervention results. Results: Participants including 4 patients received telephone calls, and 4 National Health Insurance Service staff and 4 pharmacists participated as counselors were interviewed regarding their impression of the intervention program. Three major themes arose: overall perception; necessities; and suggestions for success, of the intervention. Despite short period of intervention, educational intervention by telephone counseling involving pharmacists shows potential to improve self-management of chronic disease, and pharmacist-involvement. But more sophisticated selection of target patients requiring the intervention and complementation of electronic database system would be necessary. In addition, personal disposition of counselor was revealed to be an important factor for achieving successful outcome of intervention. Conclusion: The findings suggest that the individualized counseling intervention would be an efficient option for improved medication adherence. Further researches should include longer periods of interventions, a quantitative analysis using adherence measures based on claims data and consideration of clinical benefits associated with the intervention.