• 디지털융복합연구
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• 제14권8호
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• pp.151-161
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• 2016

ICT융합은 창조경제를 추진함에 있어서 기존 산업 및 제품과 서비스에 고부가가치를 창출할 수 있는 기회를 제공하기 때문에, 중요한 수단으로 인식되고 있다. 현재까지 연구는 ICT융합 클러스터에 참여하는 기업에 대한 세부적인 지원 정책 보다는 Porter의 다이아몬드 모델에 의존하여 ICT융합 클러스터의 전략을 포괄적으로 살펴보는데 중점을 두어 왔다. 본 연구에서는 AIDA(Attention, Interest, Desire, Action) 모델에 기반을 두고 비(非)R&D 분야의 정책적 지원이 기업의 ICT융합 클러스터에 대한 관심과 참여 의사를 이끄는 지 여부를 검증하였다. 충북지역에 위치한 중소기업을 대상으로 2주 동안 온라인 설문을 통해 수집한 181부를 바탕으로, 기술 지원, 참여여건 지원, 사업화 지원 등 비R&D 정책지원 요인이 기업의 ICT융합 클러스터 사업에 대한 관심과 참여의사에 차례로 정(+)의 영향을 준다는 점을 실증하였다. 본 연구의 결과는 AIDA 모델을 정부와 기업 간 상황(G2B)에 적용하여 정부 정책의 홍보와 기업의 관심 유도를 검증하였다는 점에서 의의가 있다. 향후 비R&D 부분에 대한 정책적 지원이 국가정책사업에 대한 기업의 관심과 참여 의사를 이끌어 내는 지 여부를 AIDA 모델을 활용하여 살펴볼 필요성이 있다.

• 한국융합학회논문지
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• 제12권5호
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• pp.275-289
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• 2021

최근 세계시민교육(이하 GCE)의 필요성이 세계적으로 강조되고 있으나, 아동기 세계시민의식에 영향을 미치는 아동복지시설 종사자 GCE가 국내에서 부족했다. 이에 본 연구는 (1) 국내외 GCE 정책·현황 조사 (2) GCE 전문가 심층 인터뷰 (3) 종사자 설문 조사로 교육 욕구 실제를 분석했다. 정책분석 결과 주로 학교체계에서만 행해진 국내 GCE와 달리 세계적으로는 다양한 방식으로 진행되었다. 심층 인터뷰에서는 국내 GCE 확대의 필요성이 강조되었고, 설문 분석 결과 GCE에 대한 인지-관심-참여 의사에 경로 관계가 유의해 인지·관심 증대가 실천에 효과적으로 나타났다. 이를 근거로 GCE가 아동복지시설 종사자에게 필요한 교육임을 확인하고 AIDA 모델에 입각해 GCE 인지도를 높여야 함을 강조하였으며 GCE 구성 시 활용할 수 있는 지표를 제시하였다.

• 디자인학연구
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• 제14권
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• pp.9-18
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• 1996

This is a study for importance of creativity in design. In the modern industrial developments in Korea has brought increase of consumptional products, by this reason. we are interested in the advertising industry for purpose of booming currents. But Comparing with the development of general advertising industry, it is not so long that understanding of advertising design has become important problm and spacially importance of creativity. Therefore According as the usefulness of advertisement is growing, we are concerned of the study of advertising effects. Now creatived expression is required without a moment delay because of the point of discrimination about too many advertisements. So, Advertising design has a role of positive communication which is passed into the life of modern man deeply. So for the dffective and impactive communication in the short time and on the limit space against implicative message, the study for importance of creativity in advertising expression has common purpose contact floody advertising everyday. Therefore this thesis try to seek Strategies of creativity in order to effective advertising expression.

• 통상정보연구
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• 제4권1호
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• pp.107-128
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• 2002

한국 중소수출기업의 인터넷을 이용한 수출마케팅 전략과 성과와의 관계를 밝히기 위하여 본고에서는 우선 이론적 배경을 바탕으로 한 연구모형의 개발 및 연구가설이 설정되었다. 이렇게 설정된 연구가설은 설문조사를 통한 자료수집과 MANOVA에 의한 통계분석을 통하여 검정되었다. 그 결과, 주로 세일즈와 광고에 적용되는 커뮤니케이션 효과계층의 고전적인 AIDA 모델을 준용한 수출성과와 인터넷 수출마케팅전략 변수간에는 거의 대부분의 경우 통계적으로 유의미한 관계가 있음을 발견하였다. 특히 AIDA의 효과계층 중 최종 수출계약성과에 비하여 그 이전 단계인 커뮤니케이션 효과가 더 크다는 사실을 확인한 것이 이 논문의 주요한 의의라고 여겨진다. 이라한 결과는 한국의 중소수출기업이 인터넷을 이용한 마케팅 전략을 짜는데 있어서 의미있는 시사점과 유의사항을 제공하고 있으며, 차후 연구자를 위한 하나의 준거가 될 수 있을 것으로 기대된다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.485-485
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• 2000

A design method of model following control system using neural networks is proposed. An unknown nonlinear single-input single-output plant is identified using a multilayer neural networks. A linear controller is designed fer the linear approximation model obtained by linearinzing the identification model. The identification model is also used as a plant emulator to obtain the prediction error. Deficient servo performance due to controlling nonlinear plant with only linear controller is mended by adjusting the linear controller output using the prediction output and the parameters of the identification model. An optimal preview controller is adopted as the linear controller by reason of having good servo performance lowering the peak of control input. Validity of proposed method is illustrated through a numerical simulation.

• 마케팅과학연구
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• 제18권2호
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• pp.19-45
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• 2008

본 연구는 인터넷쇼핑몰 비주얼 머천다이징의 주요차원을 고객이 쇼핑몰에 진입한 후 정보탐색과 대안평가를 거치는 등의 쇼핑과정을 토대로 AIDA모형 관점에서 점포, 제품, 촉진에 초점을 맞추었다. VMD의 주요차원(primary dimensions)으로는 점포디자인, 머천다이징, 그리고 머천다이징단서로 구분하였다. 선행연구 결과를 토대로 점포다자인의 하위차원으로는 차별성, 간결성, 위치확인성을, 머천다이즈의 하위차원으로는 제품구색, 명성, 정보성을, 그리고 머천다이징단서의 하위차원으로는 제품추천 및 링크를 설정하여 VMD태도와의 관계를 탐색적으로 조사하였다. 연구결과 이들 세 차원은 종속변수에 유의한 정의 영향을 미치는 것으로 나타났다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• International Journal of Internet, Broadcasting and Communication
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• 제11권2호
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• pp.19-26
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• 2019

For the last two decades, a number of information systems are developed for various aims, depending on business' needs. There are a lot of organizations in the world which are using information systems in their environment, such as telecommunications organizations, universities and banks. Using information system has become crucial for most of organizations regarding with increasing the performance of work procedures and improve productivity and efficiency in general. There are many different models that have been designed and validated to explain the effect of constructs on the adoption of technologies. The aim of this research is to review the literature on information systems adoption and to analyze the different types of models which are frequently applied by researchers in their efforts to examine the factors that estimate the adoption of technologies. The research explores information systems adoption literature that focuses on development models.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권8호
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• pp.3884-3910
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• 2016

Intrusion Detection System (IDS) in general considers a big amount of data that are highly redundant and irrelevant. This trait causes slow instruction, assessment procedures, high resource consumption and poor detection rate. Due to their expensive computational requirements during both training and detection, IDSs are mostly ineffective for real-time anomaly detection. This paper proposes a dimensionality reduction technique that is able to enhance the performance of IDSs up to constant time O(1) based on the Principle Component Analysis (PCA). Furthermore, the present study offers a feature selection approach for identifying major components in real time. The PCA algorithm transforms high-dimensional feature vectors into a low-dimensional feature space, which is used to determine the optimum volume of factors. The proposed approach was assessed using HTTP packet payload of ISCX 2012 IDS and DARPA 1999 dataset. The experimental outcome demonstrated that our proposed anomaly detection achieved promising results with 97% detection rate with 1.2% false positive rate for ISCX 2012 dataset and 100% detection rate with 0.06% false positive rate for DARPA 1999 dataset. Our proposed anomaly detection also achieved comparable performance in terms of computational complexity when compared to three state-of-the-art anomaly detection systems.