• Asia pacific journal of information systems
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• 제23권3호
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• pp.25-53
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• 2013

This study analyzes the effect of Computer Aided Innovation (CAI) to improve R&D Capabilities empirically. Survey was distributed by e-mail and Google Docs, targeting CTO of 235 SMEs. 142 surveys were returned back (rate of return 60.4%) from companies. Survey results from 119 companies (83.8%) which are effective samples except no-response, insincere response, estimated value, etc. were used for statistics analysis. Companies with less than 50billion KRW sales of entire researched companies occupy 76.5% in terms of sample traits. Companies with less than 300 employees occupy 83.2%. In terms of the type of company business Partners (called 'partners with big companies' hereunder) who work with big companies for business occupy 68.1%. SMEs based on their own business (called 'independent small companies') appear to occupy 31.9%. The present status of holding IT system according to traits of company business was classified into partners with big companies versus independent SMEs. The present status of ERP is 18.5% to 34.5%. QMS is 11.8% to 9.2%. And PLM (Product Life-cycle Management) is 6.7% to 2.5%. The holding of 3D CAD is 47.1% to 21%. IT system-holding and its application of independent SMEs seemed very vulnerable, compared with partner companies of big companies. This study is comprised of IT infra and IT Utilization as CAI capacity factors which are independent variables. factors of R&D capabilities which are independent variables are organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability. The highest average value of variables was 4.24 in organization capability 2. The lowest average value was 3.01 in IT infra which makes users access to data and information in other areas and use them with ease when required during new product development. It seems that the inferior environment of IT infra of general SMEs is reflected in CAI itself. In order to review the validity used to measure variables, Factors have been analyzed. 7 factors which have over 1.0 pure value of their dependent and independent variables were extracted. These factors appear to explain 71.167% in total of total variances. From the result of factor analysis about measurable variables in this study, reliability of each item was checked by Cronbach's Alpha coefficient. All measurable factors at least over 0.611 seemed to acquire reliability. Next, correlation has been done to explain certain phenomenon by correlation analysis between variables. As R&D capabilities factors which are arranged as dependent variables, organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability turned out that they acquire significant correlation at 99% reliability level in all variables of IT infra and IT Utilization which are independent variables. In addition, correlation coefficient between each factor is less than 0.8, which proves that the validity of this study judgement has been acquired. The pair with the highest coefficient had 0.628 for IT utilization and technology-accumulating capability. Regression model which can estimate independent variables was used in this study under the hypothesis that there is linear relation between independent variables and dependent variables so as to identify CAI capability's impact factors on R&D. The total explanations of IT infra among CAI capability for independent variables such as organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability are 10.3%, 7%, 11.9%, 30.9%, and 10.5% respectively. IT Utilization exposes comprehensively low explanatory capability with 12.4%, 5.9%, 11.1%, 38.9%, and 13.4% for organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability respectively. However, both factors of independent variables expose very high explanatory capability relatively for technology-accumulating capability among independent variable. Regression formula which is comprised of independent variables and dependent variables are all significant (P<0.005). The suitability of regression model seems high. When the results of test for dependent variables and independent variables are estimated, the hypothesis of 10 different factors appeared all significant in regression analysis model coefficient (P<0.01) which is estimated to affect in the hypothesis. As a result of liner regression analysis between two independent variables drawn by influence factor analysis for R&D capability and R&D capability. IT infra and IT Utilization which are CAI capability factors has positive correlation to organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability with inside and outside which are dependent variables, R&D capability factors. It was identified as a significant factor which affects R&D capability. However, considering adjustable variables, a big gap is found, compared to entire company. First of all, in case of partner companies with big companies, in IT infra as CAI capability, organization capability, process capability, human resources capability, and technology capability out of R&D capacities seems to have positive correlation. However, collaboration capability appeared insignificance. IT utilization which is a CAI capability factor seemed to have positive relation to organization capability, process capability, human resources capability, and internal/external collaboration capability just as those of entire companies. Next, by analyzing independent types of SMEs as an adjustable variable, very different results were found from those of entire companies or partner companies with big companies. First of all, all factors in IT infra except technology-accumulating capability were rejected. IT utilization was rejected except technology-accumulating capability and collaboration capability. Comprehending the above adjustable variables, the following results were drawn in this study. First, in case of big companies or partner companies with big companies, IT infra and IT utilization affect improving R&D Capabilities positively. It was because most of big companies encourage innovation by using IT utilization and IT infra building over certain level to their partner companies. Second, in all companies, IT infra and IT utilization as CAI capability affect improving technology-accumulating capability positively at least as R&D capability factor. The most of factor explanation is low at around 10%. However, technology-accumulating capability is rather high around 25.6% to 38.4%. It was found that CAI capability contributes to technology-accumulating capability highly. Companies shouldn't consider IT infra and IT utilization as a simple product developing tool in R&D section. However, they have to consider to use them as a management innovating strategy tool which proceeds entire-company management innovation centered in new product development. Not only the improvement of technology-accumulating capability in department of R&D. Centered in new product development, it has to be used as original management innovative strategy which proceeds entire company management innovation. It suggests that it can be a method to improve technology-accumulating capability in R&D section and Dynamic capability to acquire sustainable competitive advantage.

• 전기학회논문지
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• 제66권5호
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• pp.833-842
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• 2017

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

• 한국심리학회지ㆍ일반
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• 제36권2호
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• pp.241-270
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• 2017

본 연구는 성인 1,000명을 대상으로 트라우마 이전(pretrauma)의 성격적 특성인 성격 5요인, 외상 후 인지, 반추, PTSD 증상, 외상 후 성장간의 구조적 관계를 살펴보자 하였다. 이를 위해 본 연구에서는 성격 5요인이 외상 후 인지와 침습적 반추를 거쳐 의도적 반추에 영향을 주는 경로가 포함된 가설적 연구모형과 침습적 반추에서 의도적 반추로 가는 경로가 제외된 경쟁모형을 각각 설정하였다. 연구결과 첫째, 외향성, 우호성, 성실성은 외상 후 성장과 PTSD 증상 간에 외상 후 인지 및 반추의 매개효과가 유의하지 않았다. 둘째, 개방성은 PTSD 증상 및 외상 후 성장으로 가는 경로에서 침습적 반추와 의도적 반추가 순차적으로 매개하는 것으로 밝혀졌다. 셋째, 신경증적 경향성은 PTSD 증상 및 외상 후 성장으로 가는 경로에서 외상 후 인지, 사건관련 반추가 순차적으로 매개하는 것으로 밝혀졌다. 본 연구결과는 외상 후 성장모델의 인지과정을 지지하는 것으로 나타났다. 본 연구의 결과에 근거하여 의의와 한계점에 대하여 논의하였다.

• 한국콘텐츠학회논문지
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• 제17권10호
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• pp.307-321
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• 2017

본 연구는 중 고등학교 학생 2,260명을 대상으로 Big 5 성격요인에 따른 청소년 성격특성에 발달적 변화가 있는지 알아보고자 한다. 성별과 학년에 따라 성격 5요인의 발달적 변화에 차이를 보이는지 확인하였고, 더불어 신경증 하위요인을 추가적으로 분석함으로써 청소년기 문제행동 시기와 특성을 확인 하였다. 분석에는 일원배치변량분석(One-way ANOVA)을 사용하였으며 유의미한 차이가 난 경우 사후검증을 실시하였다. 연구 결과를 요약하면 다음과 같다. 첫째, 청소년의 성격 5요인 특성이 성별에 따라 차이가 있는 것으로 나타났다. 개방성, 성실성, 외향성은 여자가 남자보다 높게 나타났고, 신경증은 남자가 여자보다 높게 나타났다. 둘째, 청소년의 성격 5요인 특성 모두에서 학년에 따라 차이가 있는 것으로 나타났다. 셋째, 청소년기 성격특성에 대한 성별에 따른 학년별 발달적 경향성에서도 성별 간 다른 양상을 보이는 것으로 나타났다. 특히 성별과 학년에 따른 분석에서 남자는 중등 2학년, 여자는 고등 3학년에서 성격특성이 두드러질 것으로 나타났다. 그리고 신경증과 관련된 외현화 행동문제는 중등 1 2학년에서 내현화 행동문제는 고등 3학년에서 주로 발현될 것으로 나타났다. 따라서 본 연구에서는 현재 우리나라 청소년 성격특성의 발달적 변화를 확인할 수 있었으며, 또한 성별과 학년에 따라 상이한 정신건강문제가 발현 될 수 있는 것으로 나타났다.

• 대한토목학회논문집
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• 제38권5호
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• pp.693-701
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• 2018

수도권 도시철도의 환승통행은 '노선간환승'과 '역사환승'으로 구분된다. 역사환승은 1) 교통카드 Tag-In 단말기 운영노선과 초승열차 운영노선이 다른 경우와 2) 최종 하차열차 운영노선과 교통카드 Tag-Out 단말기 운영노선이 다른 경우에 발생한다. 기존연구에서 주로 교통카드자료를 이용한 환승량 추정은 '노선간 환승량'을 의미하며 '역사환승량'은 제외되어 환승통로를 이용하는 보행에 대한 과소추정의 원인이 되었다. 본 연구는 수도권 대중교통카드자료를 이용해서 역사환승량을 추정하는 방안을 제시한다. 이를 위해 승객의 경로선택모형에 역사환승량 산정에 적합하도록 변형된 Big-Node 기반 네트워크 구축기법과 자료구조 방법론을 제시한다. 1일 약 800만 건의 수도권 도시철도 이용카드자료를 대상으로 사례분석을 시행한다.

• 한국환경보건학회지
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• 제47권5호
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• pp.462-471
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• 2021

Background: The number of synthesized chemicals has rapidly increased over the past decade. For many chemicals, there is a lack of information on toxicity. With the current movement toward reducing animal testing, the use of toxicity big data and deep learning could be a promising tool to screen potential toxicants. Objectives: This study identified potential chemicals related to reproductive and estrogen receptor (ER)-mediated toxicities for 1135 cleaning products and 886 laundry products. Methods: We listed chemicals contained in cleaning and laundry products from a publicly available database. Then, chemicals that potentially exhibited reproductive and ER-mediated toxicities were identified using the European Union Classification, Labeling and Packaging classification and ToxCast database, respectively. For chemicals absent from the ToxCast database, ER activity was predicted using deep learning models. Results: Among the 783 listed chemicals, there were 53 with potential reproductive toxicity and 310 with potential ER-mediated toxicity. Among the 473 chemicals not tested with ToxCast assays, deep learning models indicated that 42 chemicals exhibited ER-mediated toxicity. A total of 13 chemicals were identified as causing reproductive toxicity by reacting with the ER. Conclusions: We demonstrated a screening method to identify potential chemicals related to reproductive and ER-mediated toxicities utilizing chemical toxicity big data and deep learning. Integrating toxicity data from in vivo, in vitro, and deep learning models may contribute to screening chemicals in consumer products.

• 지식경영연구
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• 제24권4호
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• pp.63-85
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• 2023

본 연구는 코로나-19 시대를 거치며 증가한 메타버스 공간에 관한 관심을 바탕으로 주된 이용층을 확인하고 이를 예측하는 변인을 탐색하고자 했다. 온라인 활동을 예측하기 위해서는 이용자 이용 목적과 동기 및 관련된 인구통계적 요인을 확인해야 하므로 이를 예측 변인으로 모형 분석을 진행했다. 2022년 한국미디어패널조사 데이터를 바탕으로 메타버스 이용자를 예측하는 Heckman 2단계 표본선택모형 분석을 수행했다. 분석 결과 1단계 선택모형에서 메타버스 이용을 결정하는 주된 요인으로는 오프라인 활동, 개방성, OTT 이용 여부, 그리고 유료 콘텐츠 구입 여부가 확인되었다. 또한 2단계 결과모형에서는 개방성, 성별, 유료 콘텐츠 구입 여부가 메타버스 이용 시간을 높이는 주된 변인으로 확인되었다. 이 연구 결과는 코로나-19 시대 온라인 활동 증가와 함께 메타버스 서비스에 관한 관심이 높아지고 있는 상황에서, 메타버스 이용자를 이해하고 예측하는 데 기여할 수 있을 것이다. 또한 메타버스 플랫폼 관련 기업과 개발자에게 유용한 정보를 제공할 수 있을 것이다.

• 한국통신학회논문지
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• 제36권2B호
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• pp.168-175
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• 2011

현대 사회에서 인간관계는 사회생활은 물론 개인의 성공여부를 판단하는 중요한 요소로 많은 관심을 받고 있다. 이러한 시대적 변화에 대응하기 위해 상대방의 성격을 미리 예측하고 적절한 관계를 유지하기 위해 다양한 방법들이 이용되고 있다. 따라서 본 논문에서는 면접 시스템 적용을 위해 20대 남성의 얼굴 영상에서 인중, 입 및 귀의 형태를 추출하여 5대 성격 유형별 특정과의 상관성 분석 연구를 수행하고자 한다. 이를 위해 안면 및 측면 영상을 수집하여 Visual C++을 통해 인중, 입, 귀의 영역을 추출하고 인중 비율, 입의 크기 및 귀의 형태에 따른 결과값을 도출하여 5대 성격 유형별 집단과의 비교, 분석을 수행하였다. 결과적으로 5대 성격 유형에 따라 인중 비율, 입의 크기 및 귀의 형태적 결과값을 통해 유의성을 도출하였다.

• 천문학회보
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• 제36권2호
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• pp.88.2-88.2
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• 2011

Fast Imaging Solar Spectrograph (FISS) is an instrument developed by Seoul National University and Korea Astronomy and Space Science Institute and installed at the 1.6 meter New Solar Telescope of Big Bear Solar Observatory. Using this instrument, we observed solar filaments and analyzed the data focusing on determining the temperature and non-thermal velocity. We inferred the Doppler absorption widths of $H{\alpha}$ and Ca II 8542$\bar{A}$ lines from the line profiles using the cloud model. From these values, we separately determined temperature and non-thermal velocity. Our first result came from a solar filament observed on 2010 July 29th. Temperature inside a small selected region of this ranges from 4500K to 12000K and non-thermal velocity, from 3.5km/s to 7km/s. We also found temperature varied a lot with time. For example temperature at a fixed point varied from 8000K to 18000K for 40 minutes, displaying an oscillating pattern with a period of about 8 minutes and amplitude of about 2000K. We will also present new results from filaments observed in 2011 summer.

• 영재교육연구
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• 제27권1호
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• pp.1-16
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• 2017

본 연구는 질 높은 영재교육을 제공하기 위해 영재의 성격 5요인의 요소인 개방성, 성실성, 외향성, 수용성 및 신경증이 영재교육의 성과인 자기조절학습과 학업성취도를 예측하는 지를 밝히고자 하였다. 본 연구는 경기도 A영재교육원 학생 95명을 대상으로 진행하였다. 영재의 개방성, 성실성, 외향성, 수용성, 신경증, 자기조절학습 및 학업성취도간의 관계를 살펴본 결과는 다음과 같다. 첫째, 영재의 수용성, 개방성, 성실성 순으로 자기조절학습을 유의하게 예측하였다. 둘째, 영재의 신경증, 자기조절학습 및 외향성 순으로 학업성취도를 유의하게 예측하였다. 셋째, 학업성취도에 유의한 예측력을 보이지 않은 영재의 개방성, 성실성, 수용성은 자기조절학습을 매개로 학업성취도에 대한 예측력이 유의한 것으로 밝혀졌다. 따라서 영재교육에서는 영재의 성격 5요인을 관리할 수 있는 프로그램을 통하여 영재교육의 성과인 자기조절학습과 학업성취도를 높여 나가야 할 것이다.