• 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.

• 생태와환경
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• 제42권1호
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• pp.9-18
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• 2009

본 연구에서는 우리나라의 주요한 철새도래지인 주남 저수지를 대상으로 1998년부터 2007년까지 10년간의 계절적 수질특성 분석 및 연별변이 특성, 강우분포에 따른 영양염류(N, P)의 부하특성 및 경험적 모델(Empirical model)을 이용한 부영양화 변수 간의 역동적 관계를 분석하였다. 수체내의 COD의 평균농도는 $7.8\;mg\;L^{-1}$이었고, TN 및 TP의 영양염류 농도는 각각 $1.4\;mg\;L^{-1}$, $83{\mu}g\;L^{-1}$로서 부영양-과영양 상태로 나타났다. 주남저수지의 월별 COD는 전기전도도(r=0.669, p<0.001)와 높은 상관관계를 보여 하절기 강우는 이온희석현상을 야기시키고 이는 수질향상에 기여하는 것으로 나타났다. TN 및 TP의 One-way ANOVA 분석 결과에 따르면, 철새 도래의 전 후에는 유의한 차이(F=$5.2{\sim}12.9$, p<0.05)를 보였다. 즉, 이는 철새 도래 전에 오히려 더 영영염류가 높은 것으로 나타나 철새도래에 의한 영양염류의 증가효과는 없는 것으로 나타났다. 또한, $NO_{3^-}N$ 및 $NH_{3^-}$의 Oneway ANOVA 분석 결과는 철새 도래 전 후에 유의한 차이(F=$0.37{\sim}0.48$, p>0.05)가 없는 것으로 나타났다. 부영양화에 관계된 변수를 이용한 경험적 모델(Empirical model)의 1차 회귀분석 결과에 따르면, 조류생체량은(CHL)은 TN ($R^2$=0.143, p<0.001, n=119) 및 TP ($R^2$=0.192, p<0.001, n=119)와 통계적으로 유의한 상관성을 보여 CHL의 증감에 영양염류가 영향을 미치나 그 효과는 미미한 것으로 나타났다. 이를 위해 영양상태 지수 변이성(Trophic State Index Deviation, TSID) 테스트에 따르면, "TSI (CHL)-TSI (SD)" 및 "TSI (CHL)-TSI (TP)"값은 전체 관측치 중 70% 이상이 음수값으로 나타나 CHL은 영양염류에 의한 제한보다는 무기성 부유물(Inorganic solids)에 의한 광제한 효과로 나타났다.

• 대한핵의학회지
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• 제30권3호
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• pp.315-324
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• 1996

목적 : 정상인과 파킨슨 환자의 [I-123]IPT SPECT 데이터를 이용하여 그래픽 분석방법과 축적된 특이결합과 비특이결합의 면적에 대한 비율을 이용한 분석방법으로 얻은 $k_3/k_4$와 조직방사능비를 비교하고 [I-123]IPT SPECT의 유용성을 알아보기 위한 것이다. 방법 : [I-123]IPT($6.5{\pm}1.1$ mCi)를 6명의 정상인(나이:$45{\pm}13$)과 17명의 파킨슨 환자(나이:$55{\pm}8$)에게 순간 정맥주사하였다. SPECT 데이터는 5분 간격으로 2시간 동안 얻어졌다. 운반체 매개변수 $k_3/k_4$는 동적 IPT SPECT 데이터를 이용 혈액 표본이 사용되지 않는 Ichise의 그래픽분석방법($R_v$)과 Lassen의 면적비율방법($R_A$)를 이용하여 얻어졌다. 운반체 매개변수 $R-v,\;R_A$ 그리고 115분에서 얻은(BG-OCC)/OCC비 사이의 관계들은 선형회귀분석을 사용하였다. 결과 : 정상인과 파킨슨 환자에 대한 $R_v$ 값은 각각 $2.08{\pm}0.29$와 $0.78{\pm}0.31$이었다. 그리고 $R_A$ 값은 정상인과 파킨슨 환자에 대해 각각 $1.48{\pm}0.16$과 $0.65{\pm}0.24$이었다. (BG-OCC)/OCC와 $R_v$,(BG-OCC)/OCC와 $R_A$ 그리고 $R_v$와 $R_A$ 사이의 상관계수들은 각각 0.93, 0.90, 0.99이었고, 그것들에 대응하는 기울기들은 각각 0.54, 0.34, 0.65이었다. 정상인의 $R_v$와 $R_A$는 파킨슨 환자의 것보다 높았다. 즉, 정상인의 $k_3/k_4$는 파킨슨 환자의 것과 뚜렷이 구분되었다. 그리고 그래픽 분석방법과 면적비율 분석방법에 의한 $k_3/k_4$는 조직방사능비와 좋은 상관관계를 보여주었다. 결론 : 비침습성 단순화된 정량분석 방법들은 매개변수 $k_3/k_4$를 측정하는데 유용할 것이다. 그리고 특이결합비율 분석방법은 [I-123]IPT SPECT를 이용한 사람뇌의 도파민 운반체의 정량분석연구에 대해 유용하게 사용될 것이라고 생각된다.

• 대한핵의학회지
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• 제31권1호
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• pp.19-29
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• 1997

목 적 : 본 연구의 목적은 IPT 약역학 컴퓨터시뮬레이션을 이용하여 혈류공급량, 도파민 운반체량 그리고 시간의 함수인 혈류와 도파민 운반체의 민감도를 측정하고 간편화된 정적 영상을 얻을 수 있는가의 가능성과 그 경우 도파민 운반체량의 변화를 어느 정도 반영하는가를 측정하고자 하는 것이었다. 또한 실제 결합능을 나타낸다고 가정된 $k_3/k_4$의 비를 반영하는 (BG-OCC)/OCC와 (ABBG-ABOCC)/ABOCC의 비를 이용한 방법과 그래프적 분석 방법의 정확성 및 유용성과 방사성의약품의 약역학을 분석하는 기법을 연구하고자 하였다. 방 법 : [I-123]IPT와 SPECT로 얻은 약 2시간 동적 선조체 시간방사능곡선, 동적 피 시간방사능곡선, 그리고 삼구획 역학모형을 이용, $K_1,\;k_2\;k_3\;k_4$ 속도상수들을 획득하였다. 위의 동적 피 시간방사능곡선과 속도상수들 중 혈류공급과 관련된 $K_l$ 또는 도파민 운반체량과 관련된 $k_3$의 양을 변경하면서 5분부터 5분씩 증가하여 120분까지의 혈류와 도파민 운반체의 민감도를 측정하였다. 또한 $k_3$의 양을 변화시킨 데이터를 이용하여 선조체와 후두엽에서의 시간방사능곡선을 계산한 후 각 방법의 정밀도를 측정하기 위해 (BG-OCC)/OCC와 $R_A$ 비값들과 그래프적 분석 방법을 이용하여 구한 $R_A$값들과 참값 $k_3/k_4$의 관계를 시간방사능곡선과 선형회귀 분석으로 계산하였다. 결 과 : 선조체와 후두엽에서의 $K_1,\;k_2\;k_3\;k_4$ 속도상수는 각각 $1.26{\pm}5.41%,\;0.044{\pm}19.58%,\;0.031{\pm}24.36%,\;0.008{\pm}22.78%$와 $1.36{\pm}4.76%,\;0.170{\pm}6.89%,\;0.007{\pm}23.89%,\;0.007{\pm}45.09%$이었다. 이들 속도상수를 이용하여 얻은 혈류 민감도와 도파민 운반체의 민감도는 30분, 60분, 90분, 120분에 각각 0.50, 0.35, 0.29, 0.23 그리고 0.19, 0.40, 0.53, 0.61이었다. 실제 속도상수의 비 $k_3/k_4$에 대한 (BG-OCC)/OCC와 $R_A,\;R_v$간의 상관계수는 각각 0.983, 0.984, 0.999이었으며 그때의 기울기는 각각 1.76, 0.47, 1.25이었다. 결 론 : IPT 약역학은 시간이 흐름에 따라 혈류량의 변동에 비해 도파민 운반체량의 변동에 더욱 민감한 경향을 보였으며 $k_3/k_4$에 대한 (BG-OCC)/OCC, $R_A,\;R_v$의 결과간에 좋은 상관관계를 가졌다. 따라서 이러한 약역학 컴퓨터시뮬레이션이 SPECT 영상을 이용한 도파민 운반체 또는 수용체 정량분석을 최적화하는데 매우 유용할 것으로 생각된다.