• Asia pacific journal of information systems
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• v.23 no.3
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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.

• The Journal of Art Theory & Practice
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• no.13
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• pp.73-95
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• 2012

In terms of element and process, art today has already been fully systemized, yet tends to become even more systemized. All phases of creation and exhibition, appreciation and education, promotion and marketing are planned, adjusted, and decided within the order of a globalized, networked system. Each phase is executed, depending on the system of management and control and diverse means corresponding to the system. From the step of education, artists are guided to determine their styles and not be motivated by their desire to become star artists or running counter to mainstream tendency and fashion. In the process of planning an exhibition, the level of artist awareness is considered more significant than work quality. It is impossible to avoid such systems and institutions today. No one can escape or be freed from the influence of such system. This discussion addresses a serious distortion in the selection system as part of the system connotatively called "art museum system," especially to evaluate artistic achievement and aesthetic quality. Called "studio system" or "art star system," the system distinguishes successful minority from failed absolute majority and justifies the results, deciding discriminative compensations. The discussion begins from work by Hunter Jonakin and Dan Perjovschi. The key point of this discussion is not their art worlds but the shared truth referred by the two as the collusive "art market" and "art star system." Through works based on their experiences, the two artists refer to these systems which restrict and confine them. Jonakin's Jeff Koons Must Die! is avideo game conveying a critical comment on authoritative operation of the museum system and star system. In this work, participants, whether viewer or artist, are destined to lose: the game is unwinnable. Players take the role of a person locked in a museum where artist Jeff Koons' retrospective is held. The player can either look around and quietly observe the works, which causes a game-over, or he can blow the classical paintings to pieces and cause the artist Koons to come out and reprimand the player, also resulting in a game-over. Like Jonakin, Dan Perjovschi's some drawings also focuses on the status of the artist shrunken by the system. Most artists are ruined in a process of competition to survive within the museum system. As John Burger properly pointed out, out of the art systems today, public collections (art museums) and private collections have become "something unbearable." The system justifies the selection system of art stars and its frame of reference, disregarding the problem of producing numerable victims in its process. What should be underlined above all else is that the present selection system seriously shrinks art's creative function and its function of generating meaning. In this situation, art might fall to the level of entertainment, accessible to more people and compromising with popularity. This discussion is based on assumption and consciousness on the matter that this situation might cause catastrophic results for not only explicit victims of the system but also winners, or ones defined as winners. The system of art is probably possible only by desire or distortion stemmed from such desire. The system can be flourished only under the economic system of avarice: quantitatively expanding economy, abundant style, resort economy in Venice and Miami, and luxurious shopping malls with up-to-date facilities. The catastrophe here is ongoing, not a sudden emergence, and dynamic, leading the system itself to a devastating end.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.1-10
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• 2010

A regional climate model (RCM) can be a powerful tool to enhance spatial resolution of climate and weather information (IPCC, 2001). In this study we conducted dynamical downscaling using Weather Research and Forecasting Model (WRF) as a RCM in order to obtain high resolution regional agroclimate indices over the Korean Peninsula. For the purpose of obtaining detailed high resolution agroclimate indices, we first reproduced regional weather for the period of March to June, 2002-2008 with dynamic downscaling method under given lateral boundary conditions from NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data. Normally, numerical model results have shown biases against observational results due to the uncertainties in the modelis initial conditions, physical parameterizations and our physical understanding on nature. Hence in this study, by employing a statistical method, the systematic bias in the modelis results was estimated and corrected for better reproduction of climate on high resolution. As a result of the correction, the systematic bias of the model was properly corrected and the overall spatial patterns in the simulation were well reproduced, resulting in more fine-resolution climatic structures. Based on these results, the fine-resolution agro-climate indices were estimated and presented. Compared with the indices derived from observation, the simulated indices reproduced the major and detailed spatial distributions. Our research shows a possibility to simulate regional climate on high resolution and agro-climate indices by using a proper downscaling method with a dynamical weather forecast model and a statistical correction method to minimize the model bias.

• The Journal of Korean Orthopaedic Ultrasound Society
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• v.7 no.1
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• pp.1-6
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• 2014

Purpose: In order to know the effectiveness of ultrasonographic examination in diagnosis and follow-up evaluation for the frozen shoulder, we compared those with contralateral side in initial and after steroid injection. Materials and Methods: For the 20 unilateral frozen shoulder from July 2012 to July 2013, ultrasonographic examination was tried using reference line 1 (CBT: distance from coracoid process to LHB tendon) and line 2 (CBG: distance from coracoid process to bicipital groove). We tried 1 time steroid injection and compared the improvement in gross motion and reference line with 2 month, 4 month, and 6 month's serial ultrasonographic examination. Results: The differences on CBT line between lesion side and normal side were -5.6 mm, -5.0 mm, and -4.3 mm in neutral (Neut), external rotation (ER) and internal rotation (IR), respectively. The differences on CBG line were -6.1 mm, -4.7 mm, and -5.0 mm respectively (p<0.05). The changes in the reference line after steroid injection were evaluated at 2 month (CBT: -4.8 mm, -3.5 mm, -2.6 mm / CBG: -4.7 mm, -4.0 mm, -3.6 mm), 4 month (CBT: -4.7 mm, -3.2 mm, -1.7 mm / CBG: -4.3 mm, -3.7 mm, -1.2 mm), and 6 month (CBT: -1.1 mm, -2.9 mm, -0.5 mm / CBG: -1.2 mm, -0.7 mm, -0.9 mm). The gross motion was improved at 4 month after injection, from elevation $108^{\circ}$, ER $32^{\circ}$, IR L5 in initial to $133^{\circ}$, $42^{\circ}$, L3 respectively (p<0.05). Pain improved from 7.5 in initial to 3.0 at 2 month on visual analog scale (VAS). Conclusion: The serial examination after steroid injection revealed that the delayed improvement on reference line (6 month) compared with pain (2 month) or gross motion (4 month). Dynamic ultrasonogram was useful in diagnosis and follow-up evaluation of frozen shoulder.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.398-403
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• 2023

Process-based K-cabbage model is based on physiological processes such as photosynthesis and phenology, making it possible to predict crop growth under different climate conditions that have never been experienced before. Current first-stage process-based models can be used to assess climate impact through yield prediction based on climate change scenarios, but no comparison has been performed between big data obtained from the main production area and model prediction so far. The aim of this study was to find out the direction of model improvement when using the current model for yield prediction. For this purpose, model performance evaluation was conducted based on data collected from farmers growing 'Chungwang' cabbage in Taebaek and Samcheok, the main producing areas of Chinese cabbage in highland region. The farms surveyed in this study had different cultivation methods in terms of planting date and soil water and nutrient management. The results showed that the potential biomass estimated using the K-cabbage model exceeded the observed values in all cases. Although predictions and observations at the time of harvest did not show a complete positive correlation due to limitations caused by the use of fresh weight in the model evaluation process (R²=0.74, RMSE=866.4), when fitting the model based on the values 2 weeks before harvest, the growth suitability index was different for each farm. These results are suggested to be due to differences in soil properties and management practices between farms. Therefore, to predict attainable yields taking into account differences in soil and management practices between farms, it is necessary to integrate dynamic soil nutrient and moisture modules into crop models, rather than using arbitrary growth suitability indices in current K-cabbage model.