• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.4
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• pp.195-205
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• 2013

Excessive input of organic matters from fish cage farming has been considered as one of the major factors disturbing benthic ecosystem, especially in semi-enclosed coastal waters. Recently offshore aquaculture in the vicinity of Jeju-do has been introduced to minimize that kind of negative impact. This study was conducted to investigate the ecological impacts of offshore aquaculture on the macrobenthic polychaete communities. A total of ten sampling works were carried out for 28 months, spanning from 10 days after starting giving feed to 3 months after stopping giving feed. During the study period, mean current velocity was quite strong with the range of 50 cm/s to 70 cm/s. TOC of surface sediment was constantly low. Significant changes in polychaete community were detected just three months after starting giving feed, which were the increase of the number of species and density at all stations. Up to 18 months after the start of farming, the amount of feed provided played an important role in the fluctuation of the number of species and density, especially at 0 m and 10 m stations. After reducing the amount of feed provided, dominance of some opportunistic species within 10 m distance from fish cages still lasted to the end of aquaculture. However, opportunistic species disappeared 3 months after the end of farming, which indicated the sign of recovery from the disturbance. From these results, the amount of food input and the period of cultivation were critical factors disturbing polychaete community and ensuing changes in this offshore and oligotrophic waters as well. In addition, study on the changes of polychaete community structure before and after fish farming showed more detailed changes in benthic ecological state than geochemical approach did.

• Journal of Environmental Impact Assessment
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• v.27 no.2
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• pp.181-193
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• 2018

The present study was conducted to analyze the quality of the habitats of Abies koreana WILS. by using the InVEST model based on the analytic hierarchy process (AHP) technique and to evaluate the economic value by estimating the carbon fixation. Abies koreana WILS., an original biological species of South Korea, may be an essential element in establishing the national biological sovereignty in the future. The subjects of the present study were the national parks in Mt. Halla, Mt. Jiri, and Mt. Sobaek, which are the habitats of Abies koreana WILS. As suggested by previous studies as a limitation of the InVEST model, the utilization of the data from relevant international publications as the input data, due to the lack of the domestic input data, may decrease the accuracy of the modeling. Therefore, the AHP technique was applied for the input data. The modeling was performed with reference to the years of 1980, 1990, and 2000 for the scenario analysis. The result of the modeling showed that the habitat quality was changed most in the national park in Mt. Halla, as the habitat quality score was decreased from 0.96 in 1980 to 0.97 in 1990 and 0.94 in 2000. In the national part of Mt. Sobeak, the habitat quality was changed most in the sub-alpine zone, as the habitat quality score was decreased from 0.98 in 1980 and 0.98 in 1990 to 0.97 in 2000. The habitat quality was best conserved in the national part in Mt. Jiri, as the habitat quality score was 0.98 in 1980, 0.99 in 1990, and 0.99 in 2000. The estimated economic loss by the change of the habitat quality was 19,280,000 USD for Mt. Halla and 8,030,000 USD for Mt. Sobeak. In the present study, the habitat quality of the Abies koreana WILS, the original species of South Korea, was evaluated and the economic value of the ecological services provided by the habitats was estimated quantitatively. The result showed that the ecosystem service model may be used to qualitatively analyze the quality of a habitat located in a specific region and to estimate the economic value quantitatively. The objective evaluation of ecosystem services demonstrated in the present study may be applied to promote sustainable utilization of natural resources and conservation of the ecosystem by predicting the changes that may be caused by external factors including the development of preservation areas.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.6
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• pp.1143-1152
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• 2012

We established a top-down methodology to estimate carbon footprint as national mean value (reference) with the statistical data on agri-livestock incomes in 2007. We also established LCI (life cycle inventory) DB by a bottom-up methodology with the data obtained from interview with farmers from 4 large-scale farms at Gunsan, Jeollabuk-do province to estimate carbon footprint in 2011. This study was carried out to compare top-down methodology and bottom-up methodology in performing LCA (life cycle assessment) to analyze the difference in GHGs (greenhouse gases) emission and carbon footprint under conventional rice cultivation system. Results of LCI analysis showed that most of $CO_2$ was emitted during fertilizer production and rice cultivation, whereas $CH_4$ and $N_2O$ were mostly emitted during rice cultivation. The carbon footprints on conventional rice production system were 2.39E+00 kg $CO_2$-eq. $kg^{-1}$ by top-down methodology, whereas 1.04E+00 kg $CO_2$-eq. $kg^{-1}$ by bottom-up methodology. The amount of agro-materials input during the entire rice cultivation for the two methodologies was similar. The amount of agro-materials input for the bottom-up methodology was sometimes greater than that for top-down methodology. While carbon footprint by the bottom-up methodology was smaller than that by the top-down methodology due to higher yield per cropping season by the bottom-up methodology. Under the conventional rice production system, fertilizer production showed the highest contribution to the environmental impacts on most categories except GWP (global warming potential) category. Rice cultivation was the highest contribution to the environmental impacts on GWP category under the conventional rice production system. The main factors of carbon footprints under the conventional rice production system were $CH_4$ emission from rice paddy field, the amount of fertilizer input and rice yield. Results of this study will be used for establishing baseline data for estimating carbon footprint from 'low carbon certification pilot project' as well as for developing farming methods of reducing $CO_2$ emission from rice paddy fields.

• Journal of the Korean Society for Marine Environment & Energy
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• v.13 no.4
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• pp.223-233
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• 2010

Spatio-temporal variations of long-term water qualities (COD, SS, $Chl-{\alpha}$, N-related nutrients (TN, TDN, $NO_3^-$, $NH_4^+$), P-related nutrients (TP, TDP, $PO_4^{3-}$)) at two stations (St. SD, St. GG) in the Geumgang Reservoir were investigated from August 2001 to July 2008. Statistical methods such as t-test, factor analysis, and multi-regression analysis were applied to the water quality data in the reservoir as well as mass balances on TP. From the temporal comparisons of the water qualities between 2002 and 2007, average concentrations of $NH_4^+$, $PO_4^{3-}$, and TDP gradually decreased down by 60%, 24%, 52% in 2007. However, those of TP and $Chl-{\alpha}$ increased to 99% and 423% during the period. From the spatial comparisons between the two stations, St. GG showed higher concentrations for all of the N- and P-related nutrients than in St. SD, while opposite result for the $Chl-{\alpha}$. The factor analysis showed that "the seasonal variations of N- and P-related nutrients" were the two dominant factors occupying 49% of total variances of water qualities. Based on this result, multi-regression analysis executed for the two most influential parameters (TP and $Chl-{\alpha}$) focusing on the seasonal variations of these parameters: SS and $Chl-{\alpha}$ has contributed decisively to the concentrations of TP during the wet and dry season, respectively. On the other hand, COD and TP has been important for the $Chl-{\alpha}$ during the wet and dry season, respectively. From the established mass balances of TP loadings in the Geumgang Reservoir, Other Sources (60%) occupied the greatest contribution and Fluvial Input (38%) and Sediment (1%) during the wet season. However, both Fluvial Water (48%) and Other Sources (47%) supplied comparable amount of inputs and Sediment (5%) showed significantly increased input during the dry seasons. Recently especially during the dry winter seasons, migrating bird's excretion was estimated to contribute up to 8% of total TP input and 21% of Other Sources.

• Environmental and Resource Economics Review
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• v.18 no.2
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• pp.279-309
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• 2009

Greenhouse gas emissions should be precisely forecast to reduce the emissions from industrial production processes. This study calculated the direct and indirect $CO_2$ emission intensities of 401 industries using the Input-Output tables 2003 and statistical data on the amount of energy use. This study had some limitations in drawing study findings because overseas data were used given the lack of domestic data. Other limiting factors included the oil distribution problems in the oil refinery sector, re-review of carbon neutral, and insufficient consideration of waste treatment. Nonetheless, this study is very meaningful since the direct and indirect $CO_2$ emission intensities of 401 industries were calculated. Specifically, this study considered from the zero-waste perspective the effects of waste, which attract interest worldwide since coke gas and gas from the steel industry are obtained as byproducts for the first time in Korea. According to the results of the analysis of $CO_2$ emission intensity per industry, typical industries whose indirect $CO_2$ emission intensity is high include crude steel making, Remicon, steel wire rods & track rail, cast iron, and iron reinforcing rods & bar steel. These industries produce products using the raw materials produced in the industrial sector whose $CO_2$ emission intensity is high. The representative industries whose direct $CO_2$ emission intensity is high include cement, pig iron, lime & plaster products, andcoal-based compounds. These industries extract raw ore from nature and refine them into raw materials that are useful in other industries. The findings in this study can be effectively used for the following case: estimation of target $CO_2$ emission reduction level reflecting each industrial sector's characteristics, calculation of potential emission reduction of each policy to reduce $CO_2$ emissions, identification of a firm's $CO_2$ emission level, and setting of the target level of emission reduction. Moreover, the findings in this study can be utilized widely in fields such as System of integrated Environmental and Economic Accounting(SEEA) and Material Flow Analysis(MFA) as the current topic of research in Korea.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.976-983
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• 2023

Purpose: The dynamic behavior of a bridge structure under seismic loading depends on many uncertainties, such as the nature of the seismic waves and the material and geometric properties. However, not all uncertainties have a significant impact on the dynamic behavior of a bridge structure. Since probabilistic seismic performance evaluation considering even low-impact uncertainties is computationally expensive, the uncertainties should be identified by considering their impact on the dynamic behavior of the bridge. Therefore, in this study, a global sensitivity analysis was performed to identify the main parameters affecting the dynamic behavior of bridges with I-curved girders. Method: Considering the uncertainty of the earthquake and the material and geometric uncertainty of the curved bridge, a finite element analysis was performed, and a surrogate model was developed based on the analysis results. The surrogate model was evaluated using performance metrics such as coefficient of determination, and finally, a global sensitivity analysis based on the surrogate model was performed. Result: The uncertainty factors that have the greatest influence on the stress response of the I-curved girder under seismic loading are the peak ground acceleration (PGA), the height of the bridge (h), and the yield stress of the steel (fy). The main effect sensitivity indices of PGA, h, and fy were found to be 0.7096, 0.0839, and 0.0352, respectively, and the total sensitivity indices were found to be 0.9459, 0.1297, and 0.0678, respectively. Conclusion: The stress response of the I-shaped curved girder is dominated by the uncertainty of the input motions and is strongly influenced by the interaction effect between each uncertainty factor. Therefore, additional sensitivity analysis of the uncertainty of the input motions, such as the number of input motions and the intensity measure(IM), and a global sensitivity analysis considering the structural uncertainty, such as the number and curvature of the curved girders, are required.

• Journal of Technology Innovation
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• v.19 no.1
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• pp.111-144
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• 2011

This study is to confirm the core factors of innovative capabilities and technological entrepreneurship affecting the performance of technology management and business management of small and medium-sized enterprises (SMEs). Through the consideration about the complex natures of technological innovation affecting by multidimensional factors, this study designs the research model that innovative capabilities, the performances of technology and business management are arranged in accordance with the innovation process; input-output-outcome. To meet this research purpose, the hypothesis are set up based on the previous research studies and the research samples are selected from members of the Innovative Business (INNO-BIZ) Association, located in Seoul and Geyonggi province. As a result of regression analysis to the responses gathered from 360 firms, the performance of business management is influenced positively by the technology superiority, market growth and business profitability which are the dominant factors of performance of technology management. In addition, three sub-variables of innovative capabilities such as R&D, strategic planning and learning capability, have positive effects on both the managerial performances. Innovativeness and progressiveness of technological entrepreneurship affect both the performances positively. Moreover, the co-relation between technological entrepreneurship of an innovation leader and innovative capabilities of organizational members are identified. Lastly, technological entrepreneurship has the mediating effect on the path of leading innovative capabilities to the managerial performances. In conclusion, the research results imply that technological innovation-type firms should periodically evaluate the performance of technology management which are the output of technological innovations and the reinvestment for ultimate business success. And improving and developing innovative capabilities and technological entrepreneurship is required to continuously and consistently investing and supporting resources on technological innovations at the firm-and government-level. It is considered that these are the crucial methods for securing the technologically competitive advantage of SMEs with less resources and narrow innovation range.

• Journal of Technology Innovation
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• v.25 no.4
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• pp.107-143
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• 2017

This study aims to understand the impact of internal and external environments and innovation efforts on a company's performance. First, the relationships and patterns between variables were determined through an exploratory factor analysis. Afterwards, a cluster analysis was conducted, in which the influential factors summarized in the factor analysis were classified. Finally, structural equation modeling was used to carry out an empirical analysis of the structural relationship between innovation efforts and the company's performance in the classified clusters. 7 factors were derived from the exploratory factor analysis of 40 input variables from external and internal environments. 4 clusters (n=1,022) were formed based on the 7 factors. Empirical analysis of the 4 clusters using structural equation modelling showed the following: Only independent technology development had a positive impact on the company's performance for Cluster 1, which is characterized by sensitivity to a technological/competitive environment and innovativeness. Only independent technology development and joint research had positive impacts on the company's performance for Cluster 2, which is characterized by sensitivity to a market environment and internal orientation. Joint research and the mediating variable of government support program utilization had positive impacts, while the introduction of technology had a negative impact on the company's performance for Cluster 3, which is characterized by sensitivity to a competitive environment, innovativeness, and willingness to cooperate with the government and related institutions. Independent technology development as well as the mediating variables of network utilization and government support program utilization had positive impacts on the company's performance for Cluster 4, which is characterized by openness and external cooperation.

• Journal of Environmental Impact Assessment
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• v.24 no.5
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• pp.456-471
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• 2015

Emission source term is one of the strong controlling factors for the air quality simulation capability, particularly over the urban area. Ulsan is an industrial area and frequently required to simulate for environmental assessment. In this study, two CAPSS (Clean Air Policy Support System) emission data; CAPSS-2003 and CAPSS-2010 in Ulsan, were employed as an input data for WRF-CMAQ air quality model for emission assessment. The simulated results were compared with observations for the local emission dominant synoptic conditions which had negative vorticities and lower geostrophic wind speed at 850hPa weather maps. The measurements of CO, $NO_2$, $SO_2$ and $PM_{10}$ concentrations were compared with simulations and the 'scaling factors' of emissions for CO, $NO_2$, $SO_2$, and $PM_{10}$ were suggested in in aggregative and quantitative manner. The results showed that CAPSS-2003 showed no critical discrepancies of CO and $NO_2$ observations with simulations, while $SO_2$ was overestimated by a factor of more than 12, while $PM_{10}$ was underestimated by a factor of more than 20 times. However, CAPSS-2010 case showed that $SO_2$ and $PM_{10}$ emission were much more improved than CAPSS-2003. However, $SO_2$ was still overestimated by a factor of more than 2, and $PM_{10}$ underestimated by a factor of 5, while there was no significant improvement for CO and $NO_2$ emission. The estimated factors identified in this study can be used as'scaling factors'for optimizing the emissions of air pollutants, particularly $SO_2$ and $PM_{10}$ for the realistic air quality simulation in Ulsan.