• 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.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Land and Housing Review
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• v.7 no.4
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• pp.307-314
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• 2016

Energy harvesting technique is to utilize energy that is always present but wasted. In this study, we have developed the energy harvester of the hybrid method utilizing both vibration and pressure of the vehicle traveling a road or parking lot. In the previous study, we have developed a prototype energy harvester, improved hybrid energy harvester, and developed a final product that offers improved performance in the hybrid module. The results were published in the previous paper. In this study, we installed the finally developed hybrid module in the actual parking lot. And we measured the power generation performance due to pressure and vibration, and the running speed of the vehicle when the vehicle is traveling. And we compared the results with those obtained in laboratory conditions. In a previous study performed in laboratory conditions the maximum power of the energy block was 1.066W when one single time of vibration, and 1.830W when succession with 5 times. On the other hand, in this study, we obtained the average power output of 0.310W when the vehicle is running at an average 5 km/h, 0.670W when at an average 10 km/h, and 1.250W when at an average 20 km/h, and 2.160W when at an average 5 km/h. That is, the higher the running speed of the vehicle has increased power generation performance. However, when compared to laboratory conditions, the power generation performance of the energy block in driving speed by 20km/h was lower than those in laboratory conditions. In addition, when compared to one time of vibration of laboratory conditions, power generation performance was higher when the running speed 20km/h or more and when five consecutive times in laboratory conditions, it was higher when the running speed 30km/h or more. It could be caused by a difference of load conditions between the laboratory and the actual vehicle. Thus, applying the energy block on the road would be more effective than that on the parking lot.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.3
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• pp.1-7
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• 2018

As part of a research dedicated to the field evaluation of the durability of concrete subjected to freezing-thawing, this study analyzes the relationship between the surface roughness and the relative dynamic elastic modulus through image analysis. Four mix compositions with water-to-binder ratios (W/B) of 40%, 50%, 60% and 70% and without AE agent were considered to provoke early freezing. The basic physical properties of the mixes including the relative dynamic elastic modulus and the compressive strength were first evaluated experimentally according to W/B. Then, tests were performed to measure the surface roughness followed by photographs and SEM image analysis. The measured surface roughness tended to increase with larger number of freezing-thawing cycles regardless of W/B. The relative dynamic elastic modulus appeared to increase gradually with the number of cycles for the relatively denser mixes with W/B of 40% and 50%. Besides, the surface roughness increased only at rupture for the mixes with W/B of 60% and 70%. Moreover, the analysis of the photographs of the surface of the mixes with W/B of 40% and 50% revealed that the degradation progressed gradually from the surface with the freezing-thawing cycles. However, for the mixes with W/B of 60% and 70%, apparent change of the surface remained very insignificant until rupture at which damage like cracking could be observed. Consequently, the analysis of surface photograph or the measurement of the surface roughness presented some limitation in assessing the degree of freezing-thawing-induced degradation in case of relatively porous specimens. On the other hand, the photograph and surface roughness appeared to be sufficient for assessing such degradation for the mixes with W/B of 40% and 50%. Accordingly, the image of the surface and the surface roughness are potentially applicable on site for the assessment of freezing-thawing damages in relatively dense mixes.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.18 no.6
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• pp.511-518
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• 1985

As one of the sensory factors for characterizing food quality, volatile compounds have been particularly contributed to sensory evaluation of fermented sea foods in Korea. But no chemical investigation of the volatile compounds of fermented anchovy as one of the most favored fermented sea food products has been reported. Accordinglry, for a series study of processing of low salt fermented sea foods, changes in volatile compounds and fatty acid composition of fermented anchovy with low salt contents ($4\%$ of salt contents) were experimented fermentation comparing with conventional fermented anchovy ($20\%$ of salt contents). Total lipid of raw anchovy was composed of $77.6\%$ of neutral lipid, $22.1\%$ of phospholipid and $0.3\%$ of glycolipid. And polyenoic acid was held $39.8\%$ of fatty acid composition of total lipid and the major fatty acids in those were $C_{22:6},\;C_{20:5}$. During the fermentation of anchovy saturated fatty acid ($C_{16:0},\;C_{18:0},\;C_{l4:0}$) and monoenoic acid ($C_{16:1},\;C_{18:1}$) increased while polyenoic acid ($C_{22:6},\;C_{20:5}$) decreased greatly. Thirty-eight kinds of volatile component from the whole volatile compounds obtained from fermented anchovy after 90 days fermentation were identified, and composed of some alcohols (8 kinds), carbonyl compounds (9 kinds), hydrocarbons (8 kinds) and fatty acids (8 kinds). During fermentation 8 kinds of volatile acids, 5 kinds of amines, 9 kinds of carbonyl compounds were also detected. Those volatile acids such as acetic acid, isovaleric acid, propionic acid, n-butyric acid were the major portion of total volatile fatty acids of 60 days fermented anchovy prepared with low salt contents. On the other hand, carbonyl compounds such as ethanal, 3-methyl butanal, hexanal, 2-methyl propanal were the major ones, while TMA held the most part of volatile amines in fermented anchovy with low salt contents after 60 days. Conclusively, there was little difference in composition of volatile components, but merely a little difference in content of those between low salt fermented anchovy and conventional fermented ones.

• Journal of Technology Innovation
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• v.25 no.3
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• pp.155-195
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• 2017

The goal of the present study is reviewing the literature on the scientific community and also on science, technology & society to increase interactions between innovation studies and social studies of science and technology. Up until now, various empirical studies on Korean scientists and engineers have been concentrated on researchers at universities, while they have paid inadequate attention to researchers at state-funded research institutes and private companies. In addition, these studies have tended to use concepts in Western academia to elucidate Korean cases. On the other hand, recent empirical researches on the effects of the evaluation systems in universities, PBS system, and the network of school ties suggest that these topics may reveal the unique characteristics of Korean scientific community. Empirical studies on the scientific community have also shown that Korean research institutes and researchers who are in charge of innovation in Korea have demonstrated a tendency to conform to the government's guidance due to long experiences of state-led R&D and nationalism. Research on science, technology and society has viewed the participation of citizens in science and technology as a way toward science and technology democracy, and tended to have a strong practical orientation. However, there has been a relatively small amount of research on how citizen participation influences the direction and content of technological innovation. Also, although, from the viewpoint of technological innovation, how participation of citizens in science and technology can contribute to knowledge production and innovation is a critical issue, relatively small numbers of case studies on this subject have been conducted. Therefore, as the scholars who have emphasized the democracy of science and technology have actually experimented with various ways of citizen participation, innovation researchers may have to design and implement citizen participation through which citizens' local knowledge can contribute to technological innovation.

• Journal of Soil and Groundwater Environment
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• v.8 no.2
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• pp.62-69
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• 2003

The aim of research is the evaluation of the $Cr^{6+}$ emission features of the liquid injection through emission experiments in varying conditions, based on a field-mixing ratio. The results showed that the content of $Cr^{6+}$ content in cement measured had an Ordinary Potland Cement (OPC) of 25.3 mg/kg, which constitute the largest portion among the other materials. Likewise, the emission experiment of homo-gel and sand-gel generally satisfied the standard of KSLT (Korea Standard Leaching Test) in waste of 1.5 mg/L, but in case of the standard of KSLT in soil the emission of OPC $Cr^{6+}$ of 4.85 mg/kg. These conditions is a little exceeded the criteria in the ‘Ga’ area in terms of Korea Soil Environmental Preservation Law. In addition, results generated by the mock-up injection facilities revealed that $Cr^{6+}$ emission increased as Water/Cement and injection pressure increased. At injection pressure higher than 4 kg/㎤, $Cr^{6+}$ emission exceeded the water preservation standard of 0.5 mg/L. Similarly, a pattern experiment of C $r^{6+}$ emission according to pH was conducted, in order to evaluate the $Cr^{6+}$ emission features of grout materials in leachate below pH 5 such as pH 4 acid rain or landfill. Results show that $Cr^{6+}$ emission dramatically increased in high acidic or basic state. It indicates that $Cr^{6+}$ emission will probably increase in an environment where grout materials are injected. On the other hand, concentration of leachate was determined in areas where grout materials are used. The results show that the concentration of emission in an ultra purity condition does not manifest intensity, and is affected in the OPC>MC>SC order. It means that the pollutants or $Cr^{6+}$ emission increases with decreasing concentration. As such, $Cr^{6+}$ emission will probably exceed the countermeasure criteria according to the types of gout materials. Similarly, high pressure or injection will cause increased $Cr^{6+}$ emission. Therefore, the selection of materials or mixing ratio should be considered in general as well as according to specific industries, based on the strength and pH of $Cr^{6+}$ emission.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.2
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• pp.79-87
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• 2009

Pot experiments using sand culture were conducted in 2004 under greenhouse conditions to evaluate the effect of nitrogen deficiency on red pepper biomass. Nitrogen stress was imposed by implementing 6 levels (40% to 140%) of N in Hoagland's nutrient solution for red pepper. Canopy reflectance measurements were made with hand held spectral sensors including $GreenSeeker^{TM}$, $Crop\;Circle^{TM}$, and $Field\;Scout^{TM}$ Chlorophyll meter, and a spectroradiometer as well as Minolta SPAD-502 chlorophyll meter. Canopy reflectance and dry weight of red pepper were measured at five growth stages, the 30th, 40th, 50th, 80th and 120th day after planting(DAT). Dry weight of red pepper affected by nitrogen stress showed large differences between maximum and minimum values at the 120th DAT ranged from 48.2 to $196.6g\;plant^{-1}$, respectively. Several reflectance indices obtained from $GreenSeeker^{TM}$, $Crop\;Circle^{TM}$ and Spectroradiometer including chlorophyll readings were compared for evaluation of red pepper biomass. The reflectance indices such as rNDVI, aNDVI and gNDVI by the $Crop\;Circle^{TM}$ sensor showed the highest correlation coefficient with dry weight of red pepper at the 40th, 50th, and 80th DAT, respectively. Also these reflectance indices at the same growth station was closely correlated with dry weight, yield, and nitrogen uptake of red pepper at the 120th DAT, especially showing the best correlation coefficient at the 80th DAT. From these result, the aNDVI at the 80th DAT can significantly explain for dry weight of red pepper at the 120th DAT as well as for application level of nitrogen fertilizer. Consequently ground remote sensing as a non-destructive real-time assessment of plant nitrogen status was thought to be a useful tool for in season nitrogen management for red pepper providing both spatial and temporal information.

• Nuclear Medicine and Molecular Imaging
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• v.43 no.5
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• pp.487-494
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• 2009

Purpose: Small size of recombinant scFv antibody has many advantages such as rapid blood clearances and improved targeting antibodies to tumor region. On the other hand owing to small size, number of amino group is insufficient in conjugation with chelator and fluorescence labeling. This study is to introduce poly lysine tag to the C-terminal end of scFv lym-1 sequence for fluorescence chelator conjugation. Materials and Methods: Poly lysine scFv lym-1 gene, cloned into pET-22b (+) vector, was expressed in E. coli BL21 (DE3) strain. Antibody purification was performed with Ni-NTA column and then size exclusion column chromatography. Expression and purification levels of poly lysine tagged scFv lym-1 antibody were confirmed by western blot analysis. I-124, I-125, I-131 and Tc-99m were used for radiolabeling of purified poly lysine scFv lym-1. Flow cytometry analysis of FIT( conjugated poly lysine scFv lym-1 was performed for confirmation of immunoreactivity of human Burkitt's lymphoma cells. Results: Poly lysine scFv lym-1 antibody was purified through two steps and identified as molecular weight of 48 KDa. Radiolabeling yields of I-124, I-125, I-131 and Tc-99m into poly lysine scFv lym-1 were >99%, >99%, >95% and >99%, respectively. Flow cytometry analysis of poly lysine scFv and scFv lym-1 was showed similar immunoreactivity to human Burkitt's lymphoma cells. Conclusion: Poly lysine tag was useful for the sufficient number of amino groups to scFv lym-1 antibody for chelator conjugation with minimizing loss of immunoreactivity.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.4
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• pp.185-198
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• 2001

This study was conducted to reveal the effects of local climatic conditions on reproductive growth in a mature stand of Korean white pine based on climatic estimates. For this, the reproductive growth such as production and characteristics of cone and seed were first measured and summarized for seven years from 1974 to 1980. The local climatic conditions in the study site were also estimated by both a topoclimatological method and a spatial statistical technique. The local climatic conditions were then correlated with and regressed on the growth factors to reveal the relationships between the climatic estimates and the reproductive growth. Average number of conelet formation per tree showed highly negative correlation with some climatic variables related to minimum temperature in the year of flower bud differentiation. Especially, the most significant negative correlation were found between average of the minimum temperature for June and July of flower bud differentiation year and the number of conelet formation. There was no significant correlation between the number of cone production and climatic variables. However, total precipitation from December of the flowering year to February of the cone production year showed the most high correlation (r=0.6036) with the number of cone production. It was found that significant climatic variables affecting the amount of cone drop and cone drop percentage were the sum of cloudy days from June of the flowering year to August of the cone production year. Positive correlation was significantly recognized between the average weight of empty seed per cone and total precipitation from December of the flowering year to February of the cone production year. For the percentage of empty seed, five climatic variables among 19 variables were significantly correlated at 10% level. The average weight of a cone showed negative correlation with total precipitation from June of the flowering year to August of the cone production year. It was also found that average weight of a seed had highly negative correlation with total precipitation from December of the flowering year to February of the cone production year. The average weight of cone coat was negatively correlated with two climatic variables derived from clear days, which are sum of clear days from November of the flowering year to March of the cone production year and sum of clear days from December of the flowering year to February of the cone production year. On the other hand, it showed positive correlation with mean temperature of May in the flowering year. The exactly same results were obtained in correlation analysis for the percentage of cone coat.