• Korean Journal of Soil Science and Fertilizer
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• v.25 no.2
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• pp.111-126
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• 1992

Silica sorption isotherm belonged to the C-type with weak L-type characteristics according to the classification system of adsorption isotherm. Silica sorption isothem fitted well to the Freundlich and Tempkin equation but not to the Langmuir equation. The color interference probably due to $Fe^{2+}$ during spectrometric silca determination by Molybdenum-blue method affected the sorption isotherm in reduced soils or low pH. Four parameters such as the intercept of Freundlich equation, the slope of Tempkin equation, the "Silica reactivity", and the "C-type slope", where the last two parameters were termed in the current study, were examined to assess treatment effects on silica sorption. Among them the "C-type slope" was found out to be the best parameter. The C-type isotherms showed the same high correlation coefficient as Freundlich and Tempkin equation when regressed to the sorption isothem. Plotting the C-type slope on a logarithmic scale vs. the pH showed high linearity. Using the "C-type slope" as a perameter, the pH and soil type affected the silica sorption while the effect of redox condtion was not significant. All Fe and Al extracted by the various reagents, and OM were highly correlated to silica sorption. Among them $Fe_d$ was identified as the highest influencing soil property. Since there is no equivalent reliable method to discriminate the forms of the soil Al-oxides their likely importance remains unclear.

• 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.

• Progress in Medical Physics
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• v.10 no.1
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• pp.17-22
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• 1999

INJ-I, INJ-E, PFN, BMI, and PRF were selected among the various factors which constitute a digital linear accelerator to find effects on the dose distribution by changing current and voltage within the permitted scale which Mevatron automatically maintained. We measured the absorbed dose using an ion chamber, analyzed the waveform of beam output using an oscilloscope, and measured symmetry and flatness using a dosimetry system. An RFA plus (Scanditronix, Sweden) device was used as a dosimetry system. Then an 0.6cc ion chamber (PR06C, USA), an electrometer (Capintec192, USA), and an oscilloscope (Tektronix, USA) were employed to measure the changes on the dose distribution characteristics by changing the beam-tuning parameters. When the currents and the voltages of INJ-I, INJ-E, PFN, BMI, and PRF were modified, we were able to see the notable change on the dose rate by examining the change of the output pulse using the oscilloscope and by measuring them using the ion chamber. However, the results of energy and flatness graph from RF A plus were almost identical. The factors had fine differences: INJ-I, INJ-E, PFN, BMI, and PRF had 0.01∼0.02% differences in D10/D20, 0.1∼0.2 % differences in symmetry, and 0.1∼0.4% differences in flatness. Since Mevatron controlled itself automatically to keep the reference value of the factor, it was not able to see large differences in the dose distribution. There were fine differences on the dose rate distribution when the voltage and the currents of the digitized factors were modified Nonetheless, a basic operational management information was achieved.

• Journal of Gastric Cancer
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• v.5 no.3 s.19
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• pp.174-179
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• 2005

Purpose: The 5-year survival rate is the most useful parameter for evaluating the effect of management on most malignant tumors. Recurrence after a curative resection for gastric cancer occurs mostly within 3 years of the operation, which caused us to evaluate whether a 3-year disease-free survival (3DFS) can be substituted for a 5-year overall survival (5OS). Materials and Methods: We reviewed the medical records of 656 consecutive patients who had undergone a curative resection for gastric cancer To assess whether 3DFS represents 5OS, we used a simple linear regression with survival probability calculated by using the survival function. Results: Recurrence was found in 175 cases during the follow-up periods. The accumulative frequencies of recurrence at postoperative 1 year, 3 years, and 5 years were 46% (81 cases), 89% (156 cases), and 97% (170 cases), respectively. The correlation coefficient (r) and the coefficient of determination $(r^2)$ between 3DFS and 5OS were 0.87 and 0.76, respectively, and the regression equation was $5OS=0.18+(0.80{\times}3DFS)$. The r and $R^2$ according to the type of recurrence were 0.89 and 0.80 in peritoneal seeding, 0.88 and 0.78 in hematogeneous metastasis, and 0.86 and 0.73 in local recurrence, respectively. The r (0.77) and $r^2$ (0.60) were relatively lower in low stages (stage I and II) compared to r (0.88) and $r^2(0.77)$ in high stages (stage III and IV). Conclusion: The 3DFS is an excellent predictor of 5OS. Therefore, if we use the former as the treatment evaluating method, 2-year time reduction in assessing and reporting treatment results is expected.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.157-175
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• 2012

It is difficult to evaluate the performance of process innovation of e-procurement which has large scale and complex processes. The existing evaluation methods for measuring the effects of process innovation have been mainly done with statistically quantitative methods by analyzing operational data or with qualitative methods by conducting surveys and interviews. However, these methods have some limitations to evaluate the effects because the performance evaluation of e-procurement process innovation should consider the interactions among participants who are active either directly or indirectly through the processes. This study considers the e-procurement process as a complex system and develops a simulation model based on MAS(Multi-Agent System) to evaluate the effects of e-procurement process innovation. Multi-agent based simulation allows observing interaction patterns of objects in virtual world through relationship among objects and their behavioral mechanism. Agent-based simulation is suitable especially for complex business problems. In this study, we used Netlogo Version 4.1.3 as a MAS simulation tool which was developed in Northwestern University. To do this, we developed a interaction model of agents in MAS environment. We defined process agents and task agents, and assigned their behavioral characteristics. The developed simulation model was applied to G2B system (KONEPS: Korea ON-line E-Procurement System) of Public Procurement Service (PPS) in Korea and used to evaluate the innovation effects of the G2B system. KONEPS is a successfully established e-procurement system started in the year 2002. KONEPS is a representative e-Procurement system which integrates characteristics of e-commerce into government for business procurement activities. KONEPS deserves the international recognition considering the annual transaction volume of 56 billion dollars, daily exchanges of electronic documents, users consisted of 121,000 suppliers and 37,000 public organizations, and the 4.5 billion dollars of cost saving. For the simulation, we analyzed the e-procurement of process of KONEPS into eight sub processes such as 'process 1: search products and acquisition of proposal', 'process 2 : review the methods of contracts and item features', 'process 3 : a notice of bid', 'process 4 : registration and confirmation of qualification', 'process 5 : bidding', 'process 6 : a screening test', 'process 7 : contracts', and 'process 8 : invoice and payment'. For the parameter settings of the agents behavior, we collected some data from the transactional database of PPS and some information by conducting a survey. The used data for the simulation are 'participants (government organizations, local government organizations and public institutions)', 'the number of bidding per year', 'the number of total contracts', 'the number of shopping mall transactions', 'the rate of contracts between bidding and shopping mall', 'the successful bidding ratio', and the estimated time for each process. The comparison was done for the difference of time consumption between 'before the innovation (As-was)' and 'after the innovation (As-is).' The results showed that there were productivity improvements in every eight sub processes. The decrease ratio of 'average number of task processing' was 92.7% and the decrease ratio of 'average time of task processing' was 95.4% in entire processes when we use G2B system comparing to the conventional method. Also, this study found that the process innovation effect will be enhanced if the task process related to the 'contract' can be improved. This study shows the usability and possibility of using MAS in process innovation evaluation and its modeling.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Korean Security Journal
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• no.42
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• pp.7-36
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• 2015

This study aims at analyzing difference of social Security network, Community unity and local government trust according to socio-demographical features, exploring the relation of social Security network, Community unity and local government trust according to socio-demographical features, presenting results between each variable as a model and verifying the property of mutual ones. This study sampled general citizens in Gwangju for about 15 days Aug. 15 through Aug. 30, 2014, distributed total 450 copies using cluster random sampling, gathered 438 persons, 412 persons of whom were used for analysis. This study verified the validity and credibility of the questionnaire through an experts' meeting, preliminary test, factor analysis and credibility analysis. The credibility of questionnaire was ${\alpha}=.809{\sim}{\alpha}=.890$. The inout data were analyzed by study purpose using SPSSWIN 18.0, as statistical techniques, factor analysis, credibility analysis, correlation analysis, independent sample t verification, ANOVA, multi-regression analysis, path analysis etc. were used. the findings obtained through the above study methods are as follows. First, building a social Security network has an effect on Community institution. That is, the more activated a, the higher awareness on institution. the more activated street CCTV facilities, anti-crime design, local government Security education, the higher the stability. Second, building a social Security network has an effect on trust of local government. That is, the activated local autonomous anti-crime activity, anti-crime design. local government's Security education, police public oder service, the more increased trust of policy, service management, busines performance. Third, Community unity has an effect on trust of local government. That is, the better Community institution is achieved, the higher trust of policy. Also the stabler Community institution, the higher trust of business performance. Fourth, building a social Security network has a direct or indirect effect on Community unity and local government trust. That is, social Security network has a direct effect on trust of local government, but it has a higher effect through Community unity of parameter. Such results showed that Community unity in Gwangju Region is an important factor, which means it is an important variable mediating building a social Security network and trust of local government. To win trust of local residents, we need to prepare for various cultural events and active communication space and build a social Security network for uniting them.

• Korean Journal of Soil Science and Fertilizer
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• v.28 no.3
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• pp.227-232
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• 1995

The model equations including scaling factors to estimate the soil water characteristics curve(SWCC) without direct measurement of soil water tension were developed. Scaling were applied to a data set of soil water content, soil water tension, particle size distribution, and OM contents of the 134 soil samples with the 10 soil textural classes. The capability of the model equations was tested on another 205 soil samples. The parameter, ${\theta}^*$, of soil water contents was used by scale transformation as follows : ${\theta}^*=[{\theta}i-{\theta}(1.5MPa)]$/$[{\theta}(10KPa)-{\theta}(1.5MPa)]$ Using ${\theta}^*$ a model equation to estimate SWCC, which was applicable to all textural classes, was developed as follows: $H(0.1MPa)=0.13{\cdot}({\theta}^*)^{-2.04}$. Other model equations to estimate the water content at the soil water tension of 10KPa [${\theta}(10KPa)$] and 1.5MPa [${\theta}(1.5MPa)$], which are required to ${\theta}^*$ were developed by using scale factors of sand(S) and silt(Si) content and organic matter content(OM) as foilows : ${\theta}(10KPa)=26.80-3.99ln[S]+2.36{\sqrt{[Si]}}+2.88[OM]$ ($R=0.81^{**}$) ${\theta}(1.5KPa)=15.75-2.86ln[S]+0.55{\sqrt{[Si]}}+0.70[OM]$ ($R=0.76^{**}$) The measured and estimated values of ${\theta}(1/30MPa)$ on the 205 soil samples were highly correlated on 1 : 1 corresponding line with $R=0.85^{**}$.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.5
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• pp.187-200
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• 2019

The main purpose of this study is to investigate the effects of motivation of the employment in Korea with gender, nationality, motivation of studying in Korea, major satisfaction and Korean proficiency level of foreign students who are studying in Korea. The second purpose is to analyze the current factors, to leverage educational management and to make and support some policy of Minister of Education for international students in order to promote utilization of employment of the students in Korea during 4 years of studying. This paper is used by IBM SPSS Statistics 24 Version for data analysis. As a result of the analysis, the average of students in Southeast Asia, Central Asia and South and North America Africa was higher than Japan and China in terms of motivation of employment in Korea. First, the external motivation to study in Korea has affected TOPIK with positive influence. Second, in the relationship between motivation to study in Korea and the majors satisfaction, the higher the internal motivation of study, the higher the majors' satisfaction level. Third, in the relationship between motivation to study in Korea and motivation of the employment in Korea, internal motivation to study has a positive effect on motivation of the employment. As internal motivation increases, grade of the motivation of the employment increases. Fourth, the relationship between major satisfaction and employment showed positive (+) effect. In other words, the higher the major satisfaction, the higher the motivation of the employment. Fifth, TOPIK has a negative effect on motivation of the employment. It means that external motivation was the main factor that positively influenced TOPIK and internal motivation was also main factors that positively influenced both major satisfaction and motivation of the employment in Korea. Sixth, when the internal motivation has an effect on motivation of Korea employment, the higher the major satisfaction as the parameter, the higher the motivation of Korea employment. Therefore, it will be helpful to understand the meaning of studying motivation and major satisfaction of international students, and to be used as basic data on education policies of universities and governments supporting the career and employment for international students.

• Korean Journal of Environmental Agriculture
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• v.37 no.3
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• pp.189-196
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• 2018

BACKGROUND: Water quality management of river requires quantification of pollutant loads and implementation of measures through monitoring study, but it requires labour and costs. Therefore, many researchers are performing nonpoint source pollution analysis using computer models. However, calibration of model parameters needs observed data. Nitrogen concentration in rainfall is one of the factors to be considered when estimating the pollutant loads through application of the nonpoint source pollution model, but the default value provided by the model is used when there are no observed data. Therefore, this study aims to provide the representative nitrogen concentration of the rainfall for the administrative district ensuring rational modeling and reliable results. METHODS AND RESULTS: In this study, rainfall monitoring data from June 2015 to December 2017 were used to determine the nitrogen concentration in rainfall for each administrative district. Range of the $NO_3{^-}$ and $NH_4{^+}$ concentrations were 0.41~6.05 mg/L, 0.39~2.27 mg/L, respectively, and T-N concentration was 0.80~7.71 mg/L. Furthermore, the national average of T-N concentration in this study was $2.84{\pm}1.42mg/L$, which was similar to the national average of T-N 3.03 mg/L presented by the Ministry of Environment in 2015. Therefore, the nitrogen concentrations suggested in this study can be considered to be resonable values. CONCLUSION: The nitrogen concentrations estimated in this study showed regional differences. Therefore, when estimating the pollutant loads through application of the nonpoint source pollution model, resonable parameter estimation of nitrogen concentration in rainfall is possible by reflecting the regional characteristics.