• Membrane Journal
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• v.32 no.4
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• pp.218-226
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• 2022

Hydrogen, a carrier of large-capacity chemical and clean energy, is an important industrial gas widely used in the petrochemical industry and fuel cells. In particular, hydrogen is mainly produced from fossil fuels through steam reforming and gasification, and carbon dioxide is generated as a by-product. Therefore, in order to obtain high-purity hydrogen, carbon dioxide should be removed. This review focused on free-standing polymeric membranes and mixed-matrix membranes (MMMs) that separate hydrogen from carbon dioxide reported in units of Barrer [1 Barrer = 10^-10 cm³ (STP) × cm / (cm² × s × cmHg)]. By analyzing various recently reported papers, the structure, morphology, interaction, and preparation method of the membranes are discussed, and the structure-property relationship is understood to help find better membrane materials in the future. Robeson's upper bound limits for hydrogen/carbon dioxide separation were presented through reviewing the performance and characteristics of various separation membranes, and various MMMs that improve separation properties using technologies such as crosslinking, blending and heat treatment were discussed.

• Journal of Technology Innovation
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• v.25 no.2
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• pp.153-179
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• 2017

Korean government has granted fixed indirect cost rates to several exceptional R&D programs which is lower than the predetermined rate by the government. It has been needed to evaluate the validity of exceptional R&D programs and determine the optimal indirect costs rate of the programs. This study analyzes the cost structure and explores drivers of indirect costs of exceptional R&D programs and evaluates the validity of current indirect costs rates. Finally, we propose the formulas for indirect costs rates of exceptional R&D programs. We analyze the cost structure of the exceptional R&D programs. Equipments and material costs are 50% in infra building program. Scholarship to students is 43% in HRD program. Equipments and material costs are 50% and R&D activity costs are 31% in international R&D program. Main cost components of evaluation program are salary(37%), R&D execution costs(21%) and R&D activity costs(19%). We propose three formulas of indirect costs for exceptional programs. 1) The cost items with exceptionally large amount are excluded in the base of formula for indirect costs. 2) Fixed indirect cost rate is applied for specific R&D programs. 3) Upper bound is set for the cost items with exceptionally large amount in the calculation of indirect costs rate. Our proposal is expected to contribute to the improvement of the efficiency of national R&D programs.

• 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.

• The Journal of Korean Medicine
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• v.19 no.2
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• pp.5-16
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• 1998

Hwabyung is a common emotional disorder which has symptoms expressed like firt's explosion in middle-aged after long period of emotional suppression among Koreans. It is similar in its characteristics such as neurosis, anxiety, panic attacks in Western Medicine, though the treatment method was not effective. So we have done a clinical research on Oriental Medical Method, especially on Acupuncture Therapy, and obtained following results. 1. Patients with Hwabyung complained of pressure pain around the Chunjung(?中, CV-17) point distinctively. About 70% of those were located on the CV-17 point, 25% were 1cm upper than the CV-17 point and 5% of those were 1cm lower point than the CV-17 point. 2. Degrees of pressure pain were divided into 5 grades from ade 1(feeling pain with slight pressure) to grade 5(feeling no pain with severe pressure), respectively. 3. Patients with Hwabyung showed various symptoms compared to fire's explosion such as anger, chest discomfort, difficulty in breathing. tachycardia. and feeling of epigasfric mass etc., and the degrees were divided into 5 grades according to the severities from grade 1(can't keep their usual living) to grade 5(no complaints with heavy stresses), respectively. 4. For the treatment of Hwabyung in this study, we had given Acupuncture therapy on some points such as Chunjung:?中:CV-17, Jungwan:中脘:CV-12) and Chunchu:天樞:S-25, etc. for 15 minutes a time twice a week. And Bunshimkiumgmnihang(分心氣飮加味方) was administered 3 times a day. 5. About 40% of the patients took treatment for more than 2 months, 29% of those took 1 to 2 months and 31% of those took less than 1 month. In this study, we excluded those who stopped treatment within a month without any expected effects. 6. We evaluated the changes of severity of pain according to the following categories such as - for no change, + for 1 grade, ++ for 2 grades, +++ for 3 grades, and ++++ for 4 grades of improvements. Among the patients taken 1 to 2 months of treatment. 48% of the those showed +, 7% of those showed ++, 3% of those showed +++ and 41% of those showed no change. Among the patients taken less than 2 months of treatment, 20%of those showed +, 40% of those showed ++, 28% of those showed +++ and 13% of those showed no change. 7. We evaluate the changes of symptoms according to the following categories such as - for no change, + for 1 grade, ++ for 2 grades, +++ for 3 grades and +++ for 4 grades of improvements. Among the patients taken 1 to 2 months of treatment, 34% of those showed +, 14% of those showed ++ and 52% of those showed no change. Among the patients taken more than 2 months of treatment, 20% of those showed +, 43% of those showed 20% of those showed +++, 3% of those showed +++ and 15% of those showed no change. 8. When we compare the changes of pain and symptoms according to the periods of treatment, the changes in quantity of pain in 1 to 2 months group was $0.72{\pm}0.75$, in more than 2 months group was $1.83{\pm}0.98$, and the changes in quantity of symptoms in 1 to 2 months group was $0.62{\pm}0.73$, in more than 2 months group was $1.75{\pm}1.03$. According to the above results, we have concluded that more than 2 months of treatment is more beneficial than 1 to 2 months of treatment.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.