• Radiation Oncology Journal
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• v.9 no.1
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• pp.123-130
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• 1991

A docking intraoperative electron beam applicator system, which is easily docking in the collimator for a linear accelerator after setting a sterilized transparent cone on the tumor bearing area in the operation room, has been designed to optimize dose distribution and to improve the efficiency of radiation treatment method with linear accelerator. This applicator system consisted of collimator holder with shielded metals and docking cone with transparent acrylic cylinder, A number of technical innovations have been used in the design of this system, this dooking cone gives a improving latral dose coverage at therapeutic volume. The position of $90\%$ isodose curve under suface of 8 cm diameter cone was extended $4\sim7$ mm at 12 MeV electron and the isodose measurements beneath the cone wall showed hot spots as great as $106\%$ for acrylic cone. The leakage radiation dose to tissues outside the cone wall was reduced as $3\sim5\%$ of output dose. A comprehensive set of dosimetric characteristics of the intraoperative radiation therapy applicator system is presented.

• KDI Journal of Economic Policy
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• v.13 no.1
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• pp.115-134
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• 1991

The role of pension plans in the macroeconomy has been a subject of much interest for some years. It has come to be recognized that pension plans may alter basic macroeconomic behavior patterns. The net effects on both savings and labor supply are thus matters for speculation. The aim of the present paper is to provide quantitative results which may be helpful in attaching orders of magnitude to some of the possible effects. We are not concerned with the providing empirical evidence relating to actual behavior, but rather with deriving the macroeconomic implications for a alternative possibilities. The pension plan interacts with the economy and the population in a number of ways. Demographic variables may thus affect both the economic burden of a national pension plan and the ability of the economy to sustain the burden. The tax transfer process associated with the pension plan may have implications for national patterns of saving and consumption. The existence of a pension plan may have implications also for the size of the labor force, inasmuch as labor force participation rates may be affected. Changes in technology and the associated changes in average productivity levels bear directly on the size of the national income, and hence on the pension contribution base. The vehicle for the analysis is a hypothetical but broadly realistic simulation model of an economic- demographic system into which is inserted a national pension plan. All income, expenditure, and related aggregates are in real terms. The economy is basically neoclassical; full employment is assumed, output is generated by a Cobb-Douglas production process, and factors receive their marginal products. The model was designed for use in computer simulation experiments. The simulation results suggest a number of general conclusions. These may be summarized as follows; - The introduction of a national pension plan (funded system) tends to increase the rate of economic growth until cost exceeds revenue. - A scheme with full wage indexing is more expensive than one in which pensions are merely price indexed. - The rate of technical progress is not a critical element in determining the economic burden of the pension scheme. - Raising the rate of benefits affects its economic burden, and raising the age of eligibility may decrease the burden substantially. - The level of fertility is an element in determining the long-run burden. A sustained low fertility rate increases the proportion of the aged in total population and increases the burden of the pension plan. High fertility has inverse effects.

• 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.