• Korean Journal of Agricultural Science
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• v.14 no.1
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• pp.134-143
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• 1987

In order to observe the strawberry as the raw materials and to compare the color of strawberry's products, Hunter L,a,b tristimulate color values were measured physically by color difference meter. Food textural properties of strawberry were measured by Rheo textural meter for rheological properties of strawberry jam. According to results obtained, it showed that Hunter L,a,b tristimulus color values were affected by ripening time of strawberry and Hunter color values changed regularly on different pH. Deformation of red color pigment Hunter color values changed linearlly on different pH, therefore red color pigment of elderberry showed to be used as a food color agent. The first peak of strawberry in TPA curve was high as cherry, grape and pineapple Strawberry jam showed pseudoplastic characteristic and time dependence.

• The Journal of Korean Society for Radiation Therapy
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• v.18 no.2
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• pp.81-87
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• 2006

Purpose: Few researches have been peformed on the dose distribution of the moving organ for radiotherapy so far. In order to simulate the organ motion caused by respiratory function, multipurpose phantom and moving device was used and dosimetric measurements for dose distribution of the moving organs were conducted in this study. The purpose of our study was to evaluate how dose distributions are changed due to respiratory motion. Materials and Methods: A multipurpose phantom and a moving device were developed for the measurement of the dose distribution of the moving organ due to respiratory function. Acryl chosen design of the phantom was considered the most obvious choice for phantom material. For construction of the phantom, we used acryl and cork with density of $1.14g/cm^3,\;0.32g/cm^3$ respectively. Acryl and cork slab in the phantom were used to simulate the normal organ and lung respectively. The moving phantom system was composed of moving device, moving control system, and acryl and cork phantom. Gafchromic film and EDR2 film were used to measure dose ditrbutions. The moving device system may be driven by two directional step motors and able to perform 2 dimensional movements (x, z axis), but only 1 dimensional movement(z axis) was used for this study. Results: Larger penumbra was shown in the cork phantom than in the acryl phantom. The dose profile and isodose curve of Gafchromic EBT film were not uniform since the film has small optical density responding to the dose. As the organ motion was increased, the blurrings in penumbra, flatness, and symmetry were increased. Most of measurements of dose distrbutions, Gafchromic EBT film has poor flatness and symmetry than EDR2 film, but both penumbra distributions were more or less comparable. Conclusion: The Gafchromic EBT film is more useful as it does not need development and more radiation dose could be exposed than EDR2 film without losing film characteristics. But as response of the optical density of Gafchromic EBT film to dose is low, beam profiles have more fluctuation at Gafchromic EBT. If the multipurpose phantom and moving device are used for treatment Q.A, and its corrections are made, treatment quality should be improved for the moving organs.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.