• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.17 no.4
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• pp.292-302
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• 2012

This paper reviews comparison analysis of current and latest application for stock identification methods of Todarodes pacificus, and the pros and cons of each method and consideration of how to compensate for each other. Todarodes pacificus which migrates wide areas in western North Pacific is important fishery resource ecologically and commercially. Todarodes pacificus is also considered as 'biological indicator' of ocean environmental changes. And changes in its short and long term catch and distribution area occur along with environmental changes. For example, while the catch of pollack, a cold water fish, has dramatically decreased until today after the climate regime shift in 1987/1988, the catch of Todarodes pacificus has been dramatically increased. Regarding the decrease in pollack catch, overfishing and climate changes were considered as the main causes, but there has been no definite reason until today. One of the reasons why there is no definite answer is related with no proper analysis about ecological and environmental aspects based on stock identification. Subpopulation is a group sharing the same gene pool through sexual reproduction process within limited boundaries having similar ecological characteristics. Each individual with same stock might be affected by different environment in temporal and spatial during the process of spawning, recruitment and then reproduction. Thereby, accurate stock analysis about the species can play an efficient alternative to comply with effective resource management and rapid changes. Four main stock analysis were applied to Todarodes pacificus: Morphologic Method, Ecological Method, Tagging Method, Genetic Method. Ecological method is studies for analysis of differences in spawning grounds by analysing the individual ecological change, distribution, migration status, parasitic state of parasite, kinds of parasite and parasite infection rate etc. Currently the method has been studying lively can identify the group in the similar environment. However It is difficult to know to identify the same genetic group in each other. Tagging Method is direct method. It can analyse cohort's migration, distribution and location of spawning, but it is very difficult to recapture tagged squids and hard to tag juveniles. Genetic method, which is for useful fishery resource stock analysis has provided the basic information regarding resource management study. Genetic method for stock analysis is determined according to markers' sensitivity and need to select high multiform of genetic markers. For stock identification, isozyme multiform has been used for genetic markers. Recently there is increase in use of makers with high range variability among DNA sequencing like mitochondria, microsatellite. Even the current morphologic method, tagging method and ecological method played important rolls through finding Todarodes pacificus' life cycle, migration route and changes in spawning grounds, it is still difficult to analyze the stock of Todarodes pacificus as those are distributed in difference seas. Lately, by taking advantages of each stock analysis method, more complicated method is being applied. If based on such analysis and genetic method for improvement are played, there will be much advance in management system for the resource fluctuation of Todarodes pacificus.

• Journal of radiological science and technology
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• v.33 no.2
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• pp.109-120
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• 2010

The purpose of this study was first to analyze the utilization of dental examination through questionnaire to develop a diagnostic reference level of patient doses for dental radiography in korea. 77 dental institutions were classified into three groups: A group for the dental hospitals of the college of dentistry (11 institutions), B group for dental hospitals (30 institutions) and C group for dental clinics (36 institutions). The results were as follows : The mean numbers of unit chairs and medical staffs were 140.2, 15.3 and 5.8 sets, 112.6, 7.3 and 1.7 dentists, 3.1, 0.5 and no one radiologic technologists, and 19.7, 12.5 and 3.3 dental hygienists in A, B and C groups, respectively. The mean numbers of dental X-ray equipments were 14.64, 3.21 and 2.19 in A, B and C groups, respectively. Intraoral dental X-ray unit was used the most, the following equipments were panoramic, cephalometric, and cone-beam CT units. The most used X-ray imaging system was also digital system (above 50%) in all three groups. Insight dental film (Kodak, USA) having high sensitivity was routinely used for periapical radiography. The automatic processor was not used in many dental institutions, but the film-holding device was used in many dental institutions. The utilization rates of PACS in A, B and C groups were 90.9%, 83.3% and 16.7% respectively, and the PACS software program was used the most PiView STAR (Infinitt, Korea). The annual mean number of radiographic cases in one dental institution in 2008 for A group was 6.8 times and 21.2 times more than those for B and C groups, and periapical and panoramic radiographs were taken mostly. Tube voltage (kVp) and tube current (mA) for periapical radiography were similar in all three groups, but exposure time in C group was 12.0 times and 3.5 times longer than those in B and C groups. The amount of radiation exposure in C group, in which dental hygienists take dental radiographs, was more than those in other groups. The exposure parameters for panoramic radiography were similar in all three groups. In conclusion, the exposure parameters in dental radiography should be determined with reference level, not past experiences. Use of automatic processor and film-holding devices reduces the radiation exposure in film system. The quality assurance of dental equipments are necessary for the reduction of the patient dose and the improvement of X-ray image quality.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.