• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of Chest Surgery
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• v.39 no.10 s.267
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• pp.739-748
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• 2006

Background: The purpose of this study is to ascertain the neuroprotective effect of cyclosporin A on the 25-min surgical ischemia model in the spinal cords of rabbits with neuropathological correlation and histoimmunochemical analyses, Material and Method: Thirty-two New Zealand white rabbits were randomly divided into four groups: Rabbits were randomly divided into four groups: the control 12 group (n=8), the control 17 group (n=8), the cyclosporin Cs2 group (n=8), and the cyclosporin Cs7 group (n=8). The 12 group underwent a 25-min aortic cross- clamp without intervention and were sacrificed on the 2nd day postoperatively, while the 17 group underwent a 25- min of aortic cross-clamp without intervention and were sacrificed on the 7th day postoperatively. The Cs2 group received cyclosporin A (25 mg/kg) intravenously 15 min after the 25-min cross-clamp and were sacrificed on the End day postoperatively, while the Cs7 group received cyclosporin A (25 mg/kg) intravenously 15 min after the 25-min cross-clamp and were sacrificed on the 7th day postoperatively. The rabbits underwent 25-min surgical aortic cross-clamp. Neurologic functions were evaluated on the 2nd day and 7th postoperative day using Tarlov scoring system. After scoring neurologic function, all rabbits were sacrificed for histopathologic observation. Result: All rabbits survived the experimental procedure. The values of Tarlov score did not show any differences between the control and cyclosporin groups on the 2nd day. The scores of group Cs7 ($2.75{\pm}0.89$) were significantly higher than those of group 17 ($1.25{\pm}1.39$) on the 7th day (p<0,05). On the histologic exanminations, specimens of the spinal cord showed necrosis and apoptosis. The pathologic scores of group Cs7 ($1,0{\pm}0.53$) was less than those of group 17 ($2.13{\pm}1.36$, p<0.05). TUNEL staing showed apoptosis of the specimen in group 12 and Cs2 but there was no stastically significant difference between groups on the score. There were more overexpression of HSP70 and nNOS in cyclosporine group than in control group. Conclusion: We think that cyclosporin A may decrease neuronal cell death with induced upregulation of HSP70 against 25-min ischemia of the spiral cord in the rabbit.

• Journal of agricultural medicine and community health
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• v.26 no.1
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• pp.19-35
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• 2001

This study was conducted to investigate the internal changes and the countermeasure for performance improvement by Separation of Prescribing and Dispensing Practice (SPDP) in Health Center. Data were collected from two sources: Performance report before and after SPDP of 25 Health Centers in Kyongsangbuk-do and 6 Health Centers in Daegu-City and self-administerd questionnaire survey of 221 officials at health center. The results of this study were summarized as follows: Twenty-four health centers(77.4%) of 31 health centers took convenience measures for medical treatment of citizens and convenience measures were getting map of pharmacy, improvement of health center interior, introduction of order communication system in order. After the SPDP in health centers, 19.4% of health centers increased doctors and 25.8% decreased pharmacists. 58.1% of health centers showed that number of medical treatments were decreased. 96.4%, 80.6% 80.6% 96.7% of health centers showed that number of prescriptions, total medical treatment expenses, amounts paid by the insureds and the expenses to purchase drugs, respectively, were decreased. More than fifty percent(54.2%) of health centers responded that the relative importance of health works increased compared to medical treatments after the SPDP, and number of patients decreased compared to those in before the SPDP. And there was a drastic reduction in number of prescriptions, total medical treatment expenses, amounts paid by insureds, the expenses to purchase drugs after the SPDP. Above fifty percent(57.6%) of officers at health center responded that the function of medical treatment should be reduced after the SPDP. Fields requested improvement in health centers were 'development of heath works contents'(62.4%), 'rearrangement of health center personnel'(51.6%), 'priority setting for health works'(48.4%), 'restructuring the organization'(36.2%), 'quality impro­vement for medical services'(32.1%), 'replaning the budgets'(23.1%) in order. And to better the image of health centers, health center officers replied that 'health information management'(60.7%), 'public relations for health center'(15.8%), 'kindness of health center officers'(15.3%) were necessary in order. Health center officers suggested that 'vaccination program', 'health promotion', 'maternal and children health', 'communicable disease management', 'community health planning' were relatively important works, in order, performed by health center after SPDP. In the future, medical services in health centers should be cut down with a momentum of the SPDP so that health centers might reestablish their functions and roles as public health organizations, but quality of medical services must be improved. Also health centers should pay attention to residents for improving health through 'vaccination program', 'health promotion', 'mother-children health', 'acute and chronic communicable disease management', 'community health planning', 'oral health', 'chronic degenerative disease management', etc. And there should be a differentiation of relative importance between health promotion services and medical treatment services by character of areas(metropolitan, city, county).

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• The Korean Journal of Petroleum Geology
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• v.5 no.1_2 s.6
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• pp.36-47
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• 1997

This study presents multi-variable sequence model for a broader application of sequence concept proposed by Exxon group. The concept of the multi-variable model is based on the fact that internal organization and boundary type of the sequences are determined by three varying factors including 3rd-order cycles of eustasy, and tectonic movement and sediment influx with 2nd-order changes. Instead of Exxon group's systems tracts, this model adopts parasequence sets as the fundamental building blocks of the sequence, because they are descriptive stratigraphic units simply defined by internal stacking pattern, reflecting interactions of accommodation and sediment influx. Seven sequence types which vary in number and type of internal parasequence sets are formulated as associations of four types of accommodation development and three grades of sediment influx. In the southwestern margin of Ulleung Basin, the multi-variable sequence analysis of shelf-slope sequence shows systematic changes in stratal patterns and the numbs, of constituent parasequence sets (i.e. sequence type). These changes are interpreted to reflect temporal and spatial changes in type and rate of tectonic movement and sediment influx, as a result of back-arc opening and closing. During the back-arc opening, rapid subsidence, continuous rise of relative sea level, and high sediment influx gave rise to sequences dominantly of single progradational parasequence set. In the early stage of back-arc closing accompanied by local contractional deformation, different types of sequences contemporaneously formed depending on the spatial changes in tectonically-controlled accommodation and influx rates. During the subsequent slow back-arc subsidence, rise-dominated relative sea-level cycle was coupled with moderate to high sedimentation rate to have resulted in sequences consisting of $2~3$ parasequence sets.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.7
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• pp.1064-1072
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• 2010

The objectives of this study were to gain an overview of practices and effect evaluation of oven utilization at school foodservice facilities in Daegu and Gyeongbuk province. Out of 147 dieticians, who responded for questionnaires, 44 dieticians used the oven and 103 dieticians did not use the oven. All statistical analyses were conducted with the SPSS 14.0 statistical software program. With regard to the style of foodservice system, 74.4% were urban, 23.3% were rural, and 2.3% were remote country. Also, 23.3% of school foodservices produced meals by batch cooking. According to the results of the expected effect and using effect analysis for 27 items, the average of evaluation score about expected effect was 1.64 points and that of using effect was 1.61 points. Both expected effect and using effect had higher scores than average points in 13 items out of 27 items. Using effect had higher scores than expected effect in 4 items. In conclusion, using ovens could help to increase foodservice satisfaction of students at school foodservice, because it can improve the various cooking methods and the food safety management. Therefore, it is important to modernize and automate cooking equipment for quality improvement of school foodservice operations.

• Journal of Physiology & Pathology in Korean Medicine
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• v.21 no.1
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• pp.219-225
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• 2007

This study was performed to provide basic ideas as understanding and application for oriental medicinal cuisine (Yak-Sun). To develop medicinal cuisine, it is necessary to grasp the theoretical system. And to develop medicinal cuisine for health enhancement, it is also required not only to consider constitutions but also to suggest the need of knowledge for moderation in terms of regimen along with the theory of oriental medicine. Also to develop medicinal cuisine according to the perspective of oriental medicinal theory, what should be taken into account is not only the understanding of the characteristics of food materials, but also the properties of them that the theory of oriental medicine. Lastly the scientific effect of the medicinal cuisine which is developed according to the oriental medicinal theory. And it is believed to De essential for the government to make effects to set a standard and laws to validate the medicinal effects and the process of assessment so that the systematic development can be encouraged, and to prepare guidance to food development for national health improvement. This research was planned and executed to evaluate how the composition of Yak-sun(oriental diet therapy) can effect health conditions of people who are suffering from diet-related diseases like cardiovascular related disease. by taking Yak-sun in a form of nutritional supplement with our daily meals. We produced Yak-sun tea with Mansam, Hwanggi, Tanggi and Paekchak and observed nutritional composition. We concluded that we could apply the components not only in a form of tea, but also in other forms of various food. The information we received from this conclusion will be a basic information on how we can apply oriental medicinal resources into other food and will also be a steppingstone for medicinal herbs to step foot in the field of functional food research, which already draws sizable attention world-wide.

• Journal of the Economic Geographical Society of Korea
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• v.14 no.4
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• pp.467-485
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• 2011

After Korean War, Korea focused on catching up with the world economy by concentrating on some target industries around the Capital Region and southern coastal cities. Thus, the regional disparity between Capital Region and non-Capital Regions increased drastically. At last, when Korea acquired full-fledged autonomy in 1994 in the Civilian government (1993-1998) and experienced the Asian financial crisis in 1997-1998, local governments were awakened to the notion of region-oriented development, especially for regional industrial development. The purposes of this paper are to introduce regional industrial development policies since 1998 and to suggest some recommendations in terms of how to adjust regional development for industrial policies in the future. In the introducing phase (Kim administration, 1998-2003), four provincial governments requested national funding to raise regional industries that are of strategic importance. At the same time, the central government recognized the need to nurture regional industries to overcome structural weaknesses. As a result, the Roh administration (2003-2008) gave a birth to a systematizing phase. As the ultimate regional policy objective, the balanced national development has been set and the Special Acts, Special Accounts, Committee, and National Plan have been established. Regional Industrial Promotion Project has been carried out very actively during this period. It had a good start albeit idealistic to a certain extent. Therefore, the current government has changed policy paradigm from balanced growth to regional competitiveness along with global paradigm shifts. In order to enhance regional competitiveness, regional development policies have been pursued in more efficient way. Leading Industry Nurturing Projects (LINPs) on Economic Region level, existed Regional Industrial Promotion Projects (RIPPs) on Province level, and Region Specific Industry Projects (RSIPs) on Local Area level have been implemented. Now, it is appropriate to review regional development policies including industrial policies since 1998 and to adjust them for the future sustainable regional development. Because LINPs and RIPPs will be terminated in next two years, the 2nd stage projects are on planning to reduce the redundancies in two projects. In addition, business support program would be reformed from subsiding technology development to building ecological business system. Finally some policy implications are provided in this paper, which is useful to establish the new regional industrial policies for both central and local government.