• Journal of Chest Surgery
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• v.32 no.5
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• pp.471-479
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• 1999

Background: Patients with tetralogy of Fallot(TOF), pulmonary atresia(PA) and major aortopulmonary collateral arteries(MAPCAs) have been managed by heterogenous surgical strategies. We have taken 5 different surgical approaches to this lesion. In this study, relative advantages and disadvantages of each strategy were discussed by analyzing our surgical results. Material and Method: Between January 1986 and June 1998, 50 patients aged 1 to 177(39${\pm}$31) months at a various morphologic spectrum of this lesion were treated at our institution. The groups, which were classified according to the initial approaches, consisted of one-stage unifocalization(A-1, n=9), staged unifocalization(A-2, n=11), right ventricle to pulmonary artery connection(RV-PA)(B-1, n=11), one-stage RV-PA plus unifocalization (B-2, n=11), and one-stage definitive repair(C, n=8). Morphologic charateristics, operative mortalities, and probabilities of definitive repair were compared between the groups and the causes of death, complications and the follow-up results were described. Result: Mean ages at the first operation were 57${\pm}$18(A-1), 42${\pm}$48(A-2), 18${\pm}$14(B-1), 52${\pm}$55(B-2), and 32${\pm}$34(C) months (p<0.05). Mean numbers of MAPCAs were least in group C (A-1=4.3${\pm}$1.0, A-2=4.5${\pm}$1.3, B-1=4.1${\pm}$1.9, B-2=4.1${\pm}$1.6, C=3.4${\pm}$1.8 : p<0.05). The ratios of the direct and the indirect MAPCAs in each group were not different between the grousps (A-1=91%/9%, A-2=78%/22%, B-1=80%/20%, B-2=80%/20%, C=81%/19% : p>0.05). Nineteen patients had more than 1 dependent MAPCAs. True pulmonary arteries were not present 13 patients and they were confluent in 29. A total of 101 operations were performed. Operative mortalities of initial procedures were 33%(3/9, A-1), 18%(2/11, A-2), 0%(0/11, B-1), 36%(5/11, B-2) and 13%(1/8, C) (p<0.05). Percentages of the definitive repair among the patients of each group were 22%(2/9, A-1), 18%(2/11, A-2). 45%(5/11, B-1), 27%(4/11, B-2), and 100%(8/8, C)(p<0.05). Hypoxic respiratory and cardiac failure(6), hypoxic encephalopathy(2) and sepsis(4) were the major causes of death. Phrenic nerve palsy occured in 5 patients. Thirty-one patients among the survivals have been followed up for a mean duration of 74${\pm}$42(3-145) months. One and five year actuarial survival rates were 73% and 73 %. Conclusion: In conclusion, Right ventricle to pulmonary artery connection used in patients with TOF/PA/MAPCA as an inital procedure appeared to be highly successful in enhancing the chance of satisfactory definitive repair without the significant surgical risks. One-stage total repair at an ealier age group could be performed safely with the resonable outcomes. Unifocalization approach, whether it was performed in a single stage or in the multiple stages, resulted in the high operative mortality and the lowest chance of definitive repair, however more tailored selection of the patients and the long follow-up is mandatory to prove the usefulness of this approach.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.1-22
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• 2021

Aspect Based Sentiment Analysis (ABSA), which analyzes sentiment based on aspects that appear in the text, is drawing attention because it can be used in various business industries. ABSA is a study that analyzes sentiment by aspects for multiple aspects that a text has. It is being studied in various forms depending on the purpose, such as analyzing all targets or just aspects and sentiments. Here, the aspect refers to the property of a target, and the target refers to the text that causes the sentiment. For example, for restaurant reviews, you could set the aspect into food taste, food price, quality of service, mood of the restaurant, etc. Also, if there is a review that says, "The pasta was delicious, but the salad was not," the words "steak" and "salad," which are directly mentioned in the sentence, become the "target." So far, in ABSA, most studies have analyzed sentiment only based on aspects or targets. However, even with the same aspects or targets, sentiment analysis may be inaccurate. Instances would be when aspects or sentiment are divided or when sentiment exists without a target. For example, sentences like, "Pizza and the salad were good, but the steak was disappointing." Although the aspect of this sentence is limited to "food," conflicting sentiments coexist. In addition, in the case of sentences such as "Shrimp was delicious, but the price was extravagant," although the target here is "shrimp," there are opposite sentiments coexisting that are dependent on the aspect. Finally, in sentences like "The food arrived too late and is cold now." there is no target (NULL), but it transmits a negative sentiment toward the aspect "service." Like this, failure to consider both aspects and targets - when sentiment or aspect is divided or when sentiment exists without a target - creates a dual dependency problem. To address this problem, this research analyzes sentiment by considering both aspects and targets (Target-Aspect-Sentiment Detection, hereby TASD). This study detected the limitations of existing research in the field of TASD: local contexts are not fully captured, and the number of epochs and batch size dramatically lowers the F1-score. The current model excels in spotting overall context and relations between each word. However, it struggles with phrases in the local context and is relatively slow when learning. Therefore, this study tries to improve the model's performance. To achieve the objective of this research, we additionally used auxiliary loss in aspect-sentiment classification by constructing CNN(Convolutional Neural Network) layers parallel to existing models. If existing models have analyzed aspect-sentiment through BERT encoding, Pooler, and Linear layers, this research added CNN layer-adaptive average pooling to existing models, and learning was progressed by adding additional loss values for aspect-sentiment to existing loss. In other words, when learning, the auxiliary loss, computed through CNN layers, allowed the local context to be captured more fitted. After learning, the model is designed to do aspect-sentiment analysis through the existing method. To evaluate the performance of this model, two datasets, SemEval-2015 task 12 and SemEval-2016 task 5, were used and the f1-score increased compared to the existing models. When the batch was 8 and epoch was 5, the difference was largest between the F1-score of existing models and this study with 29 and 45, respectively. Even when batch and epoch were adjusted, the F1-scores were higher than the existing models. It can be said that even when the batch and epoch numbers were small, they can be learned effectively compared to the existing models. Therefore, it can be useful in situations where resources are limited. Through this study, aspect-based sentiments can be more accurately analyzed. Through various uses in business, such as development or establishing marketing strategies, both consumers and sellers will be able to make efficient decisions. In addition, it is believed that the model can be fully learned and utilized by small businesses, those that do not have much data, given that they use a pre-training model and recorded a relatively high F1-score even with limited resources.

• Journal of Distribution Research
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• v.17 no.4
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• pp.81-101
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• 2012

A franchise system develops competitive products for a franchise store through the system established by the franchise head office. Therefore, it has advantages of expanding the marketing effect since the risk of failure is reduced for a founder and the franchise head office supports the overall sales, advertisement and promotional activities. Also, a franchise store has advantages of fulfilling necessary facilities and tools on advantageous terms, reducing expenses by purchasing in bulk, and getting a supply of products with stable qualities. However, aside from such advantages, franchise head offices are forcing franchise stores to make unnecessary investments in equipments and remodel the interior. Also, franchise business operators are being made to share the cost of marketing and multiple franchise stores are being approved within the same business district, and franchise business operators are suffering damages. Therefore, cases of shutting down a franchise store or not renewing the contract are frequent. From the position of a franchise head office, profits that are generated from franchise fees, interior remodeling fees and supplying facilities and materials will increase as the number of new franchise stores increases. However, franchise stores are faced with difficulties due to excessive competitions between similar types of businesses and the overlapping of business districts that come from increases in the number of stores, and they eventually end up shutting down. Therefore, in order for a franchise business operator and franchise head office to grow and develop continuously, opening new stores is important, but successfully renewing the contract by maintaining a relationship with an existing franchise business operator is desirable. In this aspect, a study that examines the elements that can affect the relationship between a franchise business operator and franchise head office is believed to be important for the development of the franchise industry and creating safe jobs for the public. With an emphasis on the relationship between a franchise head office and franchise store, this study attempted to examine the effect of characteristics of a franchise head office and franchise business operator on the bilateral relationship such as the faith and immersion, and wished to review the effects of such faith and immersion on the satisfaction of a franchise store, including an intention of renewing the contract. In particular, in the current situation of great uncertainties in the market, this study also wished to examine how uncertain market elements will affect the relationship between the characteristics of a franchise head office and franchise business operator, and the faith and immersion. The study revealed that among the characteristics of a franchise head office, the standardization management of a franchise head office hinders a franchise store's faith and immersion in a franchise head office. Also, a franchise head office's support was shown to increase a franchise store's faith and immersion. However, it was revealed that a franchise head office's regulation and incentive policies for a franchise store do not affect a franchise store's faith and immersion. Among characteristics of a franchise business operator, a franchise store's healthy financial status and entrepreneur spirits were shown to enhance the faith and immersion in a franchise head office. However, it was shown that excellent business abilities of a franchise business operator actually reduce the immersion for a franchise head office. Also, the faith and immersion in a franchise head office were shown to enhance the intention of renewing the contract by increasing the satisfaction for a franchise head office. In addition, it was originally believed that the effects of a franchise business operator's characteristics on the faith and immersion in a franchise head office will vary depending on the market uncertainty, but the effect of a franchise business operator's characteristics depending on the recognition of uncertainties was shown to be insignificant. Such findings show that instead of making a franchise store pay for equipment investments and marketing and obtaining profits by force, a franchise head office should actively support a franchise store so that a franchise store's business activities can be conducted well, which will bring profits to a franchise store and ultimately to a franchise head office. This is a more desirable direction for the development of both parties. Implications of such findings are summarized as follows. First, it was shown that a franchise head office's standardization management actually reduces a franchise store's faith and immersion. Therefore, it is believed that instead of conducting standardization managements for regulating and managing franchise stores, measures should be developed so that franchise stores can actually participate voluntarily. For this, a head office should put in efforts to develop and provide standardized manuals, and make sure that a self-review system takes root. Second, a franchise head office's incentives did not have significant effects on the faith and immersion, but the support was shown to be effective. Therefore, it can be seen that instead of taking post-measures for a franchise store, taking pre-measures of actively supporting is more effective in maintaining a franchise store. Third, among characteristics of a franchise head office, it was shown that a franchise store's healthy financial status increased the faith and immersion in a franchise head office. Therefore, when selecting a franchise business operator, instead of thoughtlessly opening up franchise stores for the profit of a head office, it is believed that reviewing a franchise business operator's financial firepower and credit status is necessary. As for academic implications, previous studies examined the relationship by focusing on the characteristics of a franchise head office and franchise store, but this study focused on the characteristics of a franchise business operator. Therefore, this study dealt with the importance of a franchise business operator's competence, and is significant because it revealed the fact that a franchise business operator's excellent commercialization ability can become an element that hinders the immersion in a franchise head office. It was originally believed that a franchise store's characteristics will have different effects on the faith and immersion depending on the market uncertainty, but it was shown that the effect of a franchise store's characteristics depending on the recognition of uncertainties was insignificant, and that is the limitation of this study.