• Asian Journal of Turfgrass Science
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• v.24 no.2
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• pp.211-217
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• 2010

This study was conducted to investigate a proper method of ball mark repair by comparing the creeping bentgrass recovery speed on golf course green treated by various methods of ball mark repair. Nine general repairing methods were tested and compared; control (no repair, A type), two common methods of USGA (B type) and GCSAA (C type), three methods with fork shaped hand set performing at Korean golf courses (Ansung Benest, D; Sky72, E; Lakeside, F type), and three methods using the repair machine with 6, 8, or 14 teeth (G, H, I type, respectively). Three creeping bentgrass cultivar of 'Penncross', 'T-1', and 'CY-2' were tested in this field experiment. This test was carried out from September to November in 2009 at the nursery on the Seoul Lakeside Golf course. The average speed of turfgrass recovery after various ball mark repairing methods have been ranked as in the order of E, D, C, B, F, I, H, G, and A. The methods of hand practise showed more effective results than repair method using machines. The ball mark recovery speeds of 'Penncross' were in the order of E, D, B, C, F, I, H, and A. In the case of 'T1' and 'CY-2', similar orders were showed as D, E, B, F, C, H, I, A, G and the order of D, E, C, F, B, H, G, I, A, respectively. The ball mark recovery speed among creeping bentgrass cultivar resulted in the order of 'CY-2', 'Penncross', and 'T-1'. The most proper method of ball mark repair was repair method using a hand set tool especially the method of the Sky72 Golf course (E type). At the first, remove a damaged grass area with fork and tap. And then gather the side grasses into the center area with pulling the grasses with fork. After that, make harden and flat on the turf surface by pounding and rolling with the round wooden stick. The final Nstep, water the repaired grass surface. This ball mark repairing practise showed a most rapid and proper recovery method on creeping bentgrass green.

• Clinics in Shoulder and Elbow
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• v.15 no.2
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• pp.79-85
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• 2012

Objective: It has been expected that patient with posttraumatic recurrent anterior shoulder dislocation might have limited daily life activity because of pain and apprehension of dislocation. But there have been only a small number of investigations regarding the rotator strength in this patient. The aim of this study is to find the characteristics about rotator strength of patient with posttraumatic recurrent anterior shoulder dislocation using an isokinetic testing. Method: We enrolled thirteen patients with posttraumatic recurrent anterior shoulder dislocation and fifteen sex, age-matched healthy nonathletic subjects in this controlled study. All participants were male and there were no significant differences between the two groups in age, height, weight, BMI. Isokinetic internal rotator and external rotator strength was evaluated with a Biodex Isokinetic Testing machine (Biodex Medical Systems, Shirley, NY, USA), tests were performed at 60 deg/sec and 180 deg/sec for both sides. Peak torque normalized to body weight, external rotator to internal rotator ratio, total work and fatigue were calculated for each angular velocity. The association between internal rotator and external rotator strength and shoulder instability was analyzed by comparisons with a control group. Results: Any notable differences could not be found between the two groups given all data from no symptomatic left shoulder. There were no significant differences between the two groups statistically in internal rotation strength of right shoulder. However, there has been a tendency that at all angular velocities, external rotator peak torque to body weight, total work and external rotator to internal rotator ratio were significantly lower in the anterior instability group than the control group at all angular velocities. There was no substantial difference between those groups with respect to the fatigue of external rotator and internal rotator in our study. Conclusion: The prominent characteristics of posttraumatic recurrent anterior shoulder dislocation are external rotator weakness and loss of balance with external rotator and internal rotator. Therefore selective training using this information rotator might be helpful in conservative treatment and rehabilitation.

• 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.