• Industry Promotion Research
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• v.5 no.1
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• pp.1-11
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• 2020

The purpose of this study is to analyze the industrial accidents data with survival model. EDA approach is used to explore the relationship between two variables and among three variables for the past 10 years of industrial accidents data. Survival models are also tried. Survival curve drops more rapidly for the business with fewer employees as time goes by. Industrial accidents occur more often as the total number of industrial accidents gets larger and as the number of employees gets smaller. Agriculture, fishing and forestry have a higher level of industrial accidents than construction while service industry and 'transportation·storage and telecommunication' have a fewer number of industrial accidents than construction. Korea Safety and Health Agency's and Ministry of Employment and Labor's involvement were not effective but Civilian's was. Recurrent event data analysis reveals all most the same result as for non-recurrent data analysis.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.327-336
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• 2014

Recurrent event data occurs when a subject experiences same type of event repeatedly and is found in various areas such as the social sciences, Economics, medicine and public health. To analyze recurrent event data either a total time or a gap time is adopted according to research interest. In this paper, we analyze recurrent event data with incomplete observation gap using a gap time scale. That is, some subjects leave temporarily from a study and return after a while. But it is not available when the observation gaps terminate. We adopt an interval censoring mechanism for estimating the termination time. Furthermore, to model the association among gap times of a subject, a frailty effect is incorporated into a model. Programs included in Survival package of R program are implemented to estimate the covariate effect as well as the variance of frailty effect. YTOP (Young Traffic Offenders Program) data is analyzed with both proportional hazard model and a weibull regression model.

• Korean Journal of Social Welfare Studies
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• v.48 no.4
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• pp.221-249
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• 2017

This study aims to investigate determinants that affect job retention of injured workers depending upon types of return to work in order to suggest define the intervention priority for those who returned to original works and for those who did not. After constructing explaining variables based on literature reviews, determinants were verified analyzing 1,292 people using Panel Study of Worker's Compensation Insurance(PSWCI) data. The job retention period turned out to be 46.6 months for those who returned to original work and 34.2 month for those who returned to new works. Injured workers who return to new works tend to have more unemployment experiences. As a result of Cox proportional regression analysis, the longer it takes to return to work, the longer both groups tend to retain after the accident. Age, recuperation period, health status, psycho-social rehabilitation, education and occupational training also affect on job retention probability for those who return to new work. Based upon the analyzed result, setting up an adequate duration for return-to-work, intervention for injured workers who experienced vulnerable working condition before the accident and continuous case management after return-to-work are suggested.

• The Journal of the Korean bone and joint tumor society
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• v.12 no.1
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• pp.30-36
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• 2006

Purpose: Epitheliod sarcoma is an obscure clinical entity. This study analyzes the correlation between the clinical course and the AJCC stage, presence of residual tumor and ezrin-expression. Materials and Methods: Twenty-three cases of epithelioid sarcoma were eligible. All the cases had operation. Fifteen cases had systemic chemotherapy and 6 cases had adjuvant radiotherapy. Immumohistochemical analysis was done for 15 cases. Analyzed factors were initial stage, adjuvant treatment, local recurrence, residual tumor immumohistochemical results and surgical margin. Results: The event free survival rate of 15 stage II, III cases was 47.4% at 129 months. The actual survival rate of 8 stage IV cases was 37.5% at 80 months. The presence of residual tumor tissue on re-excision specimen showed statistical significance on event free survival rate(P=0.03). Adjuvant therapy showed no impact on outcome. The stage IV and locally recurrent cases had a positive relation with Ezrin-positivity. Conclusion: Residual tumor showed correlation in the outcome of epitheliod sarcoma. Chemotherapy and radiation therapy did not affect the outcome. Further case collection and analysis is needed for the significance between Ezrin expression and clinical behavior.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.574-583
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• 2021

Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.