• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.5D
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• pp.453-461
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• 2012

Life analysis is conducted for economic analysis of equipment or facilities. The purpose of life analysis is to predict future indicators for scrapping construction equipment, and establish and utilize a wide variety of business strategies according to data predictions. First, this study shows the methods to figure out average life, life expectancy and life prediction of construction equipment and the analysis of life making methods, using survival curves. Second, the study proposes and examines the life expectancy making model depending on revenues and expenses. The result of the study reveals that the economic life of the same equipment varies with expenses, revenues and the initial cost. The life expectancy making model for construction equipment reflects respective management status for equipment and will help efficient management for companies.

• KSBB Journal
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• v.13 no.3
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• pp.251-258
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• 1998

A structured kinetic model is proposed to describe cell growth and secondary metabolite, flavone glycosides, synthesis in batch suspension culture of Scutellaria baicalensis G. The model has been developed by representing the physiological state of cell described as the activity and viability which can be estimated based on the culture fluorescence. In the model, three type of cells are considered; active-viable, nonactive-viable and dead cells. Viable cell weight could be determined based on the relative fluorescence intensity. The flavone glycosides could be produced by both active-viable and non-active viable cells with a different production rate. And the model includes the cell expansion due to glucose concentration and death phase which accounts for the release of intracellular secondary metabolite into medium. Dependent variables include substrate concentration(glucose), cell mass(dry cell weight and fresh cell weight), product concentration(flavone glycosides), activity and viability. Satisfactory agreement between the model and experimental data is obtained from shake flask culture of Scutellaria baicalensis G. The proposed model can predict the cell growth and flavone glycosides synthesis as well as intermediate materials.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.47-55
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• 2011

Hemorrhagic shock is a cause of one third of death resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physician to treat successfully. The objective of this paper was to select an optimal classifier model using physiological signals from rats measured during hemorrhagic experiment. This data set was used to train and predict survival rate using artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the best classifier according to performance measured by a 10-fold cross validation method. As a result, we selected ANN having three hidden nodes with one hidden layer and SVM with Gaussian kernel function as trained prediction model, and the ANN showed 88.9 % of sensitivity, 96.7 % of specificity, 92.0 % of accuracy and the SVM provided 97.8 % of sensitivity, 95.0 % of specificity, 96.7 % of accuracy. Therefore, SVM was better than ANN for survival prediction.

• Smart Media Journal
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• v.12 no.8
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• pp.18-25
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• 2023

The COVID-19 outbreak in 2020 caused continuous damage worldwode, especially the smart tourism industry was hit directly by the blockade of sky roads and restriction of going out. At a time when overseas travel and domestic travel have decreased significantly, the number of tourist hotels that are colsed and closed due to the continued deficit is increasing. Therefore, in this study, licensing data from the Ministry of Public Administraion and Security were collected and visualized to understand the operation status of the tourism and lodging industry. The machine learning classification algorithm was applied to implement the business status prediction model of the tourist hotel, the performance of the prediction model was optimized using the ensemble algorithm, and the performance of the model was evaluated through 5-Fold cross-validation. It was predicted that the survival rate of tourist hotels would decrease somewhat, but the actual survival rate was analyzed to be no different from before COVID-19. Through the prediction of the business status of the hotel industry in this paper, it can be used as a basis for grasping the operability and development trends of the entire tourism and lodging industry.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.459-463
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• 2024

Frequent closures among self-employed individuals lead to national economic losses. Given the high closure rates in the restaurant industry, predicting changes in this sector is crucial for business survival. While research on factors affecting restaurant industry survival is active, studies predicting commercial district changes are lacking. Thus, this study focuses on forecasting such alterations, designing a deep learning model for Seoul's administrative district commercial district changes. It collects 2023 and 2022 second-quarter variables related to these changes, converting yearly fluctuations into percentages for augmentation. The proposed deep learning model aims to predict commercial district changes. Future policies, considering this study, could support restaurant industry growth and economic development.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.687-696
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• 2011

Customer Lifetime or Customer Lifetime Value is a essential metric of differentiated CRM marketing and differentiated marketing strategy as a company core competency. However, customer lifetime used in companies is easily obtained from a confined simple customer attrition rate at some specific time point regardless of customer characteristics. In this study, in order to overcome the constraints of previous simple methods and to make practical use of it in industries, we suggest a method that estimates a customer lifetime using a customer segment based survival analysis with the censored data of customers; in addition, we apply this method to A mobile telecom company data. A method using customer segment based survival analysis is suggested in this study 1) includes all customers having different subscription dates, 2) reduces individual error, 3) can reflect trends after the observed time point and is more realistic.

• 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.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.331-340
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• 2023

Predicting the survival time of a person in water (PIW) in maritime search and rescue (SAR) operations is an important concern. Although there have been many studies on survival models in marine-developed countries, it is difficult to apply them to Koreans in Korea's oceans because they were developed using marine distress data from the United Kingdom, United States, and Canada. Data on the survival time of a P IW were collected through interviews and surveys with a special rescue team from the Korea Coast Guard, SAR cases, press releases, and Korea Meteorological Administration data to address these issues. The maximum survival time (Korean) equation was developed by performing a regression analysis of this data, and the applicability to actual marine distress was reviewed and compared to the overseas survival model. By comprehensively using the maximum survival time (Korean), domestic SAR cases, and overseas survival models, guidelines for survival time and intensive and recommended search time were suggested. The study findings can contribute to decision-making, such as the input for search and rescue units. The findings can also help to determine the end of or reductions in SAR operations and explain policy decisions to the public and families of a PIW.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.1
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• pp.35-41
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• 2010

In developing modern aircraft, the reconfiguration control that can improve the safety and the survivability against the unexpected failure by partitioning control surfaces into several parts has been actively studied. This paper deals with the reconfiguration control using model predictive control method considering the saturation of control surfaces under the control surface failure. Linearized aircraft model at trim condition is used as the internal model of model predictive control. We propose the controller that performs optimization using LMI (linear matrix inequalities) based semi-definite programming in case that control surface saturation occurs, otherwise, uses analytic solution of the model predictive control. The performance of the proposed control method is evaluated by nonlinear simulation under the flight scenario of control surface failure.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.260-268
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• 2024

Cervical cancer is the fourth most common cancer in women worldwide, and more than 604,000 new cases were reported in 2020 alone, resulting in approximately 341,831 deaths. The Cox regression model is a major model widely adopted in cancer research, but considering the existence of nonlinear associations, it faces limitations due to linear assumptions. To address this problem, this paper proposes ResSurvNet, a new model that improves the accuracy of cervical cancer mortality prediction using ResNet's residual learning framework. This model showed accuracy that outperforms the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study. As this model showed accuracy that outperformed the DNN, CPH, CoxLasso, Cox Gradient Boost, and RSF models compared in this study, this excellent predictive performance demonstrates great value in early diagnosis and treatment strategy establishment in the management of cervical cancer patients and represents significant progress in the field of survival analysis.