• Child Health Nursing Research
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• v.13 no.2
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• pp.201-211
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• 2007

Purpose: This study was designed to construct a structural model to explain the quality of life of grandmothers caring for their grandchildren. Method: Data were collected by self-report questionnaires from 232 grandmothers caring for their grandchildren living in Busan. The data collection period was from June to Oct. 2006. Data analysis was done with SAS 9.13 for descriptive statistics and PC-LISREL 8.52 program for Covariance Structural analysis. Results: The findings found that the fit of the hypothetical model to the data was good, but considering theoretical implications and statistical significances of parameter estimates, paths and variables of the model were modified by excluding 2 paths. The Modified Model with 17 paths showed a good fitness to the empirical data ($X^2=15.492$ (df=11, p=.161), GFI=.985 AGFI=.940 NFI=.982 RMSR=.037 RMSEA=.042). Health status, health problems, economical status, life events, caring stress, caring efficacy and life satisfaction had significant effects on quality of life in the grandmother caring their grandchildren, but of these variables, self-esteem was the most essential factor. All predictive variables of quality of life together explained 63.9% of the variance. Conclusion: The derived model in this study was confirmed to be proper in explaining and predicting the quality of life of the grandmothers caring their grandchildren.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.8
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• pp.423-431
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• 2000

This paper presents a systematic approach to the input-output data-based fuzzy modeling for the complex and uncertain nonlinear systems, in which the conventional mathematical models may fail to give the satisfying results. To do this, we propose a new method that can yield a successful fuzzy model using a mGA hybrid schemes with a fine-tuning method. We also propose a new coding method fo chromosome for applying the mGA to the structure and parameter identifications of fuzzy model simultaneously. During mGA search, multi-purpose fitness function with a penalty process is proposed and adapted to guarantee the accurate and valid fuzzy modes. This coding scheme can effectively represent the zero-order Takagi-Sugeno fuzzy model. The proposed mGA hybrid schemes can coarsely optimize the structure and the parameters of the fuzzy inference system, and then fine tune the identified fuzzy model by using the gradient descent method. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its applications to two nonlinear systems.

• Journal of Korean Academy of Nursing
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• v.38 no.6
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• pp.779-788
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• 2008

Purpose: This study was designed to construct a structural model explaining depression in university students. Methods: Data were collected from 1,640 university students by questionnaire, and analyzed using AMOS 5.0 to test the hypothetical model. Results: Fitness statistics for the modified model were GFI=.93, AGFI=.89, NFI=.91, and RMSEA=.081. All the 12 paths in the modified model proved to be statistically significant. Depression of university students accounted for 52% of the covariance by the factors. The factor that had the most influence on depression was individual vulnerability, and followed by sequence order, stress, social support, coping, and self-efficacy. Depression was influenced directly by individual vulnerability, stress, social support, and coping, and indirectly by individual vulnerability, stress, social support, and self-efficacy. Conclusion: A screening and management system for the high risk group is needed to effectively prevent depression and reduce rate of depression in university students. Detailed support programs which specifically deal with prevailing stressors should be developed to effectively reduce the harmful effects of individual vulnerability and stress. It is anticipated that the model constructed in this study could be utilized as a reference in developing various strategies to prevent and intervene depression in university students.

• Earthquakes and Structures
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• v.13 no.4
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• pp.397-407
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• 2017

With the growing demand for metallic dampers in engineering practice, it is urgent to establish a reasonable approach to evaluating the mechanical performance of metallic dampers under seismic excitations. This paper introduces an effective method for parameter identification of the modified Bouc-Wen model and its application to evaluating the fatigue performance of metallic dampers (MDs). The modified Bouc-Wen model which eliminates the redundant parameter is used to describe the hysteresis behavior of MDs. Relations between the parameters of the modified Bouc-Wen model and the mechanical performance parameters of MDs are studied first. A modified Genetic Algorithm using real-integer hybrid coding with relative fitness as well as adaptive crossover and mutation rates (called RFAGA) is then proposed to identify the parameters of the modified Bouc-Wen model. A reliable approach to evaluating the fatigue performance of the MDs with respect to the Chinese Code for Seismic Design of Buildings (GB 50011-2010) is finally proposed based on the research results. Experimental data are employed to demonstrate the process and verify the effectiveness of the proposed approach. It is shown that the RFAGA is able to converge quickly in the identification process, and the simulation curves based on the identification results fit well with the experimental hysteresis curves. Furthermore, the proposed approach is shown to be a useful tool for evaluating the fatigue performance of MDs with respect to the Chinese Code for Seismic Design of Buildings (GB 50011-2010).

• Smart Media Journal
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• v.8 no.1
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• pp.82-91
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• 2019

This study provides the analysis and prediction of fruits diseases related to weather conditions (temperature, wind speed, solar power, rainfall and humidity) using Linear Model and Poisson Regression. The main goal of the research is to control the method of fruits diseases and also to prevent diseases using less agricultural pesticides. So, it is needed to predict the fruits diseases with weather data. Initially, fruit data is used to detect the fruit diseases. If diseases are found, we move to the next process and verify the condition of the fruits including their size. We identify the growth of fruit and evidence of diseases with Linear Model. Then, Poisson Regression used in this study to fit the model of fruits diseases with weather conditions as an input provides the predicted diseases as an output. Finally, the residuals plot, Q-Q plot and other plots help to validate the fitness of Linear Model and provide correlation between the actual and the predicted diseases as a result of the conducted experiment in this study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1426-1447
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• 2021

The payoff game between task publishers and users in the mobile crowdsensing environment is a hot topic of research. A optimal payoff selection model based on stochastic evolutionary game is proposed. Firstly, the process of payoff optimization selection is modeled as a task publisher-user stochastic evolutionary game model. Secondly, the low-quality data is identified by the data quality evaluation algorithm, which improves the fitness of perceptual task matching target users, so that task publishers and users can obtain the optimal payoff at the current moment. Finally, by solving the stability strategy and analyzing the stability of the model, the optimal payoff strategy is obtained under different intensity of random interference and different initial state. The simulation results show that, in the aspect of data quality evaluation, compared with BP detection method and SVM detection method, the accuracy of anomaly data detection of the proposed model is improved by 8.1% and 0.5% respectively, and the accuracy of data classification is improved by 59.2% and 32.2% respectively. In the aspect of the optimal payoff strategy selection, it is verified that the proposed model can reasonably select the payoff strategy.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.691-700
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• 2023

Purpose: This study aims to analyze the diffusion pattern of the Mongolian mobile phone market. In particular, we used a generalized diffusion model to explore the factors affecting market potenial. Methods: We used three diffusion models to estimate the number of mobile subscribers in Mongolia. Based on the Logistic model with the best fitness, we introduced time-varying market potential and explored the influence of various independent variables such as GDP and inflation. Results: Among the basic diffusion models, the Logistic model was the best in terms of estimation performance and statistical significance. The estimation results of the Generalized Logistic model confirm that investment in the telecommunication sector has a significant positive effect on market potential. The estimation of the Generalized Logistic model effectively describes the continuous growth of the Mongolian telecommunications market until recently. Conclusion: We have analyzed the diffusion pattern of the Mongolian telecommunications market and found that the amount of investment in the sector leads to the growth of the market size. This study is original in terms of its subject - Mongolian telecommunications market and methodology - time-varying market potential.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2B
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• pp.217-227
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• 2004

With a rapid growth in the Internet technology, the network traffic is increasing swiftly. As for the increase of traffic, it had a large influence on performance of a total network. Therefore, a traffic management became an important issue of network management. In this paper, we study a forecast plan of network traffic in order to analyze network traffic and to establish efficient correspondence. We use time series forecast models and determine fitness whether the model can forecast network traffic exactly. In order to predict a model, AR, MA, ARMA, and ARIMA must be applied. The suitable model can be found that can express the nature of traffic for the forecast among these models. We determines whether it is satisfied with stationary in the assumption step of the model. The stationary can get the results by using ACF(Auto Correlation Function) and PACF(Partial Auto Correlation Function). If the result of this function cannot satisfy then the forecast model is unsuitable. Therefore, we are going to get the correct model that is to satisfy stationary assumption. So, we proposes a way to classify in order to get time series materials to satisfy stationary. The correct prediction method is managed traffic of a network with a way to be better than now. It is possible to manage traffic dynamically if it can be used.

• Journal of Korea Water Resources Association
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• v.57 no.4
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• pp.289-300
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• 2024

The purpose of this study is to evaluate the applicability of the GRM (Grid based rainfall-Runoff Model) to the continuous simulation by simulating the dam inflow. The GRM was previously developed for the simulation of rainfall-runoff events but has recently been improved to enable continuous simulation. The target watersheds are Chungju dam, Andong dam, Yongdam dam, and Sumjingang dam basins, and runoff models were constructed with the spatial resolution of 500 m × 500 m. The simulation period is 21 years (2001 to 2021). The simulation results were evaluated over the 17 year period (2005 to 2021), and were divided into three data periods: total duration, wet season (June to September), and dry season (October to May), and compared with the observed daily inflow of each dam. Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), correlation coefficient (CC), and total volume error (VE) were used to evaluate the fitness of the simulation results. As a result of evaluating the simulated dam inflow, the observed data could be well reproduced in the total duration and wet season, and the dry season also showed good simulation results considering the uncertainty of low-flow data. As a result of the study, it was found that the continuous simulation technique of the GRM model was properly implemented and the model was sufficiently applicable to the simulation of dam inflow in this study.

• Journal of the Korean Society of Clothing and Textiles
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• v.24 no.5
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• pp.628-639
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• 2000

The purpose of this study is to propose a model of private brands proneness form-ation considering the six private brands proneness-related variables simultaneously. Since the theoretical framework is based on previous research in various areas, it serves as an integrative one. Data were collected via intercept surveys conducted at nine regional branches of two major department stores situated in Seoul. Participants(n=1,120), who had previously purchased women's private brand apparel, were asked to complete a questionnaire during two weeks from March 15, 1999 to March 28, 1999. LISREL and SPSS PC+ were used to test the model and analyze its variables. The fitness of the model show the reasonable fit between all indices(RMSR=.036, GFI=.99, AGFI=.92, and NFI=.95). The proposed model supports all the hypothesized relationships. Private brands proneness increases as perceived money value of products, familiarity, positive store image of private brands, and satisfaction of individuals' differentiated needs increase. Furthermore, perceived money value of products increase as perceived risk of private brand purchase and perceived quality variation between private brand products and manufacture's products decrease.