• Journal of Korean Academy of Nursing
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• v.28 no.2
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• pp.344-357
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• 1998

This study was designed to construct a model that predicts the quality of life of family caregivers with a chronically ill patient. The hypothetical model was developed based on the findings from past studies on quality of life and on the family with a chronically ill patient. Data were collected by self-reported questionnaires from 200 family caregivers in Seoul & Kyung Gi-Do, from May 1 to July 21, 1997. Data were analyzed using descriptive statistics and correlation analysis. The Linear Structural Relationship(LISREL) modeling process was used to find the best fit model which predicts causal relationships among variables. The results are as follows : 1. The overall fit of the hypothetical model to the data was moderate [X$^2$=31.54(df=23, p=.11), GFI=.96, AGFI=.91, RMR=.04]. 2. Paths of the model were modified by considering both its theoretical implication and the statistical significance of the parameter estimates. Compared to the hypothetical model, the revised model has become parsimonious and had a better fit to the data expect chi-square value(GFI=.95, AGFI=.91, RMR=.04). 3. Some of predictive factors, especially economic status, physical ability to perform daily-life activity, period after disease-onset, social support and fatigue revealed indirect effects on the quality of life of family caregivers with a chronically ill patient. 4. The factors, burden and role satisfaction revealed significant direct effects on the quality of life of family caregivers with a chronically ill patient. 5. All predictive variables of quality of life of family caregivers with a chronically ill patient, especially economic status, physical ability to perform daily-life activity, period after disease-onset, social support, fatigue, burden and role satisfaction explained 38.0% of the total variance in the model. In conclusion, the derived model in this study is considered appropriate in explaining and predicting quality of life of family caregivers with a chronically ill patient. Therefore it can effectively be used as a reference model for further studies and suggests direction in nursing practice.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.40-49
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• 2004

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional SOPNN. In order to generate the structurally optimized SOPNN, GA-based design procedure at each stage (layer) of SOPNN leads to the selection of preferred nodes (or PNs) with optimal parameters- such as the number of input variables, input variables, and the order of the polynomial-available within SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based SOPNN, the model is experimented with using two time series data (gas furnace and NOx emission process data of gas turbine power plant). A comparative analysis shows that the proposed GA-based SOPNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Membrane and Water Treatment
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• v.7 no.2
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• pp.127-141
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• 2016

Electrodialysis (ED) is known to be a useful membrane process for desalination, concentration, separation, and purification in many fields. In this process, it is desirable to work at high current density in order to achieve fast desalination with the lowest possible effective membrane area. In practice, however, operating currents are restricted by the occurrence of concentration polarization phenomena. Many studies showed the occurrence of a limiting current density (LCD). The limiting current density in the electrodialysis process is an important parameter which determines the electrical resistance and the current utilization. Therefore, its reliable determination is required for designing an efficient electrodialysis plant. The purpose of this study is the development of a predictive model of the limiting current density in an electrodialysis process using response surface methodology (RSM). A two-factor central composite design (CCD) of RSM was used to analyze the effect of operation conditions (the initial salt concentration (C) and the linear flow velocity of solution to be treated (u)) on the limiting current density and to establish a regression model. All experiments were carried out on synthetic brackish water solutions using a laboratory scale electrodialysis cell. The limiting current density for each experiment was determined using the Cowan-Brown method. A suitable regression model for predicting LCD within the ranges of variables used was developed based on experimental results. The proposed mathematical quadratic model was simple. Its quality was evaluated by regression analysis and by the Analysis Of Variance, popularly known as the ANOVA.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.2
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• pp.867-870
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• 2014

Purpose: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) ependymoma data to identify predictive models and potential disparity in outcome. Materials and Methods: This study analyzed socio-economic, staging and treatment factors available in the SEER database for ependymoma. For the risk modeling, each factor was fitted by a Generalized Linear Model to predict the outcome ('brain and other nervous systems' specific death in yes/no). The area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. A random sampling algorithm was used to estimate the modeling errors. Risk of ependymoma death was computed for the predictors for comparison. Results: A total of 3,500 patients diagnosed from 1973 to 2009 were included in this study. The mean follow up time (S.D.) was 79.8 (82.3) months. Some 46% of the patients were female. The mean (S.D.) age was 34.4 (22.8) years. Age was the most predictive factor of outcome. Unknown grade demonstrated a 15% risk of cause specific death compared to 9% for grades I and II, and 36% for grades III and IV. A 5-tiered grade model (with a ROC area 0.48) was optimized to a 3-tiered model (with ROC area of 0.53). This ROC area tied for the second with that for surgery. African-American patients had 21.5% risk of death compared with 16.6% for the others. Some 72.7% of patient who did not get RT had cerebellar or spinal ependymoma. Patients undergoing surgery had 16.3% risk of death, as compared to 23.7% among those who did not have surgery. Conclusion: Grading ependymoma may dramatically improve modeling of data. RT is under used for cerebellum and spinal cord ependymoma and it may be a potential way to improve outcome.

• Journal of Korean Neurosurgical Society
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• v.52 no.6
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• pp.528-533
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• 2012

Objective : In the pediatric population the skull has not yet undergone ossification and it is assumed that the diagnostic rate of skull fractures by simple X-rays are lower than that of adults. It has been recently proposed that the diagnostic rates of skull fractures by 3-dimensional computer tomography (3D-CT) are higher than simple X-rays. The authors therefore attempted to compare the diagnostic rates of pediatric skull fractures by simple X-rays and 3D-CTs with respect to the type of fracture. Methods : One-hundred patients aged less than 12 years who visited the Emergency Center for cranial injury were subject to simple X-rays and 3D-CTs. The type and location of the fractures were compared and Kappa statistical analysis and the t-test were conducted. Results : Among the 100 pediatric patients, 65 were male and 35 were female. The mean age was $50{\pm}45$ months. 63 patients had simple skull fractures and 22 had complex fractures, and the types of fractures were linear fractures in 74, diastatic fractures 15, depressed fractures in 10, penetrating fracture in 1, and greenstick fractures in 3 patients. Statistical difference was observed for the predictive value of simple skull fractures' diagnostic rate depending on the method for diagnosis. A significant difference of the Kappa value was noted in the diagnosis of depressed skull fractures and diastatic skull fractures. Conclusion : In the majority of pediatric skull fractures, 3D-CT showed superior diagnosis rates compared to simple skull X-rays and therefore 3D-CT is recommended whenever skull fractures are suspected. This is especially true for depressed skull fractures and diastatic skull fractures.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.11 no.3
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• pp.28-38
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• 2008

The purpose of this study are to compare existing presence-absence predictive models and to predict suitable habitat for Goral (Nemorhaedus caudatus raddeanus) that is an endangered and protected species in Seoraksan national park using the best model among existing predictive models. The methods of this study are as follows. First, 375 location data and 9 environmental data layers were implemented to build a model. Secondly, 4 existing presence-absence models : Generalized Linear Model (GLM), Generalized Addictive Model (GAM), Classification and Regression Tree (CART), and Artificial Neural Network (ANN) were tested to predict the Goal habitat. Thirdly, ROC (Receiver Operating Characteristic) and Kappa statistics were used to calculate a model performance. Lastly, we verified models and created habitat suitability maps. The ROC AUC (Area Under the Curve) and Kappa values were 0.697/0.266 (GLM), 0.729/0.313 (GAM), 0.776/0.453 (CART), and 0.858/0.559 (ANN). Therefore, ANN was selected as the best model among 4 models. The models showed that elevation, slope, and distance to stream were the significant factors for Goal habitat. The ratio of predicted area of ANN using a threshold was 31.29%, but the area decreased when human effect was considered. We need to investigate the difference of various models to build a suitable wildlife habitat model under a given condition.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.227-235
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• 2018

The purpose of this study was to analyze the factors affecting on pre-service early childhood teachers' adoption intention of Robot-based education. For this purpose, the survey was conducted on 259 college students and the collected data was analyzed using SPSS 23.0. T-test and one-way ANOVA were used to compare the differences of adoption intention and predictive factors according to pre-service early childhood teachers' background variables. In addition, multiple linear regression analysis was conducted to analyze influence of perceived ease of use, perceived efficacy, innovative will, and social effect on adoption intention. The results were found that adoption intention and predictive factors did not show any significant difference according to background variables and that perceived ease of use and perceived efficacy influenced on pre-service early childhood teachers' adoption intention. Moreover, innovative will and social effect had an effect on perceived ease of use and perceived ease of use and social effect had an effect on perceived efficacy.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.126-135
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• 2002

The study is concerned with an approach to the design of new architectures of fuzzy neural networks and the discussion of comprehensive design methodology supporting their development. We propose an Adaptive Fuzzy Polynomial Neural Networks(APFNN) based on Fuzzy Neural Networks(FNN) and Self-organizing Networks(SON) for model identification of complex and nonlinear systems. The proposed AFPNN is generated from the mutually combined structure of both FNN and SON. The one and the other are considered as the premise and the consequence part of AFPNN, respectively. As the premise structure of AFPNN, FNN uses both the simplified fuzzy inference and error back-propagation teaming rule. The parameters of FNN are refined(optimized) using genetic algorithms(GAs). As the consequence structure of AFPNN, SON is realized by a polynomial type of mapping(linear, quadratic and modified quadratic) between input and output variables. In this study, we introduce two kinds of AFPNN architectures, namely the basic and the modified one. The basic and the modified architectures depend on the number of input variables and the order of polynomial in each layer of consequence structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the AFPNN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed AFPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Parasites, Hosts and Diseases
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• v.45 no.4
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• pp.273-282
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• 2007

In acute uncomplicated falciparum malaria, there is a continuum from mild to severe malaria. However, no mathematical system is available to predict uncomplicated falciparum malaria patients turning to severe malaria. This study aimed to devise a simple and reliable model of Malaria Severity Prognostic Score (MSPS). The study was performed in adult patients with acute uncomplicated falciparum malaria admitted to the Bangkok Hospital for Tropical Diseases between 2000 and 2005. Total 38 initial clinical parameters were identified to predict the usual recovery or deterioration to severe malaria. The stepwise multiple discriminant analysis was performed to get a linear discriminant equation. The results showed that 4.3% of study patients turned to severe malaria. The MSPS = 4.38 (schizontemia) + 1.62 (gametocytemia) + 1.17 (dehydration) + 0.14 (overweight by body mass index; BMI) + 0.05 (initial pulse rate) + 0.04 (duration of fever before admission)-0.50 (past history of malaria in last 1 year). 0.48 (initial serum albumin)-5.66. Based on the validation study in other malaria patients, the sensitivity and specificity were 88.8% and 88.4%, respectively. We conclude that the MSPS is a simple screening tool for predicting uncomplicated falciparum malaria patients turning to severe malaria. However, the MSPS may need revalidation indifferent geographical areas before utilized at specific places.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.20-26
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• 2019

Due to the human influence of particulate matter, various studies are being conducted to predict it using past data measured in the atmospheric environment monitoring network. However, it is difficult to precisely set the measurement environment and detailed conditions of the previously designed predictive model, and it is necessary to design a new predictive model based on the existing research results because of the problems such as the missing of the weather data. In this paper, as a previous study for particulate matter prediction, the conformity of the algorithm for particulate matter prediction was evaluated by designing the prediction model through the multiple linear regression and the artificial neural network, which are machine learning algorithms. As a result of the prediction performance comparison through RMSE, 18.13 for the MLR model and 14.31 for the MLP model, and the artificial neural network model was more conformable for predicting the particulate matter concentration.