• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.425-430
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• 2002

An automatic transmission model using the bond graph techniques is developed for analyzing shift characteristics of vehicles. Bond graph models can be systemically manipulated to yield state space equations of standard form. Bond graph techniques are applied for modeling overall automatic transmission systems and shift models. A fuzzy controller is synthesized for the verification of a shifting model in the ${1^st} gear to the {2^nd}$ gear. Simulation results show the fitness of models by the bond graph techniques.

• Journal of Technologic Dentistry
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• v.40 no.3
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• pp.115-123
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• 2018

Purpose: The purpose of this study was establishing process of manufacturing dental prosthesis by using eZIS system(DDS Inc.,Korea). Methods: To evaluate accuracy verification, the test was practiced two ways. First, Comparison of 3D printing models and stone models was practiced by using 3D superimposing software. #36 prepared master model was scanned by eZIS system and three 'Veltz3D' 3D printing models and three 'Bio3D' 3D printing models were manufactured. three stone models were manufactured by conventional impression technique. Second, Fitness test was practiced. the 3D printing models and the stone models was compared by manufacturing same resin crown. #36 prepared master model was scanned 9 times and manufactured (milled) 9 resin crowns by eZIS system. These crowns were cemented three 'Veltz3D' 3D printing models, three 'Bio3D' 3D printing models and three stone models. These crowns were sliced mesiodistal axis and gaps were measured by digital microscope. Results: The average accuracy of Bio3D models were 65.75%. Veltz3D(Hebsiba) models were 60.11% Stone models were 41.00%. Conclusion : This study results showed 3D printing model is similar with stone model. So it was under clinical allow, didn't affect final dental prothesis. There were no significant differences in the appearance of the three types of milling crowns.

• Journal of Korean Academy of Nursing
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• v.29 no.6
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• pp.1192-1207
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• 1999

The purpose of this study was to clarify the dynamic relationships among risk factors of arteriosclerosis and to develop and examine a model which could explain this relationship clearly. Data were collected from medical records of 400 male clients who visited a university hospital located in Inchon for physical examinations, from May 1996 to December 1996. Data were analyzed using the LISREL (Linear Structural Relationship) 8 program. To test the fitness of the hypothesized model, chi-square, RMSR (root mean square residual), GFI (goodness of fit index), CN (critical number) and Q-plot were used. Most of the fitness measurements, except the chi-square showed that the hypothesized model complimented the real data. According to the results, there were trends that obesity and hyperlipidemia were prevalent in heavier smokers, higher alcohol intakers, and groups who excercised less. Also, hypertension was more prevalent in older age, higher alcohol intaker, and higher serum lipid level groups. In contrast to the hypothesis, alcohol intake did not significantly affect serum lipid levels. This might be due to the serum lipid measurements (total cholesterol and trigryceride) used in this study to estimate hyperlipidemia. The direct effect of smoking on hypertension was not significant. However, the total effect of smoking on the hypertension was significant since indirect effects of smoking on hypertension, such as obesity and hyperlipidemia, were significant. The total effect of obesity on hypertension was significant since the indirect effect of obesity on hypertension via hyperlipidemia was significant, although the direct effect of smoking on hypertension was not significant. The degree of explaining hyperlipidemia with smoking, exercise, and obesity was high (60%), however, the degree of explaining obesity with age, smoking, alcohol intake, and exercise was very low (7%). On the basis of these results, high risk factors of arteriosclerosis such as hypertension, hyperlipidemia, or obesity are either directly or indirectly correlated each other. Therefore, it is difficult to predict outcomes for increasing or decreasing the risk factors by simply modulating a factor. Smoking, alcohol, and exercise both directly and indirectly affected major risk factors of arteriosclerosis. Therefore, correcting these variables is required to decrease risk factors. Finally, the relationship among other risk factors which have been known to be related with arteriosclerosis (diet, stress or hereditary) should be clarified in further studies.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.1-10
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• 2019

Since big-data text mining extracts many features and data, clustering and classification can result in high computational complexity and low reliability of the analysis results. In particular, a term document matrix obtained through text mining represents term-document features, but produces a sparse matrix. We designed an advanced genetic algorithm (GA) to extract features in text mining for detection model. Term frequency inverse document frequency (TF-IDF) is used to reflect the document-term relationships in feature extraction. Through a repetitive process, a predetermined number of features are selected. And, we used the sparsity score to improve the performance of detection model. If a spam mail data set has the high sparsity, detection model have low performance and is difficult to search the optimization detection model. In addition, we find a low sparsity model that have also high TF-IDF score by using s(F) where the numerator in fitness function. We also verified its performance by applying the proposed algorithm to text classification. As a result, we have found that our algorithm shows higher performance (speed and accuracy) in attack mail classification.

• Journal of Korean Academy of Nursing
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• v.52 no.4
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• pp.407-420
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• 2022

Purpose: This structural model study was constructed and verified a hypothetical model to examine the effects of parenting stress, social resources, family resources, and positive coping on resilience among mothers of children with developmental disabilities. Methods: Data were collected using self-report structured questionnaires, from October 19 to October 30, 2018, with 214 mothers caring for children with developmental disabilities under the age of 20 years. Results: In the fitness test results of the hypothesis model, with the fit index 𝛘² (p) = 69.27 (< .001), and the normed fit indices (𝛘² = 1.87, GFI = .94, CFI = .97, NFI = .93, and TLI = .95, RMSEA = .06, SRMR = .06), this study satisfies the good fitness in standards. There are seven statistically significant paths among the 10 paths set in the hypothetical model. The explanatory power of parenting stress and social resources, which affects the family resources was 41.4%, the explanatory power of parenting stress, social resources, and family resources affecting the positive coping was 58.9%, and the explanatory power of parenting stress, social resources, family resources, and positive coping affecting resilience was 55.5%. Conclusion: Positive coping, family resources, and social resources of mothers of children with developmental disabilities directly affect their resilience, and parenting stress indirectly affects it. Therefore, to improve the resilience of mothers of children with developmental disabilities, it is necessary to develop a systematic nursing intervention that considers parenting stress, social resources, family resources, and positive coping.

• Journal of Environmental Health Sciences
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• v.27 no.3
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• pp.35-42
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• 2001

The characteristics of water quality variation were predicted by stochastic model in Chungju dam, north Chungcheong province of south Korea, Monthly time series data of water quality from 1989 to 2001;temperature, BOD, COD and SS, were obtained from environmental yearbook and internet homepage of ministry of environment. Development of model was carried out with Box-Jenkins method, which includes model identification, estimation and diagnostic checking. ACF and PACF were used to model identification. AIC and BIC were used to model estimation. Seosonal multiplicative ARIMA(1, 0, 1)(1, 1, 0)$_{12}$ model was appropriate to explain stochastic characteristics of temperature. BOD model was ARMa(2, 2, 1), COD was seasonal multiplicative ARIMA(2. 0. 1)(1. 0, 1)$_{12}$, and SS was ARIMA(1, 0, 2) respectively. The simulated water quality data showed a good fitness to the observed data, as a result of model verification.ion.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.6
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• pp.378-387
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• 2007

To enhancing the exercise effect, exercise management systems are introduced and generally used. They create the proper exercise program through exercise prescription after determining the personal body status. When the exercise programs are created, they will consider $2weeks{\sim}3months$ period. And, existing exercise programs cannot respect with personal exercise habits or exercise period which are changing variedly. If exercise period is long, it can be caused inappropriate exercise about user current status. To solve these problems in legacy systems, this paper proposes a Context Aware Exercise Model (CAEM) to provide the exercise program considering the user context. Also, we implemented that as Intelligent Fitness Guide (IFG) System. The IFG system is selectively received necessary measurement values as input values according to user's context. If exercise kinds, frequency and strength of user are changing, that system creates the exercise program through exercise optimization algorithm and exercise knowledge base. As IFG is providing the exercise program in a real time, it can be managed the effective exercise according to user context.

• Clinical and Experimental Pediatrics
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• v.63 no.9
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• pp.361-367
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• 2020

Background: Length of stay is a significant indicator of care effectiveness and hospital performance. Owing to the limited number of healthcare centers and facilities, it is important to optimize length of stay and associated factors. Purpose: The present study aimed to investigate factors associated with neonatal length of stay in the neonatal intensive care unit (NICU) using parametric and semiparametric models and compare model fitness according to Akaike information criterion (AIC) between 2016 and 2018. Methods: This retrospective cohort study reviewed 600 medical records of infants admitted to the NICU of Bandar Abbas Hospital. Samples were identified using census sampling. Factors associated with NICU length of stay were investigated based on semiparametric Cox model and 4 parametric models including Weibull, exponential, log-logistic, and log-normal to determine the best fitted model. The data analysis was conducted using R software. The significance level was set at 0.05. Results: The study findings suggest that breastfeeding, phototherapy, acute renal failure, presence of mechanical ventilation, and availability of central venous catheter were commonly identified as factors associated with NICU length of stay in all 5 models (P<0.05). Parametric models showed better fitness than the Cox model in this study. Conclusion: Breastfeeding and availability of central venous catheter had protective effects against length of stay, whereas phototherapy, acute renal failure, and mechanical ventilation increased length of stay in NICU. Therefore, the identification of factors associated with NICU length of stay can help establish effective interventions aimed at decreasing the length of stay among infants.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.45-55
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• 2015

In this paper, we consider curriculum mining as an application of process mining in the domain of education. The basic objective of the curriculum mining is to construct a registration pattern model by using logs of registration data. However, subject registration patterns of students are very unstructured and complicated, called a spaghetti model, because it has a lot of different cases and high diversity of behaviors. In general, it is typically difficult to develop and analyze registration patterns. In the literature, there was an effort to handle this issue by using clustering based on the features of students and behaviors. However, it is not easy to obtain them in general since they are private and qualitative. Therefore, in this paper, we propose a new framework of curriculum mining applying K-means clustering based on subject attributes to solve the problems caused by unstructured process model obtained. Specifically, we divide subject's attribute data into two parts : categorical and numerical data. Categorical attribute has subject name, class classification, and research field, while numerical attribute has ABEEK goal and semester information. In case of categorical attribute, we suggest a method to quantify them by using binarization. The number of clusters used for K-means clustering, we applied Elbow method using R-squared value representing the variance ratio that can be explained by the number of clusters. The performance of the suggested method was verified by using a log of student registration data from an 'A university' in terms of the simplicity and fitness, which are the typical performance measure of obtained process model in process mining.

• Journal of the Korean Applied Science and Technology
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• v.38 no.3
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• pp.860-869
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• 2021

This study tried to provide basic data for predicting consumers by applying the structural analysis method (SEM) to the causal relationship model that applied the technology acceptance theory for mobile home training, exercise commitment, and continuous intention. Therefore, in order to identify the strategic tools due to the current COVID-19 pandemic, the survey was conducted using the mobile program "Survey Monkey" according to the sampling plan from February 1, 2021 to May 21, 2021, and a total of 287 valid samples. people were used in the final analysis of consumers. As a result of the study, it was found that the acceptance model had a significant effect(+) on exercise commitment, and the acceptance model had a significant effect(+) on the exercise continuation intention. Finally, it was found that exercise commitment had a significant(+) effect on exercise continuity intention.