• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.3
• /
• pp.17-23
• /
• 2020

This paper proposes a simulation model using an equivalent circuit for the development of an MMC system. The MMC has been chosen as the most suitable topology for high voltage power transmission, such as a voltage-type HVDC, and it has dozens to hundreds of sub-modules in the form of a half-bridge or full-bridge connected in series. A simulation study is essential for the development of an MMC algorithm. On the other hand, it is virtually impossible to construct and implement MMC simulation models, including hundreds or thousands of switching devices. Therefore, this paper presents an MMC equivalent model, which is easily expandable and implemented by modeling the dynamic characteristics. The voltage and current equation of the equivalent circuit was calculated using the direction of the arm current and switching signal. The model was implemented on Matlab/Simulink. In this paper, to show the validity of the model developed using Matlab/Simulink, the simulation results of a five-level MMC using the real switching element and the proposed equivalent model are shown. The validity of the proposed model was verified by showing that the current and voltage waveform in the two models match each other.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.10
• /
• pp.6019-6023
• /
• 2013

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

• Korean Journal of Health Education and Promotion
• /
• v.29 no.2
• /
• pp.83-91
• /
• 2012

Objectives: This study is to examine that drinking problems among university students were accounted for not only by student's individual characteristics but alcohol policy and environmental characteristics of the university in which students were enrolled. Method: Secondary data analysis was employed in which variables under study were derived from a raw data of a nationwide representative sample in 2009. Raw data under analysis included 3,665 students from 63 universities across Korea. Organizational and environmental characteristics of the university were collected from university administrators while individual characteristics and drinking behavior from the students in using self-administrated questionnaire. Multilevel regression analyses were employed to describe alcohol policy effects on students's drinking problems measured by AUDIT by using HLM7.0. Results: ICCs indicate that variation in drinking problem depends on alcohol policy of university. Multilevel regression models identified statistically significant factors in explaining variance of drinking problems. Group means on drinking problem are affected by indicators representing alcohol policy with level of drinking problem of student being decreased in accordance to level of availability of alcohol on campus. Conclusions: It is concluded that drinking problems among university students were associated with both individual characteristics and alcohol policy of the university they enrolled. This study supports policy belief that interventions at environmental as well as individual level are required to prevent drinking problem among university students.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.6
• /
• pp.1285-1296
• /
• 2013

The fundamental concerns of this paper are to analyze the effects of student course evaluation using subject characteristic and student characteristic variables. We use a 2-level hierarchical linear model since the data structure of subject characteristic and student characteristic variables is multilevel. Four models we consider are as follows; (1) null model, (2) random coefficient model, (3) mean as outcomes model, (4) intercepts and slopes as outcomes model. The results of the analysis were given as follows. First, the result of null model was that subject characteristics effects on course evaluation had much larger than student characteristics. Second, the result of conditional model specifying subject and student level predictors revealed that class size, grade, tenure, mean GPA of the class, native class for level-1, and sex, department category, admission method, mean GPA of the student for level-2 had statistically significant effects on course evaluation. The explained variance was 13% in subject level, 13% in student level.

• The Korean Journal of Applied Statistics
• /
• v.21 no.6
• /
• pp.923-932
• /
• 2008

We are likely to face complex multivariate data which can be characterized by having a non-trivial correlation structure. For instance, omitted covariates may simultaneously affect more than one count in clustered data; hence, the modeling of the correlation structure is important for the efficiency of the estimator and the computation of correct standard errors, i.e., valid inference. A standard way to insert dependence among counts is to assume that they share some common unobservable variables. For this assumption, we fitted correlated random effect models considering multilevel model. Estimation was carried out by adopting the semiparametric approach through a finite mixture EM algorithm without parametric assumptions upon the random coefficients distribution.

• Korean Journal of Comparative Education
• /
• v.28 no.5
• /
• pp.1-19
• /
• 2018

The purpose of educational assessments such as the Trends in International Mathematics and Science Study (TIMSS) is to compare groups such as countries. When the unit of measurement is above the student level, group-level diagnostic assessment based on multilevel item response theory (ML-IRT) can be considered just as cognitive diagnosis models are developed from item response theory. This study suggests an ML-IRT-based group-level diagnostic assessment model by modifying an item feature model by Park and bolt (2008). The model is illustrated on the recently released TIMSS 2015 Grade 8 mathematics assessment. The results provide skill profiles for the studied countries and the nine cognitive attributes; that is, the attribute effects can be compared across the countries and also across the attributes. By controlling unexplained variance, the suggested model may provide more reliable and more informative group-level comparisons. The results are interpreted using an example. Limitations and directions for future research are also discussed.

• Nuclear Engineering and Technology
• /
• v.46 no.6
• /
• pp.753-772
• /
• 2014

The paper presents functional modelling and its application for diagnosis in nuclear power plants. Functional modelling is defined and its relevance for coping with the complexity of diagnosis in large scale systems like nuclear plants is explained. The diagnosis task is analyzed and it is demonstrated that the levels of abstraction in models for diagnosis must reflect plant knowledge about goals and functions which is represented in functional modelling. Multilevel flow modelling (MFM), which is a method for functional modelling, is introduced briefly and illustrated with a cooling system example. The use of MFM for reasoning about causes and consequences is explained in detail and demonstrated using the reasoning tool, the MFMSuite. MFM applications in nuclear power systems are described by two examples: a PWR; and an FBR reactor. The PWR example show how MFM can be used to model and reason about operating modes. The FBR example illustrates how the modelling development effort can be managed by proper strategies including decomposition and reuse.

• Nuclear Engineering and Technology
• /
• v.37 no.1
• /
• pp.118-126
• /
• 2005

While emergency operating procedures (EOPs) occupy an important role in the management of various abnormal situations in nuclear power plants (NPPs), current technology for the validation of EOPs still largely depends on manual review. A validation method for EOPs of NPPs is thus proposed based on dynamic multi-level flow modeling (MFM). The MFM modeling procedure and the EOP validation procedure are developed and provided in the paper. Application of the proposed method to EOPs of an actual NPP shows that the proposed method provides an efficient means of validating EOPs. It is also found that the information on state transitions in MFM models during the management of abnormal situations is also useful for further analysis on EOPs including their optimization.

• The Journal of the Korea Contents Association
• /
• v.13 no.5
• /
• pp.342-353
• /
• 2013

Objectives: It attempted to analyze influencing factors on the utilization of outpatient services which were adopted to predisposing, enabling, and need factors in Anderson model. Methods: The current study analyzed "2007 Korean National Health Nutrition Survey" data, which selected 3,335 people nationwide by proportional systematic sampling. This study analyzed data of persons who used outpatient services in two weeks. It adopted Anderson Model to control contextual factors including socioeconomic factors. The study compared means and fitted logistic regression models and multilevel model. Results: The logistic regression model showed that persons purchased private medical insurance were less likely to use outpatient services than the persons did not purchase private medical insurance. Persons with hypertension and diabetes mellitus, overweight, and problem drinkers were more likely to use outpatient services. Persons with high school graduates or higher in education level and experience of accidents or intoxications were more likely to use outpatient services according to the multilevel analysis of mixed model which treated region as random effect. Conclusion: Higher level of perceived stress increased the probability to use outpatient service than lower level of perceived stress. As number of days a person had exercised increased, the probability to use outpatient service decreased. Overweight and problem alcohol drinking increased the probability of outpatient service use. Further research should be conducted to find more factors influencing outpatient service use.

• Perspectives in Nursing Science
• /
• v.15 no.2
• /
• pp.49-69
• /
• 2018

Purpose: The purpose of this study was to derive contextual indicators of medical provider quality and assess their relative importance along with the individual utilization of antenatal care (ANC) and institutional births with a skilled birth attendant (SBA) in India using a multilevel framework. Methods: The 2015~2016 Demographic and Health Survey (DHS) from India was used to assess the outcomes of neonatal, infant, and under-five child mortality. The final analytic sample included 182,980 children across 28,283 communities, 640 districts, and 36 states and union territories. The contextual indicators of medical provider quality for districts and states were derived from the individual-level number of ANC visits (<4 or ${\geq}4$) and institutional delivery with SBA. A series of random effects logistic regression models were estimated with a stepwise addition of predictor variables. Results: About half of the mothers (47.3%) had attended ${\geq}4$ ANC visits and 75.8% delivered in institutional settings with SBAs. Based on ANC visits, 276~281 districts (43.1~43.9%) and 13~16 states (36.5~44.4%) were classified as "low" quality areas, whereas 268~285 districts (41.9~44.5%) and 8~9 states (22.2~25.0%) were classified as "low" quality areas based on institutional delivery with SBAs. Conditional on a comprehensive set of covariates, the individual use of both ANC and SBA were significantly associated with all mortality outcomes (OR: 1.17, 95% CI: 1.08, 1.26, and OR: 1.10, 95% CI: 1.02, 1.19, respectively, for under-five child mortality) and remained robust even after adjusting for contextual indicators of medical provider quality. Districts and states with low quality were associated with 57~61% and 27~43% higher odds of under-five child mortality, respectively. Conclusion: When simultaneously considered, district- and state-level provider quality mattered more than individual access to care for all mortality outcomes in India. Further investigations are needed to assess the importance of improving the quality of health service delivery at higher levels to prevent unnecessary child deaths in developing countries.