• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.63-68
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• 1998

This paper proposes fuzzy regression analysis with non-symmetric fuzzy coefficients. By assuming non-symmetric triangular fuzzy coefficients and applying the quadratic programming fomulation, the center of the obtained fuzzy regression model attains more central tendency compared to the one with symmetric triangular fuzzy coefficients. For a data set composed of crisp inputs-fuzzy outputs, two approximation models called an upper approximation model and a lower approximation model are considered as the regression models. Thus, we also propose an integrated quadratic programming problem by which the upper approximation model always includes the lower approximation model at any threshold level under the assumption of the same centers in the two approximation models. Sensitivities of Weight coefficients in the proposed quadratic programming approaches are investigated through real data.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.5
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• pp.468-480
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• 2005

The overall performance of AC servo system is greatly affected the uncertainties of unpredictable mechanical parameter variations and external load disturbances. To overcome this problem, it is necessary to know different parameters and load disturbances subjected to position/speed control. This paper proposes an on-line identification method of mechanical parameters/load disturbances for AC servo system using support vector regression(SVR). The experimental results demonstrate that the proposed SVR algorithm is appropriate for control of unknown servo systems even with time-varying/nonlinear parameters.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.849-860
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• 1999

This paper addresses the issue of estimating regression function with errors in variables using wavelets. We adopt a nonparametric approach in assuming that the regression function has no specific parametric form, To account for errors in covariates deconvolution is involved in the construction of a new class of linear wavelet estimators. using the wavelet characterization of Besov spaces the question of regression estimation with Besov constraint can be reduced to a problem in a space of sequences. Rates of convergence are studied over Besov function classes $B_{spq}$ using $L_2$ error measure. It is shown that the rates of convergence depend on the smoothness s of the regression function and the decay rate of characteristic function of the contaminating error.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.21-25
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• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.5053-5061
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• 2014

The purpose of this study was to examine the relationship among drinking motivation, daily stress and problem drinking behavior, and to identify the influencing factors of problem drinking behavior in female university students. The participants were 259 female students in university. The data was collected for 1 months from Oct. 28 to Nov. 28, 2013 in a university-located Y city. Questionnaires were used to measure the levels of the drinking motivation, daily stress, and problem drinking behavior. The data was analyzed using descriptive statistics, t-test, ANOVA, Pearson's correlation, stepwise regression, and the IBMSPSS/WIN 19.0 program. The predicting factors for problem drinking behavior were drinking motivation, type of residency and smoking. The variables explained the problem drinking behavior by 35.7%. A survey of the various influencing factors of problem drinking behavior will be required and a drinking reduction program for female university students is needed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.589-598
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• 2016

The aim of this study was to investigate the relationship among the variables and the influence of academic self-efficacy and critical thinking disposition on the problem solving ability of nursing students. A descriptive research design was employed in this study. The participants were 404 sophomore nursing students in D city, who were surveyed between May 2 and May 20, 2016, using self-report questionnaire. The data were analyzed with SPSS WIN 21.0 using descriptive statistics, ANOVA, t-test, Scheffe test, Pearson's correlation coefficient, and multiple stepwise regression analysis. Significant correlations among the academic self-efficacy, critical thinking disposition, and problem solving ability were found. In academic self-efficacy and critical thinking disposition, there were significant differences in gender, academic credit, interpersonal relationship, and satisfaction with nursing as a major. The problem solving ability showed statistically significant differences in academic credit, interpersonal relationships, and satisfaction with nursing as a major. The regression model explained 51.7% of the effect on the problem solving ability. Academic self-efficacy and critical thinking disposition were factors influencing the problem solving ability of nursing students. Based on these results, to increase problem solving ability of nursing students, it will be necessary to develop an educational program and strategy for improving the academic self-efficacy and critical thinking disposition.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.801-808
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• 2018

The learning of the linear regression model is stable on the assumption that the sample size is sufficiently larger than the number of explanatory variables and there is no serious multicollinearity between explanatory variables. In this study, we investigated the difficulty of model learning when the assumption was violated by analyzing a real heavy rain damage data and we proposed to use a principal component regression model or a ridge regression model after integrating data to overcome the difficulty. We evaluated the predictive performance of the proposed models by using the test data independent from the training data, and confirmed that the proposed methods showed better predictive performances than the linear regression model.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.04a
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• pp.1-2
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• 2011

Hybrid rockets have lately attracted attention as a strong candidate of small, low cost, safe and reliable launch vehicles. A significant topic is that the first commercially sponsored space ship, SpaceShipOne vehicle chose a hybrid rocket. The main factors for the choice were safety of operation, system cost, quick turnaround, and thrust termination. In Japan, five universities including Hokkaido University and three private companies organized "Hybrid Rocket Research Group" from 1998 to 2002. Their main purpose was to downsize the cost and scale of rocket experiments. In 2002, UNISEC (University Space Engineering Consortium) and HASTIC (Hokkaido Aerospace Science and Technology Incubation Center) took over the educational and R&D rocket activities respectively and the research group dissolved. In 2008, JAXA/ISAS and eleven universities formed "Hybrid Rocket Research Working Group" as a subcommittee of the Steering Committee for Space Engineering in ISAS. Their goal is to demonstrate technical feasibility of lowcost and high frequency launches of nano/micro satellites into sun-synchronous orbits. Hybrid rockets use a combination of solid and liquid propellants. Usually the fuel is in a solid phase. A serious problem of hybrid rockets is the low regression rate of the solid fuel. In single port hybrids the low regression rate below 1 mm/s causes large L/D exceeding a hundred and small fuel loading ratio falling below 0.3. Multi-port hybrids are a typical solution to solve this problem. However, this solution is not the mainstream in Japan. Another approach is to use high regression rate fuels. For example, a fuel regression rate of 4 mm/s decreases L/D to around 10 and increases the loading ratio to around 0.75. Liquefying fuels such as paraffins are strong candidates for high regression fuels and subject of active research in Japan too. Nakagawa et al. in Tokai University employed EVA (Ethylene Vinyl Acetate) to modify viscosity of paraffin based fuels and investigated the effect of viscosity on regression rates. Wada et al. in Akita University employed LTP (Low melting ThermoPlastic) as another candidate of liquefying fuels and demonstrated high regression rates comparable to paraffin fuels. Hori et al. in JAXA/ISAS employed glycidylazide-poly(ethylene glycol) (GAP-PEG) copolymers as high regression rate fuels and modified the combustion characteristics by changing the PEG mixing ratio. Regression rate improvement by changing internal ballistics is another stream of research. The author proposed a new fuel configuration named "CAMUI" in 1998. CAMUI comes from an abbreviation of "cascaded multistage impinging-jet" meaning the distinctive flow field. A CAMUI type fuel grain consists of several cylindrical fuel blocks with two ports in axial direction. The port alignment shifts 90 degrees with each other to make jets out of ports impinge on the upstream end face of the downstream fuel block, resulting in intense heat transfer to the fuel. Yuasa et al. in Tokyo Metropolitan University employed swirling injection method and improved regression rates more than three times higher. However, regression rate distribution along the axis is not uniform due to the decay of the swirl strength. Aso et al. in Kyushu University employed multi-swirl injection to solve this problem. Combinations of swirling injection and paraffin based fuel have been tried and some results show very high regression rates exceeding ten times of conventional one. High fuel regression rates by new fuel, new internal ballistics, or combination of them require faster fuel-oxidizer mixing to maintain combustion efficiency. Nakagawa et al. succeeded to improve combustion efficiency of a paraffin-based fuel from 77% to 96% by a baffle plate. Another effective approach some researchers are trying is to use an aft-chamber to increase residence time. Better understanding of the new flow fields is necessary to reveal basic mechanisms of regression enhancement. Yuasa et al. visualized the combustion field in a swirling injection type motor. Nakagawa et al. observed boundary layer combustion of wax-based fuels. To understand detailed flow structures in swirling flow type hybrids, Sawada et al. (Tohoku Univ.), Teramoto et al. (Univ. of Tokyo), Shimada et al. (ISAS), and Tsuboi et al. (Kyushu Inst. Tech.) are trying to simulate the flow field numerically. Main challenges are turbulent reaction, stiffness due to low Mach number flow, fuel regression model, and other non-steady phenomena. Oshima et al. in Hokkaido University simulated CAMUI type flow fields and discussed correspondence relation between regression distribution of a burning surface and the vortex structure over the surface.

• Journal of Korean Clinical Nursing Research
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• v.18 no.3
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• pp.402-412
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• 2012

Purpose: The purpose of this study was to explore influencing factors of dysfunctional beliefs and emotion regulation strategy for nurses' problem solving ability. Methods: This study was a cross-sectional design with a sample of 745 nurses from 1 university hospital located in Gyeonggido. The scales were Dysfunctional Beliefs Test (70 items), Emotion Regulation Strategy Questionnaire (25 items) and Social Problem Solving Inventory (52 items). The data were analyzed using SPSS 17.0 employing ANOVA, pearson correlation coefficients and multiple regression analysis. Results: The mean score for problem solving ability was 11.26 points. Influencing factors for nurses' problem solving ability were identified as 'active regulation style' in emotion regulation strategy and 'negative concept of social self' in dysfunctional beliefs. Conclusion: It is plausible to assume that dysfunctional beliefs which are vulnerability factors in cognitive variables and emotion regulation strategy affect nurses' problem solving ability.

• Journal of Korean Elementary Science Education
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• v.26 no.3
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• pp.286-294
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• 2007

This study investigated the relationship between elementary school students' creative problem solving skills in terms of science and cognitive strategies. Creative problem solving in science was measured by 4 variables; appropriateness, scientific ability, concreteness, and originality. Cognitive strategies were measured by 6 variables; surface(rehearsal), deep(elaboration and organization), and metacognitive strategies(planning, monitoring, and regulating). The KEDI Creative Problems Solving Test in Science(Cho et al., 1997) and the Motivated Strategies for Learning Questionnaire(Pintrich & DeGroot, 1990) were administered to 72 subjects. Data were analyzed by means of Pearson's correlation and multiple regression analysis. Our findings indicated a positive correlation between creative problem solving in science and cognitive strategies. The surface cognitive strategy (rehearsal) positively predicted the total score, the scientific ability's score, the concrete score, and the original score of creative problem solving in science. The deep cognitive strategy(organization) positively predicted the appropriate score and the metacognitive strategy(planning) positively predicted the original score of scientific creative problem solving skills.