• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.629-633
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• 2005

The reduction of the Vehicle interior noise has been the main interest of NVH engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. In particular, the HVAC sound among the vehicle interior noise has been reflected sensitively in the side of psychology. In previous study, we have developed to verify identification of source for the vehicle HVAC system through multiple-dimensional spectral analysis. Also we carried out objective assessments on the vehicle HVAC noises and subjective assessments have been already performed with 30 subjects. In this study, the linear regression models were obtained for the subjective evaluation and the sound quality metrics. The regression procedure also allows you to produce diagnostic statistics to evaluate the regression estimates including appropriation and accuracy. Appropriation of regression model is necessary to $R^2$ value and F-value. And testing for regression model is necessary to Independence, Homoscedesticity and Normality. Also we selected optimum layout of damping material using Taguchi method. As a result of application, sound quality is improved by more quiet, powerful, expensive, smooth.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.2
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• pp.179-185
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• 2016

In this study, the RDII predictions were compared using two methodologies, i.e., the RTK-based and regression methods. Long-term (1/1/2011~12/31/2011) monitoring data, which consists of 10-min interval streamflow and the amount of precipitation, were collected at the domestic study area (1.36 km2 located in H county), and used for the construction of the RDII prediction models. The RTK method employs super position of tri-triangles, and each triangle (called, unit hydrograph) is defined by three parameters (i.e., R, T and K) determined/optimized using Genetic Algorithm (GA). In regression method, the MovingAverage (MA) filtering was used for data processing. Accuracies of RDII predictions from these two approaches were evaluated by comparing the root mean square error (RMSE) values from each model, in which the values were calculated to 320.613 (RTK method) and 420.653 (regression method), respectively. As a results, the RTK method was found to be more suitable for RDII prediction during extreme rainfall event, than the regression method.

• The Journal of Sustainable Design and Educational Environment Research
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• v.12 no.2
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• pp.61-70
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• 2013

As the existing school building power consumption is expressed by total power consumption, in the view of energy saving is disadvantage. The the power consumption of school building is divided as cooling, heating, lighting and others. The cooling power consumption, heating power consumption, lighting power consumption can be calculated using real total power consumption that gained from Korea Electric Power Corporation(KEPCO). The power consumption for cooling and heating can be calculated using heat transmittance, wall area and floor area, and for lighting is calculated by artificial lighting calculation. but this calculation methods is difficult for laymen. This study was carried out in order to establish the regression equation for cooling power consumption, heating power consumption, lighting power consumption and other power consumption in school building. In order to verify the validity of the regression equation, it is compared regression equation results and calculation results based on real power consumption. As the results, difference between regression result and calculation results for cooling and heating power consumption showed 0.6% and 3.6%.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.05a
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• pp.89-92
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• 2008

In generally, the regression rate equation was only expressed by function of oxidizer massflux in hybrid propulsion system. This can not represent the local value of regression rate along with oxidizer flow direction. In this study, experimental studies were performed with several pieces of solid fuel. As results, the local regression rate decreases rapidly with axial location near entrance, and increases with axial distance from the leading edge. The empirical formula for local regression rate with function of oxidizer massflux and length was derived.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.839-847
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• 2011

In this study, we derive an estimator based on autocovariance for the regression coefficients vector in the multiple linear regression model. This method is suggested by Park (2009), and although this method does not seem to be intuitively attractive, this estimator is unbiased for the regression coefficients vector. When the vectors of exploratory variables satisfy some regularity conditions, under mild conditions which are satisfied when errors are from autoregressive and moving average models, this estimator has asymptotically the same distribution as the least squares estimator and also converges in probability to the regression coefficients vector. Finally we provide a simulation study that the forementioned theoretical results hold for small sample cases.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.6 s.249
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• pp.728-733
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• 2006

The reduction of the Vehicle interior noise has been the main interest of NVH engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. In particular, the HVAC sound among the vehicle interior noise has been reflected sensitively in the side of psychology. In previous study, we have developed to verify identification of source for the vehicle HVAC system through multiple-dimensional spectral analysis. Also we carried out objective assessments on the vehicle HVAC noises and subjective assessments have been already performed with 30 subjects. In this study, the linear regression models were obtained for the subjective evaluation and the sound quality metrics. The regression procedure also allows you to produce diagnostic statistics to evaluate the regression estimates including appropriation and accuracy. Appropriation of regression model is necessary to $R^2$ value and F-value. And testing for regression model is necessary to independence, homoscedesticity and normality. Also we selected optimum layout of damping material using Taguchi method. As a result of application, sound quality is improved more quietly, powerfully, even though costly, and smoothly.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.4
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• pp.118-124
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• 2012

In this study, we turning mold steel (SKD61) using whisker reinforced ceramic tool (WA1) to get affected factor to surface roughness and regression equation. For this study, we adapt system of experiments. Results are follows; From the analysis of variance, it was found that affected factor to surface roughness was feed rate, cutting speed, depth of cut in order. From multi-regression analysis, we calculated regression equation and the coefficient of determination($R^2$). $R^2$ was 0.978 and It means regression equation is significant. Regression equation means if feed rate increase 0.039mm/rev, surface roughness will increase $0.8391{\mu}m$, if cutting speed increase 50m/min, surface roughness will decrease $0.034{\mu}m$, if depth of cut increase 0.1mm, surface roughness will increase $0.0203{\mu}m$. From the experimental verification, it was confirmed that surface roughness was predictable by system of experiments.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.21 no.2
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• pp.67-77
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• 2016

This study developed synthetic regression diagram for analyzing the linear trend of sea surface height, temperature, and salinity around the Korean marginal seas. In situ observed data had been quality controlled and they were verified by EOF comparison with objective analyzed data. From the synthetic regression diagram, we confirmed similar linear regression values with those of previous studies, but additionally provided detailed regression rate of each 5 to 30 year for the total periods of 1983-2013. We expect that quantitative results presented by this study will be useful as standard reference numbers for relevant studies analyzing oceanic long-term trend.

• Journal of the Korean Society of Mechanical Technology
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• v.13 no.3
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• pp.113-118
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• 2011

It is not easy to predict the shrinkage rate of a plastic injection mold in its design process. The shrinkage rate should be considered as one of the important performances to produce the reliable products. The shrinkage rate can be determined by using the CAE tools in the design produces. However, since the analysis can take minutes to hours, the high computational costs of performing the analysis limit their use in design optimization. Therefore this study was carried out to presume for mutual relation of analysis condition to get the optimum average shrinkage by regression analysis. The results shown that coefficient of determination of regression equation has a fine reliability over 87% and regression equation of average shrinkage is made by regression analysis.

• Corrosion Science and Technology
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• v.20 no.2
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• pp.52-61
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• 2021

This study using experimental design and linear regression technique was implemented in order to predict the pitting potential of stainless steel in marine environments, with the target materials being AL-6XN and STS 316L. The various variables (inputs) which affect stainless steel's pitting potential included the pitting resistance equivalent number (PRNE), temperature, pH, Cl^- concentration, sulfate levels, and nitrate levels. Among them, significant factors affecting pitting potential were chosen through an experimental design method (screening design, full factor design, analysis of variance). The potentiodynamic polarization test was performed based on the experimental design, including significant factor levels. From these testing methods, a total 32 polarization curves were obtained, which were used as training data for the linear regression model. As a result of the model's validation, it showed an acceptable prediction performance, which was statistically significant within the 95% confidence level. The linear regression model based on the full factorial design and ANOVA also showed a high confidence level in the prediction of pitting potential. This study confirmed the possibility to predict the pitting potential of stainless steel according to various variables used with experimental linear regression design.