• 한국환경과학회지
• /
• 제23권3호
• /
• pp.359-368
• /
• 2014

The aim of our research was to apply experimental design methodology in the optimization of N, N-Dimethyl-4-nitrosoaniline (RNO, which is indictor of OH radical formation) degradation using gas mixing-circulation plasma process. The reaction was mathematically described as a function of four independent variables [voltage ($X_1$), gas flow rate ($X_2$), liquid flow rate ($X_3$) and time ($X_4$)] being modeled by the use of the central composite design (CCD). RNO removal efficiency was evaluated using a second-order polynomial multiple regression model. Analysis of variance (ANOVA) showed a high coefficient of determination ($R^2$) value of 0.9111, thus ensuring a satisfactory adjustment of the second-order polynomial multiple regression model with the experimental data. The application of response surface methodology (RSM) yielded the following regression equation, which is an empirical relationship between the RNO removal efficiency and independent variables in a coded unit: RNO removal efficiency (%) = $77.71+10.04X_1+10.72X_2+1.78X_3+17.66X_4+5.91X_1X_2+3.64X_2X_3-8.72X_2X_4-7.80X{_1}^2-6.49X{_2}^2-5.67X{_4}^2$. Maximum RNO removal efficiency was predicted and experimentally validated. The optimum voltage, air flow rate, liquid flow rate and time were obtained for the highest desirability at 117.99 V, 4.88 L/min, 6.27 L/min and 24.65 min, respectively. Under optimal value of process parameters, high removal(> 97 %) was obtained for RNO.

• Advances in Energy Research
• /
• 제6권2호
• /
• pp.91-101
• /
• 2019

This paper proposes an efficient data-driven approach to build models for predicting energy consumption in buildings. Data used in this research is collected by installing humidity and temperature sensors at different locations in a building. In addition to this, weather data from nearby weather station is also included in the dataset to study the impact of weather conditions on energy consumption. One of the main emphasize of this research is to make feature selection independent of domain knowledge. Therefore, to extract useful features from data, two different approaches are tested: one is feature selection through principal component analysis and second is relative importance-based feature selection in original domain. The regression model used in this research is gradient boosting regression and its optimal parameters are chosen through a two staged coarse-fine search approach. In order to evaluate the performance of model, different performance evaluation metrics like r2-score and root mean squared error are used. Results have shown that best performance is achieved, when relative importance-based feature selection is used with gradient boosting regressor. Results of proposed technique has also outperformed the results of support vector machines and neural network-based approaches tested on the same dataset.

• Communications for Statistical Applications and Methods
• /
• 제5권2호
• /
• pp.339-351
• /
• 1998

Designs for discriminating between two linear regression models are studied under $\Lambda$-type optimalities maximizing the measure for the lack of fit for the designs with fixed model inadequacy. The problem of selecting an appropriate $\Lambda$-type optimalities is shown to be closely related to the estimation method. $\Lambda$-type optimalities for the least squares and minimum bias estimation methods are considered. The minimum bias designs are suggested for the designs invariant with respect to the two estimation methods. First order minimum bias designs optimal under $\Lambda$-type optimalities are then derived. Finally for the case where the lack of fit test is significant, an approach to the construction of a second order design accommodating the optimal first order minimum bias design is illustrated.

• Communications for Statistical Applications and Methods
• /
• 제22권5호
• /
• pp.463-473
• /
• 2015

We consider a nonparametric AR(1) model with nonparametric ARCH(1) errors. In order to estimate the unknown function of the ARCH part, we apply the stationary bootstrap procedure, which is characterized by geometrically distributed random length of bootstrap blocks and has the advantage of capturing the dependence structure of the original data. The proposed method is composed of four steps: the first step estimates the AR part by a typical kernel smoothing to calculate AR residuals, the second step estimates the ARCH part via the Nadaraya-Watson kernel from the AR residuals to compute ARCH residuals, the third step applies the stationary bootstrap procedure to the ARCH residuals, and the fourth step defines the stationary bootstrapped Nadaraya-Watson estimator for the ARCH function with the stationary bootstrapped residuals. We prove the asymptotic validity of the stationary bootstrap estimator for the unknown ARCH function by showing the same limiting distribution as the Nadaraya-Watson estimator in the second step.

• 대한용접접합학회:학술대회논문집
• /
• 대한용접접합학회 2004년도 춘계 학술발표대회 개요집
• /
• pp.39-41
• /
• 2004

This study is to optimize the condition of pulse parameters using the response surface method in micro pulse TIG welding of pleated type metallic filter. The input parameters used were pulse current, base current, pulse duty, frequency and welding speed and the hydraulic pressure was used as the output parameter. The central composite design was designed using second order regression model, As the results, the optimal welding condition to manufacture the pleated type metallic filter was obtained.

• 약학회지
• /
• 제46권5호
• /
• pp.324-330
• /
• 2002

This experiment was carried out to determine non-destructively the hydrogen peroxide concentration of 3％ antiseptic hydrogen peroxide solutions by portable near-infrared (NIR) system. Hydrogen peroxide standards were prepared ranging from 0 to 25.6 w/w％ and the NIR spectra of hydrogen peroxide standard solutions were collected by using a quartz cell in 1 mm pathlength. We found the variation of absorbance band due to OH vibration of hydrogen peroxide depending on the concentration around 1400 nm in the second derivatives spectra. Partial least square regression (PLSR) and multilinear regression (MLR) were explored to develop a calibration model over the spectral range 1100-1720 nm. The model using PLSR was better than that using MLR. The calibration showed good results with a standard error of prediction (SEP) of 0.16％. In order to validate the developed calibration model, routine analyses were performed using commercial antiseptic hydrogen peroxide solutions. The hydrogen peroxide values from the NIR calibration model were compared with the values from a redox titration method. The NIR routine analyses results showed good correlation with those of the redox titration method. This study showed that the rapid and non-destructive determination of hydrogen peroxide in the antiseptic solution was successfully performed by portable NIR system without very harmful solvents.

• 한국정보시스템학회:학술대회논문집
• /
• 한국정보시스템학회 1997년도 춘계학술대회논문집 지역정보단지 조성과정보기술의 활용
• /
• pp.271-285
• /
• 1997

This paper address the issues of satisfaction factors to measure the service quality in computer communication service users. In order to develope a satisfaction factors' model, we study appropriate quality factors of the service through the focus group interviews with service users, and surveys the quality levels that users have felt in services. It also analyzes the relationship between the user's quality level and the quality factors by the statistical analyses. Based on the optimal regression model, we suggest an appropriate satisfaction model in PC communication service areas. That model shows that most users are interested in the fare for use. Use-fare factor is the most powerful one to the satisfaction model. Second one is usefulness, next is correctness. But connect-status factor is the only negative one. Most users think that its factor is in the way of fluent communication. So to keep the competitiveness in the PC communication service, the sixth negative factor should be modified as soon as possible.

• 한국정보시스템학회지:정보시스템연구
• /
• 제6권2호
• /
• pp.29-50
• /
• 1997

This paper addresses the issues of affecting factors to measure the satisfaction degree in PC communication utilizations. In order to develope an optimal model, we study appropriate affecting factors in PC communication utilizations through the focus group interviews with student users, and surveys the satisfaction levels that users have felt in services. Based on the optimal regression model, we suggest an appropriate satisfaction model in PC communication utilizations. That model shows that most users are interested in the A/S area for use. A/S factor is the most powerful one to the satisfaction model. Second one is usefulness, next is DB quality. But service-ability factor and convenience one are negative ones. Most users think that their factors are in the way of fluent communication. So to keep the competitiveness in the PC communication utilizations, the negative factors should be amended as soon as possible.

• ALGAE
• /
• 제17권2호
• /
• pp.105-115
• /
• 2002

Influences of vrious environmental factors on the eutrophication of Nakdong River were analyzed statistically using water samples collected from 1 January, 1999, to 30 September, 2001 at Namji area. The relationships between the concentration of chlorophyll α (eutrophication index) and environmental factors and were analyzed to develop a statistical model which can predict the status of eutrophication. The concentation of chlorophyll α ranged from 66.2 mg · $m^{-3}$ to 70.8 mg · $m^{-3}$ during dry winter season and the average concentration during this study period was 35.5 mg · $m^{-3}$ Namji area of Nakdong River was in the hypereutrohic stage in terms of water quality. Stephanodiscus sp. and Aulacoseria granulata var. angustissima were dominant species during the witnter to spring time and summer to autumn period, respectively. Based on the correlation analysis and the analysis of variance between chlorophyll α concentration and environmental factors, significantly high positive relationships were found in the order of BOD> pH> COD > KMnO₄ consumption > DO > conductivity > alkalinity. In contrast to these factors, significantly negrative relationships were found as in the order of $PO₄^{3-}-P$ >water level>the rate of Namgang-dam discharge > NH₃-N> the rate of Andong-dam discharge> the rate of Hapchoen-dam discharge. Based on the factors analysis of environmental factors on the concentration of chlorophyll α, we obtained five factors as follows. The first factor included water level, pH, turbiditiy, conductivity, alkalinity and the rate of Namgang-dam discharge. The second factor included water temperature DO, NH₄+-N, NO₃- -N. The third factor included KMnO₄ consumption COD and BOD. The fourth factor included the rate of Andong-dam discharge, the rate of Hapcheon-dam discharge, and the rate of Imha-dam discharge. The final factor included T-N T-P and $PO₄^{3-}-P$ > concentration. We derived two statistica models that can predict the occurrence of eutrophication based on the factors by factor analysis, using regression analysis. The first model is the stepwise regression model whose independent variables are the factors produced by factor analysis : chl α (mg · $m^{-3}$ = 42.923+(18.637 factor 3) + (-17.147 factor 1) + (-12.095 factor 5) + (-4.828 factor 4). The second model is the alternative stepwise regression model whose independent variables are the sums of the standardized main component variables:chl α (mg · $m^{-3}$ = 37.295+(7.326 Zfactor 3) + (-2.704 Zfactor 1)+(-2.341 Zfactor 5).

• 정보기술응용연구
• /
• 제1권
• /
• pp.173-211
• /
• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.