• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.252-256
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• 2011

We analyze the channel capacity with considering the effect of the loading conditions of indoor PLC networks on channel state information. We consider various numbers of load for two kinds of the networks with regular length branches and a deployed network of indoor PLC. For calculating the channel capacity degradation, two noise scenarios and impedances are considered. From the simulation results, we suggest the robust regression lines for modeling the channel capacity degradation. In the cases of 0 $\Omega$ and $Z_0$ loads, natural log and linear function curve show the best goodness of fit, respectively. For the deployed indoor PLC network with 0 $\Omega$ loads, compared with the networks with regular length branches, the goodness of fit decreases by the amount of 0.12 and 0.15 for low noise and high noise scenarios, respectively. Using the regression lines, we can estimate the channel capacity degradation without measurement.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.421-433
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• 1993

Few ovservation can influence in model building procedure and can dominate the least squares fit of a selected model. An observation, however, may not have the same impact on all aspects of regression analysis. We introduce a statistic which measures the impact of individual cases on the overall goodness-of-fit statistics. We also propose an influence measure for variable selection problem. The property of uncorrelatedness between fitted values and residuals has been used to develop the influence measure. The performance of the measures are used to develop the influence measure. The performance of the measures are compared with other widely used influence measures by the analysis of real data.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.339-351
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• 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.

• Journal of Forest and Environmental Science
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• v.39 no.4
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• pp.195-202
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• 2023

In this study, we sought to compare and evaluate the accuracy and predictive performance of machine learning algorithms for estimating the growth of individual Larix kaempferi trees in Gangwon Province, Korea. We employed linear regression, random forest, XGBoost, and LightGBM algorithms to predict tree growth using monitoring data organized based on different thinning intensities. Furthermore, we compared and evaluated the goodness-of-fit of these models using metrics such as the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The results revealed that XGBoost provided the highest goodness-of-fit, with an R² value of 0.62 across all thinning intensities, while also yielding the lowest values for MAE and RMSE, thereby indicating the best model fit. When predicting the growth volume of individual trees after 3 years using the XGBoost model, the agreement was exceptionally high, reaching approximately 97% for all stand sites in accordance with the different thinning intensities. Notably, in non-thinned plots, the predicted volumes were approximately 2.1 m³ lower than the actual volumes; however, the agreement remained highly accurate at approximately 99.5%. These findings will contribute to the development of growth prediction models for individual trees using machine learning algorithms.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.515-530
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• 2009

Evaporation from surface water bodies is influenced by a number of meteorological parameters. The rate of evaporation is primarily controlled by incoming solar radiation, air and water temperature and wind speed and relative humidity. In the present study, influence of weekly meteorological variables such as air temperature, relative humidity, bright sunshine hours, wind speed, wind velocity, rainfall on rate of evaporation has been examined using 35 years(1971-2005) of meteorological data. Statistical analysis was carried out employing linear regression models. The developed regression models were tested for goodness of fit, multicollinearity along with normality test and constant variance test. These regression models were subsequently validated using the observed and predicted parameter estimates with the meteorological data of the year 2005. Further these models were checked with time order sequence of residual plots to identify the trend of the scatter plot and then new standardized regression models were developed using standardized equations. The highest significant positive correlation was observed between pan evaporation and maximum air temperature. Mean air temperature and wind velocity have highly significant influence on pan evaporation whereas minimum air temperature, relative humidity and wind direction have no such significant influence.

• Journal of Environmental Health Sciences
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• v.37 no.6
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• pp.460-466
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• 2011

Objectives: Students in elementary schools usually wash their hands in a washstand. However, little attention is paid to the washstand itself. Today, the importance of personal sanitation and hygiene is greatly emphasized. Therefore students' parents and the public are growing increasingly interested in accessibility to washstands by elementary school students in their schools. Methods: With respect to this study, a survey of students and teachers inelementary schools was performed on the installation of washstands in order to determine the proper number of washstands per school. Results: The results show that 1.1 boys (per class) need a washstand, while 1.8 girls (per class) do so in order to maintain a 50% level of crowdedness. By of the regression equation, to maintain 50% congestion (50% of all students feel congestion) there should be 18.5 boys, and the 15.76 girls per washstand. Table 3 is based on the above results, the number of students per washstand (x) and congestion (y), separated by gender according to the results of regression analysis, the correlation of male models in the linear regression analysis and correlation of girls in the regression equation can be obtained. The linear regression fit of less than 0.7 determines that the coefficients of determination are 0.5399 and 0.4195, respectively. Significance was much smaller. Also, according to the simulation using the diffusion model, with 29 students per class more than one washstand should be provided in a school. Girls (per class) need 0.7 more washstands than boys (per class). Conclusions: More washstand facilities for girls than boys are needed. If the target is based on school class size two washstands should be installed. Finally, guidelines and/or standards in the Schools Health Act of Korea forin elementary school washstands is considerably needed.

• Journal of ILASS-Korea
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• v.20 no.4
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• pp.247-253
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• 2015

Recently, the engine calibration technique has been much more complicated than that of the past engine case in order to satisfy the strict emission regulations. The current calibration method for the diesel engine which has an increasing market is both costly and time-consuming. New engine calibration method is required to develop for high-quality diesel engines with low cost and release it at the appropriate time. This study provides the optimal calibrating technique for complex engine systems using statistical modeling and numerical optimization. Firstly, it design a test plan based on Design of Experiments, a V-optimality methodology which is suitable looking for set-points, and determine the shape of test engine response. Secondly, it uses functions to make linear regression model for data analysis and optimization to fit the models of engines behavior. Finally, it generates the optimal calibrations obtained directly from empirical engine models using Grey Relational Analysis and compares the calibrations with data. This method can develop a process for systematically identifying the optimal balance of engine emissions.

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.51-59
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• 2010

In the case that the regression function has a discontinuity point in generalized linear model, Huh (2009) estimated the location and jump size using the log-likelihood weighted the one-sided kernel function. In this paper, we consider estimation of the unknown number of the discontinuity points in the regression function. The proposed algorithm is based on testing of the existence of a discontinuity point coming from the asymptotic distribution of the estimated jump size described in Huh (2009). The finite sample performance is illustrated by simulated example.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.4
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• pp.491-499
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• 2009

The determination of response characteristics for pressure sensors is routinely limited to static calibration against a deadweight pressure standard. The strength of this method is that the deadweight device is a primary standard used to generate precise pressure. Its weakness lies in the assumption that the static and dynamic responses of the sensor in question are equivalent. Differences in sensor response to static and dynamic events, however, can lead to serious measurement errors. Dynamic techniques are required to calibrate pressure sensors measuring dynamic events in milliseconds. In this paper, a dynamic calibration using negative going dynamic pressure is proposed to determine dynamic pressure response for piezoelectric sensors. Sensitivity and linearity of sensor by the dynamic calibration were compared with those by the static calibration. The uncertainty of calibration results and the goodness of fit test of linear regression analysis were presented. The results show that the dynamic calibration is applicable to determine dynamic pressure response for piezoelectric sensors.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.749-754
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• 2015

This article presents Bayesian approach to regression splines with knots on a grid of equally spaced sample quantiles of the independent variables under functional measurement error model.We consider small area model by using penalized splines of non-linear pattern. Specifically, in a basis functions of the regression spline, we use radial basis functions. To fit the model and estimate parameters we suggest a hierarchical Bayesian framework using Markov Chain Monte Carlo methodology. Furthermore, we illustrate the method in an application data. We check the convergence by a potential scale reduction factor and we use the posterior predictive p-value and the mean logarithmic conditional predictive ordinate to compar models.