• Journal of Environmental Science International
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• v.27 no.7
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• pp.509-518
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• 2018

Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.

• 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.27 no.6
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• pp.701-714
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• 2020

Quantitative high throughput screening (qHTS) assays are used to assess toxicity for many chemicals in a short period by collectively analyzing them at several concentrations. Data are routinely analyzed using nonlinear regression models; however, we propose a new method to analyze qHTS data using a nonlinear mixed effects model. qHTS data are generated by repeating the same experiment several times for each chemical; therefor, they can be viewed as if they are repeated measures data and hence analyzed using a nonlinear mixed effects model which accounts for both intra- and inter-individual variabilities. Furthermore, we apply a one-step approach incorporating robust estimation methods to estimate fixed effect parameters and the variance-covariance structure since outliers or influential observations are not uncommon in qHTS data. The toxicity of chemicals from a qHTS assay is classified based on the significance of a parameter related to the efficacy of the chemicals using the proposed method. We evaluate the performance of the proposed method in terms of power and false discovery rate using simulation studies comparing with one existing method. The proposed method is illustrated using a dataset obtained from the National Toxicology Program.

• Geophysics and Geophysical Exploration
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• v.9 no.1
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• pp.73-85
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• 2006

Array observations of the vertical component of microtremors are frequently conducted to estimate a subsurface layered-earth structure on the assumption that microtremors consist predominantly of the fundamental mode Rayleigh waves. As a useful tool in the data collection, processing and analysis, the spatial autocorrelation (SPAC) method is widely used, which in practice requires a circle array consisting of M circumferential stations and one centre station (called "M-station circle array", where M is the number of stations). The present paper considers the minimum number of stations required for a circle array for efficient data collection in terms of analytical efficacy and field effort. This study first rearranges the theoretical background of the SPAC algorithm, in which the SPAC coefficient for a circle array with M infinite is solely expressed as the Bessel function, $J_0(rk)$ (r is the radius and k the wavenumber). Secondly, the SPAC coefficient including error terms independent of the microtremor energy field for an M-station circle array is analytically derived within a constraint for the wave direction across the array, and is numerically evaluated in respect of these error terms. The main results of the evaluation are: 1) that the 3-station circle array when compared with other 4-, 5-, and 9-station arrays is the most efficient and favourable for observation of microtremors if the SPAC coefficients are used up to a frequency at which the coefficient takes the first minimum value, and 2) that the Nyquist wavenumber is the most influential factor that determines the upper limit of the frequency range up to which the valid SPAC coefficient can be estimated.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.1
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• pp.9-17
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• 2015

Interface behavior of concrete-infilled steel tube (CFT) was investigated based on the experimental observations and numerical analyses. Laboratory tests were performed for twelve CFTs that consisted of two different cases of diameters where each diameter case was composed of three different cases of shear span length. Thereby, diameter and shear span parameters were considered to prove the question of whether there exists interface slip between steel tube and infilled-concrete. Confining effect of steel tube to infilled concrete was also investigated by measuring lateral strain as well as longitudinal strain. Based on the study, it was concluded that confining effect of steel tube to infilled-concrete is not influential under flexural loading and therefore, the sectional analysis is an effective way to estimate the flexural strength of CFT.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.114-123
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• 2017

The global small and mid-sized display market is changing from thin film transistor-liquid crystal display to organic light emitting diode (OLED). Reflecting these market conditions, the domestic and overseas display panel industry is making great effort to innovate OLED technology and incease productivity. However, current OLED production technology has not been able to satisfy the quality requirement levels by customers, as the market demand for OLED is becoming more and more diversified. In addition, as OLED panel production technology levels to satisfy customers' requirement become higher, product quality problems are persistently generated in OLED deposition process. These problems not only decrease the production yield but also cause a second problem of deteriorating productivity. Based on these observations, in this study, we suggest TRIZ-based improvement of defects caused by glass pixel position deformation, which is one of quality deterioration problems in small and medium OLED deposition process. Specifically, we derive various factors affecting the glass pixel position shift by using cause and effect diagram and identify radical reasons by using XY-matrix. As a result, it is confirmed that glass heat distortion due to the high temperature of the OLED deposition process is the most influential factor in the glass pixel position shift. In order to solve the identified factors, we analyzed the cause and mechanism of glass thermal deformation. We suggest an efficient method to minimize glass thermal deformation by applying the improvement plan of facilities using contradiction matrix in TRIZ. We show that the suggested method can decrease the glass temperature change by about 23% through an experiment.

• Resources Recycling
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• v.21 no.3
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• pp.28-38
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• 2012

The purpose of this paper is to develop the alkali-activated concrete which uses 100% fly ash as a binder without any cement. The compressive strength of the mortar was examined depending on the chemical change in alkali-activator through SEM and SEM/EDS observations and the XRD analysis. It was found from the test that the higher molar concentration induced the greater effect on the initial strength, and that $Si^{4+}$ and $Al^{3+}$ were eluted relative to the compressive strength of mortar. In addition, it was confirmed that Al and Si were determined to be most influential ingredients on the microstructural development of the mortar, and that the different ingredient of the activator was almost no change in solidity from the XRD analysis.

• Journal of Korean society of Dental Hygiene
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• v.18 no.4
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• pp.535-546
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• 2018

Objectives: This study is to assess the correlation of the changes in dental health-related quality of life before, during, and after orthodontic treatments in the patients. Methods: The self-administered survey was conducted in the patients who completed orthodontic treatments living in Seoul and metropolitan areas using Oral Impact on Daily Performances (OIDP) to identify the relevant factors. Data were analyzed with descriptive statistics of variables, independent t-test, one way ANOVA, and multiple regression analysis. Results: From observations of OIDP before, during, and after orthodontic treatment, discomfort associated with three factors including physical, psychological and social ones showed the statistically significant increases during orthodontic treatment than before the treatment; whereas, it was dramatically dropped afterward. Multiple regression analysis to find the influential factors of satisfaction level on orthodontic treatment by setting before, during, and after OIDP as independent variables and satisfaction on orthodontic treatment as a dependent variable revealed that OIDP before orthodontic treatment and after orthodontic treatment significantly affected satisfaction on orthodontic treatment. Conclusions: The above analysis on the change in patients' dental health-related quality of life showed that the quality of life improved after the orthodontic treatment. Accordingly, patients' quality of life and satisfaction level on orthodontic treatment are expected to improve if they strive to maintain healthy dental health through orthodontic treatment.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.1
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• pp.1-14
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• 2021

The study has been carried out with an objective to prepare Siberian roe deer habitat potential maps in South Korea based on three geographic information system-based models including frequency ratio (FR) as a bivariate statistical approach as well as convolutional neural network (CNN) and long short-term memory (LSTM) as machine learning algorithms. According to field observations, 741 locations were reported as roe deer's habitat preferences. The dataset were divided with a proportion of 70:30 for constructing models and validation purposes. Through FR model, a total of 10 influential factors were opted for the modelling process, namely altitude, valley depth, slope height, topographic position index (TPI), topographic wetness index (TWI), normalized difference water index, drainage density, road density, radar intensity, and morphological feature. The results of variable importance analysis determined that TPI, TWI, altitude and valley depth have higher impact on predicting. Furthermore, the area under the receiver operating characteristic (ROC) curve was applied to assess the prediction accuracies of three models. The results showed that all the models almost have similar performances, but LSTM model had relatively higher prediction ability in comparison to FR and CNN models with the accuracy of 76% and 73% during the training and validation process. The obtained map of LSTM model was categorized into five classes of potentiality including very low, low, moderate, high and very high with proportions of 19.70%, 19.81%, 19.31%, 19.86%, and 21.31%, respectively. The resultant potential maps may be valuable to monitor and preserve the Siberian roe deer habitats.

• Geomechanics and Engineering
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• v.30 no.3
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• pp.259-272
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• 2022

Rockburst is a dynamic, multivariate, and non-linear phenomenon that occurs in underground mining and civil engineering structures. Predicting rockburst is challenging since conventional models are not standardized. Hence, machine learning techniques would improve the prediction accuracies. This study describes decision based uncertainty models to predict rockburst in underground engineering structures using gradient boosting algorithms (GBM). The model input variables were uniaxial compressive strength (UCS), uniaxial tensile strength (UTS), maximum tangential stress (MTS), excavation depth (D), stress ratio (SR), and brittleness coefficient (BC). Several models were trained using different combinations of the input variables and a 3-fold cross-validation resampling procedure. The hyperparameters comprising learning rate, number of boosting iterations, tree depth, and number of minimum observations were tuned to attain the optimum models. The performance of the models was tested using classification accuracy, Cohen's kappa coefficient (k), sensitivity and specificity. The best-performing model showed a classification accuracy, k, sensitivity and specificity values of 98%, 93%, 1.00 and 0.957 respectively by optimizing model ROC metrics. The most and least influential input variables were MTS and BC, respectively. The partial dependence plots revealed the relationship between the changes in the input variables and model predictions. The findings reveal that GBM can be used to anticipate rockburst and guide decisions about support requirements before mining development.