• Publications of The Korean Astronomical Society
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• v.14 no.2
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• pp.103-112
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• 1999

We have investigated the solar activity variation with period shorter than 1000 days, through Fourier transformation of solar cycle 21 and 22 data. And real time predictions of the flare maximum intensity have been made by multilinear regression method to allow the use of multivariate vectors of sunspot groups or active region characteristics. In addition, we have examined the evolution of magnetic field and current density in active regions at times before and after flare occurrence, to check short term variability of solar activity. According to our results of calculation, solar activity changes with periods of 27.1, 28.0, 52.1, 156.3, 333.3 days for solar cycle 21 and of 26.5, 27.1, 28.9, 54.1, 154, 176.7, 384.6 days for solar cycle 22. Periodic components of about 27, 28, 53, 155 days are found simultaneously at all of two solar cycles. Finally, from our intensive analysis of solar activity data for three different terms of $1977\~1982,\; 1975\~1998,\;and\;1978\~1982$, we find out that our predictions coincide with observations at hit rate of $76\%,\;63\%$, 59 respectively.

• Computers and Concrete
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• v.33 no.1
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• Journal of Korean society of Dental Hygiene
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• v.24 no.4
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• pp.353-360
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• 2024

Objectives: This study aimed to investigate the effects of conscious sedation on patient satisfaction with dental treatment. Methods: The survey included questions on the patients' general characteristics, dental treatment fear, anxiety, and satisfaction, and patient evaluation by an observer. Statistical analyses were performed using SPSS 20.0 ver. and data were analyzed using frequency analysis, independent t-test, Pearson's correlation coefficient, and multilinear regression analysis. Results: Patients who received conscious sedation therapy showed significantly lower levels of dental fear and anxiety, whereas their dental treatment satisfaction was significantly higher than that of patients who received regular dental treatment (p<0.05). Dental treatment fear, anxiety, satisfaction, and conscious sedation depth were significantly correlated in patients who received conscious sedation therapy (p<0.05). Factors influencing dental treatment satisfaction included age, weight, use of medication, smoking habits, use of conscious sedation therapy, dental treatment fear and anxiety, and conscious sedation depth (p<0.05). Conclusions: Conscious sedation therapy can be an effective method to reduce dental treatment fear and anxiety and improve patient satisfaction.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.

• Analytical Science and Technology
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• v.12 no.5
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• pp.375-381
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• 1999

Near infrared spectrophotometry(NIR) was developed as a non-invasive determination of blood alcohol. The first pure alcohol/water samples were prepared with ethanol concentration from 0.01 to 0.1%(w/w). Analysis of the second-derivative data was accomplished with multilinear regression(MLR). The standard error of calibration(SEC) of ethanol in ethanol/water solutions was approximately 0.0039%. The calibration models were established from the blood alcohol spectra by MLR and PLSR analysis. The best calibration was built with the second-derivative spectra of 2266 and 2326 nm by MLR. Second-derivative spectra in the spectral ranges of 1100~1340, 1500~1796 and 2064~2300 nm with four PLSR factors provided the standard error of prediction(SEP) of 0.030%(w/w). These results indicate that NIR may be applied for a fast non-invasive determination of alcohol in the blood.

• Journal of Korean Society of Water and Wastewater
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• v.38 no.3
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• pp.141-153
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• 2024

The purpose of this study is to analyze the correlation between ecotoxicity and water quality items using Daphnia magna in public sewage treatment plant process and to obtain operational data to control ecotoxicity through research on removal efficiency. The average value of ecotoxicity was 1.39 TU in the influent, 1.50 TU in the grit chamber, and 0.84 TU in the primary settling tank and it was found that most organic matters, nitrogen, and phosphorus were removed through biological treatment in the bioreactor. Using Pearson's correlation analysis, the positive correlation was confirmed in the order of ecotoxicity and water quality items TOC, BOD, T-N, NH3-N, SS, EC, and Cu. As a result of conducting a multilinear regression analysis with items representing positive correlation as independent variables, the regression model was found to be statistically significant, and the explanatory power of the regression model was about 81.6%. TOC was found to have a significant effect on ecotoxicity with B=0.009 (p<.001) and Cu with B=16.670 (p<.001), and since the B sign is positive (+), an increase of 1 in TOC increases the value of ecotoxicity by 0.009 and an increase in Cu by 1 increases the value of ecotoxicity by 16.670. TOC (β=0.789, p<.001) and Cu (β=0.209, p<.001) were found to have a significant positive effect on ecotoxicity. TOC and Cu have a great effect on ecotoxicity in the sewage treatment plant process, and it is judged that TOC and Cu should be considered preferentially and controlled in order to efficiently control ecotoxicity.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2565-2571
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• 2009

In this paper, cycle performance analysis of cascade refrigeration system using $NH_3-CO_2$(R717-R744) is presented to offer the basic design data for the operating parameters of the system. The operating parameters considered in this study include subcooling and superheating degree and condensing and evaporating temperature in the ammonia(R717) high temperature cycle and the carbon dioxide low temperature cycle. The COP of cascade refrigeration system increases with the increasing superheating degree, but decreases with the increasing subcooling degree. The COP of cascade refrigeration system increases with the increasing condensing temperature, but decreases with the increasing evaporating temperature. Therefore, superheating and subcoolng degree, evaporating and condensing temperature of cascade refrigeration system using $NH_3-CO_2$ have an effect on the COP of this system. A multilinear regression analysis was employed in terms of subcooling, superheating, evaporating, condensing, and cascade heat exchanger temperature difference in order to develop mathematical expressions for maximum COP and an optimum evaporating temperature.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.4
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• pp.613-630
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• 2013

This study attempts to analyze the spatial characteristics of organic agriculture in Korea during the period of 2000~2010, focusing on women's labor contribution to agricultural operations. Spatial distribution and concentration of organic farming have been investigated using Location Quotient(LQ) and Local Indicator of Spatial Association(LISA), it was found that the specialized organic farmland in the Seoul Metropolitan area has decreased significantly over the past decade, while in Chungnam and Kyungpook province it has been rapid growth in the same period. Multilinear regression analysis was also carried out to find out whether the spatial clusters in organic farming depends on women farmers. Results describing the correlation between organic farming and women farmers within identified clusters suggest that the possibility of the transition towards sustainable agriculture and farmland restructuring in Korea, by women.

• Journal of the Korean Institute of Landscape Architecture
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• v.13 no.1
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• pp.95-107
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• 1985

This study is purposed to investigate the role of grasses as the Green Foundation effect on the visual preference to flowering tree and shrub being the principal elements of natural landscape early in the spring. As the flowering shrub materials, Rhododendron mucronulatum and Forsythia Kreana were adopted. Total 48 slides were photographed at the 8 different lawn areas with the 6 planting combinations of flowering shrub materials, and 10 landscape variables - dimensional and color - were measured and preference scores were taken by slide evaluations. The results were : 1) The visual preference to the landscape of flowering shrub in the lawn area was changed with the different lawn situations. 2) With important 4 variables, multilinear regression model was established, hence Y =40.4 + 9.6($X_1$) -7.8($X_2$) -26.8($X_3$) + 15.2($X_4$) where, Y : estimated preference score $X_1$: perimeter of flower zone $X_2$: value of green covered zone $X_3$: hue of green covered zone $X_4$: chroma of green covered zone 3) Most effective variable was 'hue of green covered zone', hence the more green the lawn area ism the more preferred landscape or the more effective green foundation is.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.265-275
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• 2022

In the current fast-growing market economy, the accounting-outsourcing trend of small and medium-sized enterprises is on the increase. Studies from both foreign and domestic sources have shown that many factors influence this decision. However, each country has different economic and political characteristics, so these factors and their degree of impact on accounting outsourcing also vary. This study aimed to determine the factors affecting the decision to outsource accounting activities of small and medium transport enterprises in Vietnam. A survey of 384 transport SMEs was conducted using the convenience sampling method. A personal interview with owners/managers/CFOs in 3 major cities of Vietnam based on a research review was conducted. The model examines the influence of many independent variables on accounting outsourcing. The multilinear regression analysis shows that the higher the Assets Specificity, the lower the degree of accounting outsourcing. In addition, the degree of outsourcing is positively and significantly related to frequency and trust in accountants. Besides, when we include control variables such as gender, administrative level, firm size, company age, education, and experience into the model. The results show that small and medium enterprises with limited resources should switch from the traditional internal accounting method to a professional accountant with external knowledge. Based on this study, the author proposes several implications for the accounting outsourcing of small and medium-sized transport enterprises in Vietnam to be more effective. Finally, this study also contributes to the basic knowledge of accounting outsourcing.