• Korean Journal of Weed Science
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• v.4 no.1
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• pp.88-95
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• 1984

The study was intended to analyze the interaction effects of paraquat and oxytluorfen as an orchard herbicide-mixture. Data were prepared from the former report of authors. The algebraic expression for the actions of paraquat and oxyfluorfen on the control percentages of peach orchard weeds, and their interactions were determined from the multiple regression polynomial and plotted in three-dimensional graphs. As a result of treatments by combination of paraquat and oxyfluorfen on the field which was dominated by perennial weeds, the most effective interactions were detected at combination rates of $245\;gHa^{-1}$ paraquat and $470-705\;gHa^{-1}$ oxyfluorfen. However, to develope the long-term weeding-efficacies, the combination rates of paraquat are expected to raise up to $500-700\;gHa^{-1}$, and oxyfluorfen to fit at lower limits of rates, respectively.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.4
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• pp.183-190
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• 2018

Predicting accurate electricity prices is an important task in the electricity trading market. To address the electricity price forecasting problem, various approaches have been proposed so far and it is known that linear regression-based approaches are the best. However, the use of such linear regression-based methods is limited due to low accuracy and performance. In traditional linear regression methods, it is not practical to find a nonlinear regression model that explains the training data well. If the training data is complex (i.e., small-sized individual data and large-sized features), it is difficult to find the polynomial function with n terms as the model that fits to the training data. On the other hand, as a linear regression model approximating a nonlinear regression model is used, the accuracy of the model drops considerably because it does not accurately reflect the characteristics of the training data. To cope with this problem, we propose a new electricity price forecasting method that divides the entire dataset to multiple split datasets and find the best linear regression models, each of which is the optimal model in each dataset. Meanwhile, to improve the performance of the proposed method, we modify the proposed localized linear regression method in the map and reduce way that is a framework for parallel processing data stored in a Hadoop distributed file system. Our experimental results show that the proposed model outperforms the existing linear regression model. Specifically, the accuracy of the proposed method is improved by 45% and the performance is faster 5 times than the existing linear regression-based model.

• Journal of Ecology and Environment
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• v.33 no.2
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• pp.165-173
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• 2010

Soil respiration ($R_S$) is a critical component of the annual carbon balance of forests, but few studies thus far have attempted to evaluate empirical regression models in $R_S$. The principal objectives of this study were to evaluate the relationship between $R_S$ rates and soil temperature (ST) and soil water content (SWC) in soil from a cool-temperate deciduous broad-leaved forest, and to evaluate empirical regression models for the prediction of $R_S$ using ST and SWC. We have been measuring $R_S$, using an open-flow gas-exchange system with an infrared gas analyzer during the snowfree season from 1999 to 2001 at the Takayama Forest, Japan. To evaluate the empirical regression models used for the prediction of $R_S$, we compared a simple exponential regression (flux = $ae^{bt}$Eq. [1]) and two polynomial multiple-regression models (flux = $ae^{bt}{\times}({\theta}{\nu}-c){\times}(d-{\theta}{\nu})^f:$ Eq. [2] and flux = $ae^{bt}{\times}(1-(1-({\theta}{\nu}/c))^2)$: Eq. [3]) that included two variables (ST: t and SWC: ${\theta}{\nu}$) and that utilized hourly data for $R_S$. In general, daily mean $R_S$ rates were positively well-correlated with ST, but no significant correlations were observed with any significant frequency between the ST and $R_S$ rates on periods of a day based on the hourly $R_S$ data. Eq. (2) has many more site-specific parameters than Eq. (3) and resulted in some significant underestimation. The empirical regression, Eq. (3) was best explained by temporal variations, as it provided a more unbiased fit to the data compared to Eq. (2). The Eq. (3) (ST $\times$ SWC function) also increased the predictive ability as compared to Eq. (1) (only ST exponential function), increasing the $R^2$ from 0.71 to 0.78.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.411-424
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• 2011

The studies of congruence in organizational research have explored the concepts such as person-job fit person-organization fit, or person-environment fit. The relevant studies dealt with the fit level as an important influencing factor on the performance. In particular, researchers have agreed that employees can be motivated by the high level fit of person-organization. However, few research developing an alternative methodological approach has been done. For the purpose mentioned above the statistics like D, |D| or $D^2$ and the Q values such as Q(the correlation between two sets of interval measures) or $Q_r$(the correlation between two rankings) have been conventionally adopted in spite of numerous methodological problems. In general, these traditional indices such as difference scores, or Q values, are nondirectional and add an extra weight to differences of lager magnitude. Therefore, Edwards (1993) introduced the polynomial regression and the response surface analysis to overcome flaws with conventional approaches. However, the method-ological approaches did not reflect the profile characteristics of person-organizational value fit and wouldn't be a proper solution for the fit level of person-organization value maximizing performance. Hence, this paper investigates alternative methodological approaches, the multivariate polynomial regression and the multiple response surface analysis, to avoid the problems issued from conventional ways.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.4
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• pp.94-101
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• 2003

Recently, miniaturization and weight reduction is getting more attention due to various benefits in automotive components design. It is a trend that the design of experiment(DOE) and statical design method are frequently used for optimization. In this research, the safety of lower control arm is evaluated according to its material change form S45C to A16061 for the reduction of arm's weight. The variance analysis on the basis of structure analysis and DOE is applied to the lower control m. We have proposed a statistical design model to evaluate the effect of structural modification by performing the practical multi-objective optimization considering mass, stress and deflection.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.127-134
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• 2004

It is difficult to have the existing structural optimization using coupled field analysis from CFD to structure analysis when the structure is influenced of fluid. Therefore in an initial model of this study after doing parameter design from the background of shape using topology optimization. and it is making a approximation formula using by the CFD-structure coupled-field analysis and design of experiment. By using this result, we conducted multi-objective optimization. We could confirm efficiency of stochastic method applicable in the scene of structure reliability design to be needed multi-objective optimization. And we presented a way of design that could overcome the time and space restriction in structural design such as the butterfly valve with the less experiment.

• Journal of Welding and Joining
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• v.14 no.4
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• pp.33-42
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• 1996

In recent years there has been a significant growth in the use of the automated and/or robotic welding system, carried out as a means of improving productivity and quality, reducing product costs and removing the operator from tedious and potentially hazardous environments. One of the major difficulties with the automated and/or robotic welding process is the inherent lack of mathematical models for determination of suitable welding process parameters. Partial-penetration, single-pass bead-on-plate welds were fabricated in 12mm AS 1204 mild steel flats employing five different welding process parameters. The experimental results were used to develop three empirical equations: curvilinear; polynomial; and linear equations. The results were also employed to find the best mathematical equation under weld bend width to assist in the process control algorithms for the Gas Metal Arc Welding(GMAW) process and to correlate welding process parameters with weld bead width of bead-on-plates deposited. With the help of a standard statistical package program. SAS, multipe regression analysis was undertaken for investigating and modeling the GMAW process, and significance test techniques were applied for the interpretation of the experimental data.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.9-19
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• 2024

As using public monitoring data, analysing a trends of water quality change, establishing a criteria to determine abnormal status and constructing a regression model that can predict Chlorophyll-a, an indicator of eutrophication, was studied. Accordingly, the three freshwater lakes were selected, approximately 20 years of water quality monitoring data were analyzed for periodic changes in water quality each year using regression analysis, and a method for determining abnormalities was presented by the standard deviation at confidence level 95%. By calculating the temporal change rate of Chlorophyll-a from irregular observed data, analyzing correlations between the rate and other water quality items, and constructing regression models, a method to predict changes in Chlorophyll-a was presented. The results of this study are expected to contribute to freshwater lake water quality management as an approximate water quality prediction method using the statistical model.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.181-194
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• 2017

This study aims to develop a regression model for forest volume estimation using field-collected forest inventory information and airborne LiDAR data. The response variable of the model is forest stem volume, was measured by random sampling from each individual plot of the 30 circular sample plots collected in Bonghwa-gun, Gyeong sangbuk-do, while the predictor variables for the model are Height Percentiles(HP) and Height Bin(HB), which are metrics extracted from raw LiDAR data. In order to find the most appropriate model, the candidate models are constructed from simple linear regression, quadratic polynomial regression and multiple regression analysis and the cross-validation tests were conducted for verification purposes. As a result, $R^2$ of the multiple regression models of $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}$ among the estimated models was the highest at 0.509, and the PRESS statistic of the simple linear regression model of $HP_{25}$ was the lowest at 122.352. $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}-based$ models, thus, are comparatively considered more appropriate for Korean forests with complicated vertical structures.

• Journal of the Korean Wood Science and Technology
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• v.46 no.4
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• pp.380-392
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• 2018

The importance of polysaccharides is increasing globally due to their role as a significant source of dietary prebiotics in the human diet. In the present study, in order to maximize the yield of crude polysaccharides from Pinus densiflora, response surface methodology (RSM) was used to optimize a two-stage extraction process consisting of steam explosion and water extraction. Three independent main variables, namely, the severity factor (Ro) for the steam explosion process, the water extraction temperature ($^{\circ}C$), and the ratio of water to raw material (v/w), were studied with respect to prebiotic sugar content. A Box-Behnken design was created on the basis of the results of these single-factor tests. The experimental data were fitted to a second-order polynomial equation for multiple regression analysis and examined using the appropriate statistical methods. The data showed that both the severity factor (Ro) and the ratio of water to material (v/w) had significant effects on the prebiotic sugar content. The optimal conditions for the two-stage process were as follows: a severity factor (Ro) of 3.86, a water extraction temperature of $89.66^{\circ}C$, and a ratio of water to material (v/w) of 39.20. Under these conditions, the prebiotic sugar content in the extract was 332.45 mg/g.