• Journal of Ocean Engineering and Technology
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• v.26 no.2
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• pp.39-47
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• 2012

The objective of this study was to develop a strength prediction model using a polynomial regression analysis based on the experimental results obtained from ninety samples. As the results of a correlation analysis between various mixing factors and unconfined compressive strength using SPSS (statistical package for the social sciences), the governing factors in the strength of lightweight soil were found to be the crumb rubber content, bottom ash content,and water-cement ratio. After selecting the governing factors affecting the strength through the correlation analysis, a strength prediction model, which consisted of the selected governing factors, was developed using the polynomial regression analysis. The strengths calculated from the proposed model were similar to those resulting from laboratory tests (R2=87.5%). Therefore, the proposed model can be used to predict the strength of lightweight mixtures with various mixing ratios without time-consuming experimental tests.

• Journal of Mechanical Science and Technology
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• v.14 no.1
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• pp.93-102
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• 2000

The objective of the present study is to investigate the pressure-strain correlation terms of the Reynolds stress models for the three dimensional turbulent boundary layer in a $30^{\circ}$ bend tunnel. The numerical results obtained by models of Launder, Reece and Rodi (LRR) , Fu and Speziale, Sarkar and Gatski (SSG) for the pressure-strain correlation terms are compared against experimental data and the calculated results from the standard k-${\varepsilon}$ model. The governing equations are discretized by the finite volume method and SIMPLE algorithm is used to calculate the pressure field. The results show that the models of LRR and SSG predict the anisotropy of turbulent structure better than the standard k-${\varepsilon}$ model. Also, the results obtained from the LRR and SSG models are in better agreement with the experimental data than those of the Fu and standard k-${\varepsilon}$ models with regard to turbulent normal stresses. Nevertheless, LRR and SSG models do not effectively predict pressure-strain redistribution terms in the inner layer because the pressure-strain terms are based on the locally homogeneous approximation. Therefore, to give better predictions of the pressure-strain terms, non-local effects should be considered.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.09a
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• pp.254-257
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• 2003

Statistical analysis is performed to estimate the correlations between geological or geographical factor and groundwater inflow rates in the Seoul subway system. Correlation analysis shows that among several geological and geographical factors fractures and streams have most strong effects on inflow rate into tunnels. In particular, subway line 5∼8 are affected more by these factors than subway line 1∼4. Time series analysis is carried out to forecast groundwater inflow rate. Time series analysis is a useful empirical method for simulation and forecasts in case that physical model can not be applied to. The time series of groundwater inflow rates is calculated using the observation data. Transfer function-noise model is applied with the precipitation data as input variables. For time series analysis, statistical methods are performed to identify proper model and autoregressive-moving average models are applied to evaluation of inflow rate. Each model is identified to satisfy the lowest value of information criteria. Results show that the values by result equations are well fitted with the actual inflow rate values. The selected models could give a good explanation of inflow rates variation into subway tunnels.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.169-183
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• 2009

The simplicity and wide application of Greenberg et al. (1971) prompts to propose a set of alternative estimators of population total for multi-character surveys that elicit simultaneous information on many. sensitive study variables. The proposed estimators take into account the already known rough value of the correlation coefficient between Y(the characteristic under study) and p(the measure of size). These estimators are biased, but it is expected that the extent of bias will be smaller, since the proposed estimators are suitable for situations in between those optimum for the usual estimators and the estimators based on multi-characters for no correlation. The relative efficiency of the proposed estimators has been studied under a super population model through empirical study. It has been found through simulation study that a choice of an unrelated variable in the Greenberg et al. (1971) model could be made based on its correlation with the auxiliary variable used at estimation stage in multi-character surveys.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.585-590
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• 1997

The condonation models in the standard RELAP5/MOD3.2 code are assessed and improved based on the database, which is constructed from the previous experimental data on various condonation phenomena The default model the laminar film condonation in RELAP5/MOD3.2 does not give any reliable predictions, and its alternative model always predicts higher values than the experimental data Therefore, it is needed to develop a new correlation based on the experimental data of various operating ranges in the constructed database. The Shah correlation, which is used to calculate the turbulent film condensation heat transfer coefficients in the standard RELAP5/MOD3.2, well predicts the experimental data in the database. The horizontally stratified condonation model of RELAP5/MOD3.2 overpredicts both cocurrent and countercurrent experimental data The correlation proposed by H.J.Kim predicts the database relatively well compared with that of RELAP5/MOD3.2 The RELAP5/MOD3.2 model should use the liquid velocity for the calculation of the liquid Reynolds number and be modified to conifer the effects of the gas velocity and the film thickness.

• Journal of Korean Society of Rural Planning
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• v.18 no.3
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• pp.165-174
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• 2012

Development of transportation and communication technology has affected our daily life and has caused to separate residential places from working places. Particularly in rural areas, the life zones are incorporated into larger towns or urban areas due to their lack of cultural, social and economic infrastructures. Thus, the analysis of the depended region and the life zone is important for the planning of regional revitalization programs and related project. The purpose of this study is to propose a regional dependency model (RDM) using the origin-destination(O-D) matrix of commuters and compare it with the Nystuen & Dacey model for regional correlation. The regional characteristics are analysed and our RDM were tested using the commuting data on Seoul metropolitan area(Seoul, Gyeong-gi, Incheon) and Chungchung area. The regional correlation model can only explain the determination of regional interaction without considering the direction of regional correlation but our model can show the direction of regional dependencies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.536-564
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• 2019

Soft faults are inherent in wireless sensor networks (WSNs) due to external and internal errors. The failure of processes in a protocol stack are caused by errors on various layers. In this work, impact of errors and channel misbehavior on process execution is investigated to provide an error classification mechanism. Considering implementation of WSN protocol stack, inter-process correlations of stacked and peer layer processes are modeled. The proposed model is realized through local and global decision trees for fault diagnosis. A hybrid framework is proposed to implement local decision tree on sensor nodes and global decision tree on diagnostic cluster head. Local decision tree is employed to diagnose critical failures due to errors in stacked processes at node level. Global decision tree, diagnoses critical failures due to errors in peer layer processes at network level. The proposed model has been analyzed using fault tree analysis. The framework implementation has been done in Castalia. Simulation results validate the inter-process correlation model-based fault diagnosis. The hybrid framework distributes processing load on sensor nodes and diagnostic cluster head in a decentralized way, reducing communication overhead.

• Korean Journal of Occupational Health Nursing
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• v.31 no.1
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• pp.31-41
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• 2022

Purpose: This study aimed to identify the effects of organizational culture, organizational silence, and organizational commitment on the intention of retention as perceived by nurses Methods: The research model was designed on the basis of the PROCESS Macro model 6 proposed by Hayes. The participants were 142 nurses from general hospitals. Measurements included the scales of organizational culture, organizational silence, organizational commitment, and intention of retentione. Data were analyzed using descriptive statistics, Pearson's correlation coefficient analysis, and Hayes's PROCESS macro method for mediation. Results: Retention intention showed a significantly positive correlation with relationship-orientated culture (r=.32, p<.001), innovation-orientated culture (r=.30, p<.001), and organizational commitment (r=.48, p<.001). However retention intention showed a significantly negative correlation with organizational silence (r=-.42, p<.001). Furthermore, organizational silence and commitment had a mediating effect on the relationship between organizational culture and intention of retention. Conclusion: The impact of organizational culture on intention of retention in general hospitals was mediated by organizational silence and organizational commitment. Considering the mediating effects of organizational silence and organizational commitment on the relationship between nursing organizational culture and retention intention, a strategy should be developed to enhance organizational commitment and weaken organizational silence by strengthening related and innovative nursing culture.

• Advances in materials Research
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• v.11 no.1
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• pp.75-90
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• 2022

In this paper, functionality of soft computing algorithms such as Group method of data handling (GMDH), Random forest (RF), Random tree (RT), Linear regression (LR), M5P, and artificial neural network (ANN) have been looked out to predict the compressive strength of concrete mixed with marble powder. Assessment of result suggests that, the overall performance of ANN based model gives preferable results over the different applied algorithms for the estimate of compressive strength of concrete. The results of coefficient of correlation were maximum in ANN model (0.9139) accompanied through RT with coefficient of correlation (CC) value 0.8241 and minimum root mean square error (RMSE) value of ANN (4.5611) followed by RT with RMSE (5.4246). Similarly, other evaluating parameters like, Willmott's index and Nash-sutcliffe coefficient value of ANN was 0.9458 and 0.7502 followed by RT model (0.8763 and 0.6628). The end result showed that, for both subsets i.e., training and testing subset, ANN has the potential to estimate the compressive strength of concrete. Also, the results of sensitivity suggest that the water-cement ratio has a massive impact in estimating the compressive strength of concrete with marble powder with ANN based model in evaluation with the different parameters for this data set.

• Journal of Korean Society on Water Environment
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• v.26 no.3
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• pp.460-467
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• 2010

For D-WTP's sedimentation basin and distribution reservoir, and water tap the predictive models proposed tentatively herein included the models for estimating TTHM concentration in precipitated water, for treated water and for tap water, and the estimated correlation formula between treated water's TTHM concentration and tap water. As for TTHM-concentration predictive model in sedimentation water, the coefficient of determination is 0.866 for best-fitted short-term $DOC{\times}UV_{254}$ based Model (TTHM). As for $HAA_5$-concentration predictive model in sedimentation water, the coefficient of determination is 0.947 for the suitable $UV_{254}$-based model ($HAA_5$). In case of the predictive model in treated water, the coefficient of determination is 0.980 for best-fitted $DOC{\times}UV_{254}$ based model (TTHM) using coagulated waters, while the coefficient of determination is 0.983 for best-fitted $DOC{\times}UV_{254}$ based model ($HAA_5$) using coagulated waters, which described the $HAA_5$ concentration well. However, the predictive model for tap water could not be compatible with the one for treated water, only except for possibility inducing correlation formula for prediction, [i.e., the correlation formula between TTHM concentration and tap water was verified as TTHM (tap water) = $1.162{\times}TTHM$ (treated water), while $HAA_5$ (tap water) = $0.965{\times}HAA_5$ (treated water).] The correlation analysis between DOC and $KMnO_4$ consumption by process resulted in higher relationship with filtrated water, showing that its regression is $DOC=0.669{\times}KMnO_4$ consumption - 0.166 with 0.689 of determination coefficient. By substituting it to the existing DOC-based model ($HAA_5$) for treated water, the consequential model formula was made as follows; $HAA_5=8.35(KMnO_4\;consumption{\times}0.669-0.166)^{0.701}(Cl_2)^{0.577}t^{0.150}0.9216^{(pH-7.5)}1.022^{(Temp-20^{\circ}C)}$