• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.2
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• pp.123-131
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• 2021

The buffer is a key component of an engineered barrier system that safeguards the disposal of high-level radioactive waste. Buffers are located between disposal canisters and host rock, and they can restrain the release of radionuclides and protect canisters from the inflow of ground water. Since considerable heat is released from a disposal canister to the surrounding buffer, the thermal conductivity of the buffer is a very important parameter in the entire disposal safety. For this reason, a lot of research has been conducted on thermal conductivity prediction models that consider various factors. In this study, the thermal conductivity of a buffer is estimated using the machine learning methods of: linear regression, decision tree, support vector machine (SVM), ensemble, Gaussian process regression (GPR), neural network, deep belief network, and genetic programming. In the results, the machine learning methods such as ensemble, genetic programming, SVM with cubic parameter, and GPR showed better performance compared with the regression model, with the ensemble with XGBoost and Gaussian process regression models showing best performance.

• Journal of Bio-Environment Control
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• v.32 no.2
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• pp.115-121
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• 2023

The leopard plant has the characteristic of being used for ornamental purposes when there are yellow spots on the leaves, and is widely used as a bed plant for viewing flowers. To set several indicators to predict the growth of crops with ornamental value, and to quantitatively express the relationship between the indicators are necessary. In this study, we determine a model that estimates the leaf area and the number of flower of Farfugium japonicum Kitam. using leaf length and width, and conducting a regression analysis on some regression models. As an indicator for estimating the leaf area and the number of flower, the leaf length and width of F. japonicum were measured and applied to 8 regression models. As a result of regression analysis of 8 models that estimated leaf area and the number of flower, R² values of the linear models were all higher than 0.84 and 0.80. As a result of validation, using the most reliable model among the models for estimating the leaf area and the number of flowering, R² was 0.90 and 0.82, respectively. Using a model that estimates various indicators that can be used for quality evaluation from easy-to-measure morphological factors, the evaluation of ornamental plants will be facilitated.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.5
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• pp.502-508
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• 2012

Quantitative ultrasound technologies for osteoporosis diagnosis measure ultrasonic parameters such as speed of sound(SOS) and normalized broadband ultrasound attenuation(nBUA) in the calcaneus (heel bone). In the present study, the dependences of SOS and nBUA on bone mineral density in the proximal femur with high risk of fracture were investigated by using 20 trabecular bone samples extracted from bovine femurs. SOS and nBUA in the femoral trabecular bone samples were measured by using a transverse transmission method with one matched pair of ultrasonic transducers with a center frequency of 1.0 MHz. SOS and nBUA measured in the 20 trabecular bone samples exhibited high Pearson's correlation coefficients (r) of r = 0.83 and 0.72 with apparent bone density, respectively. The multiple regression analysis with SOS and nBUA as independent variables and apparent bone density as a dependent variable showed that the correlation coefficient r = 0.85 of the multiple linear regression model was higher than those of the simple linear regression model with either parameter SOS or nBUA as an independent variable. These high linear correlations between the ultrasonic parameters and the bone density suggest that the ultrasonic parameters measured in the femur can be useful for predicting the femoral bone mineral density.

• Journal of Korea Port Economic Association
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• v.29 no.3
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• pp.159-174
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• 2013

This paper mainly deals with the appropriation of ship voyage allocation, using a heuristic regression model, in order to reduce total costs incurred in port, yard and at sea under the specific port condition. Because of different behavior of costs incurred in port, yard and at sea, an effort to minimize these costs by adjusting the number of voyages for three ship classes(50,000, 100,000, and 150,000-ton) should be made. For instance, if the port managers attempt to reduce the sea transport cost by increasing the annual allocated number of ship voyages classed 150,000-ton for economies of scale, they have no choice but to suffer a significant increase in queueing cost due to port congestion. To put it differently, there are trade-off relationships among the costs incurred in port, yard, and at sea. We utilized a computer simulation result to perform a couple of regression analyses in order to figure out the appropriate range of allocated number of voyages of each ship class using a heuristic approach. The detailed analytical results will be shown at the main paper. We also suggested a net present value(NPV) model to make a proper investment decision for an additional berth of 200,000-ton class that alleviates port congestion and reduces transport cost incurred both in port and at sea.

• The Journal of Engineering Geology
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• v.19 no.1
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• pp.71-79
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• 2009

Step-drawdown tests were conducted at four pumping Wells, two in porous media and two in fractured rocks, respectively. In general, P = 2.0 suggested by Jacob (1947) is applied to porous media and fractured rocks in terms of drawdowns of step-drawdown test. In an attempt to review problems of linear model (Jacob's graphic method) in interpreting the step-draw down test, the outcomes of well parameters (aquifer loss coefficient (B), well loss coefficient (C) and well loss exponent (P)) calculated from linear and nonlinear model (Labadie and Helweg's least-squares method) were compared and analyzed. The values of C and P calculated from linear and nonlinear models differed according to permeability of aquifer and the conditions of pumping well. The value C obtained from nonlinear models in porous media and fractured rocks is about $10^0{\sim}10^{-2}$ and $10^{-3}{\sim}10^{-6}$ times lower than in their linear models, respectively. The value P of porous media obtained from nonlinear model ranged from 2.123 to 2.775, while it ranged from 3.459 to 5.635 for fractured rocks. In case of nonlinear model, well loss highly depends on the value P. At this time, well efficiencies calculated from linear and nonlinear models were $1.56{\sim}14.89%$ for porous media and $8.73{\sim}24.71%$ for fractured rocks, showing a significant error according to chosen models. In nonlinear model, it was found that the regression analysis using the least squares method was very useful to interpret step-drawdown test in all aquifer.

• Journal of the Korean Society for information Management
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• v.29 no.2
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• pp.193-204
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• 2012

The characteristics of citation and centrality measures in citation networks can be identified using multiple linear regression analyses. In this study, we examine the relationships between bibliometric indices and centrality measures in an article-level co-citation network to determine whether the linear model is the best fitting model and to suggest the necessity of data transformation in the analysis. 703 highly cited articles in Physics published in 2004 were sampled, and four indicators were developed as variables in this study: citation counts, degree centrality, closeness centrality, and betweenness centrality in the co-citation network. As a result, the relationship pattern between citation counts and degree centrality in a co-citation network fits a non-linear rather than linear model. Also, the relationship between degree and closeness centrality measures, or that between degree and betweenness centrality measures, can be better explained by non-linear models than by a linear model. It may be controversial, however, to choose non-linear models as the best-fitting for the relationship between closeness and betweenness centrality measures, as this result implies that data transformation may be a necessary step for inferential statistics.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.80-90
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• 2001

The aim of this study is to develop the methodology which enables to identify the mechanical properties of element such as stress intensity factor by using the AE parameters. Considering the multivariate and nonlinear properties of AE parameters such as ringdown count, rise time, energy, event duration and peak amplitude from fatigue cracks of machine element the principal component regression(PCR) and artificial neural network(ANN) models for the estimation of stress intensity factor were developed and validated. The AE parameters were found to be very significant to estimate the stress intensity factor. Since the statistical values including correlation coefficients, standard mr of calibration, standard error of prediction and bias were stable, the PCR and ANN models for stress intensity factor were very robust. The performance of ANN model for unknown data of stress intensity factor was better than that of PCR model.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.27-40
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• 2020

In this paper, we investigate the correlation between the amount of English sentences that Korean English learners (L2ers) are exposed to and their sentence processing patterns by examining what Long Short-Term Memory (LSTM) language models (LMs) can learn about implicit syntactic relationship: that is, the filler-gap dependency. The filler-gap dependency refers to a relationship between a (wh-)filler, which is a wh-phrase like 'what' or 'who' overtly in clause-peripheral position, and its gap in clause-internal position, which is an invisible, empty syntactic position to be filled by the (wh-)filler for proper interpretation. Here to implement L2ers' English learning, we build LSTM LMs that in turn learn a subset of the known restrictions on the filler-gap dependency from English sentences in the L2 corpus that L2ers can potentially encounter in their English learning. Examining LSTM LMs' behaviors on controlled sentences designed with the filler-gap dependency, we show the characteristics of L2ers' sentence processing using the information-theoretic metric of surprisal that quantifies violations of the filler-gap dependency or wh-licensing interaction effects. Furthermore, comparing L2ers' LMs with native speakers' LM in light of processing the filler-gap dependency, we not only note that in their sentence processing both L2ers' LM and native speakers' LM can track abstract syntactic structures involved in the filler-gap dependency, but also show using linear mixed-effects regression models that there exist significant differences between them in processing such a dependency.

• Journal of Korean Society of Water and Wastewater
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• v.22 no.2
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• pp.213-218
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• 2008

Prediction models for post-chlorination require complicated information of reaction time, chlorine dosage considering flow rate as well as environmental conditions such as turbidity, temperature and pH. In order to operate post-chlorination process effectively, the correlations between inlet and outlet of clear well were investigated to develop prediction models of chlorine dosages in post-chlorination process. Correlations of environmental conditions including turbidity and chlorine dosage were investigated to predict residual chlorine at the outlet of clear well. A linear regression model and autoregressive model were developed to apply for the post-chlorination which take place time delay due to detention in clear well tank. The results from autoregressive model show the correlationship of 0.915~0.995. Consequently, the autoregressive model developed in this study would be applicable for real time control for post chlorination process. As a result, the autoregressive model for post chlorination which take place time delay and have multi parameters to control system would contribute to water treatment automation system by applying the process control algorithm.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.38-39
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• 2019

Environmental problems caused by GHG emitted by various industries are emerging around the world, and accordingly, relevant regulations are being applied by countries around the world. Korea is operating a carbon credit system that trades GHG in industry for money, which is expected to be applied to the construction industry. In addition, construction equipment using fossil fuels accounts for the largest portion of $CO_2$ emissions in the construction industry, and the importance of $CO_2$ reduction and prediction is increasing. However, there is a lack of data on the directly measured $CO_2$ emissions of construction equipment and there is no accurate methodology for measuring methods. Therefore, in this study, independent variables were derived based on the $CO_2$ emission data. In addition, multiple linear regression is performed for each independent variable to derive a predictive model of carbon dioxide emission by work type of construction equipment. It is expected that the construction process plan based on environmental factors in the construction industry can be established in the future.