• Geomechanics and Engineering
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• v.30 no.1
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• pp.75-91
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• 2022

This paper aims to show how to use several Machine Learning (ML) methods to estimate the TBM penetration rate systematically (TBM-PR). To this end, 1125 datasets including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), punch slope index (PSI), distance between the planes of weakness (DPW), orientation of discontinuities (alpha angle-α), rock fracture class (RFC), and actual/measured TBM-PRs were established. To evaluate the ML methods' ability to perform, the 5-fold cross-validation was taken into consideration. Eventually, comparing the ML outcomes and the TBM monitoring data indicated that the ML methods have a very good potential ability in the prediction of TBM-PR. However, the long short-term memory model with a correlation coefficient of 0.9932 and a route mean square error of 2.68E-6 outperformed the remaining six ML algorithms. The backward selection method showed that PSI and RFC were more and less significant parameters on the TBM-PR compared to the others.

• Journal of IKEEE
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• v.26 no.4
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• pp.531-536
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• 2022

In order to efficeintly manage a battery, it is important to accurately estimate and manage the SOH(State of Health) and RUL(Remaining Useful Life) of the batteries. Even if the batteries are of the same type, the characteristics such as facility capacity and voltage are different, and when the battery for the training model and the battery for prediction through the model are different, there is a limit to measuring the accuracy. In this paper, We proposed the entropy index using voltage distribution and discharge time is generalized, and four batteries are defined as a training set and a test set alternately one by one to predict the health status of batteries through linear regression analysis of machine learning. The proposed method showed a high accuracy of more than 95% using the MAPE(Mean Absolute Percentage Error).

• Geomechanics and Engineering
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• v.37 no.2
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• pp.97-107
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• 2024

The accurate prediction of grouting upward diffusion height is crucial for estimating the bearing capacity of tip-grouted piles. Borehole construction during the installation of bored piles induces soil unloading, resulting in both radial stress loss in the surrounding soil and an impact on grouting fluid diffusion. In this study, a modified model is developed for predicting grout diffusion height. This model incorporates the classical rheological equation of power-law cement grout and the cavity reverse expansion model to account for different degrees of unloading. A series of single-pile tip grouting and static load tests are conducted with varying initial grouting pressures. The test results demonstrate a significant effect of vertical grout diffusion on improving pile lateral friction resistance and bearing capacity. Increasing the grouting pressure leads to an increase in the vertical height of the grout. A comparison between the predicted values using the proposed model and the actual measured results reveals a model error ranging from -12.3% to 8.0%. Parametric analysis shows that grout diffusion height increases with an increase in the degree of unloading, with a more pronounced effect observed at higher grouting pressures. Two case studies are presented to verify the applicability of the proposed model. Field measurements of grout diffusion height correspond to unloading ratios of 0.68 and 0.71, respectively, as predicted by the model. Neglecting the unloading effect would result in a conservative estimate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.11C
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• pp.655-661
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• 2011

The transmission issues for the scalable video coding extension of H.264/AVC (H.264 SVC) video has been widely studied. In this paper, we propose an objective video-quality assessment metric based on no-reference for H.264 SVC using scalability information. The proposed metric estimate the perceptual video-quality reflecting error conditions with the consideration of the motion vectors, error propagation patterns with the hierarchical prediction structure, quantization parameters, and number of frame which damaged by packet loss. The proposed metric reflects the human perceptual quality of video and we evaluate the performance of proposed metric by using correlation relationship between differential mean opinion score (DMOS) as a subjective quality and proposed one.

• Journal of the Korean earth science society
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• v.24 no.1
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• pp.7-21
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• 2003

The state-of-art third generation wave prediction model WAM was applied to the Korean seas for a winter monsoon period of January 1997. The wind field used in the present study is the global NSCAT-ERS/NCEP blended winds, which was further interpolated using a bi-cubic spline interpolator to fine grid limited area shallow water regime surrounding the Korean seas. To evaluate and investigate the accuracy of WAM, the hindcasted wave heights are compared with observed data from two shallow water buoys off Chil-Bal and Duk-Juk. A detailed study has been carried with the various meteorological parameters in observed buoy data and its inter-dependency on model computed wave fields was also investigated. The RMS error between the observation and model computed wave heights results to 0.489 for Chil-Bal and 0.417 for Duk-Juk. A similar comparison between the observation and interpolated winds off Duk-Juk show RMS error of 2.28 which suggest a good estimate for wave modelling studies.

• Journal of Integrative Natural Science
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• v.6 no.3
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• pp.163-169
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• 2013

The chemokine receptor CCR1 a GPCR super family protein contains seven transmembrane domains. It plays an important role in rheumatoid arthritis, organ transplant rejection, Alzheimer's disease and also causes inflammation. Because of its role in disease processes, antagonism of CCR1 became an attractive therapeutic target. In the current study, we have taken a novel series of recently reported CCR1 antagonist of 1-(4-Phenylpiperazin-1-yl_-2-(1H-Pyrazol-1-yl) ethanone derivatives and performed a HQSAR analysis. The model was developed with Atom (A) and bond (B) parameters and with different set of atom counts to improve the model. The results of HQSAR showed good predictive ability in terms of $r^2$ (0.904) and $q^2$ (0.590) with 0.710 as standard error of prediction and 0.344 as standard error of estimate. The contribution map depicted the atom contribution in inhibitory effect. Compound-14 which was reported to be a highly active compound showed positive atom contribution in three R groups ($R^3$. $R^{5a}$ and $R^{2b}$) in inhibitory effect, which could be the reason why this compound is highly active compound whereas, the lowest active compound-6 showed negative contribution to inhibitory effect.

• Journal of Forest and Environmental Science
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• v.34 no.2
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• pp.119-125
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• 2018

The tropical forests in developing countries are faced with the problem of illegal exploitation of trees. However, dearth of empirical means of expressing the dimensions, structure, quality and quantity of a removed tree has imped conviction of offenders. This study aimed at developing a model that can effectively estimate individual tree basal area (BA) from stump diameter (Ds) for Tectona grandis stands in Omo Forest Reserve, Nigeria, for timber valuation in case of illegal felling. Thirty-six $25m{\times}25m$ temporary sample plots (TSPs) were laid randomly in six age strata; 26, 23, 22, 16, 14, and 12 years specifically. BA, Ds and diameter at breast height were measured in all living T. grandis trees within the 36 TSPs. Least square method was used to convert the counted stumps into harvested stem cross-sectional areas. Six basal area models were fitted and evaluated. The BA-Ds relationship was best described by power model which gave least values of Root mean square error (0.0048), prediction error sum of squares (0.0325) and Akaike information criterion (-15391) with a high adjusted coefficient of determination (0.921). This study revealed that basal area estimation was realistic even when the only information available was stump diameter. The power model was validated using independent data obtained from additional plots and was found to be appropriate for estimating the basal area of Tectona grandis stands in Omo Forest Reserve, Nigeria.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.2
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• pp.115-125
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• 2011

An accurate estimation of software development size is an important factor in calculating reasonable cost of project development and determining its success. In this study, we propose estimation models, using function point based on the functional correlation between software, with empirical data. Three models($FP_{est}(I)$, $FP_{est}(II)$, $FP_{est}(III)$) are developed with correlation and regression analysis. The validity of the models is evaluated by the significance test by comparing values of Mean Magnitude of Relative Error (MMRE) and predictions of each model at level n%. Model $FP_{est}(III)$ proved to be superior to other models such as IFPC(Indicative Function Point Count), EFPC(Estimated Function Point Count), EPFS(Early Prediction of Function Size), $FP_{est}(I)$, and $FP_{est}(II)$. As a result, the accuracy of the model appears to be very high to determine the usefulness of the model to finally overcome weakness of other estimation models. The model can be efficiently used to estimate project development size including software size or manpower allocation.

• International journal of advanced smart convergence
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• v.7 no.3
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• pp.73-78
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• 2018

This study focuses on presenting the IT program module provided by BKLS measure in order to solve the problem of capital cost due to information asymmetry of external investors and corporate executives. Barron at al(1998) set up a BKLS measure to guide the market by intermediate analysts. The BKLS measure was measured by using the changes in the analyst forecast dispersion and analyst mean forecast error squared. This study suggests a model of the algorithm that the BKLS measure can be provided to all investors immediately by IT program in order to deliver the meaningful value in the domestic capital market as measured. This is a method of generating and analyzing real-time or non-real-time prediction models by transferring the predicted estimates delivered to the Big Data Log Analysis System through the statistical DB to the statistical forecasting engine. Because BKLS measure is not carried out in a concrete method, it is practically very difficult to estimate the BKLS measure. It is expected that the BKLS measure of Barron at al(1998) introduced in this study and the model of IT module provided in real time will be the starting point for the follow-up study for the introduction and realization of IT technology in the future.

• Steel and Composite Structures
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• v.39 no.4
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• pp.471-491
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• 2021

In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.