• 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 on Water Environment
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• v.34 no.3
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• pp.328-338
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• 2018

The objective of this study was to evaluate the applicability of a HSPF (Hydrological Simulation Program-Fortran) model for runoff estimation in the Namgang dam watershed. Spatial data, such as watershed, stream, land use, and a digital elevation map, were used as input for the HSPF model, which was calibrated and validated using observed runoff data from 2004 to 2015 for three stations (Sancheong, Shinan, Changchon) in the study watershed. Parameters for runoff calibration were selected based on the user's manual and references, and parameter calibration was done by trial and error. The $R^2$ (determination coefficient), RMSE (root-mean-square error), NSE (Nash-Sutcliffe efficiency coefficient), and RMAE (relative mean absolute error) were used to evaluate the model's performance. Calibration and validation results showed that annual mean runoff was within a ${\pm}5%$ error in Sancheong and Shinan, whereas there was a14% error in Changchon. The model performance criteria for calibration and validation showed that $R^2$ ranged from 0.80 to 0.92, RMSE was 2.33 to 2.39 mm/day, NSE was 0.71 to 0.85, and RMAE was 0.37 to 0.57 mm/day for daily runoff. Visual inspection showed that the simulated daily flow, monthly flow, and flow exceedance graph agreed well with observations for the Sancheong and Shinan stations, whereas the simulated flow was higher than observed at the Changchon station.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.5
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• pp.69-77
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• 2019

The objective of this study was to calibrate and validate the HSPF (Hydrological Simulation Program-Fortran) model for estimating the runoff of the Hapcheon dam watershed. Spatial data, such as watershed, stream, land use, and a digital elevation map, were used as input data for the HSPF model. Observed runoff data from 2000 to 2016 in study watershed were used for calibration and validation. Hydrologic parameters for runoff calibration were selected based on the user's manual and references, and trial and error method was used for parameter calibration. The $R^2$, RMSE (root-mean-square error), RMAE (relative mean absolute error), and NSE (Nash-Sutcliffe efficiency coefficient) were used to evaluate the model's performance. Calibration and validation results showed that annual mean runoff was within ${\pm}4%$ error. The model performance criteria for calibration and validation showed that $R^2$ was in the rang of 0.78 to 0.83, RMSE was 2.55 to 2.76 mm/day, RMAE was 0.46 to 0.48 mm/day, and NSE was 0.81 to 0.82 for daily runoff. The amount of inflow to Hapcheon Dam was calculated from the calibrated HSPF model and the result was compared with observed inflow, which was -0.9% error. As a result of analyzing the relation between inflow and storage capacity, it was found that as the inflow increases, the storage increases, and when the inflow decreases, the storage also decreases. As a result of correlation between inflow and storage, $R^2$ of the measured inflow and storage was 0.67, and the simulated inflow and storage was 0.61.

• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.783-794
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• 2012

The analysis for unsteady flow is necessary to design the hydraulic structures affected by water level and discharge changes through time. The numerical model has been generally used for unsteady flow analysis, however it is difficult to acquire field data to calibrate and validate the numerical model. Even though it is possible to collect field data for some case, high cost and labor are required and sometimes it is considered that the confidence of measured data is very low. In this case, the experimental data for unsteady flow can be used to calibrate and validate the numerical model as an alternative. Therefore, the discharge-supply system which could generate various type of unsteady flow hydrograph was developed in this study. Also, the accuracy of the unsteady flow hydrograph generated by developed dischargesupply system in the experiment was evaluated by comparing with target hydrograph. Accuracy errors and Root Mean Square Error (RMSE) were analyzed for the rectangular-type hydrograph with sudden changes of flow, triangular-type hydrograph with short peak time, and bell-type flood hydrograph. As a result, the generating error of the discharge-supply system for the rectangular-type hydrograph was about 59% which was maximum error among various types. Also, it was represented that RMSE for the triangular-type hydrographs with single and double peaks were approximately corresponding to 10%. However, RMSE for the bell-type flood hydrograph was lower than 2%.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.4
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• pp.274-282
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• 2009

Various location tracking sensors; such as GPS, INS, radar, and optical equipment; are used for tracking moving targets. In order to effectively track moving targets, it is necessary to develop an effective fusion method for these heterogeneous devices. There have been studies in which the estimated values of each sensors were regarded as different models and fused together, considering the different error characteristics of the sensors for the improvement of tracking performance using heterogeneous multi-sensor. However, the rate of errors for the estimated values of other sensors has increased, in that there has been a sharp increase in sensor errors and the attempts to change the estimated sensor values for the Sensor Probability could not be applied in real time. In this study, the Sensor Probability is obtained by comparing the RMSE (Root Mean Square Error) for the difference between the updated and measured values of the Kalman filter for each sensor. The process of substituting the new combined values for the Kalman filter input values for each sensor is excluded. There are improvements in both the real-time application of estimated sensor values, and the tracking performance for the areas in which the sensor performance has rapidly decreased. The proposed algorithm adds the error characteristic of each sensor as a conditional probability value, and ensures greater accuracy by performing the track fusion with the sensors with the most reliable performance. The trajectory of a UAV is generated in an experiment and a performance analysis is conducted with other fusion algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.576-584
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• 2017

Traffic demand prediction result is a primary factor for decision making such as the traffic planning and operation. The existing traffic demand prediction 4-step model only covers the trip between the origin and the destination, and not the demand followed by the accessibility improvement, due to the characteristic of this model. Therefore, the purpose of this research is to improve the limitations of the existing model by developing the inter-city trip generation and trip distribution model with more accessibility. After calculating of the trip generation and trip distribution model with more accessibility, the sign of the accessibility coefficient was positive. Commuting was the most insensitive indicator, affected by external factors among the other trip purposes. The leisure trip was the most sensitive, affected by the trip fee. According to the result of comparison with each of estimated model and observational data, it was certain that the reliability and assumption of the model have been improved by discovering the reduced weighted average error rate, Root Mean Square Error (RMSE) and total error through the model with more accessibility compared with the existing one.

• Journal of Astronomy and Space Sciences
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• v.27 no.3
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• pp.213-220
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• 2010

As an observation instrument of the longest record of tropospheric water vapor, radiosonde data provide upper-air pressure (geopotential height), temperature, humidity and wind. However, the data have some well-known elements related to inaccuracy. In this article, radiosonde precipitable water vapor (PWV) at Sokcho observatory was compared with global positioning system (GPS) PWV during each summertime of year 2007 and 2008 and the biases were calculated. As a result, the mean bias showed negative values regardless of the rainfall occurrence. In addition, on the basis of GPS PWV, the maximum root mean square error (RMSE) was 5.67 mm over the radiosonde PWV.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.6
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• pp.663-669
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• 2011

This paper examines methods to estimate torque and angle in shoulder flexion from surface electromyography(sEMG) signals for intuitive and delicate control of robotic assistance device. Five muscles on the upper arm, three for shoulder flexion and two for shoulder extension, were used to offer favorable sEMG recording conditions in the estimation. The methods tested were the mean absolute value (MAV) with linear regression and the artificial neural network (ANN) method. An optimal condition was sought by varying combination of muscles used and the parameters in each method. The estimation performance was evaluated using the correlation values and normalized root mean square error values. In addition, we discussed their possible use as an estimation of motion intent of a user or as a command input in a physical human-machine interaction system.

• Korean Journal of Hydrosciences
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• v.5
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• pp.45-55
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• 1994

A new method to estimate the mean areal precipitation using kriging is developed. Urlike the conventional approach, points for double and quadruple numerical integrations in the kriging equation are selected randomly, given the boundary of area of interest. This feature eliminates the conventional approach's necessity of dividing the area into subareas and calculating the center of each subarea, which in turn makes the developed method more powerful in the case of complex boundaries. The algorithm to select random points within an arbitrary boundary, based on the theory of complex variables, is described. The results of Monte Carlo simulation showed that the error associated with estimation using randomly selected points is inversely proportional to the square root of the number of sampling points.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.228-228
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• 2016

증발산량은 수자원 부존량 평가, 물수지 분석, 지구의 물순환 및 에너지 순환을 이해하기 위해서 알아야할 수문량이나, 이를 산정하기 위하여 단순한 가정을 하거나 경험식을 사용하는 접근에는 신뢰성에 문제가 생긴다. 본 연구에서는 아시아 지역내의 여러 지점에서 에디공분산 시스템을 활용해 플럭스 자료를 구축해놓은 Asia Fluxnet의 자료를 활용해 보완관계법(Complimentary relationship) 기반으로 제한된 기상자료를 이용해 구한 증발산량을 산정하는 방법론들을 평가하였다. Granger and Gary(GG)는 실제 증발산량은 습윤조건의 증발산량의 2배에 잠재 증발산량간의 차와 같다는 보완관계를 수정하여 일반화하고, 잠재 증발산량을 산정하는 경험식을 제시하였다. 이러한 수정된 보완관계식을 활용한 GG 방법론을 활용하여 산정한 증발산량을 측정된 증발산량과 비교한 정확성을 정량화 하기 위해 Average root mean square error (RMSE), mean absolute bias (BIAS), coefficient of determination ($R^2$)과 같은 통계값을 이용하였다. 최종적으로 각 사이트의 기후를 Aridity Index (AI)를 이용하여 분류하였으며 분류된 기후별로 GG 방법론의 적용성을 검토하였다.