• Steel and Composite Structures
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• v.52 no.2
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• pp.145-163
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• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.

• 한국체육학회지인문사회과학편
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• v.51 no.3
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• pp.373-382
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• 2012

The purpose of this study is to predict VO₂max with body index and submaximal metabolic responses. The subjects are consisted of 250 male aging from 18 to 34 and we separated them into two groups randomly; 179 for a sample, 71 for a cross-validation group. They went through maximal exercise testing with Bruce protocol, and we measured the metabolic responses in the end of the first(3 minute) and second stage(6 minute). To predict VO₂max, we applied multiple regression analysis to the sample with stepwise method. Model 1's variables are weight, 6 minute HR and 6 minute VO₂(R=0.64, SEE=4.74, CV=11.7%, p<.01), and the equation is VO₂max(ml/kg/min)= 72.256-0.340(Weight)-0.220(6minHR)+0.013(6minVO₂). Model 2's variables are weight, 6 minute HR, 6 minute VO₂, and 6 minute VCO₂(R=0.67, SEE=4.59, CV=11.3%, p<.01), and the equation is VO₂max(ml/kg/min)= 68.699-0.277(Weight) -0.206(6minHR)+0.020(6minVO₂)-0.009(6minVCO₂). And the result did not show multicolinearity for both models. Model 2 demonstrated more correlation compared to Model 1. However, when we conducted cross-validation of those models with 71 men, measured VO₂max and estimated VO₂ Max had statistical significance with correlation (R=0.53, 0.56, P<.01). Although both models are functional with validity considering their simplicity and utility, Model 2 has more accuracy.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Analytical Science and Technology
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• v.29 no.2
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• pp.57-64
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• 2016

Many phthalic acid esters (PAEs), including DMP, DEP, DBP, BBP, and DEHP, as well as DEHA are widely used as plasticizers in plastics. An analytical method was developed and used to analyze these compounds at 41 industrial facilities. The coefficient of determination (R²) for each constructed curve was higher than 0.98. The method detection limit (MDL) values were 0.4–0.7 μg/L for PAEs and 0.6 μg/L for DEHA. In addition, the recovery rate was shown to be 77.0–92.3%, while the relative standard deviation was shown to be in the range of 5.8-10.5%. DMP (n = 3), DEP (n = 2), DBP (n = 2), BBP (n = 2), and DEHA (n = 3) were detected in the range of 2.2-11.1% in the influent. DEHP was a predominant compound and was detected at > MDL in both the influent (n = 16, 35.6%) and the effluent (n = 4, 10.0%) at a high removal efficiency (92–100%). The highest levels of residue in industrial wastewater influent were 137.4 μg/L of DEHP at plastic products manufacturing facility, 12.5 μg/L of DEHA at a chemical manufacturing facility, and 14.0 μg/L of DEP at an electronics facility. The highest concentration of effluent was 12.5 μg/L of DEHP at a chemical manufacturing facility, which indicated that the effluent was below the allowable concentration (800 μg/L). Therefore, the levels of PAEs and DEHA that are discharged into nearby streams could not influence the health of the ecosystem.

• Journal of computational fluids engineering
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• v.20 no.2
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• pp.96-102
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• 2015

In this study, the green water phenomena on rectangular shaped structure is numerically simulated by STAR-CCM+ to investigate the flow pattern including the velocity profiles in bubbly water flow. 5 phases of the formation of green water in front of and over the rectangular shaped structure is simulated at the design condition which is scaled down by 1:125 from FPSO operating in GOM. All numerical results are compared with the experimental results performed in a two dimensional wave flume. The water deformation due to the green water are obtained by the high speed CCD camera with employing the shadow graphy technique, which is allowed to take the bubbly water flow into images. A series of image taken by shadow graphy technique is analyzed with MQD method to calculate the velocity in bubbly water flow.

• Structural Engineering and Mechanics
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• v.60 no.3
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• pp.489-505
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• 2016

Sinusoidal shear deformation theory (SSDT) is developed here for dynamic buckling of functionally graded (FG) nano-plates. The material properties of plate are assumed to vary according to power law distribution of the volume fraction of the constituents. In order to present a realistic model, the structural damping of nano-structure is considered using Kelvin-Voigt model. The surrounding elastic medium is modeled with a novel foundation namely as orthotropic visco-Pasternak medium. Size effects are incorporated based on Eringen'n nonlocal theory. Equations of motion are derived from the Hamilton's principle. The differential quadrature method (DQM) in conjunction with Bolotin method is applied for obtaining the dynamic instability region (DIR). The detailed parametric study is conducted, focusing on the combined effects of the nonlocal parameter, orthotropic visco-Pasternak foundation, power index of FG plate, structural damping and boundary conditions on the dynamic instability of system. The results are compared with those of first order shear deformation theory and higher-order shear deformation theory. It can be concluded that the proposed theory is accurate and efficient in predicting the dynamic buckling responses of system.

• Proceedings of the KSME Conference
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• 2001.06e
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• pp.271-275
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• 2001

Three dimensional flow characteristics in a liquid fuel ramjet combustor are investigated using PIV method. The combustors have two rectangular inlets that form 90 degree each other. Three guide vane is installed in each rectangular inlet to improve the flow stability. We made three cases of test combustors in which those inlet angles are 30 degree, 45 degree and 60 degree. Each combustor easily changes the size of combustor's recirculation zone with the replacement of combustors dome. The experiments are performed in the water tunnel test with the same Reynolds number in the case of Mach 0.3 at inlet. PIV software is developed to measure the flow field in the combustor and the accuracy of developed PIV program is verified with rotating disk experiment and standard data. The experimental results show that the two main streams from rectangular inlet collide near the plane of symmetry and generate two large longitudinal vortex, A large and complex three-dimensional recirculating flow is measured in the recirculation zone.

• International Journal of Concrete Structures and Materials
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• v.6 no.3
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• pp.177-186
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• 2012

The temperature distributions of concrete structures strongly depend on the value of thermal conductivity of concrete. However, the thermal conductivity of concrete varies according to the composition of the constituents and the temperature and moisture conditions of concrete, which cause difficulty in accurately predicting the thermal conductivity value in concrete. For this reason, in this study, back-propagation neural network models on the basis of experimental values carried out by previous researchers have been utilized to effectively account for the influence of these variables. The neural networks were trained by 124 data sets with eleven parameters: nine concrete composition parameters (the ratio of water-cement, the percentage of fine and coarse aggregate, and the unit weight of water, cement, fine aggregate, coarse aggregate, fly ash and silica fume) and two concrete state parameters (the temperature and water content of concrete). Finally, the trained neural network models were evaluated by applying to other 28 measured values not included in the training of the neural networks. The result indicated that the proposed method using a back-propagation neural algorithm was effective at predicting the thermal conductivity of concrete.

• Journal of Pharmaceutical Investigation
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• v.41 no.1
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• pp.25-30
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• 2011

Recently, Korean Food and Drug Administration (KFDA) has focused on developing quality control guidelines for all commercial products in Korea to enforce regulations, improve the quality control, and protect consumers by developing prevalently used and efficient analytical tools to determine and quantify target compounds. Because the Korean Pharmacopeia (KP) presents microbiological assays for biotin, which is laborious and time-consuming, this study is focused on applying HPLC-UV to detect and quantify biotin in complex drugs and dietary supplements like multi-vitamin. Biotin in complex drugs was extracted from methanol and analyzed using mobile phase with 10 mM potassium phosphate (monobasic, pH=3.0) in distilled water and acetonitrile. Gradient condition was used to successfully detect and quantify biotin within 20 minutes. Validation result for linearity was significant that average $r^2$ was 0.999 (n=3) and its relative standard deviation (RSD) was 0.0578% which was less than 2%. Using this method, quantification of biotin in complex drugs was completed successfully and recovery tests were finished that recovery percentage greater than 95% with relative standard deviation less than 2%.

• Journal of Power System Engineering
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• v.16 no.1
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• pp.91-97
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• 2012

It is being accelerated to develop environment-friendly vehicles to solve problems on the energy and environment of earth. The electric driving motor commonly installed in these vehicles has the excellent control capability such as fast response and accurate generation to torque control command. Especially, in-wheel motor has the additional merit such as independently driving each wheel in vehicle. Recently, being developed various control algorithm to enhance the safety and stability of vehicle motion using actively the merits of in-wheel motor. In addition to that, being issued the possibility of enhancing the ride comfort and attitude of vehicle motion such as pitching and rolling. In this paper, investigate the theoretical relationship between the braking/driving force and the motion of sprung mass of vehicle and propose the control method to enhance the ride comfort and attitude of vehicle motion. The proposed control method is proved through the simulation with vehicle model provided by TruckSim software which is commercial one and specializes in vehicle dynamics.