• Steel and Composite Structures
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• v.50 no.4
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• pp.443-458
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• 2024

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO₂ emissions in the building industry. In the present work, the compressive strength (f_c) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast-furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R² metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for CRFR respect to ARFR.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.10
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• pp.3371-3380
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• 1996

Low resolution spectral modeling of water vapor is carried out by applying the weighted-sum-of-gray-gases model (WSGGM) to a narrow band. For a given narrow band, focus is placed on proper modeling of gray gas absorption coefficients vs. temeprature relation used for any solution methods for the Radiative Transfer Equation(RTE). Comparison between the modeled emissivity and the "true" emissivity obtained from a high temperatue statistical narrow band parameters is made ofr the total spectrum as well as for a few typical narrow bands. Application of the model to nonuniform gas layers is also made. Low resolution spectral intensities at the boundary are obtained for uniform, parabolic and boundary layer type temeprature profiles using the obtained for uniform, parabolic and boundary layer type temperature profiles using the obtained WSGGM's with 9 gray gases. The results are compared with the narrow band spectral intensities as obtained by a narrow band model-based code with the Curtis-Godson approximation. Good agreement is found between them. Local heat source strength and total wall heat flux are also compared for the cases of Kim et al, which again gives promising agreement.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.13 no.3
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• pp.132-138
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• 2003

A stoichiometric mixture for $CdIn_2Te_4$ single crystal was prepared from horizontal electric furnace. The $CdIn_2Te_4$ single crystal was grown in the three-stage vertical electric furnace by using Bridgman method. The $CdIn_2Te_4$ single crystal was evaluated to be tetragonal by the power method. The (001) growth plane of oriented $CdIn_2Te_4$ single crystal was confirmed from back-reflection Laue patterns. The carrier density and mobility of $CdIn_2Te_4$ single crystal measured with Hall effect by van der Pauw method are $8.61\times 1016 \textrm {cm}^{-3}$ and 242 $\textrm{cm}^2$/V.s at 293 K, respectively. The temperature dependence of the energy band gap of the $CdIn_2Te_4$ single crystal obtained from the absorption spectra was well described by the Varshni's relation, $1.4750ev - (7.69\times10^{-3})\; ev/k)\;T^2$/(T + 2147k).The crystal field and the spin-orbit splitting energies for the valence band of the $CdIn_2Te_4$ single crystal have been estimated to be 0.2704 eV and 0.1465 eV, respectively, by means of the photocurrent spectra and the Hopfield quasicubic model. These results indicate that the splitting of the $\Delta$so definitely exists in the $\Gamma_7$ states of the valence band of the $CdIn_2Te_4$ single crystal. The three photocurrent peaks observed at 10 K are ascribed to the $A_{1-} B_{1-}$ and Cl-exciton peaks for n = 1.

• Journal of the Korea Concrete Institute
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• v.26 no.6
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• pp.723-730
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• 2014

The usage of Ground Granulated Blast Furnance Slag (GGBFS) has been increased recently. Studies on the cement hydration model incorporating GGBFS as well as the properties of cement paste done with GGBFS such as compressive strength, hydration products and hydration heat have been the subjects of many researches. However, studies on the reaction degree of GGBFS that affect the properties of cement paste incorporating GGBFS are lacking globally and specially in Korea. Thus, in this study, the reaction degree of GGBFS using the method if selective dissolution, compressive strength, the amount of chemical bound water and $Ca(OH)_2$ were measured and analysed in accordance with water-binder ratio, replacement ratio of GGBFS, and curing temperature. The results show that the reaction degree of GGBFS, the amount of chemical bound water and $Ca(OH)_2$ in cement paste with GGBFS were higher in conditions where the replacement ratio of GGBFS was low and both water-binder ratio and curing temperature were high. Finally, the reaction degree of GGBFS was achieved at a value between 0.3~0.4.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.74-82
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• 2011

To do fuzzy modelling of a nonlinear process needs to analyze the characteristics of input-output of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods. For this, fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the fuzzy rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the clusters are used for identification of fuzzy model and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. In the consequence part of the fuzzy rules fuzzy reasoning is conducted by two types of inferences such as simplified and linear inference. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. And lastly, using gas furnace process which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• Journal of the Korean Society for Marine Environment & Energy
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• v.3 no.2
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• pp.25-32
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• 2000

Fire-resistant boom is one of the most important facilities in in situ homing of spilled oil. Thermal response of a fire-resistant boom to turning is experimentally investigated in this paper by using an electric furnace and a burning test facility. This test facility is composed of a test tank, a fire boom, a hood for inhaling smoke, an incinerator for burning up gases and thermocouples, etc. Thereby a systematic method of approach in small laboratory scale is developed to study the performance of a fire-resistant boom. Burning test is carried out for the fire boom model which has been developed through the present study. It is shown that the present fire boom model has capability to withstand the high temperature around 800℃ and high rate of heat flux on it due to homing. For more realistic experimental environments, larger dimensions in devices and longer time in experiments are recommended in near future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.961-966
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• 2012

The concern about vacuum glass is enhanced as society gets greener and becomes more concerned about energy savings due to the rising cost of oil. The glass edge sealing process needs the high reliability among the main process for the vacuum glass development in order to maintain between the two glass by the vacuum. In this paper, the process of the edge sealing was performed by using the hydrogen mixture gas which is the high density heat source unlike the traditional method glass edge sealing by using the frit as the soldering process. The ambient temperature in the electric furnace was set in the edge sealing to prevents the thermal impact and transformation of the glasses and the temperature distribution uniformity was measured. The parameter of the edge sealing was set through the basic test and the mathematical relation with the area of the glass edge parts according to the parameter was drawn using the multiple regression analysis method.

• Journal of Korea Foundry Society
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• v.32 no.6
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• pp.276-283
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• 2012

This study presents the results on the changes of crucible thermal conductivity and inflow of Ar, and constructed the mathematical model about heat transfer into furnace. As process variables, simulation model was designated thermal conductivity of crucible to $0.5W{\cdot}m^{-1}{\cdot}K^{-1}$, $1W{\cdot}m^{-1}{\cdot}K^{-1}$, $2W{\cdot}m^{-1}{\cdot}K^{-1}$, $4W{\cdot}m^{-1}{\cdot}K^{-1}$, and inflow rate of Ar to 15 L/min, 30 L/min, 60 L/min. Initial condition and boundary condition were set respectively in two terms of process. Each initial conditions were set up by the preceding simulation of heat and fluid flow. The primary goal is the application of unidirectional growth of Si ingot using the result. In the result of the change of heat conductivity of crucible, the higher thermal conductivity of crucible shows the shorter solidification time and the bigger temperature difference. And the flow patterns are changed with the inflow rate of Ar. Finally, we found that the lower crucible's thermal conductivity, the better crucible is at polycrystalline Si ingot growth. But in case of Ar inflow, it is hard to say about good condition. This data will be evaluated as useful reference used in allied study or process variable control of production facilities.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.12-18
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• 2011

In this paper, we proposed Interval Type-2 polynomial Radial Basis Function Neural Networks. In the receptive filed of hidden layer, Interval Type-2 fuzzy set is used. The characteristic of Interval Type-2 fuzzy set has Footprint Of Uncertainly(FOU), which denotes a certain level of robustness in the presence of un-known information when compared with the type-1 fuzzy set. In order to improve the performance of proposed model, we used the linear polynomial function as connection weight of network. The parameters such as center values of receptive field, constant deviation, and connection weight between hidden layer and output layer are optimized by Conjugate Gradient Method(CGM) and Space Search Evolutionary Algorithm(SSEA). The proposed model is applied to gas furnace dataset and its result are compared with those reported in the previous studies.