• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• Transactions of the Korean Society of Mechanical Engineers
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• v.11 no.6
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• pp.904-912
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• 1987

The main mode of heat transfer of combustion gases at high temperature is thermal radiation of nonluminous gases, CO$_{2}$ and H$_{2}$O. Therefore the information of the emissivities of CO$_{2}$ and H$_{2}$O would be very important in the thermal performance analysis of furnace. In this study, an exponential model for the emissivities of CO$_{2}$ and H$_{2}$O was derived as function of P$_{g}$L and polynomial of reciprocal of temperature. Error analysis between the calculated values from present model and the valued of Hottel Chart was performed over temperature range of 1000-5000 R and a partial-pressure-length product range of 0.003 to 20 ft-atm. For CO$_{2}$ gray gas, the error percent between the calculated values and the values from Hottel Chart was distributed within 2.5% in case of using a polynomial in 1/T of degree 4. For H$_{2}$O gray gas, the model has an error range of 0 to 2.5% in case of using a polynomial in 1/T of degree 3.

• International Journal of Concrete Structures and Materials
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• v.7 no.3
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• pp.193-202
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• 2013

Geopolymer mortar compressed blocks were prepared using fly ash, ground granulated blast furnace slag, silica fume and metakaolin as binders and sand/quarry dust/pond ash as fine aggregate. Alkaline solution was used to activate the source materials for synthesizing the geopolymer mortar. Fresh mortar was used to obtain the compressed blocks. The strength development with reference to different parameters was studied. The different parameters considered were fineness of fly ash, binder components, type of fine aggregate, molarity of alkaline solution, age of specimen, fluid-to-binder ratio, binder-to-aggregate ratio, degree of saturation, etc. The compressed blocks were tested for compression at different ages. It was observed that some of the blocks attained considerable strength within 24 h under ambient conditions. The cardinal aim was to analyze the experimental data generated to formulate a phenomenological model to arrive at the combinations of the ingredients to produce geopolymer blocks to meet the strength development desired at the specified age. The strength data was analyzed within the framework of generalized Abrams' law. It was interesting to note that the law was applicable to the analysis of strength development of partially saturated compressed blocks when the degree of saturation was maintained constant. The validity of phenomenological model was examined with an independent set of experimental data. The blocks can replace the traditional masonry blocks with many advantages.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.512-519
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• 2003

In this paper, we propose a neuro-fuzzy modeling to improve the performance using the hierarchical clustering and Gaussian Mixture Model(GMM). The hierarchical clustering algorithm has a property of producing unique parameters for the given data because it does not use the object function to perform the clustering. After optimizing the obtained parameters using the GMM, we apply them as initial parameters for Adaptive Network-based Fuzzy Inference System. Here, the number of fuzzy rules becomes to the cluster numbers. From this, we can improve the performance index and reduce the number of rules simultaneously. The proposed method is verified by applying to a neuro-fuzzy modeling for Box-Jenkins s gas furnace data and Sugeno's nonlinear system, which yields better results than previous oiles.

• KIEAE Journal
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• v.7 no.5
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• pp.35-40
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• 2007

The purpose of this study is to execute evaluation experiment to know the evaluation property of lighting environment of residential space for senior people, considering visual characteristics along aging, and finally provide basic data for the lighting plan to ensure the visual amenity. Processes of this study are as follows;1) Analyzed the variation property of visual sensibility and visual ability of senior people along aging. 2) Selected 3 types of life behavior(rest, conversation and reading) after checking life behavior in residential space for senior people based on advanced study. 3) Made the Mock-up Model that Dimming is possible, actual furnace to model. 4) Executed sensitivity evaluation experiment about lighting environment. 5) Analyzed evaluation property of lighting environment of residential space for senior people. Results of this study are as follows, 1) With lens-filter, we got comfort and amenity in bulb-color lamp which has similar color temperature with red of lens filter. 2) Lighting environment tests during conversation : With lens filters, they felt comfort on bulb color in case of higher illuminance than 850lux and daylight color in 500lux. 3) Lighting environment tests at reading : With lens filter, bulb color got better score in brightness and appropriateness than daylight color.

• Structural Engineering and Mechanics
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• v.68 no.6
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• pp.691-700
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• 2018

Ground granulated blast furnace slag (GGBS) is a by product obtained from iron and steel industries, useful in the design and development of high quality cement paste/mortar and concrete. This paper investigates the applicability of relevance vector machine (RVM) based regression model to predict the compressive strength of various GGBS based concrete mixes. Compressive strength data for various GGBS based concrete mixes has been obtained by considering the effect of water binder ratio and steel fibres. RVM is a machine learning technique which employs Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM is an extension of support vector machine which couples probabilistic classification and regression. RVM is established based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 70% of the data has been used for development of RVM model and 30% of the data is used for validation. The predicted compressive strength for GGBS based concrete mixes is found to be in very good agreement with those of the corresponding experimental observations.

• Computers and Concrete
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• v.27 no.1
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• pp.21-28
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• 2021

The Artificial Neural Network (ANN) is a system, which is utilized for solving complicated problems by using nonlinear equations. This study aims to investigate compressive strength, rebound hammer number (RN), and ultrasonic pulse velocity (UPV) of sustainable concrete containing various amounts of fly ash, silica fume, and blast furnace slag (BFS). In this study, the artificial neural network technique connects a nonlinear phenomenon and the intrinsic properties of sustainable concrete, which establishes relationships between them in a model. To this end, a total of 645 data sets were collected for the concrete mixtures from previously published papers at different curing times and test ages at 3, 7, 28, 90, 180 days to propose a model of nine inputs and three outputs. The ANN model's statistical parameter R2 is 0.99 of the training, validation, and test steps, which showed that the proposed model provided good prediction of compressive strength, RN, and UPV of sustainable concrete with the addition of cement.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2004.06a
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• pp.105-114
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• 2004

This study investigated the thermal behavior of the nuclear graphite waste generated from the decommissioning of the Korean nuclear research reactor, The first part study investigated the decomposition rate of the nuclear graphite waste up to $1000^{\circ}C$ under various oxygen partial pressures using a thermo-gravimetric analyzer (TGA). Tested graphite waste sample not easily destroyed in the oxygen-deficient condition. However, the gas-solid oxidation reaction was found to be very effective in the presence of oxygen. No significant amount of the product of incomplete combustion was formed even in the limited oxygen concentration of 4% $O_2$. The influence of temperature and oxygen partial pressure was evaluated by the theoretical model analysis of the thermo-gravimetric data. The activation energy and the reaction order of graphite oxidation were evaluated as 128 kJ/mole and 1.1, respectively. The second part of this study investigated the behavior of radioactive elements under graphite oxidation atmosphere using thermodynamic equilibrium model. $^{22}Na$, $^{134}Cs$ and $^{137}Cs$ were found be the semi-volatile elements. Since volatile uranium species can be formulated at high temperatures above $1050^{\circ}C$, the temperature of incinerator furnace should be minimized. Other corrosion/activation products, fission products and uranium were found to be the non-volatile species.

• Journal of the Korea Institute of Building Construction
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• v.7 no.4
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• pp.145-151
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• 2007

In this paper, tests are carried out in order to investigate the strength development of concrete under various binder types, W/B and curing temperature ranged from $5{\sim}20^{\circ}C$. Fly ash and blast furnace slag were incorporated by as much as 30%, respectively. Strength development of concrete are estimated using Logistic model and strength ratio of concrete at 28days to that at early age are also investigated. According to experimental results, it is found that good agreements are obtained between measured values and calculated ones using logistic model below $20^{\circ}C$. Strength ratio of concrete at 28days to that at early age increases in case W/B decreases and curing temperature increases. Tables and graphs for strength ratio of concrete are provided in this paper. It is capable of obtaining and predicting the periods to attain design strength by considering increment factor of strength easily with the table and graphs presented in this paper. This paper presents the reference data to decide removal time of form, time to reach target strength and strength inspection of remicon whether the test specimens meet the specified criteria of compressive strength. Multi regression models with respect to the relationship between 7days compressive strength and 28 days compressive strength depending on W/B and admixture types are presented.

• Computers and Concrete
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• v.19 no.1
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• pp.51-58
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• 2017

The paper presents the experimental investigations for studying the effect of ground granulated blast furnace slag (GGBFS) on the time-dependent deflection of reinforced concrete (RC) beams due to creep and shrinkage. The RC beams were reinforced with 2-10 mm bars at tension side and subjected to constant sustained two-point loading for the period of 150 days. The amount of cement replacement by GGBFS was varied from 0 to 60% with an increment of 20%. The total deflection was measured at different ages of up to 150 days under sustained loads. The experiments revealed that the time-dependent deflection of the reinforced concrete RC beams containing GGBFS was higher than that of plain concrete RC beams. At 150 days, the average creep and shrinkage deflection of RC beams containing 20%, 40% and 60% GGBFS was 1.25, 1.45 and 1.75 times higher than the plain concrete beams. A new model, which is an extension of authors' earlier model, is proposed to incorporate the effect of GGBFS content in predicting the long-term deflection of RC beams. Besides validating the new model with the current data with higher percentage of tension reinforcement, it was also used to predict the authors' earlier data containing lesser percentage of tension reinforcement with reasonable accuracy.