• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.346-348
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• 2004

In this rarer, we introduce a new Fuzzy Polynomial Neural Networks (FPNNs)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNs based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNs-like structurenamed Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. In considering the structures of FPNN-like networks such as FPNN and FSPNN, they are almost similar. Therefore they have the same shortcomings as well as the same virtues on structural side. The proposed design procedure for networks' architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IG) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using gas furnace process dataset.

• Journal of the Korean Ceramic Society
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• v.41 no.9
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• pp.677-683
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• 2004

Nd:LLM (Nd:LiLa(MoO$_4$)$_2$) single crystals for the laser host material were grown by the Czochralski method. The Nd:LLM grown single crystals cracked easily, and the reasons of cracks are generally related with phase transition, incongruent melting, chemical heterogeneity of composition, geometric thermal structures of imbalance and growth direction. We confirmed that phase transition is not observed by TG-DTA thermal analysis, and the XRD analysis revealed congruent melting in our products. It was confirmed that the volatilization of Li$_2$O composition is the important reason of chemical heterogeneity. The geometric thermal profile of the resistance furnace of our own design was controlled with a crucible height. Also, Nd:LLM crystal affected growth direction, and was the best quality in case of (101) growth direction. The distribution and effective distribution coefficient of Nd$^{3+}$ ion were accomplished by PIXE analysis.s.

• Computers and Concrete
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• v.12 no.6
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• pp.785-802
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• 2013

Ready-mixed soil material, known as a kind of controlled low-strength material, is a new way of soil cement combination. It can be used as backfill materials. In this paper, artificial neural network and nonlinear regression approach were applied to predict the compressive strength of ready-mixed soil material containing Portland cement, slag, sand, and soil in mixture. The data used for analyzing were obtained from our testing program. In the experiment, we carried out a mix design with three proportions of sand to soil (e.g., 6:4, 5:5, and 4:6). In addition, blast furnace slag partially replaced cement to improve workability, whereas the water-to-binder ratio was fixed. Testing was conducted on samples to estimate its engineering properties as per ASTM such as flowability, strength, and pulse velocity. Based on testing data, the empirical pulse velocity-strength correlation was established by regression method. Next, three topologies of neural network were developed to predict the strength, namely ANN-I, ANN-II, and ANN-III. The first two models are back-propagation feed-forward networks, and the other one is radial basis neural network. The results show that the compressive strength of ready-mixed soil material can be well-predicted from neural networks. Among all currently proposed neural network models, the ANN-I gives the best prediction because it is closest to the actual strength. Moreover, considering combination of pulse velocity and other factors, viz. curing time, and material contents in mixture, the proposed neural networks offer better evaluation than interpolated from pulse velocity only.

• 한국연소학회:학술대회논문집
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• 2002.06a
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• pp.85-94
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• 2002

Computational flow dynamics(CFD) has been frequently applied to the waste incinerators to understand the flow performance for various design and operating parameters. Though it needs many simplifications and complicated flow models, the reasonability of its results is not fully evaluated. For example, the inlet condition is calculated from an arbitrarily assumed properties of combustion gas release from the waste bed, since the combustion in the bed is difficult to be predicted. In this study, the computational modeling and calculation procedures of CFD for the grate type waste incinerator were evaluated using comparative simulations. Though the assumption method on the generation of the combustion gas directly affected the temperature and gas species concentrations, the overall flow pattern was dominated by the secondary air jets. The gaseous reaction could be included by assuming the release of the products of incomplete combusion from the bed. However, the reaction effficiency cannot not be directly evaluated from the species concentration, since it is not possible to simulate the actual co-existence of fuel rich or oxygen rich puffs over the bed. In predicting the turbulence, the higher order model, such as Reynolds stress model, gave difference shape of local recirculation zones, but similar results was acquired from the standard $k-{\varepsilon}$ model. Introducing radiation model was required for accurate temperature prediction, but it also caused heat imbalance due to the fixed temperature of the inlet, i.e. the waste bed. Thus, the computational modeling procedures on incinerators and the analysis of the predicted results should be progressed carefully. Though not validated experimentally, current simulation method is capable of comparative evaluation on the flow-related parameters such as the furnace shape and secondary air injection using identical inlet conditions. Quantitative analysis using measures of the residence time and mixing is essential to compare the flow performance efficiently.

• Korean Journal of Optics and Photonics
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• v.7 no.4
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• pp.348-356
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• 1996

A mobile CARS spectrometer is constructed to measure the instantaneous temperature of gases, of which software include the quick fit methods and a least square fitting method to obtain temperatures from the spectra. Two quick-fit-methods give smaller variance of temperatures than the least square fitting method even though they consume much shorter time to yield temperatures. The precision and accuracy of CARS temperature is measured in the graphite tube blackbody furnace in reference to a radiation pyrometer. The accuracy of the CARS temperature is $\pm$2% from 1000K to 2400K and the precision is $\pm$35K at 1600K with the most accurate quick-fit-method. As a demonstration of the instantaneous measurement, the spectrometer is applied for measurement of the turbulent combustion at a certain condition. eograms(HS) are made using a relatively small number of synthesized 2D images. The influence of aliasing artifacts caused by insufficient or improper sampling is presented, and a new sampling theory is proposed, which is used to making holographic stereograms. Also, the optical system for extension of viewing distance and viewing zone is proposed. Results of this analysis can be applied to design normal holographic stereograms and computer based holographic stereograms.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.35-48
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• 2015

The main objective of this study is to evaluate the long-term performance of various concrete composites in natural marine environment prevailing in the Gulf region. Durability assessment studies of such nature are usually carried out under aggressive environments that constitute seawater, chloride and sulfate laden soils and wind, and groundwater conditions. These studies are very vital for sustainable development of marine and off shore reinforced concrete structures of industrial design such as petroleum installations. First round of testing and evaluation, which is presented in this paper, were performed by standard tests under laboratory conditions. Laboratory results presented in this paper will be corroborated with test outcome of ongoing three years field exposure conditions. The field study will include different parameters of investigation for high performance concrete including corrosion inhibitors, type of reinforcement, natural and industrial pozzolanic additives, water to cement ratio, water type, cover thickness, curing conditions, and concrete coatings. Like the laboratory specimens, samples in the field will be monitored for corrosion induced deterioration signs and for any signs of failureover initial period ofthree years. In this paper, laboratory results pertaining to microsilica (SF), ground granulated blast furnace slag (GGBS), epoxy coated rebars and calcium nitrite corrosion inhibitor are very conclusive. Results affirmed that the supplementary cementing materials such as GGBS and SF significantly impacted and enhanced concrete resistivity to chloride ions penetration and hence decrease the corrosion activities on steel bars protected by such concretes. As for epoxy coated rebars applications under high chloride laden conditions, results showed great concern to integrity of the epoxy coating layer on the bar and its stability. On the other hand corrosion inhibiting admixtures such as calcium nitrite proved to be more effective when used in combination with the pozzolanic additives such as GGBS and microsilica.

• Transactions of the Korean hydrogen and new energy society
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• v.20 no.5
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• pp.384-389
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• 2009

KEPRI (Korea Electric Power Research Institute) has studied planar type solid oxide fuel cell (SOFC) stacks using anode-supported cells and kW class co-generation systems for residential power generation. In this work, a 1 kW SOFC system consisted of a hot box part, a cold BOP (balance of plant) part, and a hot water reservoir. The hot box part contained a SOFC stack made up of 48 cells, a fuel reformer, a catalytic combustor, and heat exchangers. Thermal management and insulation system were especially designed for self-sustainable operation in that system. A cold BOP part was composed of blowers, pumps, a water trap, and system control units. When the 1 kW SOFC stack was tested using hydrogen at $750^{\circ}C$, the stack power was about $1.2\;kW_e$ at 30 A and $1.6\;kW_e$ at 50 A. Turning off an electric furnace, the SOFC system was operated using hydrogen and city gas without any external heat source. Under self-sustainable operation conditions, the stack power was about $1.3\;kW_e$ with hydrogen and $1.2\;kW_e$ with city gas respectively. The system also recuperated heat of about $1.1\;kW_{th}$ by making hot water.

• Korean Chemical Engineering Research
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• v.54 no.6
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• pp.775-780
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• 2016

Recently, photovoltaic industry needs a new design of Czochralski (Cz) process for higher productivity with reasonable energy consumption as well as solar cell's efficiency. If the process uses the large size reactor for increasing productivity, it is possible to produce a 12-inch, rather than the 8-inch. Also the continuous czochralski process method can be maximized to increase productivity. In this study, it was designed to improve the yield value of ingot with optimal condition which reduce consumption of electrical power. It has increased the productivity of the 12-inch ingot process condition by using CFD simulation. I have found optimal growth rate, by comparing each growth rate the interface shape, Temperature gradient, power consumption. As a result, the optimal process parameters of the growth furnace has been derived to improve for the productivity and to reduce energy. This study will contribute to the improvement of the productivity in the solar cell industry.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.433-436
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• 2004

In this paper, we introduce a new Fuzzy Polynomial Neural Networks (FPNNS)-like structure whose neuron is based on the Fuzzy Set-based Fuzzy Inference System (FS-FIS) and is different from that of FPNNS based on the Fuzzy relation-based Fuzzy Inference System (FR-FIS) and discuss the ability of the new FPNNS-like structure named Fuzzy Set-based Polynomial Neural Networks (FSPNN). The premise parts of their fuzzy rules are not identical, while the consequent parts of the both Networks (such as FPNN and FSPNN) are identical. This difference results from the angle of a viewpoint of partition of input space of system. In other word, from a point of view of FS-FIS, the input variables are mutually independent under input space of system, while from a viewpoint of FR-FIS they are related each other. The proposed design procedure for networks architecture involves the selection of appropriate nodes with specific local characteristics such as the number of input variables, the order of the polynomial that is constant, linear, quadratic, or modified quadratic functions being viewed as the consequent part of fuzzy rules, and a collection of the specific subset of input variables. On the parameter optimization phase, we adopt Information Granulation (IC) based on HCM clustering algorithm and a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized FSPNN (gFSPNN), the model is experimented with using the time series dataset of gas furnace process.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.402-410
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• 2004

In this paper, an optimal design method of clonal selection based Fuzzy-Neural Networks (FNN) model for complex and nonlinear systems is presented. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. Also Advanced Clonal Selection (ACS) is proposed to find the parameters such as parameters of membership functions, learning rates and momentum coefficients. The proposed method is based on an Immune Algorithm (IA) using biological Immune System and The performance is improved by control of differentiation rate. Through that procedure, the antibodies are producted variously and the parameter of FNN are optimized by selecting method of antibody with the best affinity against antigens such as object function and limitation condition. To evaluate the performance of the proposed method, we use the time series data for gas furnace and traffic route choice process.