• Journal of the Korean Recycled Construction Resources Institute
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• v.4 no.4
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• pp.425-430
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• 2016

Both rapid industrial development and society has achieved more comfortable life. But, behind this facts of this industrial development have current pictures that occur global warming and much more by-products by environmental pollution. Therefore, this study used BFS and CFA as by-products to reduce cement usage emitted at a high rate of $CO_2$ gas, to examine sludge recycling strategy more than 200,000ton emitted at local dyeing complex, we suggest basic data research about non-cement matrix properties of utilizing dyeing sludge carbide. As a result, the more dyeing sludge carbide replacement ratio gets higher, the more air content and flow rise. Also, as the dyeing sludge carbide replacement ratio increase more, flexural strength and compressive strength go down.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.399-406
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• 2009

In this study, Polynomial Radial Basis Function Neural Network(pRBFNN) based on Fuzzy Inference System is designed and its parameters such as learning rate, momentum coefficient, and distributed weight (width of RBF) are optimized by means of Particle Swarm Optimization. The proposed model can be expressed as three functional module that consists of condition part, conclusion part, and inference part in the viewpoint of fuzzy rule formed in 'If-then'. In the condition part of pRBFNN as a fuzzy rule, input space is partitioned by defining kernel functions (RBFs). Here, the structure of kernel functions, namely, RBF is generated from HCM clustering algorithm. We use Gaussian type and Inverse multiquadratic type as a RBF. Besides these types of RBF, Conic RBF is also proposed and used as a kernel function. Also, in order to reflect the characteristic of dataset when partitioning input space, we consider the width of RBF defined by standard deviation of dataset. In the conclusion part, the connection weights of pRBFNN are represented as a polynomial which is the extended structure of the general RBF neural network with constant as a connection weights. Finally, the output of model is decided by the fuzzy inference of the inference part of pRBFNN. In order to evaluate the proposed model, nonlinear function with 2 inputs, waster water dataset and gas furnace time series dataset are used and the results of pRBFNN are compared with some previous models. Approximation as well as generalization abilities are discussed with these results.

• Journal of the Korean Chemical Society
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• v.39 no.4
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• pp.252-256
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• 1995

A study was carried out for the development of direct determination of antimony in high purity copper metal without preseparation. The large excess of copper seemed to be performed as the matrix modifier to enhance the absorbance of antimony in copper metal. Sensitivity enhancement of Sb was obtained at the condition of the ashing temperature of $600^{\circ}C(20s){\sim}700^{\circ}C(10s)$ rather than $1000^{\circ}C(20s){\sim}1100^{\circ}C(10s)$, and the atomization temperature of $2200^{\circ}C.$ The operating pressure of argon gas was readjusted to the optimum condition at 2.0 kg/$cm^2.$ The linearity of the Sb calibration graph could be obtained in the range of 20 ppb up to 200 ppb Sb in existence of 5,000 ppm Cu with the coefficient of variation of about 5%.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.6
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• pp.264-273
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• 2006

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, the design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.3
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• pp.145-156
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• 2000

In this paper, we propose a new methodology which includes the optimal design procedure of Polynomial Neural Networks(PNN) structure for model identification of complex and nonlinear system. The proposed PNN algorithm is based on GMDA(Group Method of Data handling) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and cubic, and is connected as various kinds of multi-variable inputs. In other words, the PNN uses high-order polynomial as extended type besides quadratic polynomial used in GMDH, and the number of input of its node in each layer depends on that of variables used in the polynomial. The design procedure to obtain an optimal model structure utilizing PNN algorithm is shown in each stage. The study is illustrated with the aid of pH neutralization process data besides representative time series data for gas furnace process used widely for performance comparison, and shows that the proposed PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and prediction capabilities of model is evaluated and also discussed.

• Journal of the Korean Ceramic Society
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• v.12 no.1
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• pp.23-28
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• 1975

The object of this study is to find the optimum conditions for crystal growth and kinds of crystal in ZnO-Al2O3-SiO2 glass composition. At first, the base glass composed of ZnO (44.7%), Al2O3(14.0%) and SiO2(41.3%) was melted in propane gas furnace at 1450-150$0^{\circ}C$ for an hour, and then it was poured into the stainless steel mould heated previously at $600^{\circ}C$ to obtain the thin glass test piece. Four crystal forms from base glass such as stuffed keatite, zinc orthosilicate, zinc aluminosilicate, and cristobalite were crystallized during heat treatment between 80$0^{\circ}C$ and 110$0^{\circ}C$. For the investigation of crystal growth, X-ray diffractometer and thermal differential analysis were used and the growth rate of the four crystal forms were obtained by the method of Archimedes specific gravity and intensity comparison of X-ray diffraction peak. The results obtained were as follows. 1) Stuffed keatite peaks which started to appear after two hours at 80$0^{\circ}C$ were maximum after 11 hours and this crystal breaks down to willemite irreversibly at about 100$0^{\circ}C$. 2) Development of gahnite started at 85$0^{\circ}C$ and increased with temperature growth. 3) Stuffed keatite which had been transformed slowly into willemite at 100$0^{\circ}C$ was decreased with time and willemite increased until four hours. 4) Cristobalite began to be developed after treatment of 110$0^{\circ}C$.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.398-406
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• 2011

In this paper, we introduce a new architecture of PSO-based Polynomial Neural Networks (PNN) and discuss its comprehensive design methodology. The conventional PNN is based on a extended Group Method of Data Handling (GMDH) method, and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons located in each layer through a growth process of the network. Moreover it does not guarantee that the conventional PNN generated through learning results in the optimal network architecture. The PSO-based PNN results in a structurally optimized structure and comes with a higher level of flexibility that the one encountered in the conventional PNN. The PSO-based design procedure being applied at each layer of PNN leads to the selection of preferred PNs with specific local characteristics (such as the number of input variables, input variables, and the order of the polynomial) available within the PNN. In the sequel, two general optimization mechanisms of the PSO-based PNN are explored: the structural optimization is realized via PSO whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the PSO-based PNN, the model is experimented with using Gas furnace process data, and pH neutralization process data. For the characteristic analysis of the given entire data with non-linearity and the construction of efficient model, the given entire system data is partitioned into two type such as Division I(Training dataset and Testing dataset) and Division II(Training dataset, Validation dataset, and Testing dataset). A comparative analysis shows that the proposed PSO-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.252.2-252.2
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• 2013

ZnO nanostructures have a lot of interest for decades due to its varied applications such as light-emitting devices, power generators, solar cells, and sensing devices etc. To get the high performance of these devices, the factors of nanostructure geometry, spacing, and alignment are important. So, Patterning of vertically- aligned ZnO nanowires are currently attractive. However, many of ZnO nanowire or nanorod fabrication methods are needs high temperature, such vapor phase transport process, metal-organic chemical vapor deposition (MOCVD), metal-organic vapor phase epitaxy, thermal evaporation, pulse laser deposition and thermal chemical vapor deposition. While hydrothermal process has great advantages-low temperature (less than $100^{\circ}C$), simple steps, short time consuming, without catalyst, and relatively ease to control than as mentioned various methods. In this work, we investigate the dependence of ZnO nanowire alignment and morphology on si substrate using of nanosphere template with various precursor concentration and components via hydrothermal process. The brief experimental scheme is as follow. First synthesized ZnO seed solution was spun coated on to cleaned Si substrate, and then annealed $350^{\circ}C$ for 1h in the furnace. Second, 200nm sized close-packed nanospheres were formed on the seed layer-coated substrate by using of gas-liquid-solid interfacial self-assembly method and drying in vaccum desicator for about a day to enhance the adhesion between seed layer and nanospheres. After that, zinc oxide nanowires were synthesized using a low temperature hydrothermal method based on alkali solution. The specimens were immersed upside down in the autoclave bath to prevent some precipitates which formed and covered on the surface. The hydrothermal conditions such as growth temperature, growth time, solution concentration, and additives are variously performed to optimize the morphologies of nanowire. To characterize the crystal structure of seed layer and nanowires, morphology, and optical properties, X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), Raman spectroscopy, and photoluminescence (PL) studies were investigated.

• Korean Journal of Materials Research
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• v.20 no.3
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• pp.125-131
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• 2010

In this study, Ti powders were fabricated from Ti scrap by the Hydrogenation-Dehydrogenation (HDH) method. The Ti powders were prepared from the spark plasma sintering (SPS) and their microstructure was investigated. Hydrogenation reactions of Ti scrap occurred at near $450^{\circ}C$ with a sudden increase in the reaction temperature and the decreasing pressure of hydrogen gas during the hydrogenation process in the furnace. The dehydrogenation process was also carried out at $750^{\circ}C$ for 2 hrs in a vacuum of $10^{-4}$ torr. After the HDH process, deoxidation treatment was carried out with the Ca (purity: 99.5%) at $700^{\circ}C$ for 2 hrs in the vacuum system. It was found that the oxidation content of Ti powder that was deoxidized with Ca showed noticeably lower values, compared to the content obtained by the HDH process. In order to fabricate the Ti compacts, Ti powder was sintered under an applied uniaxial punch pressure of 40 MPa in the range of $900-1200^{\circ}C$ for 5 min under a vacuum of $10^{-4}$ torr. The relative density of the compact was 99.5% at $1100^{\circ}C$ and the tensile strength decreased with increasing sintering temperature. After sintering, all of the Ti compacts showed brittle fracture behavior, which occurred in an elastic range with short plastic yielding up to a peak stress. Ti improved the corrosion resistance of the Ti compacts, and the Pd powders were mixed with the HDH Ti powders.