• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.566-568
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• 1998

In this paper, we suggest a optimal design method of Fuzzy-Neural Networks model for complex and nonlinear systems. FNNs have the stucture of fusion of both fuzzy inference with linguistic variables and Neural Networks. The network structure uses the simpified inference as fuzzy inference system and the BP algorithm as learning procedure. And we use a clustering algorithm to find initial parameters of membership function. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance index, we use the time series data for gas furnace and the sewage treatment process.

• Korean Chemical Engineering Research
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• v.46 no.2
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• pp.340-347
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• 2008

In the case of a melting process, the velocity of waste-gas has been measured to produce the melt of an equal condition and to analyze the combustion situation of the fuel which was inputted in a furnace. Recently, there are many kinds of measuring equipments of gas-velocity on the market. But, the waste-gas produced from a melting process is high temperature, the slow speed and includes much dust. Existent measuring equipments are not suited to these conditions. Therefore, we made the measuring equipment of new method which is enough detailed to react on the slow speed and sustains in high temperature. As shown in the result of field test, the manufactured measuring equipment is so sensitive as to react on a small change of velocity and senses temperature change rapidly, we expect that this equipment helps in temperature control of a melting furnace.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.18 no.5
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• pp.191-194
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• 2008

We will focus on the horizontal Bridgman growth system to analyze the transport phenomena numerically, because the simple furnace system and the confined growth environment allow for the precise understanding of the transport phenomena in solidification process. In conventional melt growth process, the dopant concentration tends to vary significantly along the crystal. In this work, we propose the modification of crucible geometry for improving the productivity of silicon single-crystal growth by controlling axial specific resistivity distribution. Numerical analysis has been performed to study the transport phenomena of dopant impurities in conventional and proposed Bridgman silicon growth using the finite element method and implicit Euler time integration. It has been demonstrated using mathematical models and by numerical analysis that proposed method is useful for obtaining crystals with superior uniformity along the growth direction at a lower cost than can be obtained by the conventional melt growth process.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.7
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• pp.31-41
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• 1993

In this paper, a fuzzy adaptive controller is proposed for the process with large delay time and unmodelled dynamics. The fuzzy adaptive controller consists of self tuning controller and fuzzy tuning part. The self tuning controller is designed with the continuous time GMV (generalized minimum variance) using emulator and weighted least square method. It is realized by the hybrid method. The controller has robust characteristics by adapting the inference rule in design parameters. The inference processing is tuned according to the operating point of the process having the nonlinear characteristics considering the practical application. We review the characteristics of the fuzzy adaptive controller through the simulation. The controller is applied to practical electric furnace. As a result, the fuzzy adaptive controller shows the better characteristics than the simple numeric self tuning controller and the PI controller.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09a
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• pp.205-206
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• 2006

A ball-shape alumina arc-tube for low-wattage lamp was developed by the PIM process. An ultra high purity translucentgrade alumina powder was used. In injection molding process, a hot-runner type mold was developed. The translucent-grade alumina powder was extremely sensitive to contamination so that the injection molding condition and atmosphere control in the furnace should be taken care of with extreme caution. Contamination sources were pinpointed with EPMA. The arc-tube was molded in half and two halves were bonded in the middle by a new bonding technique at room temperature developed in this study.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1516-1519
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• 1997

With disseminationof Distributed Control Systems(DCS), the huge amounts of process operation data could have been available and led to figure out process behaviors better on the statistical basis. Until now, the statistical modeling technology has been susally applied to process monitoring and fault diagnosis. however, it has been also thought that these process information, extracted from statistical analysis, might serve a great opportunity for process operation improvements and process improvements. This paper proposed a general methodolgy for process operation improvements including data analysis, backing up the result of analysis based on the methodology, and the mapping physical physical phenomena to the Principal Components(PC) which is the most distinguished feature in the methodology form traditional statistical analyses. The application of the proposed methodology to the Balst Furnace(BF) process has been presented for details. The BF process is one of the complicated processes, due to the highly nonlinear and correlated behaviors, and so the analysis for the process based on the mathematical modeling has been very difficult. So the statisitical analysis has come forward as a alternative way for the useful analysis. Using the proposed methodology, we could interpret the complicated process, the BF, better than any other mathematical methods and find the direction for process operation improvement. The direction of process operationimprovement, in the BF case, is to increase the fludization and the permeability, while decreasing the effect of tapping operation. These guide directions, with those physical meanings, could save fuel cost and process operator's pressure for proper actions, the better set point changes, in addition to the assistance with the better knowledge of the process. Open to set point change, the BF has a variety of steady state modes. In usual almost chemical processes are under the same situation with the BF in the point of multimode steady states. The proposed methodology focused on the application to the multimode steady state process such as the BF, consequently can be applied to any chemical processes set point changing whether operator intervened or not.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.83-86
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• 1998

In this paper, a fuzzy model based on the Polynomial Neural Network(PNN) structure is proposed to estimate the emission pattern for air pollutant in power plants. the new algorithm uses PNN algorithm based on Group Mehtod of Data Handling (GMDH) algorithm and fuzzy reasoning in order to identify the premise structure and parameter of fuzzy implications rules, and the least square method in order to identify the optimal consequence parameters. Both time series data for the gas furnace and data for the NOx emission process of gas turbine power plants are used for the purpose of evaluating the performance of the fuzzy model. The simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy and feasibility than other works achieved previously.

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.633-635
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• 2009

It is a quite quality concerning to control temperature of single crystalline growth as it does when you get most of heat treating products. It is also important factor to control temperature when you make the $Al_2O_3$(single crystalline) used to artificial jewels, glass of watches, heat resistant transparent glasses. Thus, it is a major interest to get the proper temperature in accordance with the time process while you are making mixture of oxygen and hydrogen to have the right temperature. In this paper, we will study of electrical valve positioning system for the gas mixture to improve the quality of products.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.259-262
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• 1997

In this paper, a fuzzy model based on the polynomial Neural Network(PNN) structure is proposed to estimate the emission pattern for air pollutant in power plants. The new algorithm uses PNN algorithm based on Group Method of Data Handling (GMDH) algorithm and fuzzy reasoning in order to identify the premise structure and parameter of fuzzy implications rules, and the least square method in order to identify the optimal consequence parameters. Both time series data for the gas furnace and data for the NOx emission process of gas turbine power plants are used for the purpose of evaluating the performance of the fuzzy model. The simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy anhd feasibility than other works achieved previously.

• 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).