• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.15 no.4
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• pp.289-300
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• 2002

In this study, the experiments are carried out by boron ion implantation at energies ranging from 700keV to 2MeV in silicon. The distribution of boron profiles are measured by SIMS(Cameca 6f). Boron dopants profiles after high temp]erasure annealing are also explained by comparisons of experimental and simulated data. A new electronic stopping model for Monte Carlo simulation of high energy implantation is presented. Also the comparisons of profiles by profiles boron ion implantations are demonstrated and interpreted with theoretical models. Finally range moments of SIMS and SRP profiles are calculated and compared with simulation results.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.778-780
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• 2004

This paper presents a computing system, called MEDICOS. that enables Multidisciplinary Design Optimization (MDO) technology for engineering design on distributed environments. In MDO, various legacy softwares have to be Integrated, so dynamic configuration and seamless coordination between these legacy softwares must be supported. MEDICOS is designed to address these issues by the Linda shared memory model-based design and the agent-based wrapper technology. A prototype system for engineering designs is developed and tested with designing a super high temperature vacuum furnace.

• Journal of the Korea Institute of Building Construction
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• v.7 no.1 s.23
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• pp.71-77
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• 2007

This paper is to investigate the effect of the curing temperature on strength development of concrete incorporating cement kiln dust(CKD) and blast furnace slag (BS) quantitatively. Estimation of the compressive strength of the concrete was conducted using the equivalent age equation and the rate constant model proposed by Carino. Correction of Carino model was studied to secure the accuracy of strength development estimation by introducing correction factors regarding rate constant and age. An increasing curing temperature results in an increase in strength at early age, but with the elapse of age, strength development at high curing temperature decreases compared with that at low curing temperature. Especially, the use of BS has a remarkable strength development at early age and even at later age, high strength is maintained due to accelerated pozzolanic activity resulting from high temperature. Whereas, at low curing temperature, the use of BS leads to a decrease in compressive strength. Accordingly, much attention should be paid to prevent strength loss at low temperature. Based on the strength development estimation using equivalent age equation, good agreements between measured strength and calculated strength are obtained.

• Journal of Biosystems Engineering
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• v.37 no.1
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• pp.36-43
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• 2012

Purpose: Drying characteristics of the sliced carrot and green pumpkin were investigated by using the waste heat dryer. Methods: The effects of drying temperature ($T$) and slice thickness affecting drying time were analyzed. Mathematical models for the drying curves were determined with statistical analysis of drying data. Effective diffusivity was determined for the slices of carrot and green pumpkin under various drying conditions. Results: Drying time was reduced at the drying conditions of thinner slice and higher drying temperature. Moisture ratio ($MR$) according to drying time ($t$) was well presented as an exponential function at all of drying conditions for the slices of carrot and green pumpkin with the determination coefficient ($r^2$) of >0.99. The values of effective diffusivity ($D_{ff}$) of the slices for carrot and green pumpkin were increased with increasing the drying temperature. The relationship between Ln($D_{ff}$) and $1/T$ was linear with the determination coefficient ($r^2$) of >0.97. Conclusions: Drying model was well established as an exponential function at all of drying conditions for drying samples.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2108-2116
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• 2008

The paper concerns Fuzzy C-Means clustering based Radial Basis Function neural networks (FCM-RBFNN) and the optimization of the network is carried out by means of Particle Swarm Optimization(PSO). FCM-RBFNN is the extended architecture of Radial Basis Function Neural Network(RBFNN). In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM - RBFNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Weighted Least Square Estimator(WLSE) are used to estimates the coefficients of polynomial. Since the performance of FCM-RBFNN is affected by some parameters of FCM-RBFNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the PSO is exploited to carry out the structural as well as parametric optimization of FCM-RBFNN. Moreover The proposed model is demonstrated with the use of numerical example and gas furnace data set.

• Tribology and Lubricants
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• v.34 no.5
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• pp.175-182
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• 2018

Accumulation of coal ash at the boiler wall reduces combustion and fuel efficiency. The design of a wall blower is important to effectively remove coal ash. We present numerical results for the removal of coal ash from boiler walls of domestic coal-fired power plants, associated with the computational fluid dynamics for the flow from spray nozzle to boiler wall. The numerical model simulates an erosion process in which the multiphase fluid comprising saturated vapor and fluid water is sprayed from the nozzle, and the water particles impact the boiler wall. We adopt the Finnie erosion model for water particles. We obtain the erosion rate density as a function of nozzle angle and its injection angle. As excessive coal ash removal usually induces damage to the boiler wall, the removal operation typically focuses on a large area with uniform depth rather than the maximum removal of coal ash at a specific location. In order to estimate the removal performance of the wall blower nozzle considering several functionality and reliability factors, we evaluate the optimal injection and nozzle angles with respect to the biggest cumulative and highest erosion rates, as well as the widest range and lowest standard deviation of the erosion rate distribution.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.1
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• pp.49-57
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• 2013

The present study was conducted to investigate phosphate removal from aqueous solution using industrial wastes, red mud (RM), acid treated red mud (ATRM) and converter furnace steel slag (CFSS). The chemical composition of adsorbents was analyzed using X-ray fluorescence (XRF). Batch experiments and elution experiments using water tank were performed to examine environmental factors that influences on phosphate removal. Kinetic sorption data of RM, ATRM, and CFSS were described well by the pseudo second-order kinetic sorption model, and equilibrium sorption data of all adsorbents obeyed Freundlich isotherm model. The adsorption capacities of adsorbents followed order: ATRM (7.06 mg/g)>RM (4.34 mg/g)>CFSS (1.88 mg/g). Increasing pH from 3 to 11, the amount of adsorbed phosphate on all RM, ATRM, and CFSS were decreased. The presence of sulfate and carbonate decreased the phosphate removal of RM and ATRM but did not influence on the performance of CFSS. The phosphate removal of RM, ATRM, and CFSS was greater in seawater than deionized water, resulting from the presence of cations in seawater. The water tank elution experiments showed that RM capping blocked the elution of phosphate effectively. It was concluded that the adsorbents can be successfully used for the removal of the phosphate from the aqueous solutions.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.96-105
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• 1997

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed FPNN(Fuzzy Polynomial Neural Network) modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) method and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH method and regression polynomial fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnace and those for wastewater treatment process are used for the purpose of evaluating the performance of the proposed FPNN modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.04b
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• pp.30-33
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• 2004

Shallow p+-n junctions were formed by low-energy ion implantation and dual-step annealing processes The dopant implantation was performed into the crystalline substrates using $BF_2$ ions. The annealing was performed with a rapid thermal processor and a furnace. FA+RTA annealing sequence exhibited better junction characteristics than RTA+FA thermal cycle from the viewpoint of junction depth. A new simulator is designed to model boron diffusion in silicon, which is especially useful for analyzing the annealing process subsequent to ion implantation. The model which is used in this simulator takes into account nonequilibrium diffusion, reactions of point defects, and defect-dopant pairs considering their charge states, and the dopant inactivation by introducing a boron clustering reaction. Using a resonable parameter values, the simulator covers not only the equilibrium diffusion conditions but also the nonequilibrium post-implantation diffusion. Using initial conditions and boundary conditions, coupled diffusion equation is solved successfully. The simulator reproduced experimental data successfully.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2856-2858
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• 1999

In this paper, the FPNN(Fuzzy Polynomial Neural Networks) algorithm with multi-layer fuzzy inference structure is proposed for the model identification of a complex nonlinear system. The FPNN structure is generated from the mutual combination of PNN (Polynomial Neural Network) structure and fuzzy inference method. The PNN extended from the GMDH(Group Method of Data Handling) uses several types of polynomials such as linear, quadratic and modifled quadratic besides the biquadratic polynomial used in the GMDH. In the fuzzy inference method, simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used Each node of the FPNN is defined as a fuzzy rule and its structure is a kind of fuzzy-neural networks. Gas furnace data used to evaluate the performance of our proposed model.