• Journal of the Korean Statistical Society
• /
• v.29 no.1
• /
• pp.9-16
• /
• 2000

This article addresses the problem of maximum likelihood estimation in ARCH regression with lagged dependent variables. Some topics in asymptotics of the model such as uniform expansion of likelihood function and construction of a class of MLE are discussed, and the regularity property of MLE is obtained. The error process here is possibly non-Gaussian.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10a
• /
• pp.721-723
• /
• 1987

An Adaptation algorithm is presented and a convergence criterion is derived for parallel model reference adaptive bilinear systems. The output error converges asymptotically to zero, and the parameter estimates are bounded for stable reference models. The convergence criterion depends only upon the input sequence and a priori estimates of the maximum parameter values.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.12 no.4
• /
• pp.694-704
• /
• 1988

A recursive parameter estimation scheme utilizing the variance perturbation method is applied to the workpiece deflection model during CNC turning process, in order to improve the cylindricity of slender workpiece. It features that it is based on exponentially weighted recursive least squares method with post-process measurement of finish surfaces at two locations and it does not require a priori knowledge on the time varying deflection model parameter. The measurements of finish surfaces by using two proximity sensors mounted face to face enable one to identify the straightness, guide-way, run-out eccentricity errors. Preliminary cutting tests show that the straightness error of the finish surface due to workpiece deflection during cutting is most dominant. Identifying the errors and recursive updating the parameter, the off-line control is carried out to compensate the workpiece deflection error, through single pass cutting. Experimental results show that the proposed method is superior to the conventional multi-pass cutting and the direct compensation control in cutting accuracy and efficiency.

• Journal of Environmental Science International
• /
• v.14 no.12
• /
• pp.1155-1161
• /
• 2005

Anoxic-oxic process were analyzed numerically for the each unit and final TN effluent by Water Quality Management(WQM) model and the results were compared data from these sewage or wastewater treatment plants. No bugs and logic error were occurred during simulation work. All of the simulation results tried to two times were obtained and both results were almost same thus this model has good reappearance. A few of simulation results were deviated with measured data because lack of influent water qualities are reported however simulation results have wholly good relationship with measured data. Also each unit of simulation result was kept good relationship with that of measured data therefore this WQM model has good reliance. Finally, WQM model predicts final TN effluent within ${\pm}4.1\;mg/{\ell}$

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2000.10a
• /
• pp.230-235
• /
• 2000

In this paper, a surface generation model is presented to simulate surface roughness profile in turning operation. The simulation model takes into account the effect of tool geometry, process parameters, rotational errors of spindle, and the relative vibration between the cutting tool and workpiece. The surface roughness profiles are simulated based on the surface-shaping system. The model has been verified by comparing the experimental values with the simulation values. It is shown that the surface simulation model can properly predict the surface roughness profile.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.11a
• /
• pp.267-270
• /
• 1997

In this paper, we proposed an optimazation method using Genetic Algorithm for nonlinear system modeling. Fuzzy Neural Network(FNNs) was used as basic model of nonlinear system. FNNs was fused of Fuzzy Inference which has linguistic property and Neural Network which has learning ability and high tolerence level. This paper, We used FNNs which was proposed by Yamakawa. The FNNs was composed Simple Inference and Error Back Propagation Algorithm. To obtain optimal model, parameter of membership function, learning rate and momentum coefficient of FNNs are tuned using genetic algorithm. And we used simplex algorithm additionaly to overcome limit of genetic algorithm. For the purpose of evaluation of proposed method, we applied proposed method to traffic choice process and waste water treatment process, and then obtained more precise model than other previous optimization methods and objective model.

• Journal of electromagnetic engineering and science
• /
• v.17 no.2
• /
• pp.51-56
• /
• 2017

This study proposes a modeling process of equivalent terrains to reduce the computational load and time of a full-wave electromagnetic (EM) simulation. To verify the suitability of the proposed process, an original terrain model with a size of $3m{\times}3m$ is equivalently quantized based on the minimum range resolution of a radar, and the radar image of the quantized model is compared with that of the original model. The results confirm that the simulation time can be reduced from 407 hours to 162 hours without a significant distortion of the radar images, and an average estimation error of the quantized model (20.4 mm) is similar to that of the original model (20.3 mm).

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.7 no.5
• /
• pp.275-280
• /
• 2002

In order to control glucose concentration during fed-batch culture for antibiotic production, we applied so called “software sensor” which estimates unmeasured variable of interest from measured process variables using software. All data for analysis were collected from industrial scale cultures in a pharmaceutical company. First, we constructed an estimation model for glucose feed rate to keep glucose concentration at target value. In actual fed-batch culture, glucose concentration was kept at relatively high and measured once a day, and the glucose feed rate until the next measurement time was determined by an expert worker based on the actual consumption rate. Fuzzy neural network (FNN) was applied to construct the estimation model. From the simulation results using this model, the average error for glucose concentration was 0.88 g/L. The FNN model was also applied for a special culture to keep glucose concentration at low level. Selecting the optimal input variables, it was possible to simulate the culture with a low glucose concentration from the data sets of relatively high glucose concentration. Next, a simulation model to estimate time course of glucose concentration during one day was constructed using the on-line measurable process variables, since glucose concentration was only measured off-line once a day. Here, the recursive fuzzy neural network (RFNN) was applied for the simulation model. As the result of the simulation, average error of RFNN model was 0.91 g/L and this model was found to be useful to supervise the fed-batch culture.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.11 no.3
• /
• pp.146-153
• /
• 2012

RP system which is widely used to reduce the time of product development is to resolve the problem of cutting work. It is a method using laminated thin films to produce many forms. The RP equipment used for this experiment is FDM system. This can produce 3D model with using 3D CAD designed file within a relatively short time. Not only this, this system also through 3D file preparation, 3D product manufacture, removal support these 3 step operating process can easily produce goods, but product can be different from original design. This research has been conducted to minimize this error. To apply to the circular product made a circular specimen and measured several times with 3D scanner and find out average 99.622% of accuracy. This result is applied to RP system, and with this changed design produced a specimen, and found out the accuracy is increased to 99.958%. If this is applied to circular products, we can produce more precise products with less process.

• Proceedings of the KIEE Conference
• /
• 1990.11a
• /
• pp.396-401
• /
• 1990

The objective is to explore design concept for MIESF(Modified Integral error and State Feedback) controller. A method is outlined for designing MIESF controller that provides robust performance despite real parameter uncertainties in the process model. Insight into the design process is gained by viewing the MIESF from the perspective of IMC(Internal Model Control).