• Environmental Engineering Research
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• v.14 no.2
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• pp.102-110
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• 2009

Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.07a
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• pp.58-62
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• 2003

Silicon carbide (SiC) was etched in a $NF_3/CH_4$ inductively coupled plasma. The etch process was modeled by using a neural network called generalized regression neural network (GRNN). For modeling, the process was characterized by a $2^4$ full factorial experiment with one center point. To test model appropriateness, additional test data of 16 experiments were conducted. Particularly, the GRNN predictive capability was drastically improved by a genetic algorithm (GA). This was demonstrated by an improvement of more than 80% compared to a conventionally obtained model. Predicted model behaviors were highly consistent with actual measurements. From the optimized model, several plots were generated to examine etch rate variation under various plasma conditions. Unlike the typical behavior, the etch rate variation was quite different depending on the bias power Under lower bias powers, the source power effect was strongly dependent on induced dc bias. The etch rate was strongly correated to the do bias induced by the gas ratio. Particularly, the etch rate variation with the bias power at different gas ratio seemed to be limited by the etchant supply.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.675-678
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• 1997

In this research we continue the study of nuclear power plant steam generator's intelligent modeling. This model represents the input-output behavior and is a preliminary stage for intelligent control. Among many intelligent models available, we study neural network models that have been proven as universal function approximators. We select multilayer perceptrons, circular backpropagation networks, piecewise linearly trained networks and recurrent neural networks as the candidates for the steam generator's intelligent models. We take the input-output pairs from steam generator's reference model and train the neural network models. We validate trained neural network models as intelligent models of steam generator.

• KIEE International Transactions on Power Engineering
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• v.2A no.4
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• pp.136-144
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• 2002

Load models are very important for improving the accuracy of stability analysis and load flow studies. Various loads are connected to a power bus and their characteristics of power consumption change with voltage and frequency. Thus, the effect of voltage/frequency changes must be considered in load modeling. In this work, artificial neural networks-ANNs- were used to construct the component load models for more accurate modeling. A typical residential load was selected and subjected to a test under variable voltage/frequency conditions. Acquired data were used to construct component models by ANNs. The aggregation process of separately determined load models is also presented in the paper. Furthermore, this paper proposes a method to transform a single load model constructed by the aggregation method into a mathematical load model that can be used in traditional power system analysis software.

• Journal of electromagnetic engineering and science
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• v.11 no.3
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• pp.192-200
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• 2011

This paper proposes a new modeling method for estimating the impedance of an on-chip power/ground meshed plane. Frequency dependent R, L, and C parameters are extracted based on the proposed method so that the model can be applied from DC to high frequencies. The meshed plane model is composed of two parts: coplanar multi strip (CMS) and conductor-backed CMS. The conformal mapping technique and the scaled conductivity concept are used for accurate modeling of the CMS. The developed microstrip approach is applied to model the conductor-backed CMS. The proposed modeling method has been successfully verified by comparing the impedance of RLC circuit based on extracted parameters and the simulated impedance using a 3D-field solver.

• Proceedings of the Korean Nuclear Society Conference
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• 2003.10a
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• pp.211.1-211.1
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• 2003

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.10
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• pp.827-832
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• 2002

We propose a prediction method of the pollutant and a synchronous classification of the current state of SOx emission in the power plant. We use the auto-regressive with exogeneous (ARX) model as a predictor of SOx emission and use a radial basis function network (RBFN) as a pattem classifier. The ARX modeling scheme is implemented using recursive least squares (RLS) method to update the model parameters adaptively. The capability of SOx emission monitoring is utilized with the application of the RBFN classifier. Experimental results show that the ARX model can predict the SOx emission concentration well and ARX modeling parameters can be a good feature for the state monitoring. in addition, its validity has been verified through the power spectrum analysis. Consequently, the RBFN classifier in combination with ARX model is shown to be quite adequate for monitoring the state of SOx emission.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.6
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• pp.42-50
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• 2006

This paper addresses the optimized thermal modeling methodology for spacecraft board level thermal analysis. A direct thermal modeling of external and internal structure of active parts which have high power dissipation is newly proposed, based on conventional plate modeling for Printed Circuit Board(PCB). The parts thermal modeling results were compared with other generic methodologies and verified by thermal vacuum test. This parts thermal modeling was directly applied to thermal analysis of CS(Communication Subsystem) board of HAUSAT-2 small satellite. As a result, it was confirmed that the parts thermal modeling can complement other conventional modeling methodologies. A parts thermal modeling is very effective for thermal control design, since the existing thermal problems can be solved at the parts level in advance.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.10
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• pp.1157-1162
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• 2007

In this paper, E-TDLNN(Expanded-Tapped Delay Line Neural Network) method is suggested to make the model of power amplifier effectively. This method is the one for making the model of power amplifier through the study in neural network to the target value, the measured output spectrum of power amplifier, after adding the external value factor, gate bias, as an invariant input to the TDLNN method which suggested the memory effect of power amplifier effectively. To prove the validity of suggested method, the data at 2 points, 3.45 V and 3.50 V of gate bias range $3.4{\sim}3.6V$ with the 0.01 V step change, are studied and the predicted results at the gate bias 3.40 V, 3.48 V, 3.53 V and 3.60 V shows good coincidence with the measured values.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.212-217
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• 1993

In this paper, the hardening depth in Laser surface hardening process is estimated using a multilayered neural network. Input data of the neural network are surface temperature of five points, power and travelling speed of Laser beam. A FDM(finite difference method) is used for modeling the Laser surface hardening process. This model is used to obtain the network's training data sample and to evaluate the performance of the neural network estimator. The simulational results showed that the proposed scheme can be used to estimate the hardening depth on real time.