• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.1
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• pp.74-81
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• 2001

The sensorless scheme for Switched Reluctance Motor(SRM) drives must have the robustness and reliability because the noise and error are sensitive. These elements make electrically noisy environments due to the proximity of high current power circuits with small signal electronic circuits when SRM drives. Also, due to the leakage inductances and finite coupling capacitances, these can cause the noise on any low voltage current and voltage measurement circuit. The position estimate error occurs because the current and voltage including the noise are sued as the inputs of sensorless algorithm. In this paper the high robustness and resistance of input noise re described. The fuzzy logic based rotor estimation algorithm and the observer model are used to reduce the tolerance of input data.

• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.1
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• pp.34-42
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• 1992

This paper presents two fast least-squares lattice algorithms for adaptive nonlinear filters equipped with bilinear system models. The lattice filters perform a Gram-Schmidt orthogonalization of the input data and have very good numerical properties. Furthermore, the computational complexity of the algorithms is an order of magnitude snaller than previously algorithm is an order of magnitude smaller than previously available methods. The first of the two approaches is an equation error algorithm that uses the measured desired response signal directly to comprte the adaptive filter outputs. This method is conceptually very simple`however, it will result in biased system models in the presence of measurement noise. The second approach is an approximate least-squares output error solution. In this case, the past samples of the output of the adaptive system itself are used to produce the filter output at the current time. Results of several experiments that demonstrate and compare the properties of the adaptive bilinear filters are also presented in this paper. These results indicate that the output error algorithm is less sensitive to output measurement noise than the squation error method.

• Journal of the Korean Institute of Landscape Architecture
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• v.24 no.3
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• pp.29-41
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• 1996

The purposes of this study were to develope some techniques which can be used in the landscape simulation process using PC based computer grahics. As a result, a couple of utilities were programmed in AutoLISP language. The one(DSLINE.LSP) is to digitize 2-dimensional structuer forms in the interactive mode considering error handling, and the other one (IMPOST.LSP) is for superimposing and visualizing the digitized plan data to 3-dimension solids & surfaces referring to topographic elevations of meshes in digital terrain model. By applying utilities to present site, the followings may be described. 1) The utility DSLINE.LSP for digitizing simplified building structure form were proved to be easy to input data of polygons including orthogonal edges by handling user coordinates system and checking invalid intersection and default colsing. 2) IMPOST.LSP utility for superimposing and visualizing tool were proved to be more complicated and speedy in calculating process compared with a practical application of modeling tool before rendering process in landscape simulation of built environment on topographic relief, on specially mesospace level of assessment.

• Advances in Computational Design
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• v.2 no.3
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• pp.225-240
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• 2017

This paper presents the application of multiple linear regression (MLR) and artificial neural network (ANN) techniques for developing the models to predict the unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) of the fiber reinforced cement stabilized fly ash mixes. UCS and BTS is a highly nonlinear function of its constituents, thereby, making its modeling and prediction a difficult task. To establish relationship between the independent and dependent variables, a computational technique like ANN is employed which provides an efficient and easy approach to model the complex and nonlinear relationship. The data generated in the laboratory through systematic experimental programme for evaluating UCS and BTS of fiber reinforced cement fly ash mixes with respect to 7, 14 and 28 days' curing is used for development of the MLR and ANN model. The data used in the models is arranged in the format of four input parameters that cover the contents of cement and fibers along with maximum dry density (MDD) and optimum moisture contents (OMC), respectively and one dependent variable as unconfined compressive as well as Brazilian tensile strength. ANN models are trained and tested for various combinations of input and output data sets. Performance of networks is checked with the statistical error criteria of correlation coefficient (R), mean square error (MSE) and mean absolute error (MAE). It is observed that the ANN model predicts both, the unconfined compressive and Brazilian tensile, strength quite well in the form of R, RMSE and MAE. This study shows that as an alternative to classical modeling techniques, ANN approach can be used accurately for predicting the unconfined compressive strength and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4A
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• pp.322-331
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• 2010

This paper introduces a new efficient design scheme for spatial multiplexing (SM) systems over closed loop multiple-input multiple-output (MIMO) wireless channels. Extending the orthogonalized spatial multiplexing (OSM) scheme which was developed recently for transmitting two data streams, we propose a new SM scheme where a larger number of data streams can be supported. To achieve this goal, we partition the data streams into several subblocks and execute the block-diagonalization process at the receiver. The proposed scheme still guarantees single-symbol maximum likelihood (ML) detection with small feedback information. Simulation results verify that the proposed scheme achieves a huge performance gain at a bit error rate (BER) of $10^{-4}$ over conventional closed-loop schemes based on minimum mean-square error (MSE) or bit error rate (BER) criterion. We also show that an additional 2.5dB gain can be obtained by optimizing the group selection with extra feedback information.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.855-860
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• 2007

This paper describes a human driver model developed based on finite preview optimal control method. The human driver steering model is constructed to minimize a performance index which is a quadratic form of lateral position error, yaw angle error and steering input. Simulation studies are conducted using a vehicle simulation software, Carsim. The Carsim vehicle model is validated using vehicle test data. In order to validate the human driving steering model, the human driver steering model is compared to the driving data on a virtual test track(VTT) and the actual vehicle test data. It is shown that human driver steering behaviors can be well represented by the human driver steering model presented in this paper

• Journal of the Ergonomics Society of Korea
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• v.27 no.4
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• pp.73-83
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• 2008

With a rapid extension of functions in mobile phones, text input method has become very important for mobile phone users. Previous studies for text input methods were focused on Fitts' law, emphasizing expert's behaviors with one-handed text input method. However, it was observed that 97% of Korean mobile phone users input texts with two-hands. Therefore, this study was designed to develop a prediction model of two-handed Hangul text entry method including novice users as well as experts for mobile phone. For this study, Fitts' law was hypothesized to predict experts' movement time(MT) whereas Hick-Hyman law for visual search time was hypothesized to be added to MT for novices. The results showed that the prediction model was well fitted with the empirical data for both experts and novices with less than 3% error rates. In conclusion, this prediction model of two-handed Hangul text entry including novice users was proven to be a very effective model for modeling two-handed Hangul text input behavior for both experts.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.127-135
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• 2024

The purpose of this paper is to establish the relationship between compression wave velocity and porosity in unsaturated soil using a deep neural network (DNN) algorithm. Input parameters were examined using the error norm method to assess their impact on porosity. Compression wave velocity was conclusively found to have the most significant influence on porosity estimation. These parameters were derived through both field and laboratory experiments using a total of 266 numerical data points. The application of the DNN was evaluated by calculating the mean squared error loss for each iteration, which converged to nearly zero in the initial stages. The predicted porosity was analyzed by splitting the data into training and validation sets. Compared with actual data, the coefficients of determination were exceptionally high at 0.97 and 0.98, respectively. This study introduces a methodology for predicting dependent variables through error norm analysis by disregarding fewer sensitive factors and focusing on those with greater influence.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.572-578
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• 2020

The stock market is affected by unexpected factors, such as politics, society, and natural disasters, as well as by corporate performance and economic conditions. In recent days, artificial intelligence has become popular, and many researchers have tried to conduct experiments with that. Our study proposes an experiment using not only stock-related data but also other various economic data. We acquired a year's worth of data on stock prices, the percentage of foreigners, interest rates, and exchange rates, and combined them in various ways. Thus, our input data became diversified, and we put the combined input data into a nonlinear autoregressive network with exogenous inputs (NARX) model. With the input data in the NARX model, we analyze and compare them to the original data. As a result, the model exhibits a root mean square error (RMSE) of 0.08 as being the most accurate when we set 10 neurons and two delays with a combination of stock prices and exchange rates from the U.S., China, Europe, and Japan. This study is meaningful in that the exchange rate has the greatest influence on stock prices, lowering the error from RMSE 0.589 when only closing data are used.