• Geomechanics and Engineering
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• 제34권5호
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• pp.547-559
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• 2023

We evaluated the applicability of machine learning techniques and the Kuz-Ram model for predicting the mean fragmentation size in open-pit mines. The characteristics of the in-situ rock considered here were uniaxial compressive strength, tensile strength, rock factor, and mean in-situ block size. Seventy field datasets that included these characteristics were collected to predict the mean fragmentation size. Deep neural network, support vector machine, and extreme gradient boosting (XGBoost) models were trained using the data. The performance was evaluated using the root mean squared error (RMSE) and the coefficient of determination (r²). The XGBoost model had the smallest RMSE and the highest r² value compared with the other models. Additionally, when analyzing the error rate between the measured and predicted values, XGBoost had the lowest error rate. When the Kuz-Ram model was applied, low accuracy was observed owing to the differences in the characteristics of data used for model development. Consequently, the proposed XGBoost model predicted the mean fragmentation size more accurately than other models. If its performance is improved by securing sufficient data in the future, it will be useful for improving the blasting efficiency at the target site.

• ETRI Journal
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• 제46권4호
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• pp.708-715
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• 2024

We propose a 10-GHz 2 × 2 phased-array radio frequency (RF) receiver with an 8-bit linear phase and 15-dB gain control range using 65-nm complementary metal-oxide-semiconductor technology. An 8 × 8 phased-array receiver module is implemented using 16 2 × 2 RF phased-array integrated circuits. The receiver chip has four single-to-differential low-noise amplifier and gain-controlled phase-shifter (GCPS) channels, four channel combiners, and a 50-Ω driver. Using a novel complementary bias technique in a phase-shifting core circuit and an equivalent resistance-controlled resistor-inductor-capacitor load, the GCPS based on vector-sum structure increases the phase resolution with weighting-factor controllability, enabling the vector-sum phase-shifting circuit to require a low current and small area due to its small 1.2-V supply. The 2 × 2 phased-array RF receiver chip has a power gain of 21 dB per channel and a 5.7-dB maximum single-channel noise-figure gain. The chip shows 8-bit phase states with a 2.39° root mean-square (RMS) phase error and a 0.4-dB RMS gain error with a 15-dB gain control range for a 2.5° RMS phase error over the 10 to10.5-GHz band.

• Journal of applied mathematics & informatics
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• 제25권1_2호
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• pp.345-351
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• 2007

Using more precise majorizing sequences we provide a finer convergence analysis than before [1], [7] of Newton's method in Riemannian manifolds with the following advantages: weaker hypotheses, finer error bounds on the distances involved and a more precise information on the location of the singularity of the vector field.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 하계학술대회 논문집
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• pp.825-828
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• 1991

This paper addresses estimation of the relative shift vector from aerial image sequences. We perform similarity function tests and decide the most appropriate similarity function for the visual navigation system using aerial images. Finally, we propose the maximum variance reference line selection method for reducing the estimation error of the shift vector.

• 한국통신학회논문지
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• 제39A권3호
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• pp.155-157
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• 2014

In this paper, a novel codebook generation strategy is proposed. With the given codebooks, two codeword selection procedures are proposed and analyzed for generating the quantized multiple-input multiple-output (MIMO) channel state information (CSI). Furthermore, three different quantization and normalization strategies are analyzed. The simulation results suggest that the proposed 'quantized channel generation method 2' is the best strategy to reduce the quantization and normalization errors to generate the final quantized MIMO CSI.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.191-192
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• 2008

We give new definition of the effective-leakage and the signal to effective-leakage plus noise ratio (SELNR) to consider receiver combining motivated by the leakage. We propose a method to find jointly beamforming vector and combining vector for the two linear receivers (maximal ratio combining (MRC) receiver and minimum mean square error (MMSE) receiver) based on the SELNR.

• Advances in Computational Design
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• 제5권3호
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• pp.291-304
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• 2020

In this paper, we develop models to design the airfoil using Multilayer Feed-forward Artificial Neural Network (MFANN) and Support Vector Regression model (SVR). The aerodynamic coefficients corresponding to series of airfoil are stored in a database along with the airfoil coordinates. A neural network is created with aerodynamic coefficient as input to produce the airfoil coordinates as output. The performance of the models have been evaluated. The results show that the SVR model yields the lowest prediction error.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 전력전자학술대회 논문집
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• pp.217-220
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• 1998

In this paper, for high performance as drive, in the speed sensorless vector control of induction motor, introduced flux estimator of voltage model and error compensation algorithm using closed loop integration method, and then we proposed a improved flux estimation method of high accuracy. And the rotor speed is estimating using the stator current and the estimated flux, it is used speed information. The proposed scheme is verified through digital simulations and experiments for 3.7[kW] induction motor and shows good dynamic performance.

• Journal of the Korean Statistical Society
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• 제27권1호
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• pp.1-10
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• 1998

In the problem of estimating a vector of unknown regression coefficients under the sum of squared error losses in a hierarchical linear model, we propose the hierarchical Bayes estimator of a vector of unknown regression coefficients in a hierarchical linear model, and then prove the admissibility of this estimator using Blyth's (196\51) method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2149-2151
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• 2002

In this paper, a novel speed estimation method of an induction motor using neural networks(NNs) is presented. The NN speed estimator is trained online by using the error backpropagation algorithm, and the training starts simultaneously with the induction motor working. The neural network based vector controller has the advantage of robustness against machine parameter variation. The simulation results using Matlab/Simulink verify the useful of the proposed method.