• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.472-477
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• 2020

The purpose of this study is to correct the error of lower weight input method as an alternative to reduce the specific absorption rate(SAR) in MRI. In order to prove that the SAR values not change according to the weight entered into the patient information, the 50kg phantom is placed in the coil and the input weight is changed from 10 to 100 in 10kg units to compare the SAR values. As a result, T1-weighted images had a SAR rate of 0.2W/kg and T2-weighted images had an average of 0.4W/kg. In conclusions, the SAR does not change according to the weight input by the technician before the scan, a lower weight when inputting patient information cannot be an alternative to reduce the SAR.

• Nuclear Engineering and Technology
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• v.27 no.3
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• pp.336-343
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• 1995

The Self Tuning Regulator(STR) method which is an approach of adaptive control theory, is ap-plied to design the fully automatic power controller of the nonlinear reactor model. The adaptive control represent a proper approach to design the suboptimal controller for nonlinear, time-varying stochastic systems. The control system is based on a third­order linear model with unknown, time-varying parameters. The updating of the parameter estimates is achieved by the recursive extended least square method with a variable forgetting factor. Based on the estimated parameters, the output (average coolant temperature) is predicted one-step ahead. And then, a weighted one-step ahead controller is designed so that the difference between the output and the desired output is minimized and the variation of the control rod position is small. Also, an integral action is added in order to remove the steady­state error. A nonlinear M plant model was used to simulate the proposed controller of reactor power which covers a wide operating range. From the simulation result, the performances of this controller for ramp input (increase or decrease) are proved to be successful. However, for step input this controller leaves something to be desired.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.4
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• pp.302-310
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• 2010

In this paper, we studied Bagging neural network for predicting defect size of steam generator(SG) tube in nuclear power plant. Bagging is a method for creating an ensemble of estimator based on bootstrap sampling. For predicting defect size of SG tube, we first generated eddy current testing signals for 4 defect patterns of SG tube with various widths and depths. Then, we constructed single neural network(SNN) and Bagging neural network(BNN) to estimate width and depth of each defect. The estimation performance of SNN and BNN were measured by means of peak error. According to our experiment result, average peak error of SNN and BNN for estimating defect depth were 0.117 and 0.089mm, respectively. Also, in the case of estimating defect width, average peak error of SNN and BNN were 0.494 and 0.306mm, respectively. This shows that the estimation performance of BNN is superior to that of SNN.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.12
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• pp.1381-1388
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• 2014

This paper is proposed a wavelet-based the order statistics MAD(Median Absolute Deviation) method of SAR(Synthetic Aperture Radar) image for image enhancement. also The method of compared and defined the threshold the wavelet coefficients using MAD of the wavelet coefficients of the detail subbands was proposed to effectively image enhancement. In order to complement the disadvantage, the threshold of the proposed method sets up the image statistic and excludes the distortion. The hardware design is used FPGA of Xilinx and DSP system for the image enhancement and compressed encoding of the proposed algorithm. Therefore the proposed method is totally verified by comparing with the several other images.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.26-30
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• 2019

This paper proposes a new voice activity detection (VAD) method which is based on SNR and non-intrusive speech intelligibility estimation. In the conventional SNR-based VAD methods, voice activity probability is obtained by estimating frame-wise SNR at each spectral component. However these methods lack performance in various noisy environments. We devise a hybrid VAD method that uses non-intrusive speech intelligibility estimation as well as SNR estimation, where the speech intelligibility score is estimated based on deep neural network. In order to train model parameters of deep neural network, we use MFCC vector and the intrusive speech intelligibility score, STOI (Short-Time Objective Intelligent Measure), as input and output, respectively. We developed speech presence measure to classify each noisy frame as voice or non-voice by calculating the weighted average of the estimated STOI value and the conventional SNR-based VAD value at each frame. Experimental results show that the proposed method has better performance than the conventional VAD method in various noisy environments, especially when the SNR is very low.

• Journal of KIISE:Software and Applications
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• v.37 no.3
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• pp.170-176
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• 2010

Prediction is an important step in high-performance lossless data compression. In this paper, we propose a novel lossless image coding algorithm to increase prediction accuracy which can display low-resolution images quickly with a multi-resolution image technique. At each resolution, we use pixels of the previous resolution image to estimate current pixel values. For each pixel, we determine its estimated value by considering horizontal, vertical, diagonal edge information and average, weighted-average information obtained from its neighborhood pixels. In the experiment, we show that our method obtains better prediction than JPEG-LS or HINT.

• Journal of the Korea Society for Simulation
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• v.16 no.4
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• pp.43-55
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• 2007

Moving average(MA) and exponentially weighted moving average(EWMA) are the two most popular control methods in manufacturing. Both methods are optimized under the assumption that the exact control equation is known. This paper focuses on the problems rising from estimation errors. Based on the accuracy of the estimated parameter and the range of the weight parameter $\lambda$, the limitations are identified and the performance of methods are evaluated. Optimal adjustment for process shift with misestimated control model and its application control methods to actual process is researched. The efficiency of proposed method is evaluated through simulation.

• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.1
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• pp.124-130
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• 2012

An injection mold cooling circuit for an automotive front bumper was optimally designed in order to simultaneously minimize the average of the standard deviations of the temperature and the difference in mean temperatures of the upper and lower molds for uniform cooling. The temperature distribution for a specified design was evaluated by Moldflow Insight 2010, a commercial injection molding analysis tool. For efficient design, PIAnO (Process Integration, Automation and Optimization), a commercial PIDO tool, was used to integrate and automate injection molding analysis procedure. The weighted-sum method was used to handle the multi-objective optimization problem and PQRSM, a function-based sequential approximate optimizer equipped in PIAnO, to handle numerically noisy responses with respect to the variation of design variables. The optimal average of the standard deviations and difference in mean temperatures were found to be reduced by 9.2% and 56.52%, respectively, compared to the initial ones.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.6
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• pp.457-463
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• 2007

Various correlations of saturated vapor density in a truncated power series form are tested and compared in this study. Saturated vapor density correlation can be expressed relating logarithmic reduced density to the reduced temperature. Five types of correlation has been investigated using saturated vapor density data for 22 pure substance refrigerants from ASHRAE (American Society of Heating, Reftigerating and Air-Conditioning Engineers, Inc.) property tables and NIST (National Institute of Standards and Technology) Chemistry Webbook. Correlations are fitted to the data points by least squares method. Data points are equally weighted. The best type of correlation among the five types is suggested. The results obtained indicate that the best correlations with 3, 4, and 5 terms yield average AAD's (Average Absolute Deviation) of 0.27%, 0.04%, and 0.02%, respectively, while widely used conventional correlations with 3, 4, and 5 terms yield those of 1.19%, 0.61%, and 0.17%. The suggested type of correlation could reduce the number of terms while improving performance.

• Nuclear Engineering and Technology
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• v.22 no.4
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• pp.410-415
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• 1990

Prompt fission neutron energy spectrum as a function of energies of neutron inducing fission has been calculated en the basis of the Madland-Nix(MN) model. The resultant spectra have been weighted to excitation functions of $^{27}$ Al(n, $\alpha$), $^{32}$ S(n, p) and $^{115}$ In(n, n') threshold reactions in order to get the average cross sections and then spectral indices which are defined as the average cross section ratio for two selective threshold reactions among the above three. It is appeared that spectral indices together with the neutron spectra are varying with energies of neutron inducing fission. This may indicate that the prompt fission neutron energy spectrum can be determined by measuring experimentally the spectral index.