• Proceedings of the Korean Reliability Society Conference
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• 2000.04a
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• pp.185-191
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• 2000

A survival variable is a nonnegative random variable X with distribution function F and a survival function (equation omitted)=1－F. This variable is said to be New Better than Used of specified age $t_{0}$ if (equation omitted) for all $\chi$$\geq$0 and a fixed to. We propose the test for $H_{0}$ : (equation omitted) for all $\chi$$\geq$0 against $H_1$：(equation omitted) for all $\chi$$\geq$0 when the specified age $t_{0}$ is unknown but can be estimated from the data when $t_{0}$=${\mu}$, the mean of F, and also when $t_{0}$=$\xi_p$, the pth percentile of F. This test statistic, which is based on a linear function of the order statistics from the sample, is readily applied in the case of small sample. Also, this test statistic is more simple than the test statistic of Ahmad's test statistic (1998). Finally, the performance of this test is presented.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.3
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• pp.17-29
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• 1995

Recently, neural network models have been employed as an alternative to regression analysis for point estimation or function fitting in various field. Thus far, however, no theoretical or empirical guides seem to exist for selecting the tool which the most suitable one for a specific function-fitting problem. In this paper, we evaluate performance of three major function-fitting techniques, regression analysis and two neural network models, back-propagation and linear-Hebbian-learning neural networks. The functions to be fitted are simple linear ones of a single independent variable. The factors considered are size of noise both in dependent and independent variables, portion of outliers, and size of the data. Based on comutational results performed in this study, some guidelines are suggested to choose the best technique that can be used for a specific problem concerned.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1557-1563
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• 2015

Multiclass classification is typically performed using the voting scheme method based on combining binary classifications. In this paper we propose multiclass classification method for large data, which can be regarded as the revised one-vs-all method. The multiclass classification is performed by using the hat matrix of least squares support vector machine (LS-SVM) ensemble, which is obtained by aggregating individual LS-SVM trained on each subset of whole large data. The cross validation function is defined to select the optimal values of hyperparameters which affect the performance of multiclass LS-SVM proposed. We obtain the generalized cross validation function to reduce computational burden of cross validation function. Experimental results are then presented which indicate the performance of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.4
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• pp.277-282
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• 2014

Hybrid transformer means the distribution transformer, one of multi-function transformer equipped with the enhancement function of harmonics and unbalance. In other words, since existing transformer are equipped simply with transformation function only, a separated electric facility should be additionally installed in order to reduce the mutual harmonics and unbalance. Meanwhile, since hybrid transformer can perform the reduction of harmonics and unbalance as well as transformation function simultaneously through zigzag winding, it doesn't need any additional electric facility. This study addressed the issue of developing and testing the performance of a generator that would reduce the harmonics supplied to the grid when grid-connected with the output of a photovoltaic inverter for photovoltaic generation system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.10
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• pp.2660-2669
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• 1996

In this paper, it is called nonlinear-symbol CPFSK(NCPFSK) which is modulated by the nonlinear function of information carrying phase function within all symbol interval produce time invariant trellis structure. In general, the bit error probability performance of CPFSK modultion scheme within given signal constellation is determined from the number of memory elementsof continuous phase encoder, i.e. number of state. In this paper the number of state of analyticall designed NCPFSK is time invariant. And the nonlinear symbol mapping function of the proposed moudlation produces the nonlinear symbol andthe phase state of the modulation for updating the phase function of NCPFSK. It si shown in this paper nonlinear symbol CPFSK with multiple TCM to make further improvements in d$^{2}$, and analyzed BER performance in AWGN channel envioronments.hannel envioronments.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.813-816
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• 1995

The shape and width of fuzzy membership function has an effect on performance of fuzzy controller. In this paper, neuro-fuzzy controller is proposed to improve the control performance of fuzzy controller. It has membership function, that is adapt to plant constant by using trained neural network. This neural network has been trained with back propagation algorithm. To show the effectiveness of proposed neuro-fuzzy controller with adaptive membership function, it is applied to plant (dead time + 1st order) with various plant constant.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1319-1325
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• 2021

It is important to remove fog for accurate object recognition and detection during preprocessing because images taken in foggy adverse weather suffer from poor quality of images due to scattering and absorption of light, resulting in poor performance of various vision-based applications. This paper proposes an end-to-end deep learning-based single image de-fogging method using U-Net architecture. The loss function used in the algorithm is a loss function based on Mahalanobis distance with fog features, which solves the problem of domain shifts, and demonstrates superior performance by comparing qualitative and quantitative numerical evaluations with conventional methods. We also design it to generate fog through the VGG19 loss function and use it as the next training dataset.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.54-60
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• 2021

Various digital devices were supplied throughout the Fourth Industrial Revolution. Accordingly, the importance of data processing has increased. Data processing significantly affects equipment reliability. Thus, the importance of data processing has increased, and various studies have been conducted on this topic. This study proposes a modified Gaussian filter algorithm based on a fuzzy membership function. The proposed algorithm calculates the Gaussian filter weight considering the standard deviation of the filtering mask and computes an estimate according to the fuzzy membership function. The final output is calculated by adding or subtracting the Gaussian filter output and estimate. To evaluate the proposed algorithm, simulations were conducted using existing additive white Gaussian noise removal algorithms. The proposed algorithm was then analyzed by comparing the peak signal-to-noise ratio and differential image. The simulation results show that the proposed algorithm has superior noise reduction performance and improved performance compared to the existing method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.72-74
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• 2021

The deep learning process currently utilized in various fields consists of data preparation, data preprocessing, model generation, model learning, and model evaluation. In the process of model learning, the loss function compares the value of the model with the actual value and outputs the difference. In this paper, we analyze various loss functions used in the deep learning model for biomarker extraction, which measure the degree of loss of neural network output values, and try to find the best loss function through experiments.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.40-44
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• 2023

The defect detection based on deep learning requires minimal loss and high accuracy to pinpoint product defects. In this paper, we confirm the loss rate of deep learning training based on disc-shaped artificial scaffold images. It is intended to compare the performance of Cross-Entropy functions used in object detection algorithms. The model was constructed using normal, defective artificial scaffold images and category cross entropy and sparse category cross entropy. The data was repeatedly learned five times using each loss function. The average loss rate, average accuracy, final loss rate, and final accuracy according to the loss function were confirmed.