• Journal of electromagnetic engineering and science
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• 제8권2호
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• pp.59-63
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• 2008

The adaptive symbol selection scheme(ASSS) is popular in reducing peak to average power ratio(PAPR) for orthogonal frequency division multiplexing(OFDM) signals. The probability density function(pdf) of the samples' amplitudes of the adaptively selected OFDM signal without over-sampling has been considered to be approximately equal to the Rayleigh pdf. In this paper, we derive a more precise pdf which shows the relationship between the probability distribution of the samples' amplitudes and the number of the candidate symbols for ASSS. Using the newly derived pdf in the theoretical analysis, more accurate calculation results can be obtained.

• Communications for Statistical Applications and Methods
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• 제12권3호
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• pp.615-624
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• 2005

In this paper, we review the variable-selection properties of LASSO and SCAD in penalized regression. To improve the weakness of SCAD for high noise level, we propose a new penalty function called MSCAD which relaxes the unbiasedness condition of SCAD. In order to compare MSCAD with LASSO and SCAD, comparative studies are performed on simulated datasets and also on a real dataset. The performances of penalized regression methods are compared in terms of relative model error and the estimates of coefficients. The results of experiments show that the performance of MSCAD is between those of LASSO and SCAD as expected.

• 대한산업공학회지
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• 제15권2호
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• pp.57-64
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• 1989

An Economic selection of specification limits is considered for a given target value in a complete inspection plan. Each item is inspected, and if it meets the specification, it is accepted. Items less than the lower specification limit are scrapped or sold at a reduced price, and those greater than the upper specification limit are reworked. Cost factors to be considered are economic loss caused by quality deviations, rework cost and scrapping cost. Methods for finding the optimal specification limits are given for the cases of piecewise linear loss function and quadratic loss function with illustrative examples.

• International Journal of Precision Engineering and Manufacturing
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• 제1권1호
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• pp.106-110
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• 2000

The researches classifying the artificial flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including user defined function is developed and the total procedure is made up the digital signal processing, feature extraction, feature selection, classfier design. Specially it is composed with and discussed using the ststistical classfier such as the linear discriminant function classfier, the empirical Bayesian classfier.

• Communications for Statistical Applications and Methods
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• 제8권3호
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• pp.719-729
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• 2001

Overlap Maximization is a sampling technique to reduce survey costs and costs associated with the survey. It was first studied by Keyfitz(1951). Ernst(1998) presented a remarkable procedure for maximizing the overlap when the sampling units can be selected for two identical stratified designs simultaneously, But the approach involves mimicking the behaviour of nonlinear function by linear function and so it is less direct, even though the stratification problem for the overlap corresponds directly to the linear programming problem. furthermore, it uses the controlled selection algorithm that repeatedly needs zero-restricted controlled roundings, which are solutions of capacitated transportation problems. In this paper we suggest a comparatively simple procedure to use linear programming in order to maximize the overlap. We show how this procedure can be implemented practically.

• Journal of Positioning, Navigation, and Timing
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• 제8권4호
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• pp.193-200
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• 2019

The auto-encoder network which is a good candidate to handle the modeling of the signal strength attenuation is designed for denoising and compensating the distortion of the received data. It provides a non-linear mapping function by iteratively learning the encoder and the decoder. The encoder is the non-linear mapping function, and the decoder demands accurate data reconstruction from the representation generated by the encoder. In addition, the adaptive network width which supports the automatic generation of new hidden nodes and pruning of inconsequential nodes is also implemented in the proposed algorithm for increasing the efficiency of the algorithm. Simulation results show that the proposed method can improve the neural network training surface to achieve the highest possible accuracy of the signal modeling compared with the conventional modeling method.

• 한국조리학회지
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• 제24권2호
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• pp.1-7
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• 2018

This study aims to find what attributes of consumer satisfaction are more important when consumers choose coffee shops. Factors when customers choose coffee shops that are considered to be more crucial, such as taste, price, brand, and atmosphere, were tested and also relations between satisfaction and revisit were studied. As a result, factors as 'taste', 'price', 'brand', and 'atmosphere' were found to significantly affect satisfaction; in addition, the path that satisfaction leads to revisit was found to be significant. However, consumers' coffee shop selection attributes differed depending on their psychological consumption value. The path-coefficients from taste and price to satisfaction were more significant in the function-oriented group, meanwhile the path-coefficient from brand to satisfaction was significant in the emotion-oriented group (+) and the function-oriented group (-). The results of this study suggest attributes of selecting coffee shops and provide meaningful implications of consumer value when they choose the attributes.

• Journal of the Korean Data and Information Science Society
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• 제28권1호
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• pp.227-235
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• 2017

When the spatial information of each location is given specifically as coordinates it is popular to use the geographically weighted regression to incorporate the spatial information by assuming that the regression parameters vary spatially across locations. In this paper, we relax the linearity assumption of geographically weighted regression and propose a geographically weighted least squares-support vector machine for estimating geographically weighted mean by using the basic concept of kernel machines. Generalized cross validation function is induced for the model selection. Numerical studies with real datasets have been conducted to compare the performance of proposed method with other methods for predicting geographically weighted mean.

• Journal of the Korean Statistical Society
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• 제32권4호
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• pp.425-448
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• 2003

Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. We consider hierarchical models including selection models under a skewed heavy tailed error distribution proposed originally by Chen et al. (1999) and Branco and Dey (2001). These rich classes of models combine the information of independent studies, allowing investigation of variability both between and within studies, and incorporate weight function. Here, the testing for the skewness parameter is discussed. The score test statistic for such a test can be shown to be expressed as the posterior expectations. Also, we consider the detail computational scheme under skewed normal and skewed Student-t distribution using MCMC method. Finally, we introduce one example from Johnson (1993)'s real data and apply our proposed methodology. We investigate sensitivity of our results under different skewed errors and under different prior distributions.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 춘계 학술발표회 논문집
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• pp.7-12
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• 2005

In this paper, we review the variable-selection properties of LASSO and SCAD in penalized regression. To improve the weakness of SCAD for high noise level, we propose a new penalty function called MSCAD which relaxes the unbiasedness condition of SCAD. In order to compare MSCAD with LASSO and SCAD, comparative studies are performed on simulated datasets and also on a real dataset. The performances of penalized regression methods are compared in terms of relative model error and the estimates of coefficients. The results of experiments show that the performance of MSCAD is between those of LASSO and SCAD as expected.