• Journal of Korea Multimedia Society
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• v.23 no.5
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• pp.625-632
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• 2020

In the diagnosis of lung cancer, the tumor size is measured by the longest diameter of the tumor in the entire slice of the CT. In order to accurately estimate the size of the tumor, it is better to measure the volume, but there are some limitations in calculating the volume in the clinic. In this study, we propose an algorithm to segment lung cancer by applying a custom loss function that combines focal loss and dice loss to a U-Net model that shows high performance in segmentation problems in chest CT images. The combination of values of the various parameters in custom loss function was compared to the results of the model learned. The purposed loss function showed F1 score of 88.77%, precision of 87.31%, recall of 90.30% and average precision of 0.827 at α=0.25, γ=4, β=0.7. The performance of the proposed custom loss function showed good performance in lung cancer segmentation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.174-183
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• 2018

In the industrial fields, the process capability index has been using to evaluate the variation of quality in the process. The traditional process capability indices such as $C_p$, $C_{pk}$, $C_{pm}$ and $C^+_{pm}$ have been applied in the industrial fields. These traditional process capability indices are mainly applied in the univariate analysis. However, the main streams in the recent industry are the multivariate manufacturing process and the multiple quality characteristics are corrected each other. Therefore, the multivariate statistical method should be used in the process capability analysis. The multivariate process indices need to be enhanced with more useful information and extensive application in the recent industrial fields. Hence, the purpose of the study is to develop a more effective multivariate process index ($MC_{pI}$) using the multivariate inverted normal loss function. The multivariate inverted normal loss function has the flexibility for the any type of the symmetrical and asymmetrical loss functions as well as the economic information. Especially, the proposed modeling method for the multivariate inverted normal loss function (MINLF) and the expected loss from MINLF in this paper can be applied to the any type of the symmetrical and asymmetrical loss functions. And this modeling method can be easily expanded from a bivariate case to a multivariate case.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.20 no.2 s.33
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• pp.179-186
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• 2007

Objective : Skin barrier protects skin against harmful environment. Its function includes antimicrobial barrier as well as physical barrier. But there are few studies about skin barrier function about men and women who don't have skin disease. The aim of this study is to investigate skin barrier function healthy men and women show. Method : There are many ways to measure skin barrier function, but lately transepidermal water loss is mainly used to know skin barrier function. Transepidermal water loss was measured over eight four the Korean twenties and thirties who don't have skin disease from March 14th, 2007 to March 16th 2007. We surveyed cubital fossa and cheek beside nose to measure transepidermal water loss and used vapometer. Result : After analyzed data, we concluded that cheek showed higher transepidermal water loss than cuital fossa. At cheek, the thirties had higher transepidermal water loss than the twenties. The male got higher transepidermal water loss than the female at cheek and in the average. And temperature and humidity effected on transepidermal water loss. Conclusion: In our study, it was observed that temperature and humidity effected on transepidermal water loss. In traditional korean medicine, the inbalance of cold(寒), heat(熱), dryness(燥), wetness(濕) makes many diseases and the inbalance of them induces dameum(痰飮) and the disorder of blood flow. Dameum(痰飮) and the disorder of blood flow make skin diseases made by abnormal skin barrier function in many ways. So we need to study dameum(痰飮) and the disorder of blood flow to take care of patients who have abnormal skin barrier function.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.749-755
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• 2009

Support vector classification (SVC) provides more complete description of the lin-ear and nonlinear relationships between input vectors and classifiers. In this paper. we propose the sparse kernel classifier to solve the optimization problem of classification with a modified hinge loss function and absolute loss function, which provides the efficient computation and the sparsity. We also introduce the generalized cross validation function to select the hyper-parameters which affects the classification performance of the proposed method. Experimental results are then presented which illustrate the performance of the proposed procedure for classification.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.33-55
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• 2007

The economic world is full of patterns, many of which exert a profound influence over society and business. One of the most contentious is the distribution of wealth. Way back in 1897, an Italian engineer-turned-economist named Vilfredo Pareto discovered a pattern in the distribution of wealth that appears to be every bit as universal as the laws of thermodynamics or chemistry. The present paper proposes some Bayes estimators of shape parameter of Pareto income distribution in censored sampling. Asymmetric LINEX loss function has been considered to study the effects of overestimation and underestimation. For the prior distribution of the parameter involved a number of priors including one and two-parameter exponential, truncated Erlang and doubly truncated gamma have been contemplated to express the belief of the experimenter s/he has regarding the parameter. The estimators thus obtained have been compared theoretically and empirically with the corresponding estimators under squared error loss function, some of which were reported by Bhattacharya et al. (1999).

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.889-898
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• 2002

In decision theoretic estimation, the loss function usually emphasizes precision of estimation. However, one may have interest in goodness of fit of the overall model as well as precision of estimation. From this viewpoint, Zellner(1994) proposed the balanced loss function which takes account of both "goodness of fit" and "precision of estimation". This paper considers estimation of the parameter of a Bernoulli distribution using Zellner's(1994) balanced loss function. It is shown that the sample mean $\overline{X}$, is admissible. More general results, concerning the admissibility of estimators of the form $a\overline{X}+b$ are also presented. Finally, minimax estimators and some numerical results are given at the end of paper,at the end of paper.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.6 s.99
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• pp.680-686
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• 2005

This paper introduces the robust design of an intake system using transmission loss and the frequency weighting function. First, transmission loss is measured to evaluate the performance of the noise reduction for the intake system. The robust design parameters of the intake system are extracted by adapting a cost function with the Taguchi method. Subsequently, the frequency weighting function is developed by the subjective evaluation in which 6 special engineers were participated. Finally, the comparison between the proposed frequency weighted optimal design and unweighted optimal design for the transmission loss as the part is performed. Here, the overall levels of the transmission loss according to the methods are presented to validate the effectiveness of the proposed methodology.

• 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 Korean Society for Quality Management
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• v.31 no.1
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• pp.109-122
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• 2003

This paper presents a robust process capability index(PCI) based on the expected loss derived from the empirical distribution function(EDF). We propose the EDF expected loss in order to develop a PCI that does not depends on the underlying process distribution. The EDF expected loss depends only on the sample data, so the PCI based on it is robust and it does nor require complex calculations. The inverted normal loss function(INLF) is employed in order to overcome the drawback of the quadratic loss which may Increase unboundedly outside the specification limits. A comprehensive simulation study was performed under various process distributions, in order to compare the accuracy and the precision of the proposed PCI with those of the PCI based on the expected loss derived from the normal distribution. The proposed PCI turned out to be more accurate than the normal PCI in most cases, especially when the process distribution has high kurtosis or skewness. It is expected that the proposed PCI can be utilized In real processes where the true distribution family may not be known.

• Journal of the Korean earth science society
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• v.31 no.5
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• pp.437-447
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• 2010

Spatial estimation of environmental variables has been regarded as an important preliminary procedure for decision-making. A minimum variance criterion, which has often been adopted in traditional kriging algorithms, does not always guarantee the optimal estimates for subsequent decision-making processes. In this paper, a geostatistical framework is illustrated that consists of uncertainty modeling via stochastic simulation and risk modeling based on loss functions for the selection of optimal estimates. Loss functions that quantify the impact of choosing any estimate different from the unknown true value are linked to geostatistical simulation. A hybrid loss function is especially presented to account for the different impact of over- and underestimation of different land-use types. The loss function-specific estimates that minimize the expected loss are chosen as optimal estimates. The applicability of the geostatistical framework is demonstrated and discussed through a case study of copper mapping.