• The KIPS Transactions:PartC
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• v.12C no.4 s.100
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• pp.609-616
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• 2005

Signature-based Intrusion Detection has many false positive and many difficulties to detect new and changed attacks. Alpha-cut is introduced which reduces false positive with a combination of signature-based IDS and machine learning-based IDS in prior paper [1]. This research is a study of a succession of Alpha-cut, and we introduce Beta-rick in which attacks can be detected but cannot be detected in single signature-based detection. Alpha-cut is a way of increasing detection accuracy for the signature based IDS, Beta-pick is a way which decreases the case of treating attack as normality. For Alpha-cut and Beta-pick we use XIBL as a learning algorithm and also show the difference of result of Sd.5. To describe the value of proposed method we apply Alpha-cut and Beta-pick to signature-based IDS and show the decrease of false alarms.

• Journal of Korean Ophthalmic Optics Society
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• v.6 no.1
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• pp.107-110
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• 2001

In order to estimate the UV-A line's cut efficiency it lens, it was measured the light transmittance in the 320~400nm wavelength regions. We obtained the area to integrate the light transmittance of standard CR-39 and sample in the 320~400nm wavelength regions and established ${\alpha}$, ${\beta}$ of UV-A line's cut efficiency by contrast its area. We obtained the absolute cut efficiency ${\alpha}$ CR=0.59 value of CR-39 Lens, and if ${\alpha}$ was the absolute cut efficiency value of the lens to estimate UV-A cut effect, the relative cut efficiency ${\beta}$ was $1.69{\alpha}$. It was obtained the absolute and the relative UV exclusion index through each $a=(1-{\alpha}){\times}100%$, $b=(1-{\beta}){\times}100%$.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.14 no.2
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• pp.150-155
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• 2014

The measurement of $17{\alpha}$-hydroxyprogesterone ($17{\alpha}$-OHP) in a dried blood spot on filter paper is an important for screening of congenital adrenal hyperplasia (CAH). Since high levels of $17{\alpha}$-OHP are frequently observed in premature infants without congenital adrenal hyperplasia, we evaluated cuts-off based on birth weight and performed validation. Birth weight and $17{\alpha}$-OHP concentration data of 292,204 newborn screening subjects in Greencross labopratories were analyzed. The cut-off values based on birth weight were newly evaluated and validated with the original data. The mean $17{\alpha}$-OHP concentration were 7.25 ng/mL in very low birth weight (VLBW) group, 4.02 ng/mL in low birth weight (LBW) group, 2.53 g/mL in normal birth weight (NBW) group, and 2.24 ng/mL in heavy birth weight (HBW) group. The cut-offs for CAH were decided as follows: 21.12 ng/mL for VLBW and LBW groups and 11.14 ng/mL for NBW and HBW groups. When applied new cut-offs for original data, positive rates in VLBW and LBW groups were decreased and positive rates in NBW and HBW groups were increased. The cut-offs based on birth weight should be used in the screening for CAH. We believe that our new cut-off reduce the false positive rate and false negative rate and our experience for cut-off set up and validation will be helpful for other laboratories doing newborn screening test.

• Journal of Korean Society of Steel Construction
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• v.9 no.3 s.32
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• pp.457-465
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• 1997

The Shape optimization makes it possible to reduce the weight of structure and cost then member sizing optimization. A vast amount of imprecise information is existed in constraints of the optimum design. It is very difficult and sometimes confusing to describe and to deal with the several criteria which contain fuzzy degrees of relatives importance. This paper proposed weighting strategies in the multiobjective shape optimization of fuzzy structural system by ${\alpha}$-cut approach. The algorithm in this research is numerically tested for 2-bar truss structure. The result show that. the user can choose the one optimum solution in practices as obtaining the optimum solutions according to the ${\alpha}$-cut approach, weight of volume and displacement.

• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.681-691
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• 2006

Clustering is one of widely used methods for grouping thousands of genes by their similarities of expression levels, so that it helps to analyze gene expression profiles. This method has been used for identifying the functions of genes. Fuzzy clustering method, which is one category of clustering, assigns one sample to multiple groups according to their degrees of membership. This method is more appropriate for analyzing gene expression profiles because single gene might involve multiple genetic functions. Clustering methods, however, have the problems that they are sensitive to initialization and can be trapped into local optima. To solve these problems, this paper proposes an evolutionary fuzzy clustering method, where adaptive a-cut based evaluation is used for the fitness evaluation to apply different criteria considering the characteristics of datasets to overcome the limitation of Bayesian validation method that applies the same criterion to all datasets. We have conducted experiments with SRBCT and yeast cell-cycle datasets and analyzed the results to confirm the usefulness of the proposed method.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.89-96
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• 2006

In this paper, we propose two approaches for the character segmentation on Korean document images. One is an improved version of a projection profile-based algorithm. It involves estimating the number of characters, obtaining the split points and then searching for each character's boundary, and selecting the best segmentation result. The other is developed for low quality document images where adjacent characters are connected. In this case, parts of the projection profile are cut to resolve the connection between the characters. This is called ${\alpha}$-cut. Afterwards, the revised former segmentation procedure is conducted. The two approaches have been tested with 43,572 low-quality Korean word images punted in various font styles. The segmentation accuracies of the former and the latter are 91.81% and 99.57%, respectively. This result shows that the proposed algorithm using a ${\alpha}$-cut is effective for low-quality Korean document images.

• Communications of the Korean Mathematical Society
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• v.26 no.1
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• pp.115-124
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• 2011

In this paper, some fuzzy fixed point theorems for fuzzy mappings are established by considering the nonempty closed $\alpha$-cut sets. Some importance observations are also discussed. Our results clearly extend, generalize and improve the corresponding results in the literatures, which have given most of their attention to the class of fuzzy sets with nonempty compact or closed and bounded $\alpha$-cut sets.

• Journal of Korean Ophthalmic Optics Society
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• v.6 no.2
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• pp.17-21
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• 2001

We analyzed the luminance in the visual light region and the size of pupil by the luminance to estimate an UV-A line cut efficiency in the lens. The size of pupil by the luminance(L) was given by ${\Phi}=d-e{\cdot}tanh(f{\cdot}logL)$ and the transmittance efficiency value of a size of pupil was given by $T_r(r)=1-gr^2+hr^4$. We derived the absolute cut efficiency value ${\alpha}$ and the exclusion index $b=(1-{\alpha}){\times}100%$ about the UV-A in the $320{\sim}400nm$ regions. The ${\alpha}$ and b values were obtained respectively 0.018, 0.31, 0.273, 0.153 and 98, 69, 72, 85% of Uv-cut Lens, CR-39, red color and blue color.

• Journal of applied mathematics & informatics
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• v.31 no.1_2
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• pp.35-43
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• 2013

In this paper, we introduce the notion of $t$-convex vague sets and study their properties in details.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.133-140
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• 2007

The support vector machine has been well developed as a powerful tool for solving classification problems. In many real world applications, each training point has a different effect on constructing classification rule. Lin and Wang (2002) proposed fuzzy support vector machines for this kind of classification problems, which assign fuzzy memberships to the input data and reformulate the support vector classification. In this paper another intuitive approach is proposed by using the fuzzy ${\alpha}-cut$ set. It will show us the trend of classification functions as ${\alpha}$ changes.