• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.25-28
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• 2011

In this paper, we study some properties of condition for fuzzy data, agrement index by ratio of area and the uniformly most powerful fuzzy test of hypothesis. Also, we suggest a confidence bound for uniformly most powerful fuzzy test. For illustration, we take the most powerful critical fuzzy region from exponential distribution by likelihood ratio and test the hypothesis of ${\chi}^2$-distribution by agreement index.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.7
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• pp.1074-1084
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• 1999

In this paper the bit error probability of noncoherent M-ary FSK system is derived from the probability density function of the envelope of the received signal over a frequency-nonselective slowly m-distributed Nakagami fading channel. Also, the performance of coherent M-ray FSK system is theoretically analyzed, using union bound over the similar fading channel. Then we compare uncoded performance results for M-ary FSK and DPSK signals in Nakagami fading channel.

• Communications of the Korean Mathematical Society
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• v.36 no.4
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• pp.715-727
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• 2021

In this paper, we give some results an upper bound of Hankel determinant of H₂(1) for the classes of 𝓝 (𝜶). We get a sharp upper bound for H₂(1) = c₃ - c²₂ for 𝓝 (𝜶) by adding z₁, z₂, …, z_n zeros of f(z) which are different than zero. Moreover, in a class of analytic functions on the unit disc, assuming the existence of angular limit on the boundary point, the estimations below of the modulus of angular derivative have been obtained. Finally, the sharpness of the inequalities obtained in the presented theorems are proved.

• Communications of the Korean Mathematical Society
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• v.34 no.2
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• pp.451-464
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• 2019

In the present paper, we find the sharp bound for the fourth coefficient of starlike functions f which are normalized by f(0) = 0 = f'(0) - 1 and satisfy the following two-sided inequality: $$1+{\frac{{\gamma}-{\pi}}{2\;{\sin}\;{\gamma}}}\;<\;{\Re}\{{\frac{zf^{\prime}(z)}{f(z)}}\}\;<\;1+{\frac{{\gamma}}{2\;{\sin}\;{\gamma}}},\;z{\in}{\mathbb{D}}$$, where ${\mathbb{D}}:=\{z{\in}{\mathbb{C}}:{\left|z\right|}<1\}$ is the unit disk and ${\gamma}$ is a real number such that ${\pi}/2{\leq}{\gamma}<{\pi}$. Moreover, the sharp bound for the fifth coefficient of f defined above with ${\gamma}$ in a subset of [${\pi}/2,{\pi}$) also will be found.

• Bulletin of the Korean Mathematical Society
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• v.58 no.1
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• pp.205-215
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• 2021

In [1], Beznosova proved that the bound on the norm of the dyadic paraproduct with b ∈ BMO in the weighted Lebesgue space L2(w) depends linearly on the Ad2 characteristic of the weight w and extrapolated the result to the Lp(w) case. In this paper, we provide the weighted norm estimates of the dyadic paraproduct πb with b ∈ VMO and reduce the dependence of the Ad2 characteristic to 1/2 by using the property that for b ∈ VMO its mean oscillations are vanishing in certain cases. Using this result we also reduce the quadratic bound for the commutators of the Calderón-Zygmund operator [b, T] to 3/2.

• Journal of the Korean Mathematical Society
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• v.59 no.2
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• pp.217-233
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• 2022

Although machine learning shows state-of-the-art performance in a variety of fields, it is short a theoretical understanding of how machine learning works. Recently, theoretical approaches are actively being studied, and there are results for one of them, margin and its distribution. In this paper, especially we focused on the role of margin in the perturbations of inputs and parameters. We show a generalization bound for two cases, a linear model for binary classification and neural networks for multi-classification, when the inputs have normal distributed random noises. The additional generalization term caused by random noises is related to margin and exponentially inversely proportional to the noise level for binary classification. And in neural networks, the additional generalization term depends on (input dimension) × (norms of input and weights). For these results, we used the PAC-Bayesian framework. This paper is considering random noises and margin together, and it will be helpful to a better understanding of model sensitivity and the construction of robust generalization.

• The Pure and Applied Mathematics
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• v.30 no.1
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• pp.35-42
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• 2023

In this paper, we find a bound for all the zeros of a polynomial in terms of its coefficients similar to the bound given by Montel (1932) and Kuneyida (1916) as an improvement of Cauchy's classical theorem. In fact, we use a generalized version of Hölder's inequality for obtaining various interesting bounds for all the zeros of a polynomial as function of their coefficients.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.2
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• pp.204-213
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• 2016

A differential game theory based approach is used to develop an automated maneuver generation algorithm for Within Visual Range (WVR) air-to-air combat of unmanned combat aerial vehicles (UCAVs). The algorithm follows hierarchical decisionmaking structure and performs scoring function matrix calculation based on differential game theory to find the optimal maneuvers against dynamic and challenging combat situation. The score, implying how much air superiority the UCAV has, is computed from the predicted relative geometry, relative distance and velocity of two aircrafts. Security strategy is applied at the decision-making step. Additionally, a barrier function is implemented to keep the airplanes above the altitude lower bound. To shorten the simulation time to make the algorithm more real-time, a moving horizon method is implemented. An F-16 pseudo 6-DOF model is used for realistic simulation. The combat maneuver generation algorithm is verified through three dimensional simulations.

• Journal of the Korea Convergence Society
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• v.7 no.5
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• pp.213-219
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• 2016

This paper presents a branch-and-bound algorithm for solving the concave minimization problem with upper bounded variables whose single constraint is linear. The algorithm uses simplex as partition element. Because the convex envelope which most tightly underestimates the concave function on the simplex is uniquely determined by solving the related linear equations. Every branching process generates two subsimplices one lower dimensional than the candidate simplex by adding 0 and upper bound constraints. Subsequently the feasible points are partitioned into two sets. During the bounding process, the linear programming problems defined over subsimplices are minimized to calculate the lower bound and to update the incumbent. Consequently the simplices which do certainly not contain the global minimum are excluded from consideration. The major advantage of the algorithm is that the subproblems are defined on the one less dimensinal space. It means that the amount of work required for the subproblem decreases whenever the branching occurs. Our approach can be applied to solving the concave minimization problems under knapsack type constraints.

• Journal of Veterinary Science
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• v.22 no.3
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• pp.38.1-38.17
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• 2021

Background: The feline viral rhinotracheitis, calicivirus, and panleukopenia (FVRCP) vaccine, prepared from viruses grown in the Crandell-Rees feline kidney cell line, can induce antibodies to cross-react with feline kidney tissues. Objectives: This study surveyed the prevalence of autoantibodies to feline kidney tissues and their association with the frequency of FVRCP vaccination. Methods: Serum samples and kidneys were collected from 156 live and 26 cadaveric cats. Antibodies that bind to kidney tissues and antibodies to the FVRCP antigen were determined by enzyme-linked immunosorbent assay (ELISA), and kidney-bound antibody patterns were investigated by examining immunofluorescence. Proteins recognized by antibodies were identified by Western blot analysis. Results: The prevalences of autoantibodies that bind to kidney tissues in cats were 41% and 13% by ELISA and immunofluorescence, respectively. Kidney-bound antibodies were observed at interstitial cells, apical border, and cytoplasm of proximal and distal tubules; the antibodies were bound to proteins with molecular weights of 40, 47, 38, and 20 kDa. There was no direct link between vaccination and anti-kidney antibodies, but positive antibodies to kidney tissues were significantly associated with the anti-FVRCP antibody. The odds ratio or association in finding the autoantibody in cats with the antibody to FVRCP was 2.8 times higher than that in cats without the antibody to FVRCP. Conclusions: These preliminary results demonstrate an association between anti-FVRCP and anti-cat kidney tissues. However, an increase in the risk of inducing kidney-bound antibodies by repeat vaccinations could not be shown directly. It will be interesting to expand the sample size and follow-up on whether these autoantibodies can lead to kidney function impairment.