• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.6
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• pp.1522-1532
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• 1996

In this paper, we compare the performance of channel estimators with the L$_{1}$-norm and L$_{2}$-norm criteria in impulaive noise environment, and show than the L$_{1}$-norm criterion is appropriate for that situation. Also, it is shown that the performance of the conventional maximum likelihood sequence detector(MLSD) can be improved by applying the same principle to mobile channels. That is, the performance of the conventional MLSD, which is known to be optimal under the Gaussian noise assumption, degrades in the impulsive noise of radio mobile communication channels. So, we proposed the MLSD which can reduce the effect of impulsive noise effectively by applying the results of channel estimators. Finally, it is confirmed by computer simulation that the performance of MLSD is significantly affected depending on the types of branch metrics, and that, in the impulsive noise environments, the proposed one with new branch metrics performs better thatn the conventional branch metric, l y(k)-s(k) l$^{[-992]}$ .

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1403-1417
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• 2019

With the rapid growth of multimedia data, research on cross-media feature learning has significance in many applications, such as multimedia search and recommendation. Existing methods are sensitive to noise and edge information in multimedia data. In this paper, we propose a semi-supervised method for cross-media feature learning by means of $L_{2,q}$ norm to improve the performance of cross-media retrieval, which is more robust and efficient than the previous ones. In our method, noise and edge information have less effect on the results of cross-media retrieval and the dynamic patch information of multimedia data is employed to increase the accuracy of cross-media retrieval. Our method can reduce the interference of noise and edge information and achieve fast convergence. Extensive experiments on the XMedia dataset illustrate that our method has better performance than the state-of-the-art methods.

• Honam Mathematical Journal
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• v.42 no.3
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• pp.569-576
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• 2020

An element x ∈ E is called a norming point of P ∈ P(ⁿE) if ║x║ = 1 and |P(x)| = ║P║. For P ∈ P(ⁿE), we define Norm(P) = {x ∈ E : x is a norming point of P}. Norm(P) is called the norming set of P. We classify Norm(P) for P ∈ 𝒫(²𝑙²_∞).

• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.477-485
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• 2020

Finite element (FE) model based structural damage detection (SDD) methods play vital roles in effectively locating and quantifying structural damages. Among these methods, structural model updating should be conducted before SDD to obtain benchmark models of real structures. However, the characteristics of updating parameters are not reasonably considered in existing studies. Inspired by the l_∞ norm regularization, a novel anti-sparse representation method is proposed for structural model updating in this study. Based on sensitivity analysis, both frequencies and mode shapes are used to define an objective function at first. Then, by adding l_∞ norm penalty, an optimization problem is established for structural model updating. As a result, the optimization problem can be solved by the fast iterative shrinkage thresholding algorithm (FISTA). Moreover, comparative studies with classical regularization strategy, i.e. the l₂ norm regularization method, are conducted as well. To intuitively illustrate the effectiveness of the proposed method, a 2-DOF spring-mass model is taken as an example in numerical simulations. The updating results show that the proposed method has a good robustness to measurement noises. Finally, to further verify the applicability of the proposed method, a six-storey aluminum alloy frame is designed and fabricated in laboratory. The added mass on each storey is taken as updating parameter. The updating results provide a good agreement with the true values, which indicates that the proposed method can effectively update the model parameters with a high accuracy.

• Communications of the Korean Mathematical Society
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• v.18 no.2
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• pp.263-280
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• 2003

We prove weighted L$^{p}$ estimates with respect to the non-isotropic norm for the (equation omitted)-equation on real ellipsoids, where weights are powers of the distance to the boundary. The non-isotropic norm is smaller than the usual norm, by a factor which is equal to the distance to the boundary in the complex tangential component and which is equal to the m-th root of the distance to the boundary in the complex normal component. Here n is the maximal order of contact of the boundary of the real ellipsoid with complex analytic curves.

• Journal of Information Technology Services
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• v.2 no.2
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• pp.111-119
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• 2003

In this paper, an efficient block matching algorithm which is based on the Block Sum Pyramid Algorithm (BSPA) is presented. The cost of BSPA[1] was reduced in the proposed algorithm by using l2 norm and partial distortion elimination technique. Motion estimation accuracy of the proposed algorithm is equal to that of BSPA. The efficiency of the proposed algorithm was verified by experimental results.

• Communications of the Korean Mathematical Society
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• v.29 no.2
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• pp.263-267
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• 2014

We study the growth rate of $H^1$ Sobolev norm of the solutions to Gross-Neveu and Thirring equations. A well-known result is the double exponential rate. We show that the $H^1$ Sobolev norm grows at most an exponential rate exp($ct^2$).

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.829-835
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• 2012

When the input features are generated by factors in a classification problem, it is more meaningful to identify important factors, rather than individual features. The $F_{\infty}$-norm support vector machine(SVM) has been developed to perform automatic factor selection in classification. However, the $F_{\infty}$-norm SVM may suffer from estimation inefficiency and model selection inconsistency because it applies the same amount of shrinkage to each factor without assessing its relative importance. To overcome such a limitation, we propose the adaptive $F_{\infty}$-norm ($AF_{\infty}$-norm) SVM, which penalizes the empirical hinge loss by the sum of the adaptively weighted factor-wise $L_{\infty}$-norm penalty. The $AF_{\infty}$-norm SVM computes the weights by the 2-norm SVM estimator and can be formulated as a linear programming(LP) problem which is similar to the one of the $F_{\infty}$-norm SVM. The simulation studies show that the proposed $AF_{\infty}$-norm SVM improves upon the $F_{\infty}$-norm SVM in terms of classification accuracy and factor selection performance.

• Communications of the Korean Mathematical Society
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• v.9 no.2
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• pp.303-315
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• 1994

Let X be a real Banach space with norm ∥ㆍ∥. Let T > 0, r ≥a be fixed constants. We denote by L/sup p/ the usual L/sup p/( -r, 0; X) with norm ∥ㆍ∥/sub p/ for 1 ≤p < ∞. Our object is to study the existence of solutions of nonlinear functional evolution equations of the type (FDE) x'(t) ＋ A(t)x(t) = G(t, x/sub t/), 0 ≤t ≤T, x/sub 0/ = ø.(omitted)

• Korean Journal of Mathematics
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• v.14 no.2
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• pp.137-160
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• 2006

We describe the weight functions for which Hardy's inequality of nonincreasing functions is satisfied. Further we characterize the pairs of weight functions $(w,v)$ for which the Laplace transform $\mathcal{L}f(x)={\int}^{\infty}_0e^{-xy}f(y)dy$, with monotone function $f$, is bounded from the weighted Lebesgue space $L^p(w)$ to $L^q(v)$.