• IE interfaces
• /
• v.24 no.2
• /
• pp.151-155
• /
• 2011

In this study, we propose a signomial classification method with ₀-regularization (₀-)which seeks a sparse signomial function by solving a mixed-integer program to minimize the weighted sum of the ₀-norm of the coefficient vector of the resulting function and the $L_1$-norm of loss caused by the function. $SC_0$ gives an explicit description of the resulting function with a small number of terms in the original input space, which can be used for prediction purposes as well as interpretation purposes. We present a practical implementation of $SC_0$ based on the mixed-integer programming and the column generation procedure previously proposed for the signomial classification method with $SL_1$-regularization. Computational study shows that $SC_0$ gives competitive performance compared to other widely used learning methods for classification.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.27 no.7A
• /
• pp.705-710
• /
• 2002

In this paper, we present a method to find motion vectors in frequency domain for video data compression. The proposed algorithm is based on the Format Number Transform (FNT), and it declares the most correlated-block as the best matching block, as opposed to declaring the block with least sum of differences between blocks. We show that the proposed method is equivalent to declaring the block with the minimum L2-norm as the best matching block. Unlike other previous fast algorithms, the time requirement for the proposed algorithm does not defend on the image type for finding the optimum solution.

• 한국지구물리탐사학회:학술대회논문집
• /
• 2007.06a
• /
• pp.130-134
• /
• 2007

Classical full waveform inversion for velocity estimation defines the objective function as the $l^2$ -norm of differences between the modeled and the observed wavefields. Although widely used, the results of this method have been less than satisfactory. A moderate improvement of this method is to define the objective function as the $l^2$ -norm of differences between the logarithms of the modeled and observed wavefields. In this paper we propose new objective functions of waveform inversion. They produce better results in sub-salt imaging than those of the classical and the logarithmic objective functions. One objective function defines the residual as the difference between $L^{th}$ power of the modeled wavefields and that of the observed wavefields. Another defines the residual as the difference between the integral of the $L^{th}$ power of the modeled wavefields and that of the observed wavefields. We apply these new objective functions to the synthetic SEG/EAGE salt model, and show that our new waveform inversion algorithms provide more accurate results than those of the classical and logarithmic waveform inversion methods.

• Journal of Korean Academy of Nursing
• /
• v.33 no.3
• /
• pp.365-375
• /
• 2003

Purpose: This study was aimed to investigate the health related quality of life and related factors of organ transplant recipients. Method: The participants were 188 people who had liver(86), kidney(81), or heart(24) transplanted. Data on the demographic characteristics, transplantation-related characteristics, symptom frequency or discomfort measured by Transplant Symptom Frequency and Symptom Distress Scale by Lough et al(l987), and health related quality of life measured by SF-36(version 2) were collected. Result: Overall health related quality of life score was 492.1 for 100scoring and, 344.9 for norm based. Physical functioning showed the highest quality of life score (77.5) and vitality showed the lowest(51.l). The kidney transplanted showed the highest quality of life (504.4) and the heart transplanted showed the lowest(426.7) Quality of life was related with occupation(p=.016) and symtom discomfort(p < .0001). Conclusion: The health related quality of life of transplated patients was lower than the norm of American. Further studies need to be done to identify the norm of Korean and to investigate the effect of releving symptom discomfort on the increasing the health related quality of life.

• Journal of applied mathematics & informatics
• /
• v.30 no.1_2
• /
• pp.27-47
• /
• 2012

It has become apparent from the recent work by Choi et al. [3] on the nonlinear beam deflection problem, that analysis of the integral operator $\mathcal{K}$ arising from the beam deflection equation on linear elastic foundation is important. Motivated by this observation, we perform investigations on the eigenstructure of the linear integral operator $\mathcal{K}_l$ which is a restriction of $\mathcal{K}$ on the finite interval [$-l,l$]. We derive a linear fourth-order boundary value problem which is a necessary and sufficient condition for being an eigenfunction of $\mathcal{K}_l$. Using this equivalent condition, we show that all the nontrivial eigenvalues of $\mathcal{K}l$ are in the interval (0, 1/$k$), where $k$ is the spring constant of the given elastic foundation. This implies that, as a linear operator from $L^2[-l,l]$ to $L^2[-l,l]$, $\mathcal{K}_l$ is positive and contractive in dimension-free context.

• Current Optics and Photonics
• /
• v.5 no.4
• /
• pp.431-443
• /
• 2021

Sparse unmixing has been proven to be an effective method for hyperspectral unmixing. Hyperspectral images contain rich spectral and spatial information. The means to make full use of spectral information, spatial information, and enhanced sparsity constraints are the main research directions to improve the accuracy of sparse unmixing. However, many algorithms only focus on one or two of these factors, because it is difficult to construct an unmixing model that considers all three factors. To address this issue, a novel algorithm called multiview-based spectral weighted and low-rank row-sparsity unmixing is proposed. A multiview data set is generated through spectral partitioning, and then spectral weighting is imposed on it to exploit the abundant spectral information. The row-sparsity approach, which controls the sparsity by the l_2,0 norm, outperforms the single-sparsity approach in many scenarios. Many algorithms use convex relaxation methods to solve the l_2,0 norm to avoid the NP-hard problem, but this will reduce sparsity and unmixing accuracy. In this paper, a row-hard-threshold function is introduced to solve the l_2,0 norm directly, which guarantees the sparsity of the results. The high spatial correlation of hyperspectral images is associated with low column rank; therefore, the low-rank constraint is adopted to utilize spatial information. Experiments with simulated and real data prove that the proposed algorithm can obtain better unmixing results.

• Journal of the Chungcheong Mathematical Society
• /
• v.25 no.2
• /
• pp.319-329
• /
• 2012

For a norm 1 function ${\sigma}$ in the Fourier-Stieltjes algebra of a locally compact group we define the space of ${\sigma}$-harmonic operators in the non-commutative $L^p$-space associated to the group von Neumann algebra of G. We will investigate some properties of the space and will obtain a precise description of it.

• Kyungpook Mathematical Journal
• /
• v.53 no.2
• /
• pp.295-306
• /
• 2013

First we present the explicit formula for the norm of a symmetric bilinear form on the 2-dimensional real predual of the Lorentz sequence space $d_*(1,w)^2$. Using this formula, we classify the extreme points of the unit ball of $\mathcal{L}_s(^2d_*(1,w)^2)$.

• Kyungpook Mathematical Journal
• /
• v.55 no.1
• /
• pp.119-126
• /
• 2015

First we present the explicit formula for the norm of a (continuous) linear functional of $\mathcal{L}(^2d_*(1,w)^2)^*$. Using this formula and results of [16] and [17], we show that every extreme bilinear form of the unit ball of $\mathcal{L}(^2d_*(1,w)^2)$ is exposed.

• Kyungpook Mathematical Journal
• /
• v.53 no.4
• /
• pp.625-638
• /
• 2013

First we present the explicit formula for the norm of a bilinear form on the 2-dimensional real predual of the Lorentz sequence space $d_*(1,w)^2$. Using this formula, we classify the extreme points of the unit ball of $\mathcal{L}(^2d_*(1,w)^2)$.