• Geophysics and Geophysical Exploration
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• v.9 no.1
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• pp.44-49
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• 2006

In this study, the resolution capabilities of electrical resistivity tomography (ERT) in the monitoring of $CO_2$ injection are investigated. The pole-pole and bipole-bipole electrode configuration types are used between two uncased boreholes straddling the $CO_2$ plume. Forward responses for an initial pre-injection model and three models for subsequent stages of $CO_2$ injection are calculated for the two different electrode configuration types, noise is added and the theoretical data are inverted with both L1- and L2-norm optimisation. The results show that $CO_2$ volumes over a certain threshold can be detected with confidence. The L1-norm proved superior to the L2-norm in most instances. Normalisation of the inverted models with the pre-injection inverse model gives good images of the regions of changing resistivity, and an integrated measure of the total change in resistivity proves to be a valid measure of the total injected volume.

• Economic and Environmental Geology
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• v.28 no.4
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• pp.433-438
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• 1995

The most popular minimization method is based on the least-squares criterion, which uses the $L_2$ norm to quantify the misfit between observed and synthetic data. The solution of the least-squares problem is the maximum likelihood point of a probability density containing data with Gaussian uncertainties. The distribution of errors in the geophysical data is, however, seldom Gaussian. Using the $L_2$ norm, large and sparsely distributed errors adversely affect the solution, and the estimated model parameters may even be completely unphysical. On the other hand, the least-absolute-deviation optimization, which is based on the $L_1$ norm, has much more robust statistical properties in the presence of noise. The solution of the $L_1$ problem is the maximum likelihood point of a probability density containing data with longer-tailed errors than the Gaussian distribution. Thus, the $L_1$ norm gives more reliable estimates when a small number of large errors contaminate the data. The effect of outliers is further reduced by M-fitting method with Cauchy error criterion, which can be performed by iteratively reweighted least-squares method.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.271-282
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• 1996

this paper deal with the estimation of the power spectral density function of time series. A kernel estimator which is based on local average is defined and the rates of convergence of the pointwise, $$L_2$-norm; and; $L{\infty}$-norm associated with the estimator are investigated by restricting as to kernels with suitable assumptions. Under appropriate regularity conditions, it is shown that the optimal rate of convergence for 0$N^{-r}$ both in the pointwiseand $$L_2$-norm, while; $N^{r-1}(logN)^{-r}$is the optimal rate in the $L{\infty}-norm$. Some examples are given to illustrate the application of main results.

• Journal of the Korean Statistical Society
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• v.16 no.2
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• pp.80-91
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• 1987

Minimum $L_i$ norm estimation is a robust procedure ins the sense that it leads to an estimator which has greater statistical eficiency than the least squares estimator in the presence of outliers. And the $L_1$ norm estimator has some desirable statistical properties. In this paper a new computational procedure for $L_1$ norm estimation is proposed which combines the idea of reweighted least squares method and the linear programming approach. A modification of the projective transformation method is employed to solve the linear programming problem instead of the simplex method. It is proved that the proposed algorithm terminates in a finite number of iterations.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.9-21
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• 2015

The support vector machine has been successfully applied to various classification areas due to its flexibility and a high level of classification accuracy. However, when analyzing imbalanced data with uneven class sizes, the classification accuracy of SVM may drop significantly in predicting minority class because the SVM classifiers are undesirably biased toward the majority class. The weighted $L_2$-norm SVM was developed for the analysis of imbalanced data; however, it cannot identify irrelevant input variables due to the characteristics of the ridge penalty. Therefore, we propose the weighted $L_1$-norm SVM, which uses lasso penalty to select important input variables and weights to differentiate the misclassification of data points between classes. We demonstrate the satisfactory performance of the proposed method through simulation studies and a real data analysis.

• Journal of the Korean Mathematical Society
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• v.44 no.1
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• pp.109-127
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• 2007

We classify complete, locally homogeneous metrics with finite volume on four-dimensional manifolds which are critical points for the squared $L^2-norm$ functionals of either the full Riemannian curvature tensor or the Weyl curvature tensor defined on the space of Riemannian metrics.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.4
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• pp.20-28
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• 2008

We propose a radial image processing method in a discrete polar coordinate system based on L1-norm. For this purpose, we first verified that the polar coordinate based on L2-norm can not exist in discrete system and then develop a method converting the Cartesian coordinate to the discrete polar coordinate. We apply the proposed method to smooth mass images of breast tissue and to detect the boundaries of extremely deformable objects. Compared to the Gaussian smoothing method performed in the Cartesian coordinate system, the proposed method stabilized the image signal while maintaining the overall radial shape of mass images. The proposed boundary detection method can detect shapes with high precision while conventional edge detectors can not accurately detect the shape of deformable objects. We also exploit the method to perform pupil detection and have had good experimental results.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04a
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• pp.692-694
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• 2000

본 논문에서는 이진 트리 형태를 가지는 다관절체의 균형을 잡거나 이진 트리 모양으로 연결된 네트워크 상에서 단말 노드들의 부하를 균형 있게 하는데 이용할 수 있는 무게 있는 리프 이진 트리 균형 문제를 제안한다. 또한 무게 있는 리프 이진 트리 균형 문제를 리프들의 무게 변화량의 쌍의 {{{{ { l}_{ 1} }}}}-norm, {{{{ { l}_{2 } }}}}-norm, {{{{ { l}_{3 } }}}}-norm 각각을 최소로 하면서 해결하는 방법들을 제안한다. 이 방법들은 무게 있는 리프 이진 트리 균형 문제의 특성을 이용하여 n개 변수를 하나의 변수의 양의 상수배로 나타냄으로써 해결할 수 있음을 보인다.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.124-129
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• 2007

For seismic imaging and inversion, the inverted image depends on how we define the objective function. ${\ell}^1$-norm is more robust than ${\ell}^2$-norm. However, it is difficult to apply the Newton-type algorithm directly because the partial derivative for ${\ell^1$-norm has a singularity. In our paper, to overcome the difficulties of singularities, Huber function given by hybrid ${\ell}^1/{\ell}^2$-norm is used. We tested the robustness of our new object function with several noisy data set. Numerical results show that the new objective function is more robust to band limited spiky noise than the conventional object function.

• Bulletin of the Korean Mathematical Society
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• v.60 no.1
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• pp.171-184
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• 2023

In this paper, we show that a complete translating soliton Σ^m in ℝⁿ for the mean curvature flow is stable with respect to weighted volume functional if Σ satisfies that the L^m norm of the second fundamental form is smaller than an explicit constant that depends only on the dimension of Σ and the Sobolev constant provided in Michael and Simon [12]. Under the same assumption, we also prove that under this upper bound, there is no non-trivial f-harmonic 1-form of L²_f on Σ. With the additional assumption that Σ is contained in an upper half-space with respect to the translating direction then it has only one end.