• 한국수학교육학회지시리즈B:순수및응용수학
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• 제9권1호
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• pp.81-90
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• 2002

The least informative (favorable) distributions, minimizing Fisher information for a multivariate location parameter, are derived in the parametric class of the exponential-power spherically symmetric distributions under the following characterizing restrictions; (i) a bounded variance, (ii) a bounded value of a density at the center of symmetry, and (iii) the intersection of these restrictions. In the first two cases, (i) and (ii) respectively, the least informative distributions are the Gaussian and Laplace, respectively. In the latter case (iii) the optimal solution has three branches, with relatively small variances it is the Gaussian, them with intermediate variances. The corresponding robust minimax M-estimators of location are given by the $L_2$-norm, the $L_1$-norm and the $L_{p}$ -norm methods. The properties of the proposed estimators and their adaptive versions ar studied in asymptotics and on finite samples by Monte Carlo.

• 대한수학회보
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• 제44권1호
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• pp.125-130
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• 2007

We propose a simple and intuitive method to derive the exact convergence rate of global $L_{2}-norm$ error for strong numerical approximation of stochastic differential equations the result of which has been reported by Hofmann and $M{\"u}ller-Gronbach\;(2004)$. We conclude that any strong numerical scheme of order ${\gamma}\;>\;1/2$ has the same optimal convergence rate for this error. The method clearly reveals the structure of global $L_{2}-norm$ error and is similarly applicable for evaluating the convergence rate of global uniform approximations.

• 한국정보전자통신기술학회논문지
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• 제12권5호
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• pp.521-528
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• 2019

Epilepsy is one of the most prevalent neurological diseases. Electroencephalogram (EEG) signals are widely used for monitoring and diagnosis tool for epileptic seizure. Typically, a huge amount of EEG signals is needed, where they are visually examined by experienced clinicians. In this study, we propose a simple automatic seizure detection framework using intracranial EEG signals. We suggest a sparse approximation based classification (SAC) scheme by solving overdetermined system. L1-norm minimization algorithms are utilized for efficient sparse signal recovery. For evaluation of the proposed scheme, the public EEG dataset obtained by five healthy subjects and five epileptic patients is utilized. The results show that the proposed fast L1-norm minimization based SAC methods achieve the 99.5% classification accuracy which is 1% improved result than the conventional L2 norm based method with negligibly increased execution time (42msec).

• Journal of applied mathematics & informatics
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• 제5권3호
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• pp.735-748
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• 1998

We analyze the error in the p version of the of the finite element method when the effect of the quadrature error is taken in the load vector. We briefly study some results on the $H^{1}$ norm error and present some new results for the error in the $L^{2}$ norm. We inves-tigate the quadrature error due to the numerical integration of the right hand side We present theoretical and computational examples showing the sharpness of our results.

• 지구물리와물리탐사
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• 제9권1호
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• pp.44-49
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• 2006

이 연구에서는 $CO_2$ 주입 모니터링에서의 전기비저항 토모그래피(ERT)의 분해능에 대해 조사한다. $CO_2$ 주입체 양쪽의 케이싱 없는 시추공에서 단극자 및 양극자 전극 배열이 사용된다. 주입 전 및 주입에 따른 3단계 모델에 대한 반응이 계산되고, 잡음이 더해진 후의 이론 값들이 L1 과 L2 norm을 사용해 역산된다. 역산결과는 어느 정도 수준이상의 $CO_2$ 부피가 확실히 감지됨을 보여준다. 대부분의 경우에 L1 norm의 경우가 L2에 비해 우월함이 판명되었다. 역산 결과를 주입 전 모델의 역산결과로 정규화하면 전기비저항이 변화하는 부분의 훌륭한 영상을 보여주며, 비저항의 전체적인 변화를 통합해서 판단하면 전체 주입된 부피에 대한 타당한 측정이 이루어짐이 입증된다.

• 자원환경지질
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• 제28권4호
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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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• 제3권3호
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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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• 제16권2호
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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.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2000년도 봄 학술발표논문집 Vol.27 No.1 (A)
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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.

• 물과 미래
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• 제22권3호
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• pp.315-322
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• 1989

해양수치모형에 적용되는 상이한 방사조건을 포함한 개방경계조건의 영향이 $L^{2_}$-norm과 RMS오차해석에 의하여 비교되었다. 수치실험에서는 M2조석, 격자망의 영향, 해저마찰의 영향 등이 각각 고려되었다. 연구결과 개선방사조건이 고려될 때 단순한 구형만에서 해석해와 비교된 $M_2$조석의 경우는 방사조건이 고려되지 않을때보다 $L^{2_}$-norm에 의하면 40%, RMS오차에 의하면 96%나 신뢰성이 향상되었다. 이는 반격자를 이용할 때 보다도 더욱 만족스러운 결과인 것으로 나타났다. 해저마찰이 고려된 경우도 개선방사조건의 도입이 필요한 것으로 판단되었다.