• 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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.1205-1208
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• 1996

This paper proposes balanced model reduction of non-minimum phase plant. The algorithm presented in this paper is to convert high-order non-minimum phase plant into low-oder minimum phase plant using balanced model reduction. Balanced model reduction requires the error bound that Hankel singular value produces. This algorithm shows the tolerance that admits the method of this paper.

• Journal of applied mathematics & informatics
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• 제22권1_2호
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• pp.373-385
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• 2006

To solve a class of nonlinear parameter estimation problems, a method combining the regularized structured nonlinear total least norm (RSNTLN) method and parameter separation scheme is suggested. The method guarantees the convergence of parameters and has an advantages in reducing the residual norm over the use of RSNTLN only. Numerical experiments for two models appeared in signal processing show that the suggested method is more effective in obtaining solution and parameter with minimum residual norm.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2008년도 춘계학술대회 논문집
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• pp.606-609
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• 2008

Deconvolution is one of the most used techniques for processing seismic reflection data. It is applied to improve temporal resolution by wavelet shaping and removal of short period reverberations. Several deconvolution algorithms such as predicted, spike, minimum entropy deconvolution and so on has been proposed to obtain such above purposes. Among of them, $\iota_1$ norm proposed by Taylor et al., (1979) and used to compared to minimum entropy deconvolution by Sacchi et al., (1994) has given some advantages on time computing and high efficiency. Theoritically, the deconvolution can be considered as inversion technique to invert the single seismic trace to the reflectivity, but it has not been successfully adopted due to noisy signals of the real data set and unknown source wavelet. After stacking, the seismic traces are moved to zero offset, thus each seismic traces now can be a single trace that is created by convolving the seismic source wavelet and reflectivity. In this paper, the fundamental of $\iota_1$ norm deconvolution method will be introduced. The method will be tested by synthetic data and applied to improve the stacked section of gas hydrate.

• 대한수학회지
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• 제46권5호
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• pp.967-977
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• 2009

There are three standard weight functions on a linear code viz. Hamming weight, Lee weight, and Euclidean weight. Euclidean weight function is useful in connection with the lattice constructions [2] where the minimum norm of vectors in the lattice is related to the minimum Euclidean weight of the code. In this paper, we obtain an upper bound over the number of parity check digits for Euclidean weight codes detecting and correcting burst errors.

• 조명전기설비학회논문지
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• 제21권4호
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• pp.96-103
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• 2007

In this paper, we present a novel and numerically efficient algorithm for high resolution TOA(Time Of Arrival) estimation under indoor radio propagation channels. The proposed algorithm is not dependent on the structure of receivers, i.e, it can be used with either coherent or non-coherent receivers. The TOA estimation algorithm is based on a high resolution frequency estimation algorithm of Minimum-norm. The efficiency of the proposed algorithm relies on numerical analysis techniques in computing signal or noise subspaces. The algorithm is based on the two step procedures, one for transforming input data to frequency domain data and the other for estimating the unknown TOA using the proposed efficient algorithm. The efficiency in number of operations over other algorithms is presented. The performance of the proposed algorithm is investigated by means of computer simulations.. Throughout the analytic and computer simulation results, we show that the proposed algorithm exhibits superior performance in estimating TOA estimation with limited computational cost.

• Progress in Superconductivity
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• 제4권1호
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• pp.48-52
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• 2002

When applying a SQUID system for diagnosing heart disease, it is informative to obtain the source current distributions from the measured MCG (magnetocardiogram) signals since the bioelectric activity in the heart is generally represented by distributed current sources. In order to estimate the Primary current distribution in a heart, the minimum norm estimate was computed, assuming a source plane below the chest surface. In the simulation, current distributions, which were computed for the test dipoles represented well the essential feature of the test-current configurations. Source current reconstruction was performed for MCG signal of a healthy volunteer, which was recorded using a 40-channel SQUID system in a magnetically shielded room. It was found that the obtained current distribution is consistent with the electrical activity in a heart.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3194-3216
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• 2018

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method inspired by biological mechanism. In this paper, a novel method called Two Dimensional Slow Feature Discriminant Analysis via $L_{2,1}$ norm minimization ($2DSFDA-L_{2,1}$) is proposed. $2DSFDA-L_{2,1}$ integrates $L_{2,1}$ norm regularization and 2D statically uncorrelated constraint to extract discriminant feature. First, $L_{2,1}$ norm regularization can promote the projection matrix row-sparsity, which makes the feature selection and subspace learning simultaneously. Second, uncorrelated features of minimum redundancy are effective for classification. We define 2D statistically uncorrelated model that each row (or column) are independent. Third, we provide a feasible solution by transforming the proposed $L_{2,1}$ nonlinear model into a linear regression type. Additionally, $2DSFDA-L_{2,1}$ is extended to a bilateral projection version called $BSFDA-L_{2,1}$. The advantage of $BSFDA-L_{2,1}$ is that an image can be represented with much less coefficients. Experimental results on three face databases demonstrate that the proposed $2DSFDA-L_{2,1}/BSFDA-L_{2,1}$ can obtain competitive performance.

• 지구물리와물리탐사
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• 제9권1호
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• pp.121-128
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• 2006

헬리콥터를 이용한 항공자력탐사는 정해진 고도를 따라 지표면에 평행하게 이루어지지만, 고해상도 탐사에서는 특히 측정이 이루어지는 고도가 너무 변화하여 평탄면으로 간주할 수 없는 경우가 있다. 이 연구에서는 모서리 효과를 조절할 수 있도록 주변 자력원이 포함되는 등가원 방법을 이용하여 이러한 자료를 변환하는 방법을 개발하였고, 3차원적으로 무작위하게 분포하는 점의 자료를 직접적으로 모델화하였다. 이 문제는 일반적으로 under-determined 이지만 CG 법은 최소 norm 해를 찾을 수 있으며, 자력이상을 자력원과 연관시키는 조화함수를 선택할 자유가 있는데, 상향연속 함수 연산자가 선택되면 등가원 자체가 자력이상이 된다. 기본자기장의 방향으로의 자기 쌍극자분포를 자력원으로 선택하면, 자기 쌍극자의 방향을 수직으로 돌려줌으로써 쉽게 자극화 변환 이상을 유도할 수 있다.

• 지구물리와물리탐사
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• 제20권2호
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• pp.78-87
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• 2017

MWNI (Minimum Weighted Norm Interpolation)를 이용한 내삽 방법은 고차원으로 확장이 용이하고 상대적으로 계산 속도가 빠르다는 장점을 가지고 있으나 알리아스 효과가 존재하는 자료의 내삽에 취약하다. 이런 문제의 개선을 위해 제안된 방법이 모델제약(model-constrained) MWNI이다. 이 논문에서는 MWNI를 이용한 방법과 모델제약 MWNI 방법의 두가지 모듈을 개발한 후 알리아스 효과가 존재하는 자료의 내삽 결과를 비교하였다. 시공간 영역(t-x domain)과 주파수-파수 영역(f-k domain)의 결과 그림을 통해서 모델제약 MWNI를 적용했을 때의 결과가 더 효과적임을 확인할 수 있었다. 동해 울릉분지의 가스 하이드레이트 부존 지역의 현장 자료에 내삽을 적용한 결과, 가스침니 구간 전후로 진폭이 급격하게 변하는 자료에서도 내삽이 가능함을 확인할 수 있었다. 또한 매우 불규칙하고 넓은 구간에서 누락된 인공지진파 자료의 정규화를 통해 신호의 연결성 향상이 가능함을 보일 수 있었다. 결과적으로 이 논문에서 개발된 모듈은 현장의 다양한 여건에 의해 불규칙하거나 넓은 간격으로 얻어진 탄성파 자료의 정규화나 내삽에 효율적으로 활용될 수 있음을 확인하였다.