• 호남수학학술지
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• 제40권4호
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• pp.719-732
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• 2018

In this manuscript, hybrid fixed point results for four maps using (E.A) and tangential properties in the setting of metric space are studied. Application to system of functional equations is also studied.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(C)
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• pp.393-396
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• 2011

In this paper, we propose a constrained alternating least squares nonnegative matrix factorization algorithm (cALSNMF) to detect active brain regions from single subject's task-related fMRI data. In cALSNMF, we define a new cost function which considers the uncorrelation and noisy problems of fMRI data by adding decorrelation and smoothing constraints in original Euclidean distance cost function. We also generate a novel training procedure by modifying the update rules and combining with optimal brain surgeon (OBS) algorithm. The experimental results on visuomotor task fMRI data show that our cALSNMF fits fMRI data better than original ALSNMF in detecting task-related brain activation from single subject's fMRI data.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제25권3호
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• pp.107-116
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• 2021

We introduce optimization algorithms using Bregman Divergence for solving non-negative matrix factorization (NMF) problems. Bregman divergence is known a generalization of some divergences such as Frobenius norm and KL divergence and etc. Some algorithms can be applicable to not only NMF with Frobenius norm but also NMF with more general Bregman divergence. Matrix Factorization is a popular non-convex optimization problem, for which alternating minimization schemes are mostly used. We develop the Bregman proximal gradient method applicable for all NMF formulated in any Bregman divergences. In the derivation of NMF algorithm for Bregman divergence, we need to use majorization/minimization(MM) for a proper auxiliary function. We present algorithmic aspects of NMF for Bregman divergence by using MM of auxiliary function.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2145-2157
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• 2018

Congestion Management (CM) is an attractive research area in the electrical power transmission with the power compensation abilities. Reconfiguration and the Flexible Alternating Current Transmission Systems (FACTS) devices utilization relieve the congestion in transmission lines. The lack of optimal power (real and reactive) usage with the better transfer capability and minimum cost is still challenging issue in the CM. The prediction of suitable place for the energy resources to control the power flow is the major requirement for power handling scenario. This paper proposes the novel optimization principle to select the best location for the energy resources to achieve the real-reactive power compensation. The parameters estimation and the selection of values with the best fitness through the Symmetrical Distance Travelling Optimization (SDTO) algorithm establishes the proper controlling of optimal power flow in the transmission lines. The modified fitness function formulation based on the bus parameters, index estimation correspond to the optimal reactive power usage enhances the power transfer capability with the minimum cost. The comparative analysis between the proposed method with the existing power management techniques regarding the parameters of power loss, cost value, load power and energy loss confirms the effectiveness of proposed work in the distributed renewable energy systems.