• Journal of the Korean Mathematical Society
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• v.61 no.3
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• pp.427-444
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• 2024

The matrix completion problem is to predict missing entries of a data matrix using the low-rank approximation of the observed entries. Typical approaches to matrix completion problem often rely on thresholding the singular values of the data matrix. However, these approaches have some limitations. In particular, a discontinuity is present near the thresholding value, and the thresholding value must be manually selected. To overcome these difficulties, we propose a shrinkage and thresholding function that smoothly thresholds the singular values to obtain more accurate and robust estimation of the data matrix. Furthermore, the proposed function is differentiable so that the thresholding values can be adaptively calculated during the iterations using Stein unbiased risk estimate. The experimental results demonstrate that the proposed algorithm yields a more accurate estimation with a faster execution than other matrix completion algorithms in image inpainting problems.

• Journal of the Chungcheong Mathematical Society
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• v.16 no.1
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• pp.25-36
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• 2003

We study the question of whether a partial (0, 1)-normal matrix has a non-symmetric normal completion. Matrix sandwich problems are an important and special case of matrix completion problems. In this paper, we give some properties for the (0, 1)-normal matrices and some large classes that satisfies the normal sandwich completion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3057-3075
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• 2020

End-to-end network delay plays an vital role in distributed services. This delay is used to measure QoS (Quality-of-Service). It would be beneficial to know all node-pair delay information, but unfortunately it is not feasible in practice because the use of active probing will cause a quadratic growth in overhead. Alternatively, using the measured network delay to estimate the unknown network delay is an economical method. In this paper, we adopt the state-of-the-art matrix completion technology to better estimate the network delay from limited measurements. Although the number of measurements required for an exact matrix completion is theoretically bounded, it is practically less helpful. Therefore, we propose an online adaptive sampling algorithm to measure network delay in which statistical leverage scores are used to select potential matrix elements. The basic principle behind is to sample the elements with larger leverage scores to keep the traits of important rows or columns in the matrix. The amount of samples is adaptively decided by a proposed stopping condition. Simulation results based on real delay matrix show that compared with the traditional sampling algorithm, our proposed sampling algorithm can provide better performance (smaller estimation error and less convergence pressure) at a lower cost (fewer samples and shorter processing time).

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.11a
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• pp.4-7
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• 2015

In this paper, we propose a matrix completion algorithm for Internet of Things (IoT) localization. The proposed algorithm recovers the Gram matrix of sensors by performing optimization over the Riemannian manifold of fixed-rank positive semidefinite matrices. We compute and show the closed forms of all the differentially geometric components required for applying nonlinear conjugate gradients combined with Armijo line search method. The numerical experiments show that the performance of the proposed algorithm in solving IoT localization is outstanding compared with the state-of-the-art matrix completion algorithms both in noise and noiseless scenarios.

• The KIPS Transactions:PartA
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• v.9A no.3
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• pp.371-378
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• 2002

The EMFG (Extended Mark Flow Graph) is known as a graph model for representing the discrete event systems. In this paper, we introduce input/output matrixes representing the marking variance of input/output boxes when each transition fires in an EMFG, and compute an incidence matrix. We represent firing conditions of transitions to a firing condition matrix for computing a firable vector, and introduce the firing completion vector to decide completion of each transition’s firing. By using them, we improve an analysis algorithm of the EMFG’s operation to be represented all the process of EMFG’s operation mathematically. We apply the proposed algorithm to the system repeating the forward and reverse revolution, and then confirm that it is valid. The proposed algorithm is useful to analysis the variant discrete event systems.

• Journal of the Korean Mathematical Society
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• v.52 no.5
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• pp.1003-1021
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• 2015

A matrix completion problem has been exploited amply because of its abundant applications and the analysis of contractions enables us to have insight into structure and space of operators. In this article, we focus on a specific completion problem related to Hankel partial contractions. We provide concrete necessary and sufficient conditions for the existence of completion of Hankel partial contractions for both extremal and non-extremal types with lower dimensional matrices. Moreover, we give a negative answer for the conjecture presented in [8]. For our results, we use several tools such as the Nested Determinants Test (or Choleski's Algorithm), the Moore-Penrose inverse, the Schur product techniques, and a congruence of two positive semi-definite matrices; all these suggest an algorithmic approach to solve the contractive completion problem for general Hankel matrices of size $n{\times}n$ in both types.

• Journal of the Chungcheong Mathematical Society
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• v.24 no.3
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• pp.587-593
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• 2011

We find concrete necessary and sufficient conditions for the existence of contractive completions of Stochastic Hankel partial contractions of size $4{\times}4$, extremal type.

• Journal of the Korean Mathematical Society
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• v.57 no.3
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• pp.641-653
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• 2020

To extend the well-known extremal characterization of the geometric mean of two n × n positive definite matrices A and B, we solve the following problem: $${\max}\{X:X=X^*,\;\(\array{A&V&X\\V&B&W\\X&W&C}\){\geq}0\}$$. We find an explicit expression of the maximum value with respect to the matrix geometric mean of Schur complements.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.3
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• pp.604-611
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• 2017

In wireless sensor networks, an energy efficient operation is important since the energy of the sensors is limited. This paper proposes an energy efficient method that reduces a packet generation with Matrix Completion method where sensor value matrix has low-rank and decreases a collision rate and an overhead by transmitting only sensor ID to a time slot corresponding to the sensor value. Computer simulations demonstrates that the proposed method shows 17% of transmission failure and 73% of the packet generation compared to a conventional CSMA/CS. Delay time of transmitting information of the proposed method exhibits 22% of the CSMA/CA and the MSE error after reconstructing sensor values by Singular Value Thresholding(SVT) in Fusion Center is 87% of the CSMA/CA.

• Kyungpook Mathematical Journal
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• v.54 no.2
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• pp.157-163
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• 2014

For a finite subset ${\Lambda}$ of $\mathbb{N}_0{\times}\mathbb{N}_0$, where $\mathbb{N}_0$ is the set of nonnegative integers, we say that a complex data ${\gamma}_{\Lambda}:=\{{\gamma}_{ij}\}_{(ij){\in}{\Lambda}}$ in the unit disc $\mathbf{D}$ of complex numbers has a cyclic subnormal completion if there exists a Hilbert space $\mathcal{H}$ and a cyclic subnormal operator S on $\mathcal{H}$ with a unit cyclic vector $x_0{\in}\mathcal{H}$ such that ${\langle}S^{*i}S^jx_0,x_0{\rangle}={\gamma}_{ij}$ for all $i,j{\in}\mathbb{N}_0$. In this note, we obtain some sufficient conditions for a cyclic subnormal completion of ${\gamma}_{\Lambda}$, where ${\Lambda}$ is a finite subset of $\mathbb{N}_0{\times}\mathbb{N}_0$.