• Journal of the Korean Mathematical Society
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• v.41 no.5
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• pp.883-894
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• 2004

We discuss in this paper the strong convergence for weighted sums of negative associated (in abbreviation: NA) arrays. Meanwhile, the central limit theorem for weighted sums of NA variables and linear process based on NA variables is also considered. As corollary, we get the results on iid of Li et al. ([10]) in NA setting.

• Bulletin of the Korean Mathematical Society
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• v.54 no.3
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• pp.935-952
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• 2017

It is natural to try to find a kernel such that its convolution of integrable functions converges faster than that of the $Fej{\acute{e}}r$ kernel. In this paper, we introduce a weighted Fourier partial sums which are written as the convolution of signed good kernels and prove that the weighted Fourier partial sum converges in $L^2$ much faster than that of the $Ces{\grave{a}}ro$ means. In addition, we present two numerical experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2626-2646
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• 2014

This study presents a power and Physical Resource Blocks (PRBs) joint allocation algorithm to coordinate uplink (UL) interference in the device-to-device (D2D) underlaying Long Term Evolution-Advanced (LTE-A) networks. The objective is to find a mechanism to mitigate the UL interference between the two subsystems and maximize the weighted sum throughput as well. This optimization problem is formulated as a mixed integer nonlinear programming (MINLP) which is further decomposed into PRBs assignment and transmission power allocation. Specifically, the scenario of applying imperfect channel state information (CSI) is also taken into account in our study. Analysis reveals that the proposed PRBs allocation strategy is energy efficient and it suppresses the interference not only suffered by the LTE-A system but also to the D2D users. In another side, a low-complexity technique is proposed to obtain the optimal power allocation which resides in one of at most three feasible power vectors. Simulations show that the optimal power allocation combined with the proposed PRBs assignment achieves a higher weighted sum throughput as compared to traditional algorithms even when imperfect CSI is utilized.

• Journal of Communications and Networks
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• v.12 no.5
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• pp.449-458
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• 2010

In this paper, we present a distributed resource allocation algorithm for multi-cell uplink systems that increases the weighted sum of the average data rates over the entire network under the average transmit power constraint of each mobile station. For the distributed operation, we arrange each base station (BS) to allocate the resource such that its own utility gets maximized in a noncooperative way. We define the utility such that it incorporates both the weighted sum of the average rates in each cell and the induced interference to other cells, which helps to instigate implicit cooperation among the cells. Since the data rates of different cells are coupled through inter-cell interferences, the resource allocation taken by each BS evolves over iterations. We establish that the resource allocation converges to a unique fixed point under reasonable assumptions. We demonstrate through computer simulations that the proposed algorithm can improve the weighted sum of the average rates substantially without requiring any coordination among the base stations.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.423-429
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• 2014

This paper discusses a method for getting Type I sums of squares by projections under a two-way fixed-effects model when variances of errors are not equal. The method of weighted least squares is used to estimate the parameters of the assumed model. The model is fitted to the data in a sequential manner by using the model comparison technique. The vector space generated by the model matrix can be composed of orthogonal vector subspaces spanned by submatrices consisting of column vectors related to the parameters. It is discussed how to get the Type I sums of squares by using the projections into the orthogonal vector subspaces.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.4
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• pp.324-333
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• 2013

The purpose of this paper is to improve the efficiency of multi-objective topology optimization using a genetic algorithm (GA) with bar-system representation. We proposed a new GA using an elite initial population obtained from a Solid Isotropic Material with Penalization (SIMP) using a weighted sum method. SIMP with a weighted sum method is one of the most established methods using sensitivity analysis. Although the implementation of the SIMP method is straightforward and computationally effective, it may be difficult to find a complete Pareto-optimal set in a multi-objective optimization problem. In this study, to build a more convergent and diverse global Pareto-optimal set and reduce the GA computational cost, some individuals, with similar topology to the local optimum solution obtained from the SIMP using the weighted sum method, were introduced for the initial population of the GA. The proposed method was applied to a structural topology optimization example and the results of the proposed method were compared with those of the traditional method using standard random initialization for the initial population of the GA.

• Journal of the Chungcheong Mathematical Society
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• v.26 no.3
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• pp.615-623
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• 2013

In this paper we establish a central limit theorem for weighted sums of $Y_n={\sum_{i=1}^{n}}a_n,_iX_i$, where $\{a_{n,i},\;n{\in}N,\;1{\leq}i{\leq}n\}$ is an array of nonnegative numbers such that ${\sup}_{n{\geq}1}{\sum_{i=1}^{n}}a_{n,i}^2$ < ${\infty}$, ${\max}_{1{\leq}i{\leq}n}a_{n,i}{\rightarrow}0$ and $\{X_i,\;i{\in}N\}$ is a sequence of linear negatively quadrant dependent random variables with $EX_i=0$ and $EX_i^2$ < ${\infty}$. Using this result we will obtain a central limit theorem for partial sums of linear processes.

• Journal of Integrative Natural Science
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• v.8 no.2
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• pp.145-146
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• 2015

In this note, we provide some examples of polynomials $z^n-p(z)$, where $p(z)={\limits\sum_{k=o}^{n-1}}a_kz^k$, and ${\limits\sum_{k=o}^{n-1}}a_kz^k=1$, $a_k{\geq}0$ for each k such that p(z) has all its zeros on ${\mid}z{\mid}=c<1$, and $z^n-p(z)$ has all its zeros on two circles ${\mid}z{\mid}=1$ and ${\mid}z{\mid}=d<1$.

• Communications for Statistical Applications and Methods
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• v.29 no.4
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• pp.487-498
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• 2022

Social network analysis (SNA) techniques have recently been developed to monitor and detect abnormal behaviors in social networks. As a useful tool for process monitoring, control charts are also useful for network monitoring. In this paper, the degree and closeness centrality measures, in which each has global and local perspectives, respectively, are applied to an exponentially weighted moving average (EWMA) chart and a multinomial cumulative sum (CUSUM) chart for monitoring undirected weighted networks. In general, EWMA charts monitor only one variable in a single chart, whereas multinomial CUSUM charts can monitor a categorical variable, in which several variables are transformed through classification rules, in a single chart. To monitor both degree centrality and closeness centrality simultaneously, we categorize them based on the average of each measure and then apply to the multinomial CUSUM chart. In this case, the global and local attributes of the network can be monitored simultaneously with a single chart. We also evaluate the performance of the proposed procedure through a simulation study.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.321-327
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• 2014

Feature selection is the technique to improve the classification performance by using a minimal set by removing features that are not related with each other and characterized by redundancy. This study proposed new feature selection using the distance between the center of gravity of the bounded sum of weighted fuzzy membership functions (BSWFMs) provided by the neural network with weighted fuzzy membership functions (NEWFM) in order to improve the classification performance. The distance-based feature selection selects the minimum features by removing the worst features with the shortest distance between the center of gravity of BSWFMs from the 24 initial features one by one, and then 22 minimum features are selected with the highest performance result. The proposed methodology shows that sensitivity, specificity, and accuracy are 97.7%, 99.7%, and 98.7% with 22 minimum features, respectively.