• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.367-374
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• 2022

A sampling set selection algorithm is proposed to reconstruct original graph signals from the sampled signals generated on the nodes in the sampling set. Instead of directly minimizing the reconstruction error, we focus on minimizing the upper bound on the reconstruction error to reduce the algorithm complexity. The metric is manipulated by using QR factorization to produce the upper triangular matrix and the analytic result is presented to enable a greedy selection of the next nodes at iterations by using the diagonal entries of the upper triangular matrix, leading to an efficient sampling process with reduced complexity. We run experiments for various graphs to demonstrate a competitive reconstruction performance of the proposed algorithm while offering the execution time about 3.5 times faster than one of the previous selection methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1682-1687
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• 2020

We study the problem of sampling a subset of nodes of graphs for bandlimited graph signals such that the signal values on the sampled nodes provide the most information in order to reconstruct the original graph signal. Instead of directly minimizing the reconstruction error, we focus on minimizing the upper bound of the reconstruction error to reduce the complexity of the selection process. We further simplify the upper bound by applying useful approximations to propose a low-weight greedy selection process that is iteratively conducted to find a suboptimal sampling set. Through the extensive experiments for various graphs, we inspect the performance of the proposed algorithm by comparing with different sampling set selection methods and show that the proposed technique runs fast while preserving a competitive reconstruction performance, yielding a practical solution to real-time applications.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1023-1030
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• 2020

We address the sampling set selection problem for arbitrary graph signals such that the original graph signal is reconstructed from the signal values on the nodes in the sampling set. We introduce a variation difference as a new indirect metric that measures the error of signal variations caused by sampling process without resorting to the eigen-decomposition which requires a huge computational cost. Instead of directly minimizing the reconstruction error, we propose a simple and fast greedy selection algorithm that minimizes the variation differences at each iteration and justify the proposed reasoning by showing that the principle used in the proposed process is similar to that in the previous novel technique. We run experiments to show that the proposed method yields a competitive reconstruction performance with a substantially reduced complexity for various graphs as compared with the previous selection methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.153-160
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• 2022

A greedy algorithm is proposed to select a subset of nodes of a graph for bandlimited graph signals in which each signal value is generated with its weight. Since graph signals are weighted, we seek to minimize the weighted reconstruction error which is formulated by using the QR factorization and derive an analytic result to find iteratively the node minimizing the weighted reconstruction error, leading to a simplified iterative selection process. Experiments show that the proposed method achieves a significant performance gain for graph signals with weights on various graphs as compared with the previous novel selection techniques.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.79-89
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• 1999

A counting rule that allows an element to be linked to more than one enumeration unit is called a multiplicity counting rule. Sample designs that use multiplicity counting rules are called network samples. Defining a network to be a set of observation units with a given linkage pattern, a network may be linked with more than one selection unit, and a single selection unit may be linked with more than one network. This paper considers allocation for multivariate stratified random sampling with multiplicity.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.169-180
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• 1999

A Markov Chain Monte Carlo method with development of computation is used to be the software system reliability probability model. For Bayesian estimator considering computational problem and theoretical justification we studies relation Markov Chain with Gibbs sampling. Special case of GOS with Superposition for Goel-Okumoto and Weibull models using Gibbs sampling and Metropolis algorithm considered. In this paper discuss Bayesian computation and model selection using posterior predictive likelihood criterion. We consider in this paper data using method by Cox-Lewis. A numerical example with a simulated data set is given.

• Journal of Applied Reliability
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• v.1 no.2
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• pp.183-192
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• 2001

Jelinski-Moranda model and modified Jelinski-Moranda model in software reliability are studied and we consider maximum likelihood estimator and Bayes estimates of the number of faults and the fault-detection rate per fault. A gibbs sampling approach is employed to compute the Bayes estimates, future survival function is examined. Model selection based on prequential likelihood of the conditional predictive ordinates. A numerical example with simulated data set is given.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.719-729
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• 2003

We propose a Bayesian model selection procedure for nonlinear regression models under noninformative prior. For informative prior, Na and Kim (2002) suggested the Bayesian model selection procedure through MCMC techniques. We extend this method to the case of noninformative prior. The difficulty with the use of noninformative prior is that it is typically improper and hence is defined only up to arbitrary constant. The methods, such as Intrinsic Bayes Factor(IBF) and Fractional Bayes Factor(FBF), are used as a resolution to the problem. We showed the detailed model selection procedure through the specific real data set.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.12 no.19
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• pp.25-30
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• 1989

This study is concerned with a job sequencing method using the concept of sampling technique. This sampling technique has never been applied to develop the scheduling algorithms. The most job sequencing algorithms have been developed to determine the best or good solution under the special conditions. Thus, it is not only very difficult, but also taken too much time to develop the appropriate job schedules that satisfy the complex work conditions. The application areas of these algorithms are also very narrow. Under these circumstances it is very desirable to develop a simple job sequencing method which can produce the good solution with the short tine period under any complex work conditions. It is called a sampling job sequencing method in this study. This study is to examine the selection of the good job sequence of 1%-5% upper group by the sampling method. The result shows that there is the set of 0.5%-5% job sequence group which has to same amount of performance measure with the optimal job sequence in the case of experiment of 2/n/F/F max. This indicates that the sampling job sequencing method is a useful job sequencing method to find the optimal or good job sequence with a little effort and time consuming. The results of ANOVA show that the two factors, number of jobs and the range of processing time are the significant factors for determining the job sequence at $\alpha$=0.01. This study is extended to 3 machines to machines job shop problems further.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.33-44
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• 2003

Typically, it should be use auxiliary variable for calibrating the survey nonreponse in census or sampling survey. Where, if the dimension of auxiliary information is large, then it nay be spend a lot of computing time, and difficult to handle data set. Also because the variance estimator depends on the dimension of auxiliary variables, the variance estimator becomes underestimator. To deal with this problem, we propose the variable selection methods for calibration estimation procedure in unit nonreponse situation and we compare the efficiency by simulation study.