• ETRI Journal
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• v.31 no.1
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• pp.31-41
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• 2009

Optical burst switching is a promising switching paradigm for the next IP-over-optical network backbones. However, its burst loss performance is greatly affected by burst contention. Several methods have been proposed to address this problem, some of them requiring the network to be flooded by frequent state dissemination signaling messages. In this work, we present a traffic engineering approach for path selection with the objective of minimizing contention using only topological information. The main idea is to balance the traffic across the network to reduce congestion without incurring link state dissemination protocol penalties. We propose and evaluate two path selection strategies that clearly outperform shortest path routing. The proposed path selection strategies can be used in combination with other contention resolution methods to achieve higher levels of performance and support the network reaching stability when it is pushed under stringent working conditions. Results show that the network connectivity is an important parameter to consider.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.259-270
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• 2013

This paper considers a penalized composite quantile regression (CQR) that performs a variable selection in the linear model with grouped variables. An adaptive sup-norm penalized CQR (ASCQR) is proposed to select variables in a grouped manner; in addition, the consistency and oracle property of the resulting estimator are also derived under some regularity conditions. To improve the efficiency of estimation and variable selection, this paper suggests the two-stage penalized CQR (TSCQR), which uses the ASCQR to select relevant groups in the first stage and the adaptive lasso penalized CQR to select important variables in the second stage. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.673-683
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• 2017

The least absolute shrinkage and selection operator (LASSO) has been a popular regression estimator with simultaneous variable selection. However, LASSO does not have the oracle property and its robust version is needed in the case of heavy-tailed errors or serious outliers. We propose a robust penalized regression estimator which provide a simultaneous variable selection and estimator. It is based on the rank regression and the non-convex penalty function, the smoothly clipped absolute deviation (SCAD) function which has the oracle property. The proposed method combines the robustness of the rank regression and the oracle property of the SCAD penalty. We develop an efficient algorithm to compute the proposed estimator that includes a SCAD estimate based on the local linear approximation and the tuning parameter of the penalty function. Our estimate can be obtained by the least absolute deviation method. We used an optimal tuning parameter based on the Bayesian information criterion and the cross validation method. Numerical simulation shows that the proposed estimator is robust and effective to analyze contaminated data.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.651-662
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• 2017

We propose an alternative procedure to select penalty parameter in $L_1$ penalized robust regression. This procedure is based on marginalization of prior distribution over the penalty parameter. Thus, resulting objective function does not include the penalty parameter due to marginalizing it out. In addition, its estimating algorithm automatically chooses a penalty parameter using the previous estimate of regression coefficients. The proposed approach bypasses cross validation as well as saves computing time. Variable-wise penalization also performs best in prediction and variable selection perspectives. Numerical studies using simulation data demonstrate the performance of our proposals. The proposed methods are applied to Boston housing data. Through simulation study and real data application we demonstrate that our proposals are competitive to or much better than cross-validation in prediction, variable selection, and computing time perspectives.

• Communications for Statistical Applications and Methods
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• v.31 no.3
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• pp.337-347
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• 2024

Kernel density estimation is a prevalent technique employed for nonparametric density estimation, enabling direct estimation from the data itself. This estimation involves two crucial elements: selection of the kernel function and the determination of the appropriate bandwidth. The selection of the bandwidth plays an important role in kernel density estimation, which has been developed over the past decade. A range of methods is available for selecting the bandwidth, including the plug-in bandwidth. In this article, the proposed plug-in bandwidth is introduced, which leverages shifted Chebyshev series-based approximation to determine the optimal bandwidth. Through a simulation study, the performance of the suggested bandwidth is analyzed to reveal its favorable performance across a wide range of distributions and sample sizes compared to alternative bandwidths. The proposed bandwidth is also applied for kernel density estimation on real dataset. The outcomes obtained from the proposed bandwidth indicate a favorable selection. Hence, this article serves as motivation to explore additional plug-in bandwidths that rely on function approximations utilizing alternative series expansions.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.397-405
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• 1998

We develop parametric bootstrap model selection criteria in an example to fit a random sample to either a general normal distribution or a normal distribution with prespecified mean. We apply the bootstrap methods in two ways; one considers the direct substitution of estimated parameter for the unknown parameter, and the other focuses on the bias correction. These bootstrap model selection criteria are compared with AIC. We illustrate that all the selection rules reduce to the one sample t-test, where the cutoff points converge to some certain points as the sample size increases.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.17-20
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• 2005

In this work, we compared several feature selection methods in the field of spam mail filtering. The proposed fuzzy inference method outperforms information gain and chi squared test methods as a feature selection method in terms of error rate. In the case of junk mails, since the mail body has little text information, it provides insufficient hints to distinguish spam mails from legitimate ones. To address this problem, we follow hyperlinks contained in the email body, fetch contents of a remote web page, and extract hints from both original email body and fetched web pages. A two-phase approach is applied to filter spam mails in which definite hint is used first, and then less definite textual information is used. In our experiment, the proposed two-phase method achieved an improvement of recall by 32.4% on the average over the $1^{st}$ phase or the $2^{nd}$ phase only works.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.9
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• pp.1612-1616
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• 2006

For map services in the mobile environment, it should be considered that resource restriction or the mobile device. on, if a map database dedicated to mobile services may not be developed, the spatial data extracted from general map databases should be simplified before transmitting. % is paper suggests the filtering methods to manipulate the spatial data, which are changed to be able to displayed on the mobile devices. The suggested methods are evaluated by experiments. This method is based on the map generalization operator 'selection' and is refined to adapt on mobile phone environments.

• Journal of Construction Engineering and Project Management
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• v.6 no.2
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• pp.16-23
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• 2016

The use of inappropriate procurement methods to deliver construction projects has long been acknowledged as a major source of poor project performance and is particularly problematic for the Libyan Construction Industry. Poor procurement method selection has been recognised as a major contributory factor to frequent time and cost overruns. This paper offers a way of selecting specific procurement methods to maximize successful project performance. The methodology involves an intensive review of relevant literature, followed by a semi-structured questionnaire survey. The key findings of the study reveal that 11 out of its 12 common selection criteria exhibit a significant contribution to one or more project performance criteria (time, cost and quality). Project clients should therefore prioritise these criteria when selecting a design-bid-build method. Knowledge of the criteria that contribute positively to project performance will also enable clients to work out, prior to and during construction, the best measures and provisions for successful project outcomes.

• Journal of the Ergonomics Society of Korea
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• v.29 no.4
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• pp.435-444
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• 2010

To identify and evaluate the risk factors in WMSD work, a number of ergonomic workload evaluation methods have been developed. In the legal examination of WMSD risk factors, simple observational workload evaluation methods are widely used instead of using costly measurement equipments such as EMG and motion analyzer. The simple workload evaluation methods can be categorized into three groups; risk factor checklist methods, posture observation methods, and manual material handling task evaluation methods. In terms of the categories, this survey summarized several representative workload evaluation methods and compared them each other. Then some industrial application cases referring each the workload evaluation methods were surveyed. Due to the characteristics of each method, the selection and application procedure of workload evaluation method should be appropriate for the corresponding work. Therefore, some guidelines for the selection and application procedure of workload evaluation method were suggested.