• Journal of Communications and Networks
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• 제13권3호
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• pp.240-249
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• 2011

Cooperative diversity protocols have attracted a great deal of attention since they are thought to be capable of providing diversity multiplexing tradeoff among single antenna wireless devices. In the high signal-to-noise ratio (SNR) region, cooperation is rarely required; hence, the spectral efficiency of the cooperative protocol can be improved by applying a proper cooperation selection technique. In this paper, we present a simple "cooperation selection" technique based on instantaneous channel measurement to improve the spectral efficiency of cooperative protocols. We show that the same instantaneous channel measurement can also be used for relay selection. In this paper two protocols are proposed-proactive and reactive; the selection of one of these protocols depends on whether the decision of cooperation selection is made before or after the transmission of the source. These protocols can successfully select cooperation along with the best relay from a set of available M relays. If the instantaneous source-to-destination channel is strong enough to support the system requirements, then the source simply transmits to the destination as a noncooperative direct transmission; otherwise, a cooperative transmission with the help of the selected best relay is chosen by the system. Analysis and simulation results show that these protocols can achieve higher order diversity with improved spectral efficiency, i.e., a higher diversity-multiplexing tradeoff in a slow-fading environment.

• Communications for Statistical Applications and Methods
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• 제27권5호
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• pp.535-546
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• 2020

In genetic association studies, pleiotropy is a phenomenon where a variant or a genetic region affects multiple traits or diseases. There have been many studies identifying cross-phenotype genetic associations. But, most of statistical approaches for detection of pleiotropy are based on individual tests where a single variant association with multiple traits is tested one at a time. These approaches fail to account for relations among correlated variants. Recently, multivariate regularization methods have been proposed to detect pleiotropy in analysis of high-dimensional genomic data. However, they suffer a problem of tuning parameter selection, which often results in either too many false positives or too small true positives. In this article, we applied selection probability to multivariate regularization methods in order to identify pleiotropic variants associated with multiple phenotypes. Selection probability was applied to individual elastic-net, unified elastic-net and multi-response elastic-net regularization methods. In simulation studies, selection performance of three multivariate regularization methods was evaluated when the total number of phenotypes, the number of phenotypes associated with a variant, and correlations among phenotypes are different. We also applied the regularization methods to a wild bean dataset consisting of 169,028 variants and 17 phenotypes.

• ETRI Journal
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• 제27권6호
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• pp.747-758
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• 2005

The problem of selection among competing models has been a fundamental issue in statistical data analysis. Good fits to data can be misleading since they can result from properties of the model that have nothing to do with it being a close approximation to the source distribution of interest (for example, overfitting). In this study we focus on the preference among models from a family of polynomial regressors. Three decades of research has spawned a number of plausible techniques for the selection of models, namely, Akaike's Finite Prediction Error (FPE) and Information Criterion (AIC), Schwartz's criterion (SCH), Generalized Cross Validation (GCV), Wallace's Minimum Message Length (MML), Minimum Description Length (MDL), and Vapnik's Structural Risk Minimization (SRM). The fundamental similarity between all these principles is their attempt to define an appropriate balance between the complexity of models and their ability to explain the data. This paper presents an empirical study of the above principles in the context of model selection, where the models under consideration are univariate polynomials. The paper includes a detailed empirical evaluation of the model selection methods on six target functions, with varying sample sizes and added Gaussian noise. The results from the study appear to provide strong evidence in support of the MML- and SRM- based methods over the other standard approaches (FPE, AIC, SCH and GCV).

• 대한지역사회영양학회지
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• 제13권4호
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• pp.531-539
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• 2008

The purpose of this study is to examine how nutritionists, principals and parents evaluated the importance and priorities of different selection factors for food materials suppliers in an attempt to suggest how to ensure the best supplier choice, since the selection of the best supplier was mandatory for the successful TQM of school food services. This study especially aims to develop priority alternatives based on relative rather than absolute assignments on selection of suppliers for school food service materials. To apply AHP, the selection factors of a food materials suppliers were grouped into five categories, which included quality, service, reliability, hygiene and price. And the five categories involved 28 elements. As the result, quality was considered most crucial on the whole, followed by service, reliability, hygiene and price; and food labeling in the category of quality evaluated as that of first priority in the totality evaluation elements.

• Journal of Electrical Engineering and Technology
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• 제7권2호
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• pp.245-254
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• 2012

In this study, we introduce a prototype-based classifier with feature selection that dwells upon the usage of a biologically inspired optimization technique of Particle Swarm Optimization (PSO). The design comprises two main phases. In the first phase, PSO selects P % of patterns to be treated as prototypes of c classes. During the second phase, the PSO is instrumental in the formation of a core set of features that constitute a collection of the most meaningful and highly discriminative coordinates of the original feature space. The proposed scheme of feature selection is developed in the wrapper mode with the performance evaluated with the aid of the nearest prototype classifier. The study offers a complete algorithmic framework and demonstrates the effectiveness (quality of solution) and efficiency (computing cost) of the approach when applied to a collection of selected data sets. We also include a comparative study which involves the usage of genetic algorithms (GAs). Numerical experiments show that a suitable selection of prototypes and a substantial reduction of the feature space could be accomplished and the classifier formed in this manner becomes characterized by low classification error. In addition, the advantage of the PSO is quantified in detail by running a number of experiments using Machine Learning datasets.

• Journal of Information Science Theory and Practice
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• 제9권4호
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• pp.15-34
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• 2021

Text clustering is one of the most commonly used methods for detecting themes or types of documents. Text clustering is used in many fields, but its effectiveness is still not sufficient to be used for the understanding of Arabic text, especially with respect to terms extraction, unsupervised feature selection, and clustering algorithms. In most cases, terms extraction focuses on nouns. Clustering simplifies the understanding of an Arabic text like the text of the Quran; it is important not only for Muslims but for all people who want to know more about Islam. This paper discusses the complexity and limitations of Arabic text clustering in the Quran based on their themes. Unsupervised feature selection does not consider the relationships between the selected features. One weakness of clustering algorithms is that the selection of the optimal initial centroid still depends on chances and manual settings. Consequently, this paper reviews literature about the three major stages of Arabic clustering: terms extraction, unsupervised feature selection, and clustering. Six experiments were conducted to demonstrate previously un-discussed problems related to the metrics used for feature selection and clustering. Suggestions to improve clustering of the Quran based on themes are presented and discussed.

• 한국식생활문화학회지
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• 제39권1호
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• pp.30-37
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• 2024

Consumption market research was conducted on gradually increasing vegetarians using various selection attributes. Factors were extracted to identify vegetarian selection attributes and to divide the study cohort into groups, continuous variables (health, animal welfare, eco-friendliness, religion, familiarity, convenience, stability, and cost) and categorical variables (age, marital status, vegetarian duration, and vegetarian frequency) were simultaneously subjected to two-step cluster analysis. Cluster 1 contained high proportions of 20-29 and 30-39 year-olds, which are MZ-generation age groups. A high proportion had a vegetarian duration of 1-3 years, and the popular reasons for vegetarian selection were animal welfare and eco-friendliness. Cluster 2 contained high proportions of 50-59 and 40-49 year-olds, and many in this cluster were married, and mean vegetarian duration was ≥15 years. In addition, significant differences were observed between Clusters 1 and 2 in terms of religion, health, familiarity, cost, stability, and convenience. This study should contribute significantly to predicting vegetarian consumers' selection decisions and consumption behaviors and provide reliable marketing data for foodservice companies that develop vegetarian foods.

• Journal of the Korean Data and Information Science Society
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• 제19권2호
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• pp.621-634
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• 2008

This paper addresses the problem of testing whether the means in several inverse Gaussian populations with heterogeneity are equal. The analysis of reciprocals for the equality of inverse Gaussian means needs the assumption of equal scale parameters. We propose Bayesian model selection procedures for testing equality of the inverse Gaussian means under the noninformative prior without the assumption of equal scale parameters. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian model selection procedures based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and real data analysis are provided.

• 한국소음진동공학회논문집
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• 제25권4호
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• pp.232-237
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• 2015

Complex dynamic systems are composed of several subsystems. Each subsystems affect the dynamics of other subsystems since they are connected to each other in the whole system. Theoretically, we can derive the exact mass and stiffness matrix of a system if we have the natural frequencies and mode shapes of that system. In real situation, the modal parameters for the higher modes are not available and the number of degree of freedom concerned are not so high. This paper shows a simple method to derive the mass and stiffness matrix of a system considering the connecting points of subsystems. Since the accuracy of reconstructed structure depends on the selection of node and mode, the rule for selection of node and mode are derived from the numerical examples.

• 품질경영학회지
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• 제32권2호
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• pp.77-92
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• 2004

It is difficult but very necessary to measure the productivity of container terminals as logistics service provider. It is meaningful to find the appropriate inputs and outputs of the logistics service delivery systems and to measure the relationship between these inputs and outputs. This study proposes a model of evaluating the efficiency of container terminals. The evaluation consists of three phases. First, DEA(Data Envelopment Analysis) phase, determines the efficiency score and weights of DMUs(Decision Making Unit). This phase performs through four steps : selection of DMU, selection of DEA model, determination of input and output factors, calculation of efficiency score and weights for each DMU. Secondly, CEM (Cross Evaluation Model) phase, is to calculate the cross-efficiency scores of DMUs. This phase performs through three steps: selection of CEM, determination of cross-efficiency score for each DMU and development of cross-efficiency matrix. Finally, average cross-efficiency analysis phase is to compute the average cross-efficiency score. The proposed model discriminates among DMUs and ranks DMUs, whether they are efficient or inefficient.