• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.147-159
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• 2021

This study was conducted to analyse the dine out behaviors for single-person households using the Consumer Behavior Survey for Food in 2019. The results showed that the respondents had different dine out behaviors according to their age. 20s~30s and 40s~50s single-person household tend to dine out more frequently than 60s~70s. On the other hand, there was no significant difference in the most important selection criteria when choosing a restaurant (p=0.063), but 39.7% of 20s~30s 43.1% of 40s~50s, and the 38.3% of 60s~70s respondents selected 'the taste of food', and the young people (20s~30s) who chose 'cleanliness of the restaurant' were second with 39 people (15.5%), but in the opinion of 40s~50s and 60s~70s, 'price level' was the second most important selection criteria. Besides, frequency of buying food at home was the influencing factor for 20s~30s' monthly expenditure of dinning out, while frequency of buying food at home, monthly expenditure of buying delivery or take-out food were the factors for 40s~50s. Lastly, gender, occupation as well as monthly expenditure of buying delivery or take-out food were the factors for 60s~70s' monthly expenditure of dinning out. As many studies have shown that the expenditures single-person households play an important role in the restaurant business, the results of this study are necessary for food service industry to generate different business strategy to single-person household by age.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.12
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• pp.1912-1921
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• 2020

Objective: This study assessed genomic prediction accuracies based on different selection methods, evaluation procedures, training population (TP) sizes, heritability (h²) levels, marker densities and pedigree error (PE) rates in a simulated Korean beef cattle population. Methods: A simulation was performed using two different selection methods, phenotypic and estimated breeding value (EBV), with an h² of 0.1, 0.3, or 0.5 and marker densities of 10, 50, or 777K. A total of 275 males and 2,475 females were randomly selected from the last generation to simulate ten recent generations. The simulation of the PE dataset was modified using only the EBV method of selection with a marker density of 50K and a heritability of 0.3. The proportions of errors substituted were 10%, 20%, 30%, and 40%, respectively. Genetic evaluations were performed using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with different weighted values. The accuracies of the predictions were determined. Results: Compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection. However, an increase in the marker density did not yield higher accuracy in either method except when the h² was 0.3 under the EBV selection method. Based on EBV selection with a heritability of 0.1 and a marker density of 10K, GBLUP and ssGBLUP_0.95 prediction accuracy was higher than that obtained by phenotypic selection. The prediction accuracies from ssGBLUP_0.95 outperformed those from the GBLUP method across all scenarios. When errors were introduced into the pedigree dataset, the prediction accuracies were only minimally influenced across all scenarios. Conclusion: Our study suggests that the use of ssGBLUP_0.95, EBV selection, and low marker density could help improve genetic gains in beef cattle.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.1-8
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• 2008

This paper introduces three efficient frame selection schemes to reduce the computation complexity for the multi-reference and variable block size Motion Estimation (ME). The proposed RSP (Reference Selection Pass) scheme can minimize the overhead of frame selection. The MFS (Modified Frame Selection) scheme can reduce the number of search points about 18% compared with existing schemes considering the motion of image during the reference frame selection process. In addition, the TPRFS (Two Pass Reference frame Selection) scheme can minimize the frame selection operation for the variable block size ME in H.264/AVC using the character of selected reference frame according to the block size. The simulation results show the proposed schemes can save up to 50% of the ME computation without degradation of image Qualify. Because the proposed schemes can be separated from the block matching process, they can be used with any existing single reference fast search algorithms.

• Journal of Korean Library and Information Science Society
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• v.44 no.1
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• pp.27-47
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• 2013

This study investigated the classification performance of a kNN classifier using the feature selection methods based on document frequency(DF) and collection frequency(CF). The results of the experiments, which used HKIB-20000 data, were as follows. First, the feature selection methods that used high-frequency terms and removed low-frequency terms by the CF criterion achieved better classification performance than those using the DF criterion. Second, neither DF nor CF methods performed well when low-frequency terms were selected first in the feature selection process. Last, combining CF and DF criteria did not result in better classification performance than using the single feature selection criterion of DF or CF.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.105-115
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• 2016

Ensemble classification involves combining individually trained classifiers to yield more accurate prediction, compared with individual models. Ensemble techniques are very useful for improving the generalization ability of classifiers. The random subspace ensemble technique is a simple but effective method for constructing ensemble classifiers; it involves randomly drawing some of the features from each classifier in the ensemble. The instance selection technique involves selecting critical instances while deleting and removing irrelevant and noisy instances from the original dataset. The instance selection and random subspace methods are both well known in the field of data mining and have proven to be very effective in many applications. However, few studies have focused on integrating the instance selection and random subspace methods. Therefore, this study proposed a new hybrid ensemble model that integrates instance selection and random subspace techniques using genetic algorithms (GAs) to improve the performance of a random subspace ensemble model. GAs are used to select optimal (or near optimal) instances, which are used as input data for the random subspace ensemble model. The proposed model was applied to both Kaggle credit data and corporate credit data, and the results were compared with those of other models to investigate performance in terms of classification accuracy, levels of diversity, and average classification rates of base classifiers in the ensemble. The experimental results demonstrated that the proposed model outperformed other models including the single model, the instance selection model, and the original random subspace ensemble model.

• Journal of Communications and Networks
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• v.13 no.3
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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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• v.27 no.5
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5898-5916
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• 2018

In this paper, the downlink performance of multi-cell distributed antenna systems (DAS) with a single user in each cell is investigated. Assuming the channel state information is available at the transmitter, four transmission modes are formulated as combinations of remote antenna units (RAUs) selection and cooperative transmission, namely, non-cooperative transmission without RAU selection (NCT), cooperative transmission without RAU selection (CT), non-cooperative transmission with RAU selection (NCT_RAUS), and cooperative transmission with RAU selection (CT_RAUS). By using probability theory, the cumulative distribution function (CDF) of a user's signal to interference plus noise ratio (SINR) and the system ergodic capacity under the above four modes are determined, and their closed-form expressions are obtained. Furthermore, the system energy efficiency (EE) is studied by introducing a realistic power consumption model of DAS. An expression for determining EE is formulated, and the closed-form tradeoff relationship between spectral efficiency (SE) and EE is derived as well. Simulation results demonstrate their consistency with the theoretical analysis and reveal the factors constraining system EE, which provide a scientific basis for future design and optimization of DAS.

• The Korean Journal of Food & Health Convergence
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• v.8 no.4
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• pp.23-30
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• 2022

This study aims to analyze the premium hamburger market, which has recently become popular, the effect of the importance of the customer selection attribute of premium hamburgers on customer satisfaction, and the effect of customer satisfaction on repurchase intention. Existing research has focused on the importance of the selection attributes of premium hamburgers. Quality, convenience, experience, and presentation visuals were selected as customer selection attributes. This study analyzed 158 customers who had purchased and tasted premium hamburgers. To verify reliability and validity, a confirmatory factor analysis and discriminant validity analysis were performed, and a path analysis was carried out using structural equation modeling. The results showed that the quality, convenience, experience, and presentation visuals of premium hamburgers had a statistically significant effect on satisfaction. Moreover, satisfaction was verified to have a significant effect on repurchase intention. Customers' preference for premium burgers will continue to increase, thanks to the growth in national income, single-person families, and healthy food wellness. It was empirically proven that the selection attributes of premium burgers have a statistically significant effect on customer satisfaction and that satisfaction significantly affects repurchase intention. This study broadens the research horizon and has practical implications.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.1-5
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• 2002

We consider the problem of optimal bandwidth choice for nonparametric classification, based on kernel density estimators, where the problem of interest is distinguishing between two univariate distributions. When the densities intersect at a single point, optimal bandwidth choice depends on curvatures of the densities at that point. The problem of empirical bandwidth selection and classifying data in the tails of a distribution are also addressed.