• Journal of Information Processing Systems
• /
• v.2 no.2
• /
• pp.67-75
• /
• 2006

In order to acquire a precise and quick response to an analytical query, proper selection of the views to materialize in the data warehouse is crucial. In traditional view selection algorithms, all relations are considered for selection as materialized views. However, materializing all relations rather than a part results in much worse performance in terms of time and space costs. Therefore, we present an improved algorithm for selection of views to materialize using the clustering method to overcome the problem resulting from conventional view selection algorithms. In the presented algorithm, ASVMRT (Algorithm for Selection of Views to Materialize using Reduced Table), we first generate reduced tables in the data warehouse using clustering based on attribute-values density, and then we consider the combination of reduced tables as materialized views instead of a combination of the original base relations. For the justification of the proposed algorithm, we reveal the experimental results in which both time and space costs are approximately 1.8 times better than conventional algorithms.

• Journal of information and communication convergence engineering
• /
• v.9 no.2
• /
• pp.155-160
• /
• 2011

In distributed source localization where sensors transmit measurements to a fusion node, we address the sensor selection problem where the goal is to find the best set of sensors that maximizes localization accuracy when quantization of sensor measurements is taken into account. Since sensor selection depends heavily upon rate assigned to each sensor, joint optimization of rate allocation and sensor selection is required to achieve the best solution. We show that this task could be accomplished by solving the problem of allocating rates to each sensor so as to minimize the error in estimating the position of a source. Then we solve this rate allocation problem by using the generalized BFOS algorithm. Our experiments demonstrate that the best set of sensors obtained from the proposed sensor selection algorithm leads to significant improvements in localization performance with respect to the set of sensors determined from a sensor selection process based on unquantized measurements.

• Journal of the Korea Safety Management & Science
• /
• v.5 no.1
• /
• pp.69-82
• /
• 2003

The sequence which minimizes overall utility work in car assembly lines reduces the cycle time, the number of utility workers, and the risk of conveyor stopping. This study suggests mathematical formulation of the sequencing problem to minimize overall utility work, and present a genetic algorithm which can provide a near optimal solution in real time. To apply a genetic algorithm to the sequencing problem in car assembly lines, the representation, selection methods, and genetic parameters are studied. Experiments are carried out to compare selection methods such as roullette wheel selection, tournament selection and ranking selection. Experimental results show that ranking selection method outperforms the others in solution quality, whereas tournament selection provides the best performance in computation time.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.6B
• /
• pp.358-366
• /
• 2007

Recently, FCC(Federal Communications Commission) has considered for that unlicensed device leases licensed devices' channel to overcome shortage of communication channels. Therefore, IEEE 802.22 WRAN(Wireless Regional Area Networks) working group progresses CR (Cognitive Radio) technique that is able to sense and adopt void channels that are not being occupied by the licensed devices. Channel selection is of the utmost importance because it can affect the whole system performance in CR network. Thus, we propose a policy-based dynamic channel selection architecture for cognitive radio network to achieve an efficient communication. We propose three kinds of method for channel selection; the first one is weighted channel selection, the second one is sequential channel selection, and the last one is combined channel selection. We can obtain the optimum channel list and allocates channels dynamically using the proposed protocol.

• Asian-Australasian Journal of Animal Sciences
• /
• v.21 no.11
• /
• pp.1559-1571
• /
• 2008

A rule was developed to constrain the annual rate of inbreeding to a predefined value in a population with different lifetimes between sires and dams, and to maximize the selection response over generations. This rule considers that the animals in a population should be divided into sex-age classes based on the theory of gene flow, and restricts the increase of average inbreeding coefficient for new offspring by limiting the increase of the mean additive genetic relationship for parents selected. The optimization problem of this rule was formulated as a quadratic programming problem. Inputs for the rule were the BLUP estimated breeding values, the additive genetic relationship matrix of all animals, and the long-term contributions of sex-age classes. Outputs were optimal number and contributions of selected animals. In addition, this rule was combined with the optimization of emphasis given to QTL, and further increased the genetic gain over the planning horizon. Stochastic simulations of closed nucleus schemes for pigs were used to investigate the potential advantages obtained from this rule by combining the standard QTL selection, optimal QTL selection and conventional BLUP selection. Results showed that the predefined rates of inbreeding were actually achieved by this rule in three selection strategies. The rule obtained up to 9.23% extra genetic gain over truncation selection at the same rates of inbreeding. The combination of the extended rule and the optimization of emphasis given to QTL allowed substantial increases in selection response at a fixed annual rate of inbreeding, and solved substantially the conflict between short-term and long-term selection response in QTL-assisted selection schemes.

• The Research Journal of the Costume Culture
• /
• v.23 no.2
• /
• pp.213-227
• /
• 2015

This study understood what effect was produced on the purchasing decision making of outdoor wear by a shopping orientation, fashion involvement and demographic characteristics offered practical suggestions as to what effect was produced on the store selection criteria, product selection criteria for purchasing decision making in purchasing outdoor wear. This research was conducted through a questionnaire survey, and 397 males in were collected for analysis. The results were as follows. First, shopping orientation group was classified into hedonic shopping orientation group and utilitarian shopping orientation group. And it was classified into high fashion involvement group and low fashion involvement group according to fashion involvement. Product selection criteria were classified into 2 factors such as intrinsic attributes and extrinsic attributes. And store selection criteria were classified into 4 factors such as store atmosphere, store environment, promotion and salesmen. Second, there was partly significant difference in product selection criteria, and store selection criteria between utilitarian shopping group and hedonic shopping group. Third, there was significant difference in product selection criteria and store selection criteria between high fashion involvement group and low fashion involvement group. Finally, there was significant difference in the and according to age, job, and income among demographic characteristics.

• Journal of the Korean Society of Food Culture
• /
• v.27 no.1
• /
• pp.49-56
• /
• 2012

The purpose of this study was to understand the influence of instant noodle's selection attribution on satisfaction and to empirically analyze whether or not grade (elementary schoolchildren, middle school students, high school students) plays a moderating role in the relationship between selection attribution and satisfaction. Further, this study examined the differences in demographic characteristics among two groups of subjects divided by instant noodle's selection attribution. Based on a total of 1021 samples, this study verified a total of 3 hypotheses using the SPSS program. Data were analyzed by frequency analysis, chi-square, t-test, factor analysis, reliability analysis, cluster analysis, discriminant analysis, and hierarchical regression analysis. Results of the study were as follows. There were three different instant noodle's selection attributions among the children and adolescents investigated: internal element, external element, and company reliability. The multiple regression results show that internal element (=.391), external element (=.239), and company reliability (=.063) among customers' selection attributions had significant positive effects on satisfaction. In addition, the effect of selection attribution upon satisfaction was partially moderated according to grade. Further, cluster analysis divided subjects into two groups according to instant noodle's selection attribution: high-selection group vs. low-selection group. The wo groups of subjects classified by instant noodle's selection attribution were also different from each other in demographic characteristics. Limitations and future research directions are also discussed.

• Bulletin of the Korean Chemical Society
• /
• v.33 no.5
• /
• pp.1527-1535
• /
• 2012

Selection of the most informative molecular descriptors from the original data set is a key step for development of quantitative structure activity/property relationship models. Recently, mutual information (MI) has gained increasing attention in feature selection problems. This paper presents an effective mutual information-based feature selection approach, named mutual information maximization by replacing collinear variables (MIMRCV), for nonlinear quantitative structure-property relationship models. The proposed variable selection method was applied to three different QSPR datasets, soil degradation half-life of 47 organophosphorus pesticides, GC-MS retention times of 85 volatile organic compounds, and water-to-micellar cetyltrimethylammonium bromide partition coefficients of 62 organic compounds.The obtained results revealed that using MIMRCV as feature selection method improves the predictive quality of the developed models compared to conventional MI based variable selection algorithms.

• Asian-Australasian Journal of Animal Sciences
• /
• v.29 no.5
• /
• pp.640-645
• /
• 2016

The objective of this study was to evaluate the present conventional selection program of a swine nucleus farm and compare it with a new selection strategy employing genomic enhanced breeding value (GEBV) as the selection criteria. The ZPLAN+ software was employed to calculate and compare the genetic gain, total cost, return and profit of each selection strategy. The first strategy reflected the current conventional breeding program, which was a progeny test system (CS). The second strategy was a selection scheme based strictly on genomic information (GS1). The third scenario was the same as GS1, but the selection by GEBV was further supplemented by the performance test (GS2). The last scenario was a mixture of genomic information and progeny tests (GS3). The results showed that the accuracy of the selection index of young boars of GS1 was 26% higher than that of CS. On the other hand, both GS2 and GS3 gave 31% higher accuracy than CS for young boars. The annual monetary genetic gain of GS1, GS2 and GS3 was 10%, 12%, and 11% higher, respectively, than that of CS. As expected, the discounted costs of genomic selection strategies were higher than those of CS. The costs of GS1, GS2 and GS3 were 35%, 73%, and 89% higher than those of CS, respectively, assuming a genotyping cost of $120. As a result, the discounted profit per animal of GS1 and GS2 was 8% and 2% higher, respectively, than that of CS while GS3 was 6% lower. Comparison among genomic breeding scenarios revealed that GS1 was more profitable than GS2 and GS3. The genomic selection schemes, especially GS1 and GS2, were clearly superior to the conventional scheme in terms of monetary genetic gain and profit.

• Journal of Internet Computing and Services
• /
• v.15 no.3
• /
• pp.109-116
• /
• 2014

This study proposed new instance selection using neural network with weighted fuzzy membership functions(NEWFM) based on Takagi-Sugeno(T-S) fuzzy model to improve the classification performance. The proposed instance selection adopted weighted average defuzzification of the T-S fuzzy model and an interval selection, same as the confidence interval in a normal distribution used in statistics. In order to evaluate the classification performance of the proposed instance selection, the results were compared with depending on whether to use instance selection from the case study. The classification performances of depending on whether to use instance selection show 77.33% and 78.19%, respectively. Also, to show the difference between the classification performance of depending on whether to use instance selection, a statistics methodology, McNemar test, was used. The test results showed that the instance selection was superior to no instance selection as the significance level was lower than 0.05.