• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1325-1344
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• 2019

Recently, to satisfy mobile users' increasing data transmission requirement, energy efficiency (EE) resource allocation in distributed antenna systems (DASs) has become a hot topic. In this paper, we aim to maximize EE in DASs subject to constraints of the minimum data rate requirement and the maximum transmission power of distributed antenna units (DAUs) with different density distributions. Virtual cell is defined as DAUs selected by the same user equipment (UE) and the size of virtual cells is dependent on the number of subcarriers and the transmission power. Specifically, the selection rule of DAUs is depended on different scenarios. We develop two scenarios based on the density of DAUs, namely, the sparse scenario and the dense scenario. In the sparse scenario, each DAU can only be selected by one UE to avoid co-channel interference. In order to make the original non-convex optimization problem tractable, we transform it into an equivalent fractional programming and solve by the following two sub-problems: optimal subcarrier allocation to find suitable DAUs; optimal power allocation for each subcarrier. Moreover, in the dense scenario, we consider UEs could access the same channel and generate co-channel interference. The optimization problem could be transformed into a convex form based on interference upper bound and fractional programming. In addition, an energy-efficient DAU selection scheme based on the large scale fading is developed to maximize EE. Finally, simulation results demonstrate the effectiveness of the proposed algorithm for both sparse and dense scenarios.

• Journal of Practical Agriculture & Fisheries Research
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• v.20 no.1
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• pp.175-185
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• 2018

This study was conducted to evaluate the physiological and ecological characters of rice cultivars suitable for low fertilized condition. 5 rice cultivars(Jinmibyeo, Sobibyeo, Hwayeongbyeo, Nagdongbyeo and Junambyeo) were cultivated for selection under 3 different nitrogen application levels, and 1 cultivars were selected. The results obtained are summarized as follows ; High yielded rice cultivars under low N application level were Junambyeo, Jinheng and Sobibyeo. Also these cultivars were yielded highly under conventional level(11kg/10a). Milled rice yield under conventional level(11kg/10a) was positively correlated with them under low N levels. Milled rice yield was most affected by no. of grain/m². Rice cultivars that were high crop growth rate(CGR) before heading stage were Junambyeo, Sobibyeo and Nagdongbyeo. Grain filling rate was increased mostly until 20 days after heading, and decreased after this stage. Nitrogen use efficiency was higher under low N level(5.5kg/10a) than conventional level(11kg/10a). Especially, Junambyeo was most low in Apparent recovery of applied N(AR) under low N application level, but most high in Agronomic N use efficiency(ANUE). This characteristics of Junambyeo will to be useful for selection of variety suitable for growing under low fertilized condition.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.4
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• pp.13-22
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• 2018

In this study, we examined the accuracy of the assessment items for selecting start-ups used in the project to support start-ups and verified their validity in determining whether they are appropriate assessment items based on selection criteria. The results of 973 start-ups that applied for the project fostering startup leading universities were collected and logistic regression was performed using SPSS 18.0. The study results are summarized as follows. First, the differences in characteristics of start-ups were identified in terms of selection. Second, the impact of selection by assessment items was gender in 2015, capability of the founder, business establishment in 2016, performance and potential in the global market, and business startup in 2017. Third, the overall selection accuracy analysis for the last three years confirmed that the accuracy of the selection is lower each year and that the accuracy of the selection is lower than the accuracy of the non-selection. This means that the current assessment items for selecting start-ups are inaccurate for selection, and that changes in the items due to changes in the start-up environment each year have led to lower accuracy of selection. It is meaningful that this study raised the importance of assessment items and the need for improvement of assessment items for the screening functions of good start-ups to enhance efficiency of the policies for startup support.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.38 no.6
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• pp.499-506
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• 1993

The contents of sulfur, sulfur-containing protein and amino acids of soybean seeds of 518 genotypes as well as their inheritance and selection efficiency in early breeding generation were measured to facilitate breeding for soybean with high sulfur-containing amino acids. Average seed sulfur content of 518 cultivars was 0.33%, and ranged from 0.20 to 0.45%, and that of 30 wild soybeans was 0.35%, and ranged form 0.19 to 0.62%. Correlation coefficients between seed sulfur content and sulfur-containing protein and amino acids were 0.924$^{**}$ and 0.974$^{**}$, respectively. Seed sulfur content was tended to be high in soybean genotypes with late maturity, seed coat bloom, or green cotyledon. Sulfur content had -0.312$^{**}$ correlation coeficient with sugar content and -0.384$^{**}$ with 100 seed weight. Seed sulfur content was inherited quantitatively, in which additive effect was greater than dominant one, and proportion of genes with positive effects was similar to those with negative ones. Estimated narrow- and broad-sense heritabilities were 0.75 and 0.88 for seed sulfur content, respectively. Heritability measured from selection in early breeding lines for high or low seed sulfur content was 60～62.5% or 50～62,5%, respectively. And selection for high sulfur content increased by 14.7～18.8%, whereas that for low one decreased by 8.8～15.6%, when compared to that of random population. Therefore selection in early generation seemed to be clearly effective.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.5A
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• pp.370-375
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• 2005

While the international standards bodies recommend the dual leaky buckets for the traffic specification for VBR service, video traffic shows burstiness in multiple time-scales. In order to fill this gap between the current standards and real traffic characteristics, we present a standard-compatible traffic parameter selection method based on the notion of a critical time scale (CTS). Since the defined algorithm minimizes the required link capacity under a maximum delay constraint, it could be used as a benchmark even when it can not be implemented easily. Simulation results with compressed video traces demonstrate the efficiency of the defined traffic parameter selection algorithm in resource allocation.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.365-373
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• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.8
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• pp.1039-1046
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• 2004

A model Indigenous Thai beef cattle breeding structure consisting of nucleus, multiplier and commercial units was used to evaluate the effect of changes in heritabilities of and genetic correlations between adaptation traits on genetic gain and profitability. A breeding objective that incorporated adaptation was considered. Two scenarios for improving both the production and the adaptation of animals where also compared in terms of their genetic and economic efficiency. A base scenario was modelled where selection is for production traits and adaptation is assumed to be under the forces of natural selection. The second scenario (+Adaptation) included all the information available for base scenario with the addition of indirect measures of adaptation. These measures included tick count (TICK), faecal egg count (FEC) and rectal temperature (RECT). Therefore, the main difference between these scenarios was seen in the records available for use as selection criteria and hence the level of investments. Additional genetic gain and profitability was generated through incorporating indirect measures of adaptation as criteria measured in the breeding program. Unsurprisingly, the results were sensitive to the changes in heritabilities and genetic correlations between adaptation traits. However, there were more changes in the genetic gain and profitability of the breeding program when the genetic correlations of adaptation and its indirect measures were varied than when the correlations between these measures were. The changes in the magnitudes of the genetic gain and profit per cow stresses the importance of using reliable estimates of these traits in any breeding program.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.6
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• pp.1286-1291
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• 2013

Intensive researches on multiuser-based Massive MIMO are performed to increase the spectral efficiency. Although the Massive MIMO scheme based on huge number of antennas inevitably causes hardware and computational complexity in baseband and radio frequency (RF) elements, the problem can be mitigated without serious performance degradation by limiting the number of baseband and RF elements below the number of transmit antennas of base station and opportunistically selecting transmit antennas according to channel states. Accordingly, this paper proposes a simple antenna selection scheme for multiuser-based Massive MIMO systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.1911-1930
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• 2017

Overlay routing has emerged as a promising approach to improve reliability and efficiency of the Internet. The key to overlay routing is the placement and maintenance of the overlay infrastructure, especially, the selection and placement of key relay nodes. Spurred by the observation that a few relay nodes with high betweenness centrality can provide more optimal routes for a large number of node pairs, we propose a resilient routing overlay network construction method by introducing Super-Relay nodes. In detail, we present the K-Minimum Spanning Tree with Super-Relay nodes algorithm (SR-KMST), in which we focus on the selection and connection of Super-Relay nodes to optimize the routing quality in a resilient and scalable manner. For the simultaneous path failures between the default physical path and the overlay backup path, we also address the selection of recovery path. The objective is to select a proper one-hop recovery path with minimum cost in path probing and measurement. Simulations based on a real ISP network and a synthetic Internet topology show that our approach can provide high-quality overlay routing service, while achieving good robustness.

• Smart Structures and Systems
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• v.20 no.2
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• pp.219-229
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• 2017

Outlier detection is an imperative task to identify the occurrence of abnormal events before the structures are suffered from sudden failure during their service lives. This paper proposes a two-phase method for the outlier detection of Global Positioning System (GPS) monitoring data. Prompt judgment of the occurrence of abnormal data is firstly carried out by use of the relational analysis as the relationship among the data obtained from the adjacent locations following a certain rule. Then, a negative selection algorithm (NSA) is adopted for further accurate localization of the abnormal data. To reduce the computation cost in the NSA, an improved scheme by integrating the adjustable radius into the training stage is designed and implemented. Numerical simulations and experimental verifications demonstrate that the proposed method is encouraging compared with the original method in the aspects of efficiency and reliability. This method is only based on the monitoring data without the requirement of the engineer expertise on the structural operational characteristics, which can be easily embedded in a software system for the continuous and reliable monitoring of civil infrastructure.