• International journal of advanced smart convergence
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• v.7 no.4
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• pp.92-100
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• 2018

This paper presents a link adaptation scheme for adaptive full duplex (AFD) systems. The signal modulation levels and communication link patterns are adaptively selected according to the changing channel conditions. The link pattern selection process consists of two successive steps such as a transmit-receive antenna pair selection based on maximum sum rate or minimum maximum symbol error rate, and an adaptive modulation based on maximum minimum norm. In AFD systems, the antennas of both nodes are jointly determined with modulation levels depending on the channel conditions. An adaptive algorithm with relatively low complexity is also proposed to select the link parameters. Simulation results show that the proposed AFD system offers significant bit error rate (BER) performance improvement compared with conventional full duplex systems with perfect or imperfect self-interference cancellation under the same fixed sum rate.

• Journal of Communications and Networks
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• v.17 no.2
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• pp.162-171
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• 2015

This paper provides an analytic performance evaluation of the bit error rate (BER) of underlay decode-and-forward cognitive networks with best relay selection over Rayleigh multipath fading channels. A generalized BER expression valid for arbitrary operational parameters is firstly presented in the form of a single integral, which is then employed for determining the diversity order and coding gain for different best relay selection scenarios. Furthermore, a novel and highly accurate closed-form approximate BER expression is derived for the specific case where relays are located relatively close to each other. The presented results are rather convenient to handle both analytically and numerically, while they are shown to be in good agreement with results from respective computer simulations. In addition, it is shown that as in the case of conventional relaying networks, the behaviour of underlay relaying cognitive networks with best relay selection depends significantly on the number of involved relays.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5A
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• pp.457-463
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• 2011

This paper proposes theorthogonal reference vectors selection method of the subspace interference alignment. The proposed method selects multiple orthogonal reference vectors instead of using one reference vector for all users at the same time. The proposed scheme selects a reference vector which maximizes a sum-rate for a certain cell, generates orthogonal vectors to the previous selected vector and selects the one of orthogonal vectors whose sum rate is maximized for each cell. Larger channel gain and sum-rate than the previous method can be obtained by selection degree of freedom. The computer simulation demonstrates the proposed method gives higher sum-rate compared with that of the previous reference vector selection method.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.52-56
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• 1996

In trainir~ga back-propagation neural network, the learning speed of the network is greatly affected by its learning rate. Most of off-line fashioned learning-rate selection methods, however, are empirical except for some deterministic methods. It is very tedious and difficult to find a good learning rate using the empirical methods. The deterministic methods cannot guarantee the quality of the quality of the learning rate. This paper proposes a new learning-rate selection method. Our off-line fashioned method selects a good learning rate through stochastically searching process using evolutionary programming. The simulation results show that the learning speed achieved by our method is superior to that of deterministic and empirical methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.42-49
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• 2014

The conventional best relay selection based on all the channel information for the first and second hops in dual-hop systems has a large consumption of resources for channel feedback. In this paper, we analyze the average bit error rate for partial relay selection based on the channel information only for the first hop in dual-hop decode-and-forward relaying systems, where we assume independent Rayleigh fading channels. In particular, we provide an exact and closed-form expression for the average bit error rate of M-ary QAM. Also, through numerical investigation, the performance of the partial relay selection is compared with the performance of the best relay selection, and the performance is evaluated for different numbers of relays and various average channel power ratios for the first and second hops.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.3
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• pp.1-21
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• 2016

The Markowitz portfolio selection model uses estimators to deduce input parameters. However, the estimation errors of input parameters negatively influence the performance of portfolios. Therefore, this model cannot be reliably applied to real-world investments. To overcome this problem, we suggest an algorithm that can exclude stocks with large estimation error from the portfolio by applying a tracking signal to the Markowitz portfolio selection model. By calculating the tracking signal of each stock, we can monitor whether unexpected departures occur on the outcomes of the forecasts on rate of returns. Thereafter, unreliable stocks are removed. By using this approach, portfolios can comprise relatively reliable stocks that have comparatively small estimation errors. To evaluate the performance of the proposed approach, a 10-year investment experiment was conducted using historical stock returns data from 6 different stock markets around the world. Performance was assessed and compared by the Markowitz portfolio selection model with additional constraints and other benchmarks such as minimum variance portfolio and the index of each stock market. Results showed that a portfolio using the proposed approach exhibited a better Sharpe ratio and rate of return than other benchmarks.

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

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.31-40
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• 2013

In this paper, a novel method is proposed to build an ensemble of classifiers by using a feature selection schema. The feature selection schema identifies the best feature sets that affect the arrhythmia classification. Firstly, a number of feature subsets are extracted by applying the feature selection schema to the original dataset. Then classification models are built by using the each feature subset. Finally, we combine the classification models by adopting a voting approach to form a classification ensemble. The voting approach in our method involves both classification error rate and feature selection rate to calculate the score of the each classifier in the ensemble. In our method, the feature selection rate depends on the extracting order of the feature subsets. In the experiment, we applied our method to arrhythmia dataset and generated three top disjointed feature sets. We then built three classifiers based on the top-three feature subsets and formed the classifier ensemble by using the voting approach. Our method can improve the classification accuracy in high dimensional dataset. The performance of each classifier and the performance of their ensemble were higher than the performance of the classifier that was based on whole feature space of the dataset. The classification performance was improved and a more stable classification model could be constructed with the proposed approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.560-569
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• 2005

This paper proposes an algorithm for transmit antenna selection in a multi-input multi-output(MIMO) spatial multiplexing system with per antenna rate control(PARC) and an ordered successive interference cancellation (OSIC) receiver. The active antenna subset is determined at the receiver and conveyed to the transmitter using feedback information on transmission rate per antenna. We propose a serial decision procedure consisting of a successive process that tests whether antenna selection gain exists when the antenna with the lowest pre-processing signal to interference and noise ratio(SINR) is discarded at each stage. Furthermore, we show that 'reverse detection ordering', whereby the signal with the lowest SINR is decoded at each stage of successive decoding, widens the disparities among fractions of the whole capacity allocated to each individual antenna and thus maximizes a gain of antenna selection. Numerical results show that the proposed reverse detection ordering based serial antenna selection approaches the closed-loop MIMO capacity and that it induces a negligible capacity loss compared with the heuristic selection strategy even with considerably reduced complexity.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.3
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• pp.77-85
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• 2018

Multi-User Multiple-Input Multiple-Output (MU-MIMO) is the core technology for improving the channel capacity compared to Single-User MIMO (SU-MIMO) by using multiuser gain and spatial diversity. Key problem for the MU-MIMO is the user selection which is the grouping the users optimally. To solve this problem, we adopt Extreme Value Theory (EVT) at the beginning of the proposed algorithm, which defines a primary user set instead of a single user that has maximum channel power according to a predetermined threshold. Each user in the primary set is then paired with all of the users in the system to define user groups. By comparing these user groups, the group that produces a maximum sum rate can be determined. Through computer simulations, we have found that the proposed method outperforms the conventional technique yielding a sum rate that is 0.81 bps/Hz higher when the transmit signal to noise ratio (SNR) is 30 dB and the total number of users is 100.