• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1961-1974
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• 2019

In this letter, a harmonic-mean-based dual-antenna selection scheme at relay node is proposed in two-way relaying networks (TWRNs). With well-designed distributed orthogonal concatenated Alamouti space-time block code (STBC), a dual-antenna selection problem based on the instantaneous achievable sum-rate criterion is formulated. We propose a low-complexity selection algorithm based on the harmonic-mean criterion with linearly complexity $O(N_R)$ rather than the directly exhaustive search with complexity $O(N^2_R)$. From the analysis of network outage performance, we show that the asymptotic diversity gain function of the proposed scheme achieves as $1/{\rho}{^{N_R-1}}$, which demonstrates one degree loss of diversity order compared with the full diversity. This slight performance gap is mainly caused by sacrificing some dual-antenna selection freedom to reduce the algorithm complexity. In addition, our proposed scheme can obtain an extra coding gain because of the combination of the well-designed orthogonal concatenated Alamouti STBC and the corresponding dual-antenna selection algorithm. Compared with the common-used selection algorithms in the state of the art, the proposed scheme can achieve the best performance, which is validated by numerical simulations.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.113-122
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• 2015

Feature subset selection is as a pre-processing step in learning algorithms. In this paper, we propose an efficient algorithm, ModifiedFAST, for feature subset selection. This algorithm is suitable for text datasets, and uses the concept of information gain to remove irrelevant and redundant features. A new optimal value of the threshold for symmetric uncertainty, used to identify relevant features, is found. The thresholds used by previous feature selection algorithms such as FAST, Relief, and CFS were not optimal. It has been proven that the threshold value greatly affects the percentage of selected features and the classification accuracy. A new performance unified metric that combines accuracy and the number of features selected has been proposed and applied in the proposed algorithm. It was experimentally shown that the percentage of selected features obtained by the proposed algorithm was lower than that obtained using existing algorithms in most of the datasets. The effectiveness of our algorithm on the optimal threshold was statistically validated with other algorithms.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.310-313
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• 2011

Broadcasting nature of wireless communications makes it possible to apply opportunistic network coding (OPNC) by overhearing transmitted packets from a source to sink nodes. However, it is difficult to apply network coding to the topology of multiple relay and sink nodes. We propose to use relay node selection, which finds a proper node for network coding since the OPNC alone in the topology of multiple relays and sink nodes cannot guarantee network coding gain. The proposed system is a novel combination of wireless network coding and relay selection, which is a key contribution of this paper. In this paper, with the consideration of channel state and potential network coding gain, we propose relay node selection techniques, and show performance gain over the conventional OPNC and a channel-based selection algorithm in terms of average system throughput.

• Journal of Information Technology Applications and Management
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• v.26 no.3
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• pp.19-27
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• 2019

In this study, we propose a methodology for predicting the movie audience based on movie information that can be easily acquired before opening and effectively distinguishing qualitative variables. In addition, we constructed a model to estimate the number of movie audiences at the time of data acquisition through the configured variables. Another purpose of this study is to provide a criterion for categorizing success of movies with qualitative characteristics. As an evaluation criterion, we used information gain ratio which is the node selection criterion of C4.5 algorithm. Through the procedure we have selected 416 movie data features. As a result of the multiple linear regression model, the performance of the regression model using the variables selection method based on the information gain ratio was excellent.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.10A
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• pp.943-951
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• 2006

Multi-input-multi-output (MIMO) systems are considered to improve the capacity and reliability of next generation mobile communication. However, the multiple RF chains associated with multiple antennas are costly in terms of size, power and hardware. Antenna selection is a low-cost low-complexity alternative to capture many of the advantages of MIMO systems. We proposed new joint Tx/Rx antenna selection algorithm with low complexity. The proposed algorithm is a method selects $L_R{\times}L_T$ channel matrix out of $L_R{\times}L_T$ entire channel gain matrix where $L_R{\times}L_T$ matrix selects alternate Tx antenna with Rx antenna which have the largest channel gain to maximize Frobenius norm. The feature of this algorithm is very low complexity compare with Exhaustive search which have optimum capacity. In case of $4{\times}4$ antennas selection out of $8{\times}8$ antennas, the capacity decreases $0.5{\sim}2dB$ but the complexity also decreases about 1/10,000 than optimum exhaustive search.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8B
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• pp.657-667
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• 2012

In the MPR selection algorithm of Optimized Link State Routing (OLSR), each node selects own MPRs independently, so most of nodes are selected to MPR at least once. To cope with this problem, the MPR candidate selection algorithm was proposed. The MPR candidate selection algorithm can reduce the number of MPRs, but the efficiencies of route and connectivity decline due to decreased number of MPRs. So, in this paper, we propose the Significant Multi-Point Relay (S-MPR) selection algorithm which can enhance the performance of ad hoc network by improving the MPR selection algorithm of OLSR. In proposed S-MPR selection algorithm, each node selects the most important node to S-MPR to guarantee the connectivity then selects remaining MPRs in MPR candidates. So proposed S-MPR selection algorithm can reduce the overhead of many MPRs without decline of routing performance. To show the performance gain of proposed S-MPR selection algorithm, we simulate the proposed S-MPR selection algorithm by using OPNET.

• ETRI Journal
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• v.41 no.3
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• pp.358-370
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• 2019

The main aim of this study is to select the optimal set of genes from microarray cancer datasets that contribute to the prediction of specific cancer types. This study proposes the enhancement of the feature selection filter algorithm based on Joe's normalized mutual information and its use for gene selection. The proposed algorithm is implemented and evaluated on seven benchmark microarray cancer datasets, namely, central nervous system, leukemia (binary), leukemia (3 class), leukemia (4 class), lymphoma, mixed lineage leukemia, and small round blue cell tumor, using five well-known classifiers, including the naive Bayes, radial basis function network, instance-based classifier, decision-based table, and decision tree. An average increase in the prediction accuracy of 5.1% is observed on all seven datasets averaged over all five classifiers. The average reduction in training time is 2.86 seconds. The performance of the proposed method is also compared with those of three other popular mutual information-based feature selection filters, namely, information gain, gain ratio, and symmetric uncertainty. The results are impressive when all five classifiers are used on all the datasets.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.1
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• pp.9-16
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• 2008

This paper presents a frequency selection algorithm for minimization power consumption of processor in Mobile System. The proposed algorithm has processor designed low power processor using clock gating method. Clock gating method has improved the power dissipation by control main clock through the bus which is embedded clock block applying the method of clock gating. Proposed method has compared power consumption considered the dynamic power for processor, selected frequency has considered energy gain and energy consumption for designed processor. Or reduced power consumption with decreased processor speed using slack time. This technique has improved the life time of the mobile systems by clock gating method, considered energy and using slack time. As an results, the proposed algorithm reduce average power saving up to 4% comparing to not apply processor in mobile system.

• Journal of IKEEE
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• v.24 no.1
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• pp.106-119
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• 2020

In this paper, we propose a control scheme for quadrotor system using a pole placement method. When using a state feedback controller, a lot of trial and error in selection of control gains are often required to improve system performance. In order to relax this complicated process, we analyze the closed-loop system associated with control gains. Then, we present a control gain selection algorithm for control gains using a pole placement method to improve the system performance. The proposed control method is applied to the actual quadrotor system to illustrate the validity of the proposed method.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.167-174
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• 2022

In metaheuristic algorithms such as Genetic Algorithm (GA), initial population has a significant impact as it affects the time such algorithm takes to obtain an optimal solution to the given problem. In addition, it may influence the quality of the solution obtained. In the machine learning field, feature selection is an important process to attaining a good performance model; Genetic algorithm has been utilized for this purpose by scientists. However, the characteristics of Genetic algorithm, namely random initial population generation from a vector of feature elements, may influence solution and execution time. In this paper, the use of a statistical algorithm has been introduced (Chi2) for feature relevant checks where p-values of conditional independence were considered. Features with low p-values were discarded and subject relevant subset of features to Genetic Algorithm. This is to gain a level of certainty of the fitness of features randomly selected. An ensembled-based learning model for Hepatitis has been developed for Hepatitis C stage classification. 1385 samples were used using Egyptian-dataset obtained from UCI repository. The comparative evaluation confirms decreased in execution time and an increase in model performance accuracy from 56% to 63%.