• Journal of Communications and Networks
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• v.18 no.1
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• pp.50-64
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• 2016

Nowadays, smart meter (SM) technology is widely effectively used. In addition, power allocation (PA) and channel selection (CS) are considered problems with many proposed approaches. In this paper, we will suggest a specific scenario for an SM configuration system and show how to solve the optimization problem for transmission between SMs and the data concentrator unit (DCU), the center that collects the data from several SMs, via simulation. An efficient CS with PA scheme is proposed in the TV white space system, which uses the TV band spectrum. On the basic of the optimal configuration requirements, SMs can have a transmission schedule and channel selection to obtain the optimal efficiency of using spectrum resources when transmitting data to the DCU. The optimal goals discussed in this paper are the maximum capacity or maximum channel efficiency and the maximum allowable power of the SMs used to satisfy the quality of service without harm to another wireless system. In addition, minimization of the interference to the digital television system and other SMs is also important and needs to be considered when the solving coexistence scenario. Further, we propose a process that performs an interference analysis scheme by using the spectrum engineering advanced Monte Carlo analysis tool (SEAMCAT), which is an integrated software tool based on a Monte-Carlo simulation method. Briefly, the process is as follows: The optimization process implemented by genetic evolution optimization engines, i.e., a genetic algorithm, will calculate the best configuration for the SM system on the basis of the interference limitation for each SM by SEAMCAT in a specific configuration, which reaches the solution with the best defined optimal goal satisfaction.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10A
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• pp.760-768
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• 2009

In this paper, we propose the energy efficient transmission parameter selection method for Wireless Personal Area Network (WPAN) system which is applied to e-Navigation system considering various ship models environment. An appropriate selection of transmission parameters of HR-WPAN system is very essential to be considered for saving WPAN devices' energy consumption, when HR-WPAN system is applied to ship area network (SAN). Therefore, we propose an energy consumption model for a ship area network employing IEEE 802.15.3 based CSMA/CA HR-WPAN model and analyze the effect of transmission parameter selection on the performance of energy consumption. In particular, the path loss is the major performance decision parameter for the SAN employing HR-WPAN system, since it varies according to the material of shipbuilding such as steel(for large ship), FRP(for medium size ship) and compound wood(for small ship). Thus, we analyze and demonstrate that the proper transmission parameter selection of transmit power, PHY data rate and fragment size for each ship model could guarantee energy efficiency.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1202-1211
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• 2018

In this paper, induction machine operation efficiency and torque is improved using Machine Learning based Gene Optimization (ML-GO) Technique is introduced. Optimized Genetic Algorithm (OGA) is used to select the optimal induction machine data. In OGA, selection, crossover and mutation process is carried out to find the optimal electrical machine data for induction machine design. Initially, many number of induction machine data are given as input for OGA. Then, fitness value is calculated for all induction machine data to find whether the criterion is satisfied or not through fitness function (i.e., objective function such as starting to full load torque ratio, rotor current, power factor and maximum flux density of stator and rotor teeth). When the criterion is not satisfied, annealed selection approach in OGA is used to move the selection criteria from exploration to exploitation to attain the optimal solution (i.e., efficient machine data). After the selection process, two point crossovers is carried out to select two crossover points within a chromosomes (i.e., design variables) and then swaps two parent's chromosomes for producing two new offspring. Finally, Adaptive Levy Mutation is used in OGA to select any value in random manner and gets mutated to obtain the optimal value. This process gets iterated till finding the optimal value for induction machine design. Experimental evaluation of ML-GO technique is carried out with performance metrics such as torque, rotor current, induction machine operation efficiency and rotor power factor compared to the state-of-the-art works.

• Journal of Internet Computing and Services
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• v.13 no.4
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• pp.1-10
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• 2012

In this paper, an optimal feature selection method considering sematic of features, which is preprocess of document classification is proposed. The feature selection is very important part on classification, which is composed of removing redundant features and selecting essential features. LSA (Latent Semantic Analysis) for considering meaning of the features is adopted. However, a supervised LSA which is suitable method for classification problems is used because the basic LSA is not specialized for feature selection. We also apply GA (Genetic Algorithm) to the features, which are obtained from supervised LSA to select better feature subset. Finally, we project documents onto new selected feature subset and classify them using specific classifier, SVM (Support Vector Machine). It is expected to get high performance and efficiency of classification by selecting optimal feature subset using the proposed hybrid method of supervised LSA and GA. Its efficiency is proved through experiments using internet news classification with low features.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.435-446
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• 2004

In this paper we introduced a new score generating function for the rank dispersion function in a multiple linear model. Based on the new score function, we derived the asymptotic relative efficiency, ARE(11, rs), of our score function with respect to the Wilcoxon scores for the generalized F distributions which show very flexible distributions with a variety of shape and tail behaviors. We thoroughly explored the selection of r and s of our new score function that provides improvement over the Wilcoxon scores.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.712-716
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• 2004

Because of rapid development of software technology, a number of software professionals have been concerned with component-based development methodologies. Up to date, the evaluation of component quality has been focused on object-oriented metric based methodology. But this paper presents the selection process and evaluation criteria based on an MCDM(Multiple Criteria Decision Making) technique for the selection of optimal COTS component from consumers' viewpoints. We considered functionality, efficiency and usability based on ISO/IEC 9126 for quality measurement and conducted practical analysis into commercial EJB component in internet. This paper shows that the proposed selection technique is applicable for the selection of the optimal COTS component.

• The Journal of the Korea institute of electronic communication sciences
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• v.2 no.3
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• pp.174-179
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• 2007

In this paper, we present a optimal module selection using genetic algorithm under the power, area, delay constraint. The proposed algorithm use the way of optimal module selection it will be able to minimize power consumption. In the comparison and experimental results, The proposed application algorithm reduce maximum power saving up to 26.9% comparing to previous non application algorithm, and reduce minimum power saving up to 9.0%. It also show the average power saving up to 15.525% and proved the power saving efficiency.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.259-270
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• 2013

This paper considers a penalized composite quantile regression (CQR) that performs a variable selection in the linear model with grouped variables. An adaptive sup-norm penalized CQR (ASCQR) is proposed to select variables in a grouped manner; in addition, the consistency and oracle property of the resulting estimator are also derived under some regularity conditions. To improve the efficiency of estimation and variable selection, this paper suggests the two-stage penalized CQR (TSCQR), which uses the ASCQR to select relevant groups in the first stage and the adaptive lasso penalized CQR to select important variables in the second stage. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.275-282
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• 2014

One of the most widely used methods for dimensionality reduction is principal component analysis (PCA). However, the reduced dimensions from PCA do not provide a clear interpretation with respect to the original features because they are linear combinations of a large number of original features. This interpretation problem can be overcome by feature selection approaches that identifying the best subset of given features. In this study, we propose an unsupervised feature selection method based on the geometrical information of PCA loading vectors. Experimental results from a simulation study demonstrated the efficiency and usefulness of the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.11-20
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• 2015

In order to prevent the elderly people from committing suicide attempts, it is necessary to verify attributes that affect the suicide attempts. It is noted that previous studies have focused on qualitative approaches, and simple correlation analyses to determine the attributes related to the suicide attempts in the elderly people. However, such previous approaches had led to insufficient performance when facing with complicated data sets. In this sense, this study suggests an alternative method in which attribute selection techniques are adopted to determine more relevant attributes of the suicide attempts occurring in Korean elderly people. To verify empirical validity of our proposed method, we used Korea National Health and Nutrition Examination Survey (KNHANES) from January 2007 to December 2012. Empirical results proved that the proposed attribute selection techniques showed better predictive effectiveness; 94.4% compared to the simple statistical methods. This study proposes a way to determining the elderly suicide and preventing it to happen.