• Journal of the Korea Institute of Building Construction
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• v.10 no.4
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• pp.95-103
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• 2010

The purpose of this study is to propose a decision-making technique for selecting waterproofing methods using the Analytic Hierarchy Process (AHP) technique. In this study, a questionnaire survey was given to a group of specialists, which included design specialists, construction specialists, and maintenance specialists, regarding their experience with roof waterproofing methods. The 1st level hierarchy of the questionnaire survey addressed the function, economics, and maintenance of each of the roof waterproofing methods. The rank of 13 items of questionnaire, which is the 2nd level hierarchy of the questionnaire survey, is calculated using Expert Choice Solution. The analysis of questionnaire survey shows that each specialist selects different roof waterproofing methods, and all specialists make much of waterproof performance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1774-1779
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• 2005

Most of materials receive force in using, therefore, the characteristics of materials must be considered in system design not to occur deformation or destruction. Mechanical properties about materials can be expressed as responsible level of material itself under the exterior operation. Main mechanical properties is strength, hardness, ductility and stiffness etc. Currently, among major measure facilities to measure such mechanical properties, advanced indentation technique has focused in industrial areas as reason of nondestructive and easy applications for mechanical tensile properties and evaluation of residual stress of materials. This study is to find the optimum experimental condition about residual stress advanced indentation technique for accurate analysis of the welded joint of steel materials through indentation load-depth curve obtained from cruciform specimen experiment. Optimum selection was applied to the welded joint of real steel materials to give non-equi-biaxial stress state and compared with general residual stress analyzing method for verification.

• Journal of Computing Science and Engineering
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• v.10 no.4
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• pp.111-117
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• 2016

An accurate approach for diagnosis of attention deficit hyperactivity disorder (ADHD) is presented in this paper. The presented technique efficiently classifies three subtypes of ADHD (ADHD-C, ADHD-H, ADHD-I) and typically developing control (TDC) by using only structural magnetic resonance imaging (MRI). The research examines structural MRI of the hippocampus from the ADHD-200 database. Each available MRI has been processed by a region-of-interest (ROI) to build a set of features for further analysis. The presented ADHD diagnostic approach unifies feature selection and classification techniques. The feature selection technique based on the proposed binary-coded genetic algorithm searches for an optimal subset of features extracted from the hippocampus. The classification technique uses a chosen optimal subset of features for accurate classification of three subtypes of ADHD and TDC. In this study, the famous Extreme Learning Machine is used as a classification technique. Experimental results clearly indicate that the presented BCGA-ELM (binary-coded genetic algorithm coupled with Extreme Learning Machine) efficiently classifies TDC and three subtypes of ADHD and outperforms existing techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.1866-1888
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• 2017

Efficient energy consumption in WSN is one of the key design issues for improving network stability period. In this paper, we propose a new Heterogeneity-aware Energy-efficient Clustering (HEC) technique which considers two types of heterogeneity - network lifetime and of sensor nodes. Selection of cluster head nodes is done based on the three network lifetime phases: only advanced nodes are allowed to become cluster heads in the initial phase; in the second active phase all nodes are allowed to participate in cluster head selection process with equal probability, and in the last dying out phase, clustering is relaxed by allowing direct transmission. Simulation-based performance analysis of the proposed technique as compared to other relevant techniques shows that HEC achieves longer stable region, improved throughput, and better energy dissipation owing to judicious consumption of additional energy of advanced nodes. On an average, the improvement observed for stability period over LEACH, SEP, FAIR and HEC- with SEP protocols is around 65%, 30%, 15% and 17% respectively. Further, the scalability of proposed technique is tested by varying the field size and number of sensing nodes. The results obtained are found to be quite optimistic. The impact of energy heterogeneity has also been assessed and it is found to improve the stability period though only upto a certain extent.

• Journal of Communications and Networks
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• v.10 no.2
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• pp.175-185
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• 2008

In this paper, we investigate the effect of cooperative and selection relaying schemes on multiuser diversity in downlink cellular systems with fixed relay stations (RSs). Each mobile station (MS) is either directly connected to a base station (BS) and/or connected to a relay station. We first derive closed-form solutions or upper-bound of the ergodic and outage capacities of four different downlink data relaying schemes: A direct scheme, a relay scheme, a selection scheme, and a cooperative scheme. The selection scheme selects the best access link between the BS and an MS. For all schemes, the capacity of the BS-RS link is assumed to be always larger than that of RS-MS link. Half-duplex channel use and repetition based relaying schemes are assumed for relaying operations. We also analyze the system capacity in a multiuser diversity environment in which a maximum signal-to-noise ratio (SNR) scheduler is used at a base station. The result shows that the selection scheme outperforms the other three schemes in terms of link ergodic capacity, link outage capacity, and system ergodic capacity. Furthermore, our results show that cooperative and selection diversity techniques limit the performance gain that could have been achieved by the multiuser diversity technique.

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

• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.217-223
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• 2013

This research tries to study the effect that the welfare field training the course selection. Because of being the important place where the welfare field training trains the expert, the knowledge, value, and practical lodging tries to be identify instructed. In this aspect, we try to analyze about the extend of satisfaction of the actual training, that is the extent of desiring the link including the class of school subject extend of satisfaction of the department of social welfare engine, and etc. and factor about the actual training of the department of social welfare, because the welfare field training the technique is connected directly with the route. First, the individual characteristic of the social welfare and trainees is grasped. Second, the relationship of the social welfare and factor and course selection is investigated. Third, the social welfare and university (circle) lives look into whether the social welfare field training has an effect on the course selection or not.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7A
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• pp.640-648
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• 2011

We propose relay selection schemes using STBC (Space Time Block Coding) technique in OFDM (Orthogonal Frequency Division Multiplexing)-based wireless systems. The proposed schemes select the optimum relay having the maximum instantaneous equivalent channel gain among multiple candidate relays. Also, in order to reduce the system overhead, a symbol grouping method which groups some amount of symbols before selecting the optimum relay is proposed. The simulation results show that the proposed relay selection schemes can obtain more selection diversity gain as the number of selectable relay candidates increases. Furthermore, the proposed scheme with the symbol grouping can reduce system overhead without any degradation of the performance in fading channels with low frequency selectivity.

• 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.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.