• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.11
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• pp.989-996
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• 2016

This paper intends to develop the rotor track and balance (RTB) algorithm using the nonlinear RTB models and a real-coded hybrid genetic algorithm. The RTB response data computed using the trim solutions with variation of the adjustment parameters have been used to build nonlinear RTB models based on the quadratic interpolation functions. Nonlinear programming problems to minimize the track deviations and the airframe vibration responses have been formulated to find optimum settings of balance weights, trim-tab deflections, and pitch-link lengths of each blade. The results are efficiently resolved using the real-coded genetic algorithm hybridized with the particle swarm optimization techniques for convergence acceleration. The nonlinear RTB models and the optimized RTB parameters have been compared with those computed using the linear models to validate the proposed techniques. The results showed that the nonlinear models lead to more accurate models and reduced RTB responses than the linear counterpart.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2546-2566
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• 2016

This paper investigates an efficient transmission scheme for the remote wireless sensors to receive information which is rarely discussed in the integrated remote wireless sensor and multibeam satellite networks (IWSMSNs). The networks can be employed to exchange sensing information for emergency scenario, ocean scenario, and so on, which are isolated from available terrestrial networks. As the efficient transmission link is important to the IWSMSNs, we propose a hybrid full frequency (HFF) precoding by taking advantage of frequency reuse and multiple-input multiple-output (MIMO) precoding. Considering energy efficiency and sinks fairness are crucial to transmission link, thus the HFF precoding problems are formulated as transmit power minimization (TPM) and max-min fair (MMF) received signal to interference plus noise ratio (SINR) problems, which can be transformed to indefinite quadratic optimization programs. Then this paper presents a semi-definite programming (SDP) algorithm to solve the problems for the IWSMSNs. The promising potential of HFF for the real IWSMSNs is demonstrated through simulations.

• Industrial Engineering and Management Systems
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• v.13 no.2
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• pp.163-171
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• 2014

Under amazing increase in markets and certain demand on qualified service in the delivery system, global logistic optimization is becoming a keen interest to provide an essential infrastructure coping with modern competitive prospects. As a key technology for such deployment, we have been engaged in the practical studies on vehicle routing problem (VRP) in terms of Weber model, and developed a hybrid approach of meta-heuristic methods and the graph algorithm of minimum cost flow problem. This paper extends such idea to multi-depot VRP so that we can give a more general framework available for various real world applications including those in green or low carbon logistics. We show the developed procedure can handle various types of problem, i.e., delivery, direct pickup, and drop by pickup problems in a common framework. Numerical experiments have been carried out to validate the effectiveness of the proposed method. Moreover, to enhance usability of the method, Google Maps API is applied to retrieve real distance data and visualize the numerical result on the map.

• Computers and Concrete
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• v.27 no.5
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• pp.489-512
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• 2021

In this study, two powerful techniques, namely particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were selected and combined with a pre-developed ANN model aiming at improving its performance prediction of the compressive strength of concrete modified with fly ash. To achieve this study's aims, a comprehensive database with 379 data samples was collected from the available literature. The output of the database is the compressive strength (CS) of concrete samples, which are influenced by 9 parameters as model inputs, namely those related to mix composition. The modeling steps related to ICA-ANN (or neuro-imperialism) and PSO-ANN (or neuro-swarm) were conducted through the use of several parametric studies to design the most influential parameters on these hybrid models. A comparison of the CS values predicted by hybrid intelligence techniques with the experimental CS values confirmed that the neuro-swarm model could provide a higher degree of accuracy than another proposed hybrid model (i.e., neuro-imperialism). The train and test correlation coefficient values of (0.9042 and 0.9137) and (0.8383 and 0.8777) for neuro-swarm and neuro-imperialism models, respectively revealed that although both techniques are capable enough in prediction tasks, the developed neuro-swarm model can be considered as a better alternative technique in mapping the concrete strength behavior.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.348-358
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• 2022

Software fault prediction is a method to compute fault in the software sections using software properties which helps to evaluate the quality of software in terms of cost and effort. Recently, several software fault detection techniques have been proposed to classifying faulty or non-faulty. However, for such a person, and most studies have shown the power of predictive errors in their own databases, the performance of the software is not consistent. In this paper, we propose a hybrid soft computing technique for SFP based on optimal feature extraction and classification (HST-SFP). First, we introduce the bat induced butterfly optimization (BBO) algorithm for optimal feature selection among multiple features which compute the most optimal features and remove unnecessary features. Second, we develop a layered recurrent neural network (L-RNN) based classifier for predict the software faults based on their features which enhance the detection accuracy. Finally, the proposed HST-SFP technique has the more effectiveness in some sophisticated technical terms that outperform databases of probability of detection, accuracy, probability of false alarms, precision, ROC, F measure and AUC.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.253-256
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• 2001

Today's rapid development in the computer and network technology makes the environment which enables the companies to consider their decisions on the wide point of view and enables the software vendors to make the software packages to help these decisions. To make these software packages, many algorithms should be developed. The production and distribution planning problem belongs to those problems that industry manufacturers daily face in organizing their overall production plan. However, this combinatorial optimization problem can not be solved optimally in a reasonable time when large instances are considered. This legitimates the search for heuristic techniques. As one of these heuristic techniques, genetic algorithm has been considered in many researches. A standard genetic algorithm is a problem solving method that apply the rules of reproduction, gene crossover, and mutation to these pseudo-organisms so those organisms can Pass beneficial and survival-enhancing traits to new generation. This standard genetic algorithm should not be applied to this problem directly because when we represent the chromosome of this problem, there may exist high epitasis between genes. So in this paper, we proposed the hybrid genetic algorithm which turns out to better result than standard genetic algorithms

• Journal of the Society of Naval Architects of Korea
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• v.45 no.6
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• pp.646-655
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• 2008

A main issue about production management of shipyards is to efficiently manage the work in process and logistics. However, so far the management of a transporter for moving building blocks has not been efficiently performed. To solve the issues, optimal block transporting scheduling system is developed for minimizing of the travel distance without overload of a transporter. To implement the developed system, a hybrid optimization algorithm for an optimal block transportation scheduling is proposed by combining the genetic algorithm and the ant algorithm. Finally, to evaluate the applicability of the developed system, it is applied to a block transportation scheduling problem of shipyards. The result shows that the developed system can generate the optimal block transportation scheduling of a transporter which minimizes the travel distance without overload of the transporter.

• Composites Research
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• v.32 no.1
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• pp.13-20
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• 2019

In this study, in order to improve the quality issue and component characteristics of the battery module, which is one of the major parts of the electric vehicle. The structure is reinforced by using the composite material and the mechanism structure optimization of Hybrid concept which can overcome the disadvantages of single material was performed and the performance was compared. For this purpose, figure out the main design variables of composite materials according to Classical Laminated Plate Theory (CLPT) and the algorithm for predicting composite material properties have been studied. Based on the mechanical properties of the designed composite materials, finite element analysis (FEM) and the performance of the battery module was verified. Consequently, according to the verification result, Hybrid Battery Module reinforced with Selective Composite Patch can reduce the weight by 30% and reduce the product thickness by 32.5% compared with the existing Al battery module and proved the merit of Hybrid structure such as maintaining impact performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1859-1864
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• 2007

Existing barcode signal restoration algorithms are not robust to unmodeled outliers that may exist in scanned barcode images due to scratches, dirts, etc. In this paper, we describe a robust barcode signal restoration algorithm that uses the hybrid $L_1-L_2$ norm as a similarity measure. To optimze the similarity measure, we propose a modified iterative reweighted least squares algorithm based on the one step minimization of a quadratic surrogate function. In the simulations and experiments with barcode images, the proposed method showed better robustness than the conventional MSE based method. In addition, the proposed method converged quickly during optimization process.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.173-176
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• 2001

Several dielectric as well as conducting cylinders buried in the lossy half space are reconstructed from the scattered fields measured along the interface between the air and the lossy ground. Iterative inversion method by using the hybrid optimization algorithm combining the genetic and the Levenberg-Marquardt algorithm enables us to find the positions, the sizes, and the medium parameters such as the permittivities and the conductivities of the buried cylinders as well as those of the background lossy half space. Illposedness of the inversion caused by the errors in the measured scattered fields are regularized by filtering the evanescent modes of the scattered fields out.