• Structural Engineering and Mechanics
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• v.73 no.4
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• pp.463-479
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• 2020

This paper is aimed to address a simultaneous optimization of the size, shape, and topology of steel lattice towers through a combination of the radial basis function (RBF) neural networks and the artificial bee colony (ABC) metaheuristic algorithm to reduce the computational time because mere metaheuristic optimization algorithms require much time for calculations. To verify the results, use has been made of the CIGRE Tower and a 132 kV transmission towers as numerical examples both based on the design requirements of the ASCE10-97, and the size, shape, and topology have been optimized (in both cases) once by the RBF neural network and once by the MSTOWER analyzer. A comparison of the results shows that the neural network-based method has been able to yield acceptable results through much less computational time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.182-188
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• 2019

Autonomous mobile robots need SLAM (simultaneous localization and mapping) to look for the location and simultaneously to make the map around the location. In order to achieve visual SLAM, it is necessary to form an algorithm that detects and extracts feature points from camera images, and gets the camera pose and 3D points of the features. In this paper, we propose MPROSAC algorithm which combines MSAC and PROSAC, and compare the performance of optimization method and the filtering method for feature-based monocular visual SLAM. Sparse Bundle Adjustment (SBA) is used for the optimization method and the extended Kalman filter is used for the filtering method.

• Structural Engineering and Mechanics
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• v.69 no.1
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• pp.21-31
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• 2019

Optimizing composite structures to exploit their maximum potential is a realistic application with promising returns. In this research, simultaneous maximization of the fundamental frequency and frequency separation between the first two modes by optimizing the fiber angles is considered. A high-fidelity design optimization methodology is developed by combining the high-accuracy of finite element method with iterative improvement capability of metaheuristic algorithms. Three powerful nature-inspired optimization algorithms viz. a genetic algorithm (GA), a particle swarm optimization (PSO) variant and a cuckoo search (CS) variant are used. Advanced memetic features are incorporated in the PSO and CS to form their respective variants-RPSOLC (repulsive particle swarm optimization with local search and chaotic perturbation) and CHP (co-evolutionary host-parasite). A comprehensive set of benchmark solutions on several new problems are reported. Statistical tests and comprehensive assessment of the predicted results show CHP comprehensively outperforms RPSOLC and GA, while RPSOLC has a little superiority over GA. Extensive simulations show that the on repeated trials of the same experiment, CHP has very low variability. About 50% fewer variations are seen in RPSOLC as compared to GA on repeated trials.

• ETRI Journal
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• v.38 no.5
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• pp.941-951
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• 2016

In this paper, cognitive radio and simultaneous wireless information and power transfer (SWIPT) are effectively combined to design a spectrum-efficient and energy-efficient transmission paradigm. Specifically, a novel SWIPT-based primary-secondary cooperation model is proposed to increase the transmission rate of energy/spectrum constrained users. In the proposed model, a multi-antenna secondary user conducts simultaneous energy harvesting and information forwarding by means of power splitting (PS), and tries to maximize its own transmission rate under the premise of successfully assisting the data delivery of the primary user. After the problem formulation, joint power splitting and beamforming optimization algorithms for decode-and-forward and amplify-and-forward modes are presented, in which we obtain the optimal PS factor and beamforming vectors using a golden search method and dual methods. Simulation results show that the proposed SWIPTbased primary-secondary cooperation schemes can obtain a much higher level of performance than that of non-SWIPT cooperation and non-cooperation schemes.

• Food Science and Preservation
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• v.13 no.5
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• pp.623-628
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• 2006

This study was performed to develop extract materials from Elsholtzia splendens by analyzing the functionality and aroma profile by the simultaneous steam distillation extraction. The qualities of extracts such as total yield, total phenolic compound and electron donation ability were affected by extraction temperature than time. The main flavor compounds were analyzed as estragole, thymol and beta-caryophyllene. The response variables had significant with extraction temperature than tim. The established model was suitable to predict calculated value in experimental ranges. The optimum extinction conditions, which were limited of maximum value for dependent variables were $108^{\circ}C$ and 2.1 hr in the simultaneous steam distillation extraction method.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.621-632
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• 2013

In this paper, we introduce advanced architectures of genetically-oriented Fuzzy Neural Networks (FNNs) based on fuzzy set and fuzzy relation and discuss a comprehensive design methodology. The proposed FNNs are based on 'if-then' rule-based networks with the extended structure of the premise and the consequence parts of the fuzzy rules. We consider two types of the FNNs topologies, called here FSNN and FRNN, depending upon the usage of inputs in the premise of fuzzy rules. Three different type of polynomials function (namely, constant, linear, and quadratic) are used to construct the consequence of the rules. In order to improve the accuracy of FNNs, the structure and the parameters are optimized by making use of genetic algorithms (GAs). We enhance the search capabilities of the GAs by introducing the dynamic variants of genetic optimization. It fully exploits the processing capabilities of the FNNs by supporting their structural and parametric optimization. To evaluate the performance of the proposed FNNs, we exploit a suite of several representative numerical examples and its experimental results are compared with those reported in the previous studies.

• Computational Structural Engineering
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• v.9 no.3
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• pp.179-186
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• 1996

A simple nonlinear programming(NLP) formulation for the optimal topology problem of structures is developed and examined. The NLP formulation is general, and can handle arbitrary objective functions and arbitrary stress, displacement constraints under multiple loading conditions. The formulation is based on simultaneous analysis and design approach to avoid stiffness matrix singularity resulting from zero sizing variables. By embedding the equilibrium equations as equality constraints in the nonlinear programming problem, we avoid constructing and factoring a system stiffness matrix, and hence avoid its singularity. The examples demonstrate that the formulation is effective for finding an optimal solution, and shown to be robust under a variety of constraints.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.516-525
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• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.

• Analytical Science and Technology
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• v.34 no.4
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• pp.153-159
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• 2021

In this study, we describe the development of a new high-performance liquid chromatography (HPLC) method for the simultaneous analysis of six bioactive compounds (including gallic acid, epicatechin 3-gallate, quercitrin, afzelin, quercetin, and kaempferol) from Orostachys japonicus. The extraction method was investigated and optimization of the extraction time (min), solvent composition (%), and solvent to material ratio were conducted. As a result, 30 min extraction with 50% methanol and 40:1 mL/g of solvent: material ratio achieved the highest extraction efficiency with a yield of 3.32 mg/g. Furthermore, the developed HPLC method was validated and the correlation coefficient (R) values were within the satisfactory range of 0.9995-0.9999 over the linearity range of 1.53-417 ㎍/mL. The limit of detection and limit of quantification for the six active components were between 0.03-0.08 ㎍/mL and 0.08-0.26 ㎍/mL, respectively. With these newly optimized and developed methods, four batches of O. japonicus were analyzed to confirm the high extraction efficiency of the method and the feasibility of an application.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.10
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• pp.947-953
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• 2008

MDO(Multidisciplinary Design and Optimization) framework can be an integrated environment or a system, which is for synthetic and simultaneous analysis and design optimization in various design fields of aerospace systems. MDO framework has to efficiently use and integrate distributed resources such as various analysis codes, optimization codes, CAD tools, DBMS and etc. in heterogeneous environment, and to provide graphical and easy-to-use user interfaces. Also, its development method can be changed by design objects and development environment. In this paper, we classify MDO frameworks into three types according to the development environments: Single PC-based, PLinda-based and Web Services-based MDO framework. And, we compare and analyze these frameworks.