• Structural Engineering and Mechanics
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• 제73권4호
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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.

• 전기학회논문지
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• 제68권1호
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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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• 제69권1호
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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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• 제38권5호
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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.

• 한국식품저장유통학회지
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• 제13권5호
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• pp.623-628
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• 2006

꽃향유로부터 정유물을 추출하여 천연향료로 개발하기 위하여 수증기증류법으로 추출하고 추출공정의 최적 조건을 반응표면 분석법으로 얻고자 하였다. 수증기증류법의 추출조건에 따른 꽃향유 추출물의 품질특성으로 수율과 총페놀화합물, 전자공여능과 주요 향기성분으로 estragole 과 thymol, beta-caryophyllene의 함량을 분석하였다. 추출온도가 추출시간보다 더 큰 영향을 주는 것으로 나타났으며, 각 품질 특성에 대하여 수립된 예측모델식이 모두 유의성이 높아 각 변수의 예측이 가능함을 보였다. 실험구간내에서 능선분석을 통하여 최대점과 추출조건을 예측하였으며 품질특성 값을 최대로 하는 제한 조건으로 얻어진 최적 추출조건은 $108^{\circ}C$와 2.1시간이었다.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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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.

• 전산구조공학
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• 제9권3호
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• pp.179-186
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• 1996

본 연구에서는 구조체의 위상학적 최적화를 위한 비선형 formulation(NLP)가 개발, 검토되었다. 이 NLP는 multiple-loading하에서 임의의 오브젝티브 함수, 응력, 변위 제약조건들을 쉽게 다룰 수가 있다. 또한 이 NLP는 해석과 최적화 디자인을 동시에 실시함으로써 요소 사이즈가 영으로 접근함에 따른 강성 매트릭스의 singularity를 피할 수 있다. 즉, 평형 방정식을 등제약조건으로 치환함으로써 강성 매트릭스 그 자체나 그의 역매트릭스를 구할 필요도 없어진다. 이 NLP는 multiple-loading conditon하에서 테스트되었으며, 이를 통해 이 NLP가 다양한 제약조건하에서 강력하게 작용함이 입증되었다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2005년도 공동추계학술대회
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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.

• 분석과학
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• 제34권4호
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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.

• 한국항공우주학회지
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• 제36권10호
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• pp.947-953
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• 2008

다분야 통합 최적설계(MDO) 프레임워크는 항공우주시스템의 설계에 고려해야 할 다양한 설계 분야의 통합적이고 동시적인 해석 및 설계 최적화를 위한 통합 환경으로 해석자원 및 최적화자원은 물론 CAD 툴과 DBMS 또한 통합해야하며 사용자편의환경을 제공해야한다. 또한 설계하고자 하는 대상 및 개발환경에 따라 프레임워크의 구축방법은 달라질 수 있다. 본 논문에서는 개발환경에 따라 단일 PC기반 프레임워크와 PLinda기반 프레임워크, 그리고 웹서비스 기반 프레임워크로 분류하여 이들을 비교 분석하였다.