• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.4
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• pp.183-190
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• 2018

This paper presents optimization of the main spar of Human-Powered Aircraft (HPA) wing. Mass minimization was attempted, while considering large torsional deformation of the beam. Sequential Quadratic Programming (SQP) method was adopted as a relevant tool to conduct structural optimization algorithm. An inner diameter and ply thicknesses of the main spar were selected as the design variables. The objective function includes factors such as mass minimization, constant tip bending displacement, and constant tip twist of the beam. For estimation of bending and torsional deformation, the geometrically exact beam model, which is appropriate for large deflection, was adopted. Properties of the cross sectional area which the geometrically exact beam model requires were obtained by Variational Asymptotic Beam Sectional Analysis (VABS), which is a cross sectional analysis program. As a result, maintaining tip bending displacement and tip twist within 1.45%, optimal design that accomplished 7.88% of the mass reduction was acquired. By the stress and strain recovery, structural integrity of the optimal design and validity of the present optimization procedure were authenticated.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.5 no.1
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• pp.59-64
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• 2011

In this paper, the lumped element model parameter estimation method and its implemented estimation software for fabricated floating mass transducers of IMEHDs have been presented so that the estimated parameter values could be compared with the designed ones and applied to predict the output performance when the transducers were implanted into human ears. The presented method is based on the sequential quadratic programming (SQP) for estimating parameters in the transducer's lumped model and has been implemented by the use of LabVIEW graphical language. Using the implemented estimation software, the accuracy of parameter estimation has been verified and our implemented estimation method has been evaluated by the comparison of the estimated transducer parameter values with the designed ones for a practically fabricated floating mass transducer for IMEHDs.

• Journal of the Korea Society of Computer and Information
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• v.17 no.3
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• pp.67-76
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• 2012

Traffic classification plays an important role in traffic management. To traditional methods, P2P and encryption traffic may become a problem. Support Vector Machine (SVM) is a useful classification tool which is able to overcome the traditional bottleneck. The main disadvantage of SVM algorithms is that it's time-consuming to train large data set because of the quadratic programming (QP) problem. However, the useful support vectors are only a small part of the whole data. If we can discard the useless vectors before training, we are able to save time and keep accuracy. In this article, we discussed the feasibility to remove the useless vectors through a sequential method to accelerate training speed when dealing with large scale data.