• Journal of Korean Society of Steel Construction
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• v.19 no.1
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• pp.29-41
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• 2007

Structural design, like other complex decision problems, involves many trade-offs among competing criteria. Although mathematical programming models are becoming increasingly realistic, they often have design limitations, that is, there are often relevant issues that cannot be easily captured. From the understanding of these limitations, a decision-support system is developed that can generate some useful alternatives as well as a single optimum value in the optimization of steel frame structures. The alternatives produced using this system are "good" with respect to modeled objectives, and yet are "different," and are often better, with respect to interesting objectives not present in the model. In this study, we created a decision-support system for designing the most cost-effective moment-resisting steel frame structures for resisting lateral loads without compromising overall stability. The proposed approach considers the cost of steel products and the cost of connections within the design process. This system makes use of an optimization formulation, which was modified to generate alternatives of optimum value, which is the result of the trade-off between the number of moment connections and total cost. This trade-off was achieved by reducing the number of moment connections and rearranging them, using the combination of analysis based on the LRFD code and optimization scheme based on genetic algorithms. To evaluate the usefulness of this system, the alternatives were examined with respect to various design aspects.

• Journal of IKEEE
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• v.17 no.3
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• pp.339-345
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• 2013

In this paper, the probabilistic exclusion based orthogonal matching pursuit (PEOMP) algorithm for the sparse signal reconstruction is proposed. Some of recent greedy algorithms such as CoSaMP, gOMP, BAOMP improved the reconstruction performance by deleting unsuitable atoms at each iteration. They still often fail to converge to the solution because the support set could not escape from a local minimum. PEOMP helps to escape by excluding a random atom in the support set according to a well-chosen probability function. Experimental results show that PEOMP outperforms several OMP based algorithms and the $l_1$ optimization method in terms of exact reconstruction probability.

• Journal of the Society of Naval Architects of Korea
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• v.43 no.4 s.148
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• pp.512-520
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• 2006

The effects of flexibilities of supporting structures on shaft alignment are growing as ship sizes are Increasing mainly for container carrier and LNG carrier. But, most of classification societies not only do not suggest any quantitative guidelines about the flexibilities but also do not have shaft alignment design program considering the flexibility of supporting structures. A newly developed program, which is based on innovative shaft alignment technologies including nonlinear elastic multi-support bearing concept and hull deflection database approach, has S basic modules : 1)fully automated finite element generation module, 2) hull deflection database and it's mapping module on bearings, 3) squeezing and oil film pressure calculation module, 4) optimization module and 5) gap & sag calculation module. First module can generate finite element model including shafts, bearings, bearing seats, hull and engine housing without any misalignment of nodes. Hull deflection database module has built-in absolute deflection data for various ship types, sizes and loading conditions and imposes the transformed relative deflection data on shafting system. The squeezing of lining material and oil film pressures, which are relatively solved by Hertz contact theory and built-in hydrodynamic engine, can be calculated and visualized by pressure calculation module. One of the most representative capabilities is an optimization module based on both DOE and Hooke-Jeeves algorithm.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.41-50
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• 2020

Sentimental analysis begins with the search for words that determine the sentimentality inherent in data. Managers can understand market sentimentality by analyzing a number of relevant sentiment words which consumers usually tend to use. In this study, we propose exploring performance of feature selection methods embedded with Particle Swarm Optimization Multi Objectives Evolutionary Algorithms. The performance of the feature selection methods was benchmarked with machine learning classifiers such as Decision Tree, Naive Bayesian Network, Support Vector Machine, Random Forest, Bagging, Random Subspace, and Rotation Forest. Our empirical results of opinion mining revealed that the number of features was significantly reduced and the performance was not hurt. In specific, the Support Vector Machine showed the highest accuracy. Random subspace produced the best AUC results.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1021-1028
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• 2011

The objective of this paper is to optimize the design parameters of a novel mechanism for a robotic knee orthosis. The feature of the proposed knee othosis is to drive a knee joint with independent actuation during swing and stance phases, which can allow an actuator with fast rotation to control swing motions and an actuator with high torque to control stance motions, respectively. The quadriceps device operates in five-bar links with 2-DOF motions during swing phase and is changed to six-bar links during stance phase by the contact motion to the patella device. The hamstring device operates in a slider-crank mechanism for entire gait cycle. The suggested kinematic model will allow a robotic knee orthosis to use compact and light actuators with full support during walking. However, the proposed orthosis must use additional linkages than a simple four-bar mechanism. To maximize the benefit of reducing the actuators power by using the developed kinematic design, it is necessary to minimize total weight of the device, while keeping necessary actuator performances of torques and angular velocities for support. In this paper, we use a SGA (Simple Genetic Algorithm) to minimize sum of total link lengths and motor power by reducing the weight of the novel knee orthosis. To find feasible parameters, kinematic constraints of the hamstring and quadriceps mechanisms have been applied to the algorithm. The proposed optimization scheme could reduce sum of total link lengths to half of the initial value. The proposed optimization scheme can be applied to reduce total weight of general multi-linkages while keeping necessary actuator specifications.

• Advances in aircraft and spacecraft science
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• v.1 no.2
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• pp.197-218
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• 2014

Quite a demanding task frequently arises in space engineering, when dealing with the cargo accommodation of modules and vehicles. The objective of this effort usually aims at maximizing the loaded cargo, or, at least, at meeting the logistic requirements posed by the space agencies. Complex accommodation rules are supposed to be taken into account, in compliance with strict balancing conditions and very tight operational restrictions. The context of the International Space Station (ISS) has paved the way for a relevant research and development activity, providing the company with a remarkable expertise in the field. CAST (Cargo Accommodation Support Tool) is a dedicated in-house software package (funded by the European Space Agency, ESA, and achieved by Thales Alenia Space), to carry out the whole loading of the Automated Transfer Vehicle (ATV). An ad hoc version, tailored to the Columbus (ISS attached laboratory) on-board stowage issue, has been further implemented and is to be used from now on. This article surveys the overall approach followed, highlighting the advantages of the methodology put forward, both in terms of solution quality and time saving, through an overview of the outcomes obtained to date. Insights on possible extensions to further space applications, especially in the perspective of the paramount challenges of the near future, are, in addition, presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.4
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• pp.781-787
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• 2007

Network Mobility (NEMO) basic support protocol doesn't execute the process of route optimization and has not presented the particular security mechanism in other blocks except hi-directional tunnel between Mobile Router (MR) and its Home Agent (HA). Therefore in this paper we process secure route optimization courses through authenticated binding update protocol between MR and its Correspondent Node (CN) and the protocol of the competency of mandate between MR and its Mobile Network Node (MNN); its block also uses an bi-directional tunnel as the block between MR and its HA. The address of each node are generated by the way of Cryptographically Generated Address (CGA) for proving the ownership of address. Finally we analyze the robustness of proposed protocol using security requirements of MIPv6 and existing attacks and the efficiency of this protocol using the connectivity recovery and end-to-end packet transmission delay time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.632-637
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• 2014

In this paper the concept of the performance-based logistics (PBL) support for weapon systems is discussed and an enhancement is studied such that prior to the Operational phase, the development of the PBL can begin from the Engineering & Manufacturing Development (EMD) phase together with multiple performance indices considered. The genetic algorithm should be considered for the complex system to solve the maintenance policy optimization. In particular, the requirement of repair level analysis model is developed based on reflecting the PBL concept. To decide the maintenance policy prior to Operational phase in accordance with customer requirements, the PIDO(Process Integration and Design Optimization) technique useful in choosing the performance indices and changing the constraints was used. The genetic algorithm of PIDO tool, like PIAnO and ModelCenter, was verified that it could be applied to optimize the maintenance policy.

• Smart Structures and Systems
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• v.22 no.5
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• pp.561-574
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• 2018

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.3
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• pp.241-249
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• 2004

There is an over head hoist transporter(OHT) by the system for delivering the wafer in semiconductor processing. The transfer system consist of carrier, vehicle, rail and support. The Tail supporting the wafer and the transfer system should maintain enough strength and stiffness. To achieve lightness and enough strength and stiffness, optimization algorithm should be introduced in design process. In this study, two kinds of section shapes as L-type, C-type is carried out the structure analysis and optimization. Total weight of rail is to be minimized while displacement should not exceed limit. To improve the initial model, topology optimization is done by the plain problem. Size optimization is done with 3D solid element and PLBA algorithm, the RQP algorithm. The weight of optimum model as L-type, C-type is decreased by 2.3%, 10% respectively. It is improved better than the initial model in the strength and stiffness of the structure.