• Structural Engineering and Mechanics
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• v.68 no.5
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• pp.563-573
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• 2018

Temperature-induced responses, such as strains and displacements, are related to the boundary conditions. Therefore, it is required to determine the boundary conditions to establish a reliable bridge model for temperature-induced responses analysis. Particularly, bridge bearings usually present nonlinear behavior with an increase in load, and the nonlinear boundary conditions cause significant effect on temperature-induced responses. In this paper, the bridge nonlinear boundary conditions were simulated as bilinear translational or rotational springs, and the boundary parameters of the bilinear springs were identified based on the measured temperature-induced responses. First of all, the temperature-induced responses of a simply support beam with nonlinear translational and rotational springs subjected to various temperature loads were analyzed. The simulated temperature-induced strains and displacements were assumed as measured data. To identify the nonlinear translational and rotational boundary parameters of the bridge, the objective function based on the temperature-induced responses is then created, and the nonlinear boundary parameters were further identified by using the nonlinear least squares optimization algorithm. Then, a beam structure with nonlinear translational and rotational springs was simulated as a numerical example, and the nonlinear boundary parameters were identified based on the proposed method. The numerical results show that the proposed method can effectively identify the parameters of the nonlinear boundary conditions. Finally, the boundary parameters of a real arch bridge were identified based on the measured strain data and the proposed method. Since the bearings of the real bridge do not perform nonlinear behavior, only the linear boundary parameters of the bridge model were identified. Based on the bridge model and the identified boundary conditions, the temperature-induced strains were recalculated to compare with the measured strain data. The recalculated temperature-induced strains are in a good agreement with the real measured data.

• Journal of the Ergonomics Society of Korea
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• v.13 no.2
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• pp.65-79
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• 1994

A biomechanical model of lower extremity in seated postures was developed to assess muscular activities of lower extremity involved in a variety of foot pedal operations. The model incorporated four rigid body segments with the twenty-four muscles to represent lower extremity. This study deals with quasi-static movement to investigate dymanic movement effect in seated foot operation. It is found that optimization method which has been used for modeling the articulated body segments does not predict the forces generated from biarticular muscles and antagonistic muscles reasonably. So, the revised nonlinear optimization scheme was employed to consider the synergistic effects of biarticular muscles and the antagonistic muscle effects from the stabilization of the joint. For the model validation, three male subjects performen the experiments in which EMG activities of the nine lower extremity muscles were measured. Predicted muscle forces were compared with the corresponding EMG amplitudes and it showed no statistical difference. For the selection of optimal seated posture, a physiological meaningful criterion for muscular load sharing developed.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.240-245
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• 1990

A on-line optimization scheme based on model in a binary distillation process is proposed. A reduced-order model utilized the concept of collocation is used as a process model and the recursive prediction error method is employed to identify the reduced-order model. The concentrations of end products are controlled by nonlinear adaptive predictive control algorithm. The objective function is constructed to find optimum operate condition for saving utility cost. The proposed optimization is scheme is tested through simulation studies in 13-staged water-methanol distillation column.

• Journal of the Korean Society of Hazard Mitigation
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• v.1 no.3 s.3
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• pp.73-82
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• 2001

This study examines the applicability of the Nash model and the Diskin model, which are linear and nonlinear runoff models, respectively, by applying optimization techniques to the parameter calibration of the two models. Nonlinear programming which is one of traditional optimization techniques and Genetic Algorithm which has been actively applied recently are used in this study. The Nash and Diskin models which use the calibrated parameter with a flood events are applied to a different flood event in Soyang Dam basin. The results obtained from the parameter calibration show slight discrepancy depending upon the flood events. It has been found in the comparion between the observed hydrograph and the hydrographs obtained from the parameter calibration that the Diskin model can better simulate the observed hydrograph than the Nash model can, especially, for the peak flow. This can be analyzed that the Diskin model which is a nonlinear runoff model is better off in simulating the nonlinear characteristic of the rainfall-runoff process.

• Journal of the Korean Statistical Society
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• v.32 no.1
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• pp.1-10
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• 2003

We consider obtaining graphical summaries of uncertainty in estimates of parameters in nonlinear models. A nonlinear constrained optimization algorithm is developed for likelihood based confidence intervals for the functions of parameters in the model The results are applied to the problem of finding significance levels in nonlinear models.

• ETRI Journal
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• v.46 no.3
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• pp.461-472
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• 2024

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

• Structural Engineering and Mechanics
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• v.70 no.2
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• pp.221-231
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• 2019

This paper performs for the first time a simultaneous optimization for members sections along with semi-rigid beam-to-column connections for space steel frames with fixed, semi-rigid, and hinged bases using a biogeography-based optimization algorithm (BBO) and a genetic algorithm (GA). Furthermore, a member's sections optimization for a fully fixed space frame is carried out. A real and accurate simulation of semi-rigid connection behavior is considered in this study, where the semi-rigid base connections are simulated using Kanvinde and Grilli (2012) nonlinear model, which considers deformations in different base connection components under the applied loads, while beam-to-column connections are modeled using the familiar Frye and Morris (1975) nonlinear polynomial model. Moreover, the $P-{\Delta}$ effect and geometric nonlinearity are considered. AISC-LRFD (2016) specification constraints of the stress and displacement are considered as well as section size fitting constraints. The optimization is applied to two benchmark space frame examples to inspect the effect of semi-rigidity on frame weight and drift using BBO and GA algorithms.

• Steel and Composite Structures
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• v.6 no.5
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• pp.435-457
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• 2006

The paper presents the cost optimization of composite floor trusses composed from a reinforced concrete slab of constant depth and steel trusses consisting of hot rolled channel sections. The optimization was performed by the nonlinear programming approach, NLP. Accordingly, a NLP optimization model for composite floor trusses was developed. An accurate objective function of the manufacturing material, power and labour costs was proposed to be defined for the optimization. Alongside the costs, the objective function also considers the fabrication times, and the electrical power and material consumption. Composite trusses were optimized according to Eurocode 4 for the conditions of both the ultimate and the serviceability limit states. A numerical example of the optimization of the composite truss system presented at the end of the paper demonstrates the applicability of the proposed approach.

• Computers and Concrete
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• v.17 no.1
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• pp.141-156
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• 2016

The search for new structural systems capable of associating performance and safety requires deeper knowledge regarding the mechanical behavior of structures subject to different loading conditions. The Strut-and-Tie Model is commonly used to structurally designing some reinforced concrete elements and for the regions where geometrical modifications and stress concentrations are observed, called "regions D". This method allows a better structural behavior representation for strength mechanisms in the concrete structures. Nonetheless, the topological model choice depends on the designer's experience regarding compatibility between internal flux of loads, geometry and boundary/initial conditions. Thus, there is some difficulty in its applications, once the model conception presents some uncertainty. In this context, the present work aims to apply the Strut-and-Tie Model to nonlinear structural elements together with a topological optimization method. The topological optimization method adopted considers the progressive stiffness reduction of finite elements with low stress values. The analyses performed could help the structural designer to better understand structural conceptions, guaranteeing the safety and the reliability in the solution of complex problems involving structural concrete.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.923-928
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• 1993

Genetic algorithms are powerful optimization methods based on the mechanism of natural genetics and natural selection. Genetic algorithms reduce chance of searching local optima unlike most conventional search algorithms and especially show good performances in complex nonlinear optimization problems because they do not require any information except objective function value. This paper presents a new model based on sexual reproduction in nature. In the proposed Sexual Reproduction model(SR model), individuals consist of the diploid of chromosomes, which are artificially coded as binary string in computer program. The meiosis is modeled to produce the sexual cell(gamete). In the artificial meiosis, crossover between homologous chromosomes plays an essential role for exchanging genetic informations. We apply proposed SR model to optimization of the design parameters of Single-sided Linear Induction Motor(SLIM). Sequential Unconstrained Minimization Technique(SUMT) is used to transform the nonlinear optimization problem with many constraints of SLIM to a simple unconstrained problem, We perform optimal design of SLIM available to FA conveyer systems and discuss its results.