• Journal of the Earthquake Engineering Society of Korea
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• v.21 no.3
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• pp.145-152
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• 2017

The objective of this study is to propose a simple and accurate analytical model for HSS braces. For this purpose, a physical theory model is adopted. Rectangular hollow section steel (HSS) braces are considered in this study. To accurately simulate the cyclic behavior of braces using the physical theory model, empirical equations calculating constituent parameters are implemented on the analytical model, which were proposed in the companion paper. The constituent parameters are cyclic brace growth, cyclic buckling load, and the incidence of local buckling and fracture. The analytical model proposed in this study was verified by comparing actual and simulated cyclic curves of brace specimens. It is observed that the proposed model accurately simulates the cyclic behavior of the braces throughout whole response range.

• Smart Structures and Systems
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• v.15 no.2
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• pp.259-281
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• 2015

In this study, a structural identification method is proposed to assess the integrity of gravity-type caisson structures by analyzing vibration features. To achieve the objective, the following approaches are implemented. Firstly, a simplified structural model with a few degrees-of-freedom (DOFs) is formulated to represent the gravity-type caisson structure that corresponds to the sensors' DOFs. Secondly, a structural identification algorithm based on the use of vibration characteristics of the limited DOFs is formulated to fine-tune stiffness and damping parameters of the structural model. Finally, experimental evaluation is performed on a lab-scaled gravity-type caisson structure in a 2-D wave flume. For three structural states including an undamaged reference, a water-level change case, and a foundation-damage case, their corresponding structural integrities are assessed by identifying structural parameters of the three states by fine-tuning frequency response functions, natural frequencies and damping factors.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.1
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• pp.51-57
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• 2010

This paper presents temperature control of engine cooling system using a controllable magnetorheological(MR) fan clutch. An appropriate size of MR fan clutch is devised and modeled on the basis of Bingham model. Subsequently, an optimization to determine design parameters such as width of housing is undertaken by choosing the reciprocal of the controllable torque as an objective function. Under consideration of spatial limitation, design parameters are optimally determined using finite element analysis. A sliding mode controller is then designed to control the angular velocity of the MR fan clutch using experimentally determined parameters. The designed controller is experimentally implemented and control performances of the MR fan clutch system are evaluated.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2001.10a
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• pp.29-32
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• 2001

The objective of this work was to optimize processing parameters of hybrid thermoplastic composites in compression molding. The mechanical properties of the composites manufactured with various forming conditions were measured to characterize processing parameters. Polypropylene(PP) composites containing randomly oriented long carbon fiber and carbon black were used in this work. The composite materials contained 5%, 10%, 15%, and 20% carbon fiber and 5%, 10%, 15%, 20%, and 25% carbon black by weight. Compression molding was conducted at various mold temperatures. The temperature of the material in the mid-plain was monitored during the forming. Crystallinity was also measured by using XRD. The tensile modulus of the composites increase, with increasing the mold temperature. However, the impact strength of the composites decreases as mold temperature increases.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.187-188
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• 2011

This paper proposes the hybrid Complex-PSO algorithm based on the complex search method and particle swarm optimization (PSO) for unconstrained optimization. This hybridization intends to produce faster and more accurate convergence to the optimum value. These hybrid will concentrate on determining the dynamic load model parameters, the ZIP model and induction motor model parameters. Measurement-based parameter estimation, which employs measurement data to derive load model parameters, is used. The theoretical foundation of the measurement-based approach is system identification. The main objective of this paper is to demonstrate how the standard particle swarm optimization and complex method can be improved through hybridization of the two methods and the results will be compared with that of their original forms.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.379-382
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• 2004

Abrasive jet machining (AJM) has a large number of parameters such as powder flow rate, air pressure, diameter of abrasive, stand off distance, material hardness and fracture toughness, etc. It is not easy matter to control those parameter. To achieve high accurate machining, in this study, Taguchi method was used to select process parameters. The objective of the optimization was to get higher material removal rate (MRR). From the experiments and analysis, some process parameters were found to make efficient machining.

• Journal of Construction Engineering and Project Management
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• v.7 no.2
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• pp.1-10
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• 2017

The Clonal Selection Algorithm (CSA) is an algorithm inspired by the human immune system mechanism. In CSA, several parameters needs to be optimized by large amount of sensitivity analysis for the optimal results. They limit the accuracy of the results due to the uncertainty and subjectivity. Adaptive Clonal Selection (ACS), a modified version of CSA, is developed as an algorithm without controls by pre-defined parameters in terms of selection process and mutation strength. In this paper, we discuss the ACS in detail and present its implementation in construction site utilization planning (CSUP). When applied to a developed model published in research literature, it proves that the ACS are capable of searching the optimal layout of temporary facilities on construction site based on the result of objective function, especially when the parameterization process is considered. Although the ACS still needs some improvements, obtaining a promising result when working on a same case study computed by Genetic Algorithm and Electimze algorithm prove its potential in solving more complex construction optimization problems in the future.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1191-1203
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• 2007

In this paper, we consider the hypothesis testing for the homogeneity of the shape parameters in the gamma distributions. The noninformative priors such as Jeffreys# prior or reference prior are usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian testing procedure for the homogeneity of the shape parameters based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and a real data example are provided.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.51-55
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• 1995

An adaptive unified predictive control (UPC) algorithm is applied to a batch polymerization reactor for poly(methyl methancrylate) (PMMA) and the effects of controller parameters are investigated. Computational studies are performed for a batch polymerization system model developed in this study. A transfer function in parametric form is estimated by recursive least squares (RLS) method, and the UPC algorithm is implemented to control the reactor temperature on the basis of this transfer function. The adaptive unified predictive controller shows a better performance than the PID controller for tracking set point changes, especially in the latter part of reaction course when gel effect becomes significant. Various performance can be acquired by selecting adequate values for parameters of the adaptive unified predictive controller; in other words, the optimal set of parameters exists for a given set of reaction conditions and control objective.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.175-182
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• 1994

In this study, we present unvoiced consonant recognition system using characteristic parameters of the phoneme of the each syllable. For the recognition, the characteristic parameters on the time domain such as ZCR, total energy of the consonant region and half region energy of the consonant region, and those on the frequency domain such as the frequency spectrum of the transition region are used. The objective unvoiced consonants in this study are /ㄱ/,/ㄷ/,/ㅂ/,/ㅈ/,/ㅋ/,/ㅌ/,/ㅍ/ and /ㅊ/. Each characteristic parameter of two regions extracted from these segmented unvoiced consonants are used for each recognition system of the region, independently, And complementing two outputs of each other system, the final output is to be produced. The recognition system is implemented using MLP which has learning ability. The recognition simulation results for 112 unvoiced consonant samples are that average recognition rates are 96.4$\%$ under 80$\%$ learning rates and 93.7$\%$ under 60$\%$ learning rates.