• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.2
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• pp.56-65
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• 1998

In this paper, a neural network(NN) control system is proposed and practically implemented, which is adequate to the induction motor speed control system with frequent load variation. The back propagation neural network technique is used to provide a real adaptive estimation of the motor parameter. The error between the desired state variable and the actual one is back-propagated to adjust the motor parameter, so that the actual state variable will coincide with the desired one. Designed control system is based on PC-DSP structure for the purposed of easiness of applying NN algorithm. Through computer simulation and experimental results, it is verified that proposed control system is robust to the load variation and practical implementation is possible.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.555-558
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• 2002

Sales Rate is a key parameter whose indications on real estate market plays a key role in prospecting and establishing governmental policies and strategies for Condominiums. However, it's not easy to present systematic model for tracing the effects of this parameter on sales rate without definite concept and relations with sales rate. Therefore, this study (1) derives factors affecting Sales Rate of Condominiums, (2) specially, gives an analysis of correlation with variable for the rest of factors based on economic factors, and then finds out its influential relation, (3) presents diagrammatic analysis model of all impact variables by factor to grasp on the whole for Sales Rate of Condominiums.

• Structural Engineering and Mechanics
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• v.59 no.6
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• pp.1037-1054
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• 2016

Structural parameter evaluation and external force estimation are two important parts of structural health monitoring. But the structural parameter identification with limited input information is still a challenging problem. A new simultaneous identification method in time domain is proposed in this study to identify the structural parameters and evaluate the external force. Each sampling point in the time history of external force is taken as the unknowns in force evaluation. To reduce the number of unknowns for force evaluation the time domain measurements are divided into several windows. In each time window the structural excitation is decomposed by orthogonal polynomials. The time-variant excitation can be represented approximately by the linear combination of these orthogonal bases. Structural parameters and the coefficients of decomposition are added to the state variable to be identified. The extended Kalman filter (EKF) is augmented and selected as the mathematical tool for the implementation of state variable evaluation. The proposed method is validated numerically with simulation studies of a time-invariant linear structure, a hysteretic nonlinear structure and a time-variant linear shear frame, respectively. Results from the simulation studies indicate that the proposed method is capable of identifying the dynamic load and structural parameters fairly accurately. This method could also identify the time-variant and nonlinear structural parameter even with contaminated incomplete measurement.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.11
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• pp.861-867
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• 2014

In this study, a semi-active isolator was constructed from applying a MR damper that used the MR fluid to an isolator. The parameter identification was also performed to determine the characteristics of this semi-active isolator during which the least squares method and the auxiliary variable method were applied to produce a value closest to the true value. In addition, the MR damper's nonlinear damping force was closely analyzed to greatly reduce the range of error. Based on this analysis, it was discovered that the parameter tended to increase with more electric current. Such analysis of the dynamic properties of semi-active isolator proved that constructing an isolator that provides a more stable operation could be achieved.

• Earthquakes and Structures
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• v.22 no.1
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• pp.53-64
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• 2022

This study proposes an intelligent semi-active isolation system combining a variable-stiffness control device and ground motion characteristic prediction. To determine the optimal control parameter in real-time, a genetic algorithm (GA)-fuzzy control law was developed in this study. Data on various types of ground motions were collected, and the ground motion characteristics were quantified to derive a near-fault (NF) characteristic ratio by employing an on-site earthquake early warning system. On the basis of the peak ground acceleration (PGA) and the derived NF ratio, a fuzzy inference system (FIS) was developed. The control parameters were optimized using a GA. To support continuity under near-fault and far-field ground motions, the optimal control parameter was linked with the predicted PGA and NF ratio through the FIS. The GA-fuzzy law was then compared with other control laws to verify its effectiveness. The results revealed that the GA-fuzzy control law could reliably predict different ground motion characteristics for real-time control because of the high sensitivity of its control parameter to the ground motion characteristics. Even under near-fault and far-field ground motions, the GA-fuzzy control law outperformed the FPEEA control law in terms of controlling the isolation layer displacement and the superstructure acceleration.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4102-4111
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• 2023

In order to better perform thermal hydraulic calculation and analysis of supercritical water reactor, based on the experimental data of supercritical water, the model training and predictive analysis of the heat transfer coefficient of supercritical water were carried out by using the support vector machine (SVM) algorithm. The changes in the prediction accuracy of the supercritical water heat transfer coefficient are analyzed by the changes of the regularization penalty parameter C, the slack variable epsilon and the Gaussian kernel function parameter gamma. The predicted value of the SVM model obtained after parameter optimization and the actual experimental test data are analyzed for data verification. The research results show that: the normalization of the data has a great influence on the prediction results. The slack variable has a relatively small influence on the accuracy change range of the predicted heat transfer coefficient. The change of gamma has the greatest impact on the accuracy of the heat transfer coefficient. Compared with the calculation results of traditional empirical formula methods, the trained algorithm model using SVM has smaller average error and standard deviations. Using the SVM trained algorithm model, the heat transfer coefficient of supercritical water can be effectively predicted and analyzed.

• Journal of Korea Foresty Energy
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• v.18 no.2
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• pp.84-91
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• 1999

The objective of this study was to present creep and the effects of mechano-sorptive deflection of drift pin moment resisting joint between LVL members under changing relative humidity (RH) conditions. The LVL members with steel gusset were jointed by a square pattern of eight injected drift pin. Three diameter drift pins were used to test specimens (6mm, 10mm, and 16mm). The creep test was conducted under two constant loading conditions : one at 30 kgf(840 kgf-cm) and the other at 60 kgf(1680 kgf-cm). The experiment was conducted in an open shed outside. (1)The total rotation creep model of moment resisting joing can be expressed as the sum of the creep of controlled environment (3-parameter model), dimensional change and mechano-sorptive deflection resulting from the variable environment. (2)Mechanosorptive rotation creep is recoverable as moisture content increases during adsorption. Least squares method for linear regression analysis was performed using mechano-sorptive rotation creep as the dependent variable and moisture content as the independent variable. The slope of low moment specimens are compared with those of high moment. This means that low moment condition is more easily affected by changes in humidity than high moment conditions. (3)Although creep deflection is higher for small diameter drift pin than for large diameter drift pin, the shape of creep deflection curves for all specimens is similar.

• Structural Engineering and Mechanics
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• v.66 no.6
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• pp.737-748
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• 2018

The current study presents a new technique in the framework of the nonlocal elasticity theory for a comprehensive buckling analysis of Euler-Bernoulli nano-beams made up of bidirectional functionally graded material (BDFGM). The mechanical properties are considered by exponential and arbitrary variations for axial and transverse directions, respectively. The various circumstances including tapering, resting on two-parameter elastic foundation, step-wise or continuous variations of axial loading, various shapes of sections with various distribution laws of mechanical properties and various boundary conditions like the multi-span beams are taken into account. As far as we know, for the first time in the current work, the buckling analyses of BDFGM nano-beams are carried out under mentioned circumstances. The critical buckling loads and mode shapes are calculated by using energy method and a new technique based on calculus of variations and collocation method. Fast convergence and excellent agreement with the known data in literature, wherever possible, presents the efficiency of proposed technique. The effects of boundary conditions, material and taper constants, foundation moduli, variable axial compression and small-scale of nano-beam on the buckling loads and mode shapes are investigated. Moreover the analytical solutions, for the simpler cases are provided in appendices.

• International Journal of Highway Engineering
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• v.14 no.5
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• pp.189-199
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• 2012

PURPOSES: The purpose of this study is to propose a new methodology for developing statistical collision models and to show the validation results of the methodology. METHODS: A new modeling method of introducing variables into the model one by one in a multiplicative form is suggested. A method for choosing explanatory variables to be introduced into the model is explained. A method for determining functional forms for each explanatory variable is introduced as well as a parameter estimating procedure. A model selection method is also dealt with. Finally, the validation results is provided to demonstrate the efficacy of the final models developed using the method suggested in this study. RESULTS: According to the results of the validation for the total and injury collisions, the predictive powers of the models developed using the method suggested in this study were better than those of generalized linear models for the same data. CONCLUSIONS: Using the methodology suggested in this study, we could develop better statistical collision models having better predictive powers. This was because the methodology enabled us to find the relationships between dependant variable and each explanatory variable individually and to find the functional forms for the relationships which can be more likely non-linear.

• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.173-191
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• 2017

This study develops a new type of latent class analysis (LCA) in order to explain the associations between one latent variable and several other categorical latent variables. Our model postulates that the prevalence of the latent variable of interest is affected by another latent variable composed of other several latent variables. For the parameter estimation, we propose deterministic annealing EM (DAEM) to deal with local maxima problem in the proposed model. We perform simulation study to demonstrate how DAEM can find the set of parameter estimates at the global maximum of the likelihood over the repeated samples. We apply the proposed LCA model in an investigation of the effect of and joint patterns for drug-using behavior to violent behavior among US high school male students using data from the Youth Risk Behavior Surveillance System 2015. Considering the age of male adolescents as a covariate influencing violent behavior, we identified three classes of violent behavior and three classes of drug-using behavior. We also discovered that the prevalence of violent behavior is affected by the type of drug used for drug-using behavior.