• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.30-34
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• 1998

Path Planning is one of the important fields in robot technologies. Local path planning may be done in on-line modes while recognizing an environment of robot by itself. In dynamic environments to obtain fluent information for environments vision system as a sensing equipment is a one of the most necessary devices for safe and effective guidance of robots. If there is a predictor that tells what future sensing outputs will be, robot can respond to anticipated environmental changes in advance. The tracking of obstacles has a deep relationship to the prediction for safe navigation. We tried to deal with active contours, that is snakes, to find out the possibilities of stable tracking of objects in image plane. Snakes are defined based on energy functions, and can be deformed to a certain contour form which would converge to the minimum energy states by the forces produced from energy differences. By using point algorithm we could have more speedy convergence time because the Brent's method gives the solution to find the local minima fast. The snake algorithm may be applied to sequential image frames to track objects in the images by these characteristics of speedy convergence and robust edge detection ability.

• Structural Engineering and Mechanics
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• v.27 no.2
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• pp.243-257
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• 2007

A numerically efficient superelement is proposed as a low degree of freedom model for dynamic analysis of rotating tapered beams. The element uses a combination of polynomials and trigonometric functions as shape functions in what is also called the Fourier-p approach. Only a single element is needed to obtain good modal frequency prediction with the analysis and assembly time being considerably less than for conventional elements. The superelement also allows an easy incorporation of polynomial variations of mass and stiffness properties typically used to model helicopter and wind turbine blades. Comparable results are obtained using one superelement with only 14 degrees of freedom compared to 50 conventional finite elements with cubic shape functions with a total of 100 degrees of freedom for a rotating cantilever beam. Excellent agreement is also shown with results from the published literature for uniform and tapered beams with cantilever and hinged boundary conditions. The element developed in this work can be used to model rotating beam substructures as a part of complete finite element model of helicopters and wind turbines.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.171-174
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• 2009

Generally the neural network and the fuzzy compensative algorithm are applied to forecast the time series for power demand with a characteristic of non-linear dynamic system, but it has a few prediction errors relatively. It also makes long term forecast difficult for sensitivity on the initial condition. On this paper, we evaluate the chaotic characteristic of electrical power demand with analysis methods of qualitative and quantitative and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction, time series forecast for multi dimension using Lyapunov exponent quantitatively. We compare simulated results with the previous method and verify that the purpose one being more practice and effective than it.

• Journal of Korean Association for Spatial Structures
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• v.18 no.4
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• pp.69-80
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• 2018

In this study, an algorithm applying deep learning to the truss structures was proposed. Deep learning is a method of raising the accuracy of machine learning by creating a neural networks in a computer. Neural networks consist of input layers, hidden layers and output layers. Numerous studies have focused on the introduction of neural networks and performed under limited examples and conditions, but this study focused on two- and three-dimensional truss structures to prove the effectiveness of algorithms. and the training phase was divided into training model based on the dataset size and epochs. At these case, a specific data value was selected and the error rate was shown by comparing the actual data value with the predicted value, and the error rate decreases as the data set and the number of hidden layers increases. In consequence, it showed that it is possible to predict the result quickly and accurately without using a numerical analysis program when applying the deep learning technique to the field of structural analysis.

• Smart Structures and Systems
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• v.24 no.3
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• pp.361-377
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• 2019

The stiffness of shape memory alloy (SMA) spring while in actuation is represented by an empirical model that is derived from the logistic differential equation. This model correlates the stiffness to the alloy temperature and the functionality of SMA spring as active variable stiffness actuator (VSA) is analyzed based on factors that are the input conditions (activation current, duty cycle and excitation frequency) and operating conditions (pre-stress and mechanical connection). The model parameters are estimated by adopting the nonlinear least square method, henceforth, the model is validated experimentally. The average correlation factor of 0.95 between the model response and experimental results validates the proposed model. In furtherance, the justification is augmented from the comparison with existing stiffness models (logistic curve model and polynomial model). The important distinction from several observations regarding the comparison of the model prediction with the experimental states that it is more superior, flexible and adaptable than the existing. The nature of stiffness variation in the SMA spring is assessed also from the Dynamic Mechanical Thermal Analysis (DMTA), which as well proves the proposal. This model advances the ability to use SMA integrated mechanism for enhanced variable stiffness actuation. The investigation proves that the stiffness of SMA spring may be altered under controlled conditions.

• Structural Engineering and Mechanics
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• v.69 no.2
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• pp.155-162
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• 2019

It is very common to find an empirical formulation in an earthquake design code to calculate fundamental vibration period of a structural system. Fundamental vibration period or frequency is a key parameter to provide adequate information pertinent to dynamic characteristics and performance assessment of a structure. This parameter enables to assess seismic demand of a structure. It is possible to find an empirical formulation related to reinforced concrete structures, masonry towers and slender masonry structures. Calculated natural vibration frequencies suggested by empirical formulation in the literatures has not suits in a high accuracy to the case of rest of the historical masonry bridges due to different construction techniques and wide variety of material properties. For the listed reasons, estimation of fundamental frequency gets harder. This paper aims to present an empirical formulation through Mean Square Error study to find ambient vibration frequency of historical masonry bridges by using a non-linear regression model. For this purpose, a series of data collected from literature especially focused on the finite element models of historical masonry bridges modelled in a full scale to get first global natural frequency, unit weight and elasticity modulus of used dominant material based on homogenization approach, length, height and width of the masonry bridge and main span length were considered to predict natural vibration frequency. An empirical formulation is proposed with 81% accuracy. Also, this study draw attention that this accuracy decreases to 35%, if the modulus of elasticity and unit weight are ignored.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.4
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• pp.153-162
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• 2019

Recently, it is necessary to develop a technology for quantitatively evaluating railway vibration to prevent civil complaints about orbital structures caused by railway noise and normal operation of ultra-precise equipment of orbital industrial complexes. The existing analytical method requires a very complicated dynamic response model, and it is difficult to secure the reliability of the result due to the inaccuracy of the demand model. Therefore, in this paper, we propose a railway vibration evaluation algorithm and system that deduce the vibration value generated from railway operation by using Linear Regression and Gradient Descent technique based on actual measurement railway vibration database that classifies factors affecting railway vibration. The prediction results obtained by the proposed algorithm show higher efficiency and accuracy than the existing analytical methods.

• Wind and Structures
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• v.32 no.5
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• pp.453-469
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• 2021

Snow accumulation on the road frequently induces a big traffic problem in the cold snowy region. Accurate prediction on snow distribution is fundamental for solving drifting snow disasters on roads. The present study adopts the transient method to simulate the wind-induced snow distribution on embankment based on the mixture multiphase model and dynamic mesh technique. The simulation and field measurement are compared to confirm the applicability of the simulation. Furthermore, the process of snow accumulation is revealed. The effects of friction velocity and snow concentration on snow accumulation are analyzed to clarify its mechanism. The results show that the simulation agrees well with the field measurement in trends. Moreover, the snow accumulation on the embankment can be approximately divided into three stages with time, the snow firstly deposited on the windward side, then, accumulation occurs on the leeward side which induced by the wake vortex, finally, the snow distribution reaches an equilibrium state with the slope of approximately 7°. The friction velocity and duration have a significant influence on the snow accumulation, and the vortex scale directly affected the snow deposition range on the embankment leeward side.

• Advances in concrete construction
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• v.11 no.1
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• pp.1-9
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• 2021

This article discusses the issue of optimizing controller design issues, in which the artificial intelligence (AI) evolutionary bat (EB) optimization algorithm is combined with the fuzzy controller in the practical application of the building. The controller of the system design includes different sub-parts such as system initial condition parameters, EB optimal algorithm, fuzzy controller, stability analysis and sensor actuator. The advantage of the design is that for continuous systems with polytypic uncertainties, the integrated H2/H∞ robust output strategy with modified criterion is derived by asymptotically adjusting design parameters. Numerical verification of the time domain and the frequency domain shows that the novel system design provides precise prediction and control of the structural displacement response, which is necessary for the active control structure in the fuzzy model. Due to genetic algorithm (GA), we use a hierarchical conditions of the Hurwitz matrix test technique and the limits of average performance, Hierarchical Fitness Function Structure (HFFS). The dynamic fuzzy controller proposed in this paper is used to find the optimal control force required for active nonlinear control of building structures. This method has achieved successful results in closed system design from the example.

• Composites Research
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• v.20 no.3
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• pp.31-36
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• 2007

A quantitative prediction method for initial crack length in a carbon/epoxy (CF/EP) composite beam using active piezoelectric transducers was established in this study. Wavelet Transform (WT)-based signal processing and identification technique in time-frequency domain was developed to facilitate the determination of damage presence and severity. Dynamic response of a CF/EP composites beam containing a continuously expanding crack, coupled with a pair of active piezoelectric disks, was examined under a narrow band excitation, and then applied with the proposed signal processing technique.