• Structural Engineering and Mechanics
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• v.53 no.3
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• pp.607-623
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• 2015

The wind-induced dynamic response of large-span flexible structures includes two important components-background response and resonant response. However, it is difficult to separate the two components in time-domain. To solve the problem, a relational expression of wavelet packet coefficients and power spectrum is derived based on the principles of digital signal processing and the theories of wavelet packet analysis. Further, a new approach is proposed for separation of the background response from the resonant response. Then a numerical example of frequency detection is provided to test the accuracy and the spectral resolution of the proposed approach. In the engineering example, the approach is applied to compute the power spectra of the wind-induced response of a large-span roof structure, and the accuracy of spectral estimation for stochastic signals is verified. The numerical results indicate that the proposed approach is efficient and accurate with high spectral resolution, so it is applicable for power spectral computation of various response signals of structures induced by the wind. Moreover, the background and the resonant response time histories are separated successfully using the proposed approach, which is sufficiently proved by detailed verifications. Therefore, the proposed approach is a powerful tool for the verification of the existing frequency-domain formulations.

• Structural Engineering and Mechanics
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• v.68 no.3
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• pp.345-358
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• 2018

Heterogeneous structure and, particularly, low resistance to tension stresses leads to different mechanical properties of the concrete in different loading situations. To solve this problem, the tension zone of concrete elements is reinforced. Development of the cracks, however, becomes even more complicated in the presence of bar reinforcement. Direct tension test is the common layout for analyzing mechanical properties of reinforced concrete. This study investigates scatter of the test results related with arrangement of bar reinforcement. It employs results of six elements with square $60{\times}60mm$ cross-section reinforced with one or four 5 mm bars. Differently to the common research practice (limited to the average deformation response), this study presents recordings of numerous strain gauges, which allows to monitor/assess evolution of the deformations during the test. A simple procedure for variation assessment of elasticity modulus of the concrete is proposed. The variation analysis reveals different deformation behavior of the concrete in the prisms with different distribution of the reinforcement bars. Application of finite element approach to carefully collected experimental data has revealed the effects, which were neglected during the test results interpretation stage.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.2
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• pp.93-100
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• 2006

In this paper, a new-type Extended Kalman Filter (EKF) is proposed as a robust nonlinear filter for a stochastic nonlinear system. The original EKF is widely used for various nonlinear system applications. But it is fragile to its estimation errors because they give rise to linearization errors that affect the system mode1 as the modeling errors. The linearization errors are nonlinear functions of the estimation errors therefore it is very difficult to obtain the accurate error covariance of the EKF using the linear form. The inaccurately estimated error covariance hinders the EKF from being a sub-optimal estimator. The proposed filter tries to obtain the upper bound of the error covariance tolerating the uncertainty of the error covariance instead of trying to obtain the accurate one. It treats the linearization errors as uncertain modeling errors that can be handled by the robust linear filtering. In order to be more robust to the estimation errors than the original EKF, the proposed filter minimizes the upper bound like the robust linear filter that is applied to the linear model with uncertainty. The in-flight alignment problem of the inertial navigation system with GPS position measurements is a good example that the proposed robust filter is applicable to. The simulation results show the efficiency of the proposed filter in the robustness to initial estimation errors of the filter.

• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.74-81
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• 2017

Recently, there are many companies that use systems based on artificial intelligence. The accuracy of artificial intelligence depends on the amount of learning data and the appropriate algorithm. However, it is not easy to obtain learning data with a large number of entity. Less data set have large generalization errors due to overfitting. In order to minimize this generalization error, this study proposed DGA(Dropout Genetic Algorithm) which can expect relatively high accuracy even though data with a less data set is applied to machine learning based genetic algorithm to deep learning based dropout. The idea of this paper is to determine the active state of the nodes. Using Gradient about loss function, A new fitness function is defined. Proposed Algorithm DGA is supplementing stochastic inconsistency about Dropout. Also DGA solved problem by the complexity of the fitness function and expression range of the model about Genetic Algorithm As a result of experiments using MNIST data proposed algorithm accuracy is 75.3%. Using only Dropout algorithm accuracy is 41.4%. It is shown that DGA is better than using only dropout.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.881-891
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• 2014

Graphical models can be used as an intuitive tool for modeling a complex stochastic system with a large number of variables related each other because the conditional independence between random variables can be visualized as a network. Graphical least absolute shrinkage and selection operator (LASSO) is considered to be effective in avoiding overfitting in the estimation of Gaussian graphical models for high dimensional data. In this paper, we consider the model selection problem in graphical LASSO. Particularly, we compare various model selection criteria via simulations and analyze a real financial data set.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.7
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• pp.38-50
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• 1997

This paper concerns a VHDL design and simulation of an accurate and cost-effective fuzzy logic controller (FLC). The accurcy of the proposed FLC is obtained by using the center of gravity (COG) defuzzifier that considers both membership values and spans of membership functions in calculating a crisp value. The cost-effectiveness of the proposed FLC is obtained by restructuring the conventional FLC in the following ways: Firstly, the MAX-MIN inference is inference is replaced by a read-modify-write operation that can be implemented economically in the structure of register files. Secondly, the division in the COG defuzzifier is avoided by finding the moment equilibrium point. The proposed COG defuzzifier has two disadvantages that it requires additional multipliers and it takes a lot of computation time to find the moment equilibrium point. The first disadvantage is overcome by replacing the mulitpliers with stochastic AND operations and the second disadvantage is alleviated by using a coarse-to-fine searching algorithm. The proposed FLC is described in VHDL structurally and behaviorally and whether it is working well or not is checked on SYNOPSYS VHDL simulator by using the truck backer-upper control problem.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.649-652
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• 1999

In this paper, the design method of target tracking systems using the extended $H^{\infty}$ filter(EHF) is proposed. Usually, a Cartesian coordinate frame is tell suited to describe the target dynamics. However, the measurements made in radar-centered polar coordinates are expressed as nonlinear equations in Cartesian coordinates. Thus the tacking problem is concerned with the nonlinear estimation. The extended $H^{\infty}$ filter is able to deal with the problems arising in the target tacking systems such as the parameter uncertainty included inevitably in modeling physical systems mathematically, the unavailableness of the stochastic information about exogenous disturbances, and errors due to the linearization of measurement equations. We show the proposed filter is robuster than the extended Kalman filter(EKF) through a simple target tracking example.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.69.1-69.1
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• 2017

According to structure formation simulations, weak shocks with typical Mach number, M<3, are expected to form in merging galaxy clusters. The presence of such shocks has been indicated by X-ray and radio observations of many merging clusters. In particular, diffuse radio sources known as radio relics could be explained by synchrotron-emitting electrons accelerated via diffusive shock acceleration (Fermi I) at quasi-perpendicular shocks. Here we also consider possible roles of stochastic acceleration (Fermi II) by compressive MHD turbulence downstream of the shock. Then we explore a puzzling discrepancy that for some radio relics, the shock Mach number inferred from the radio spectral index is substantially larger than that estimated from X-ray observations. This problem could be understood, if shock surfaces associated with radio relics consist of multiple shocks with different strengths. In that case, X-ray observations tend to pick up the part of shocks with lower Mach numbers and higher kinetic energy flux, while radio emissions come preferentially from the part of shocks with higher Mach numbers and higher cosmic ray (CR) production. We also show that the Fermi I reacceleration model with preexisting fossil electrons supplemented by Fermi II acceleration due to postshock turbulence could reproduce observed profiles of radio flux densities and integrated radio spectra of two giant radio relics. This study demonstrates the CR electrons can be accelerated at collisionless shocks in galaxy clusters just like supernova remnant shock in the interstellar medium and interplanetary shocks in the solar wind.

• Journal of the Korean Solar Energy Society
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• v.37 no.2
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• pp.23-33
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• 2017

Accurate prediction of stochastic behavior of occupants is a well known problem for improving prediction performance of building energy use. Many researchers have been tried various sensors that have information on the status of occupant such as $CO_2$ sensor, infrared motion detector, RFID etc. to predict occupants, while others have been developed some algorithm to find occupancy probability with those sensors or some indirect monitoring data such as energy consumption in spaces. In this research, various sensor data and energy consumption data are utilized for decision tree algorithms (C4.5 & CART) for estimation of sub-hourly occupancy status. Although the experiment is limited by space (private room) and period (cooling season), the prediction result shows good agreement of above 95% accuracy when energy consumption data are used instead of measured $CO_2$ value. This result indicates potential of IoT data for awareness of indoor environmental status.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.135-150
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• 2012

This paper presents a portfolio model for a long-term power generation mix problem. The proposed portfolio model evaluates generation mix by considering the tradeoffs between the expected cost for power generation and its variability. Unlike conventional portfolio models measuring variance, we introduce Conditional Value-at-Risk (CVaR) in designing the variability with aims to considering events that are enormously expensive but are rare such as nuclear power plant accidents. Further, we consider uncertainties associated with future electricity demand, fuel prices and their correlations, and capital costs for power plant investments. To obtain an objective generation by each energy source, we employ the sample average approximation method that approximates the stochastic objective function by taking the average of large sample values so that provides asymptotic convergence of optimal solutions. In addition, the method includes Monte Carlo simulation techniques in generating random samples from multivariate distributions. Applications of the proposed model and method are demonstrated through a case study of an electricity industry with nuclear, coal, oil (OCGT), and LNG (CCGT) in South Korea.