• Title/Summary/Keyword: Gauss model

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The State Space Identification Model of the Dynamic System using Neural Networks (신경회로망을 이용한 동적 시스템의 상태 공간 인식 모델)

  • 이재현;탁환호;이상배
    • Journal of the Korean Institute of Intelligent Systems
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    • v.10 no.5
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    • pp.442-448
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    • 2000
  • The conventional control of dynamic systems needs accurate mathematical modeling of control systems. But the modeling of dynamic systems require very complex computation process due to complex state equation and many control parameters. Accordingly this paper proposes a state space identification model of the dynamic system using neural networks. The Gauss-Newton method is used to train the proposed neural network and the effectiveness of proposed method is verified through the computer simulation of the Seesaw system identification problem.

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ㄷ자형 개방형 단면부에 의해 보강되 등방성 평판의 음압레벨에 관한 연구

  • 김택현
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 1998.03a
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    • pp.214-220
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    • 1998
  • The determination of sound pressure radiated from periodic plate structures is fundamental in the estimation of noise levels in aircraft fuselages and ship hull structures. As a robust approach to this problem, here a very general and comprehensive analytical model for prediction the sound radiated by a vibration plate stiffened by periodically spaced orthogonal symmetric beams subjected to a sinusoidally time varying point load is developed. The plate is assumed to be infinite in extent, and the beams are considered to exert both line force and moment reactions on it. Structural damping is included in both plate and beam materials. From this theoretical model, the sound pressure levels on axis in a semi-infinite fluid(water) bounded by the plate with the variation in the loactions of an external using three numerical tools such as the Gauss-Jordan method, the LU decomposition method and the IMSL numerical package.

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Finding Declinations Using Lookup Tables and Bilinear Interpolation in the Application of Digital Magnetic Compass

  • Yim, Jeong-Bin;Kim, Dae-Hee
    • Journal of Navigation and Port Research
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    • v.36 no.9
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    • pp.699-706
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    • 2012
  • This paper describes Declination interpolation method in the application of Digital Magnetic Compass(DMC) with small capacity memory for relatively low-priced system. The purpose of this study is to find accurate declination values using Declination Lookup Table(DLT) and Secular Variation Table(SVT) with the Bilinear interpolation method having four points data only. World Magnetic Model with Gauss coefficients produced by NOAA is used in the calculation of DLT and SVT at desired years. To verify the applicability of the proposed method, the simulation tests and the error analysis are carried out in this work. As results from tests, the interpolation error is within 0.01 degree that is much enough to implement high accurate DMC comparing with commercial DMCs on the market. In summary, the declination interpolation method, proposed in this study, can be useful in the application of DMC.

Effects of Earth-Tube Characteristics on the Soil-Air Heat Exchanger Performances (지중매설관의 특성이 토양 - 공기 열교환기 성능에 미치는 영향)

  • 김영복
    • Journal of Biosystems Engineering
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    • v.22 no.4
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    • pp.459-468
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    • 1997
  • To optimize the design and operation of a soil- air heat exchanger system, the effects of variables characterizing system design and operation on the performance of the system were analyzed by a theoretical model which included the three-dimensional transient heat conduction equation. The solution of the theoretical model was acquired by a computer program that uses Finite Difference Methods and Gauss-Seidel iteration computation, in which the time discretization scheme was an implicit difference appoximation. The computer program was validated first by comparison of the results for different grid sizes. Air outlet temperature, energy gain, and heat exchange efficiency of the system were analyzed based upon the tube diameter, tube length, tube thickness, and tube thermal diffusivity.

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Topology optimization for thin plate on elastic foundations by using multi-material

  • Banh, Thien Thanh;Shin, Soomi;Lee, Dongkyu
    • Steel and Composite Structures
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    • v.27 no.2
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    • pp.177-184
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    • 2018
  • This study contributes to evaluate multiphase topology optimization design of plate-like structures on elastic foundations by using classic plate theory. Multi-material optimal topology and shape are produced as an alternative to provide reasonable material assignments based on stress distributions. Multi-material topology optimization problem is solved through an alternative active-phase algorithm with Gauss-Seidel version as an optimization model of optimality criteria. Stiffness and adjoint sensitivity formulations linked to thin plate potential strain energy are derived in terms of multiphase design variables and Winkler-Pasternak parameters considering elastic foundation to apply to the current topology optimization. Numerical examples verify efficiency and diversity of the present topology optimization method of elastic thin plates depending on multiple materials and Winkler-Pasternak parameters with the same amount of volume fraction and total structural volume.

Estimation of Viscoelastic Properties of Trabecular Bone Using An Inverse Method (역추기법을 이용한 해면골의 점탄성 특성 해석)

  • Kang, Shin-Ill;Lee, Won-Hee;Hong, Jung-Hwa
    • Journal of Biomedical Engineering Research
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    • v.18 no.3
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    • pp.211-216
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    • 1997
  • An inverse method with regularization has been developed to determine the viscoelastic properties of trabecular bone. A series of stress relaxation experiments were performed under the condition of uniaxial compression stress state. Optimization has been formulated within the framework of nonlinear least-squares and a modified Gauss-Newton method with a zeroth-order regularization technique. The stress relaxation behavior of trabecular bone was analyzed using a standard viscoelastic model. The present study clearly shows that trabecular bone exhibits typical viscoelastic stress relaxation behavior, and the obtained material parameters well represent the viscoelastic behavior of trabecular bone.

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Fraud Detection in E-Commerce

  • Alqethami, Sara;Almutanni, Badriah;AlGhamdi, Manal
    • International Journal of Computer Science & Network Security
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    • v.21 no.6
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    • pp.200-206
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    • 2021
  • Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models.

PM2.5 Estimation Based on Image Analysis

  • Li, Xiaoli;Zhang, Shan;Wang, Kang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.2
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    • pp.907-923
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    • 2020
  • For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

Damage detection using finite element model updating with an improved optimization algorithm

  • Xu, Yalan;Qian, Yu;Song, Gangbing;Guo, Kongming
    • Steel and Composite Structures
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    • v.19 no.1
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    • pp.191-208
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    • 2015
  • The sensitivity-based finite element model updating method has received increasing attention in damage detection of structures based on measured modal parameters. Finding an optimization technique with high efficiency and fast convergence is one of the key issues for model updating-based damage detection. A new simple and computationally efficient optimization algorithm is proposed and applied to damage detection by using finite element model updating. The proposed method combines the Gauss-Newton method with region truncation of each iterative step, in which not only the constraints are introduced instead of penalty functions, but also the searching steps are restricted in a controlled region. The developed algorithm is illustrated by a numerically simulated 25-bar truss structure, and the results have been compared and verified with those obtained from the trust region method. In order to investigate the reliability of the proposed method in damage detection of structures, the influence of the uncertainties coming from measured modal parameters on the statistical characteristics of detection result is investigated by Monte-Carlo simulation, and the probability of damage detection is estimated using the probabilistic method.

CAVITATION FLOW SIMULATION FOR A 2-D HYDROFOIL USING A HOMOGENEOUS MIXTURE MODEL ON UNSTRUCTURED MESHES (비정렬 격자계에서 균질혼합 모델을 이용한 2차원 수중익형 주위의 캐비테이션 유동 해석)

  • Ahn, S.J.;Kwon, O.J.
    • Journal of computational fluids engineering
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    • v.17 no.1
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    • pp.94-100
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    • 2012
  • In this paper, the cavitating flows around a hydrofoil have been numerically investigated by using a 2-d multi-phase RANS flow solver based on pseudo-compressibility and a homogeneous mixture model on unstructured meshes. For this purpose, a vertex-centered finite-volume method was utilized in conjunction with 2nd-order Roe's FDS to discretize the inviscid fluxes. The viscous fluxes were computed based on central differencing. The Spalart-Allmaras one equation model was employed for the closure of turbulence. A dual-time stepping method and the Gauss-Seidel iteration were used for unsteady time integration. The phase change rate between the liquid and vapor phases was determined by Merkle's cavitation model based on the difference between local and vapor pressure. Steady state calculations were made for the modified NACA66 hydrofoil at several flow conditions. Good agreements were obtained between the present results and the experiment for the pressure coefficient on a hydrofoil surface. Additional calculation was made for cloud cavitation around the hydrofoil. The observation of the vapor structure, such as cavity size and shape, was made, and the flow characteristics around the cavity were analyzed. Good agreements were obtained between the present results and the experiment for the frequency and the Strouhal number of cavity oscillation.