• Smart Structures and Systems
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• 제15권2호
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• pp.425-445
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• 2015

Parametric identification of structures is one of the important aspects of structural health monitoring. Most of the techniques available in the literature have been proved to be effective for structures with small degree of freedoms. However, the problem becomes challenging when the structure system is large, such as bridge structures. Therefore, it is highly desirable to develop parametric identification methods that are applicable to complex structures. In this paper, the LSE based techniques will be combined with the substructure approach for identifying the parameters of a cable-stayed bridge with large degree of freedoms. Numerical analysis has been carried out for substructures extracted from the 2-dimentional (2D) finite element model of a cable-stayed bridge. Only vertical white noise excitations are applied to the structure, and two different cases are considered where the structural damping is not included or included. Simulation results demonstrate that the proposed approach is capable of identifying the structural parameters with high accuracy without measurement noises.

• 대한산업공학회지
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• 제37권1호
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• pp.74-82
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• 2011

The Bass model is a cornerstone in diffusion theory which is used for forecasting demand of durables or new services. Three well-known estimation methods for parameters of the Bass model are Ordinary Least Square (OLS), Maximum Likelihood Estimator (MLE), Nonlinear Least Square (NLS). In this paper, a hybrid method incorporating OLS and NLS is presented and it's performance is analyzed and compared with OLS and NLS by using simulation data and empirical data. The results show that NLS has the best performance in terms of accuracy and our hybrid method has the best performance in terms of stability. Specifically, hybrid method has better performance with less data. This result means much in practical aspect because the avaliable data is little when a diffusion model is used for forecasting demand of a new product.

• 한국항공운항학회지
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• 제18권4호
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• pp.30-37
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• 2010

This paper deals with attitude determination and parameter estimation problems for a lunar module. For this we first derive equations of motion for the lunar module by considering allocation locations (configurations) of reaction thruster and a reaction wheel assembly. The lunar module is assumed as a rigid body. In order to include the effect of fuel sloshing on the dynamics of the lunar module, we model it as a spherical pendulum for a simple analysis. For estimating angular rates and moment of inertia of the module, we employ an extended Kalman filter and the least mean square algorithms, respectively. Finally we construct a dynamical model for the lunar module by combining all these elements.

• 제어로봇시스템학회논문지
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• 제17권8호
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• pp.731-738
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• 2011

In this paper, we introduce an advanced architecture of K-Means clustering-based polynomial Radial Basis Function Neural Networks (p-RBFNNs) designed with the aid of SSOA (Space Search Optimization Algorithm) and develop a comprehensive design methodology supporting their construction. In order to design the optimized p-RBFNNs, a center value of each receptive field is determined by running the K-Means clustering algorithm and then the center value and the width of the corresponding receptive field are optimized through SSOA. The connections (weights) of the proposed p-RBFNNs are of functional character and are realized by considering three types of polynomials. In addition, a WLSE (Weighted Least Square Estimation) is used to estimate the coefficients of polynomials (serving as functional connections of the network) of each node from output node. Therefore, a local learning capability and an interpretability of the proposed model are improved. The proposed model is illustrated with the use of nonlinear function, NOx called Machine Learning dataset. A comparative analysis reveals that the proposed model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• 전기학회논문지
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• 제65권6호
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• pp.1070-1079
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• 2016

In this paper, the design of recursive radial basis function neural networks based on incremental fuzzy c-means is introduced for processing the big data. Radial basis function neural networks consist of condition, conclusion and inference phase. Gaussian function is generally used as the activation function of the condition phase, but in this study, incremental fuzzy clustering is considered for the activation function of radial basis function neural networks, which could effectively do big data processing. In the conclusion phase, the connection weights of networks are given as the linear function. And then the connection weights are calculated by recursive least square estimation. In the inference phase, a final output is obtained by fuzzy inference method. Machine Learning datasets are employed to demonstrate the superiority of the proposed classifier, and their results are described from the viewpoint of the algorithm complexity and performance index.

• 대한기계학회논문집B
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• 제26권1호
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• pp.110-115
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• 2002

The tunnels built in recent years are equipped with traffic counters and pollution sensors (mostly, CO and Vl sensors). Utilizing these built-in sensors, it is possible to develop an algorithm to estimate the amount of pollutants exhausted from the each class of cars passing through the tunnel. These estimated data can be effectively utilized not only for ventilation control but also for designing ventilation facilities. The diffusion of pollutants in a tunnel can be described with one-dimensional diffusion-convection equation. This equation is approximated with interpolation functions and weighted residual method converting to adequate form for standard state estimate algorithms. With this converted equations, a least square optimization based algorithm is developed, whose outputs are the estimated amounts of pollutants emitted from each class of cars. In order to verify the feasibility of the developed algorithms, simulations are performed with the real data acquisitioned from the Tunnae tunnel located in Young-Dong highway in Korea.

• 한국소음진동공학회논문집
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• 제25권2호
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• pp.116-123
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• 2015

This paper proposed a new formula for predicting acoustic power transmission loss of simple expansion chamber with two internal partitions. Seventeen cases of computational results were performed and the database was constructed for the TL according to the various positions of internal partitions. Using this database, firstly, the formula for the peak values of TL at certain frequencies was developed using the least square estimation. Secondly, the formula for the TL curve could be obtained automatically with the input data of the positions of two internal partitions. The formula of TL developed in this paper showed good agreement with computational results. This formula will be helpful for the positioning of internal partitions to improve TL at target frequencies.

• 한국통신학회논문지
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• 제33권4C호
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• pp.305-311
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• 2008

본 논문에서는 실시간 자동차 환경에서 VSSNLMS(variable step size normalized least mean square)를 이용하여 적응 잡음 제거 알고리즘을 제안한다. 기본적인 ANC(adaptive noise canceller)알고리즘인 LMS알고리즘은 알고리즘의 간단성 때문에 가장 많이 사용되고 있다. 그러나 LMS알고리즘은 수렴율과 실시간 환경에서의 정확성 사이에서 문제를 가지고 있다. 이러한 문제를 풀기 위해, 비정장성 환경에서 잡음제거를 위해 VSSLMS알고리즘이 사용된다. 본 논문에서 실시간 데이터 입력 시스템을 사용하여 컴퓨터 시뮬레이션 함으로써, VSSLMS알고리즘이 LMS알고리즘에 비해 수렴율과 정확성 이 모두에 더 효율적이라는 것을 입증한다.

• 한국소음진동공학회논문집
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• 제23권11호
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• pp.943-950
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• 2013

This paper discusses a harmonic response estimation method on the L$\acute{e}$vy plate with two opposite edges simply supported and the other two edges having free boundary conditions. Since the equation of motion of the plate is not self-adjoint, the modes are not orthogonal to each other on the domain. Noting that the L$\acute{e}$vy plate can be expressed using one term sinusoidal function that is orthogonal to other sinusoidal functions, this paper suggested the calculation method that is equivalent to finding a least square error minimization solution of the finite number of algebraic equations. Example problems subjected to a distributed area loading and a distributed line loading are defined and their solutions are provided. The solutions are compared to those of the commercial code, ANSYS. According to the verification results, it is expected that the suggested method will be useful to predict the forced response on the L$\acute{e}$vy plate with the distributed area or line loading conditions.

• 한국산업정보학회논문지
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• 제13권1호
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• pp.57-67
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• 2008

본 논문에서는 광류를 이용한 적응 블록 정합 움직임 추정 방법을 제안하였다. 제안 방법에서는 먼저 각 화소의 시간 경사값과 공간 경사값을 미분필터를 통하여 계산한 후, 이 경사값들로부터 최소 자승 추정법을 이용하여 광류를 추정하여 탐색영역의 위치와 크기를 결정하였다. 특히 움직임 특성에 따라 탐색영역을 결전함으로써 움직임 추정 오차가 큰 영역인 크고 복잡한 움직임을 갖는 영상에 대해서 뛰어난 성능을 갖는다. 다양한 움직임 특성을 가지는 실험 영상들에 대한 기존의 방법과 제안한 방법의 움직임 추정 성능 평가를 위한 컴퓨터 모의실험을 통하여, 제안한 방법이 움직임이 크고 복잡한 영상에 대해서 기존의 방법에 비해 우수한 PSNR을 나타냄을 확인하였다.