• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 추계학술대회논문집
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• pp.963-966
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• 2005

Model updating method is known to the area to correct finite element models by the results of the experimental modal analysis. Most common methods in model updating depend on a parametric model of the structure. In this case, the number of parameters is normally smaller than that of modal data obtained from an experiment. In order to overcome this limitation, many researchers are trying to get modal data as many as possible to date. 1 want to name this method multiple modified-system generation method. These Methods consist of direct system modification method and feedback controller method. The direct system modification Is to add a mass or stiffness on the original structure or perturb the boundary conditions. The feedback controller method is to make the closed food system with sensor and actuator so as to get the closed loop modal data. In this paper, we need to focus on the feedback controller method because of its simplicity. Several methods related the feedback controller methods are virtual passive controller (VPC) sensitivity enhancement controller (SEC) and mode decoupling controller (MDC). Among them, we will apply MDC to the model updating problem. MDC has various advantages compared with other controllers, such as VPC and SEC. To begin with, only the target mode can be changed without changing modal property of non-target modes. In addition, it is possible to fix any modes if the number of sensors is equal to that of the system modes. Finally, the required control power to achieve desired change of target mode is always lower than those of other methods such as VPC. However, MDC can make the closed loop system unstable when using incomplete modal data. So we need to take action to avoid undesirable instability from incomplete modal data. In this paper, we address the method to design the unique and robust MDD obtained from incomplete modal data. The associated simulation will be Incorporated to demonstrate the usefulness of this method.

• Smart Structures and Systems
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• 제10권4_5호
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• pp.471-483
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• 2012

In this paper, the structural health monitoring (SHM) benchmark problem of the Canton tower is studied. Based on the field monitoring data from the 20 accelerometers deployed on the tower, some modal frequencies and mode shapes at measured degrees of freedom of the tower are identified. Then, these identified incomplete modal data are used to update the reduced finite element (FE) model of the tower by a novel algorithm. The proposed algorithm avoids the problem of subjective selection of updated parameters and directly updates model stiffness matrix without model reduction or modal expansion approach. Only the eigenvalues and eigenvectors of the normal finite element models corresponding to the measured modes are needed in the computation procedures. The updated model not only possesses the measured modal frequencies and mode shapes but also preserves the modal frequencies and modes shapes in their normal values for the unobserved modes. Updating results including the natural frequencies and mode shapes are compared with the experimental ones to evaluate the proposed algorithm. Also, dynamic responses estimated from the updated FE model using remote senor locations are compared with the measurement ones to validate the convergence of the updated model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.810-831
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• 2019

Recently, continuous dimensional emotion recognition from audiovisual clues has attracted increasing attention in both theory and in practice. The large amount of data involved in the recognition processing decreases the efficiency of most bimodal information fusion algorithms. A novel algorithm, namely the incomplete Cholesky decomposition based kernel cross factor analysis (ICDKCFA), is presented and employed for continuous dimensional audiovisual emotion recognition, in this paper. After the ICDKCFA feature transformation, two basic fusion strategies, namely feature-level fusion and decision-level fusion, are explored to combine the transformed visual and audio features for emotion recognition. Finally, extensive experiments are conducted to evaluate the ICDKCFA approach on the AVEC 2016 Multimodal Affect Recognition Sub-Challenge dataset. The experimental results show that the ICDKCFA method has a higher speed than the original kernel cross factor analysis with the comparable performance. Moreover, the ICDKCFA method achieves a better performance than other common information fusion methods, such as the Canonical correlation analysis, kernel canonical correlation analysis and cross-modal factor analysis based fusion methods.

• Structural Engineering and Mechanics
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• 제81권1호
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• pp.103-115
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• 2022

The prediction error variances for frequencies are usually considered as unknown in the Bayesian system identification process. However, the error variances for mode shapes are taken as known to reduce the dimension of an identification problem. The present study attempts to explore the effectiveness of Bayesian approach of model parameters updating using Markov Chain Monte Carlo (MCMC) technique considering the prediction error variances for both the frequencies and mode shapes. To remove the ergodicity of Markov Chain, the posterior distribution is obtained by Gaussian Random walk over the proposal distribution. The prior distributions of prediction error variances of modal evidences are implemented through inverse gamma distribution to assess the effectiveness of estimation of posterior values of model parameters. The issue of incomplete data that makes the problem ill-conditioned and the associated singularity problem is prudently dealt in by adopting a regularization technique. The proposed approach is demonstrated numerically by considering an eight-storey frame model with both complete and incomplete modal data sets. Further, to study the effectiveness of the proposed approach, a comparative study with regard to accuracy and computational efficacy of the proposed approach is made with the Sequential Monte Carlo approach of model parameter updating.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1998년도 춘계학술대회논문집; 용평리조트 타워콘도, 21-22 May 1998
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• pp.244-249
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• 1998

The present paper improves the direct identification scheme based upon the equation error formulation with incomplete modal data. First, an indirect estimation technique is considered for estimating unmeasured elements of latent vectors by the combined use of a model and measured incomplete eigen vectors. It is used for estimating the other elements of eigen vectors, which are essential for identification but not available. Next an index is introduced here to indicate the quality of estimation with respect to the mode and the measured positions. A sensitivity formula for eigenvalues with respect to the unknown joint coefficient is also derived to select the modes appropriate for identification. An identification strategy is suggested to meet with practical problems with the help of the index and sensitivity formula. The index and the sensitivity are proved to be useful for selecting measurement positions and modes appropriate for identification A comprehensive simulation study is performed to test the proposed method.

• 한국멀티미디어학회논문지
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• 제7권3호
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• pp.427-437
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• 2004

3차원 모델간의 형상을 비교하는 것은 형상을 기반으로 하는 인식, 검색, 분류 등을 위해 매우 중요하다. 본 논문에서는 모델의 이동, 회전, 스케일 변화에 영향받지 않고, 모델을 구성하는 정점들이 비균일 하고 불완전한 경우에도 강인한 3차원 형상 비교 방법을 제안한다. 먼저 입력 데이터로부터 고유 모드를 이용한 모달 모델을 구성하고 모달 스트레인 에너지를 이용하여 형상 간의 유사성을 비교한다. 제안된 방법은 고유 진동수에 따라 고유 모드들을 순서화 함으로써 형태 변형을 전역적인 것에서부터 지역적인 것으로 체계화한다. 이렇게 체계화된 형상 표현과 모달 스트레인 에너지를 이용함으로써 국부적인 형태에 치우치지 않고 전체적인 형태의 유사성을 평가하였다.

• Structural Engineering and Mechanics
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• 제68권1호
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• pp.39-51
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• 2018

In this study, a frequency domain model updating method is presented using power spectral density (PSD) data. It uses the sensitivity of PSD function with respect to the unknown structural parameters through a decomposed form of transfer function. The stiffness parameters are captured with high accuracy through solving the sensitivity equations utilizing the least square approach. Using numerically noise polluted data, the model updating results of a truss model prove robustness of the method against measurement and mass modelling errors. Results prove the capabilities of the method for parameter estimation using highly noise polluted data of low ranges of excitation frequency.

• 한국실천공학교육학회논문지
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• 제3권1호
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• pp.45-50
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• 2011

유한요소 해석(FEA: finite element analysis)의 발달로 복잡한 기계나 자동차 및 구조물에 대해서도 상세한 진동해석을 할 수 있게 되었다. 그러나, 복잡한 구조물을 정확하게 모형화하기 어렵고, 특히 접합부의 강성과 감쇠 특성을 알기 어렵고, 복잡한 형상을 단순화하는 과정에서 발생하는 오차 등의 이유로 유한요소해석 결과는 부정확할 수 있다. 반면에 실제 구조물의 실험 데이터로부터 추출한 실험적 모드해석(modal testing) 결과는 상세하지는 않지만 정확하다고 볼 수 있다. 그러나 실험 결과가 구조물의 진동 특성을 정확하게 나타낸다는 가정은 여러 가지 측정 오차로 인하여 정확하지 않을 수 있다. 이 논문에서는 실험적 모드해석의 기본이 되는 FRF(frequency response function; 주파수 응답함수)의 측정에 영향을 미치는 오차들을 측정 오차와 신호처리 오차로 구분하여 각각에 대해 세밀히 살펴보고, 그러한 오차들을 감소함으로써 보다 정확한 FRF를 구하는 방법에 대해서 고찰해보았다.

• Journal of Electrical Engineering and Technology
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• 제12권5호
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• pp.1754-1763
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• 2017

This paper analyses the characteristics of incomplete-journey double-circuit transmission lines on the same tower formed by single-circuit lines and double-circuit lines, and then presents a fault location algorithm based on identification of fault branch. With the relationship between the three-phase system and the double-circuit line system, a phase-mode transformation matrix for double-circuit lines can be derived. Based on the derived matrix, the double-circuit lines with faults can be decoupled, and then the fault location for an incomplete-journey double-circuit line is achieved by using modal components in the mode domain. The algorithm is divided into two steps. Firstly, the fault branch is identified by comparing the relationships of voltage amplitudes at the bonding point. Then the fault location, on the basis of the identification result, is calculated by using a two-terminal method, and only the fault distance of the actual fault branch can be obtained. There is no limit on synchronization of each terminal sampling data. The results of ATP-EMTP simulation show that the proposed algorithm can be applied within the entire line and can accurately locate faults in different fault types, fault resistances, and fault distances.

• Structural Engineering and Mechanics
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• 제70권3호
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• pp.339-350
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• 2019

This research focuses on finite element model updating and damage assessment of structures at element level based on global nondestructive test results. For this purpose, an optimization system is generated to minimize the structural dynamic parameters discrepancies between numerical and experimental models. Objective functions are selected based on the square of Euclidean norm error of vibration frequencies and modal assurance criterion of mode shapes. In order to update the finite element model and detect local damages within the structural members, modern optimization techniques is implemented according to the evolutionary algorithms to meet the global optimized solution. Using a simulated numerical example, application of genetic algorithm (GA), particle swarm (PSO) and artificial bee colony (ABC) algorithms are investigated in FE model updating and damage detection problems to consider their accuracy and convergence characteristics. Then, a hybrid multi stage optimization method is presented merging advantages of PSO and ABC methods in finding damage location and extent. The efficiency of the methods have been examined using two simulated numerical examples, a laboratory dynamic test and a high-rise building field ambient vibration test results. The implemented evolutionary updating methods show successful results in accuracy and speed considering the incomplete and noisy experimental measured data.