• Title/Summary/Keyword: Structure identification

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Identification of Fuzzy Inference Systems Using a Multi-objective Space Search Algorithm and Information Granulation

  • Huang, Wei;Oh, Sung-Kwun;Ding, Lixin;Kim, Hyun-Ki;Joo, Su-Chong
    • Journal of Electrical Engineering and Technology
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    • v.6 no.6
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    • pp.853-866
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    • 2011
  • We propose a multi-objective space search algorithm (MSSA) and introduce the identification of fuzzy inference systems based on the MSSA and information granulation (IG). The MSSA is a multi-objective optimization algorithm whose search method is associated with the analysis of the solution space. The multi-objective mechanism of MSSA is realized using a non-dominated sorting-based multi-objective strategy. In the identification of the fuzzy inference system, the MSSA is exploited to carry out parametric optimization of the fuzzy model and to achieve its structural optimization. The granulation of information is attained using the C-Means clustering algorithm. The overall optimization of fuzzy inference systems comes in the form of two identification mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and the polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by the MSSA and C-Means, whereas the parameter identification is realized via the MSSA and least squares method. The evaluation of the performance of the proposed model was conducted using three representative numerical examples such as gas furnace, NOx emission process data, and Mackey-Glass time series. The proposed model was also compared with the quality of some "conventional" fuzzy models encountered in the literature.

System Identification and Damage Estimation via Substructural Approach

  • Tee, K.-F.;Koh, C.-G.;Quek, S.-T.
    • Computational Structural Engineering : An International Journal
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    • v.3 no.1
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    • pp.1-7
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    • 2003
  • For system identification of large structures, it is not practical to identify the entire structure due to the prohibitive computational time and difficulty in numerical convergence. This paper explores the possibility of performing system identification at substructure level, taking advantage of reduction in both the number of unknowns and the number of degrees of freedom involved. Another advantage is that different portions (substructures) of a structural system can be identified independently and even concurrently with parallel computing. Two substructural identification methods are formulated on the basis whether substructural approach is used to obtain first-order or second-order model. For substructural first-order model, identification at the substructure level will be performed by means of the Observer/Kalman filter Identification (OKID) and the Eigensystem Realization Algorithm (ERA) whereas identification at the global level will be performed to obtain second-order model in order to evaluate the system's stiffness and mass parameters. In the case of substructural second-order model, identification will be performed at the substructure level throughout the identification process. The efficiency of the proposed technique is shown by numerical examples for multi-storey shear buildings subjected to random forces, taking into consideration the effects of noisy measurement data. The results indicate that both the proposed methods are effective and efficient for damage identification of large structures.

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Damage detection of multi-storeyed shear structure using sparse and noisy modal data

  • Panigrahi, S.K.;Chakraverty, S.;Bhattacharyya, S.K.
    • Smart Structures and Systems
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    • v.15 no.5
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    • pp.1215-1232
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    • 2015
  • In the present paper, a method for identifying damage in a multi storeyed shear building structure is presented using minimum number of modal parameters of the structure. A damage at any level of the structure may lead to a major failure if the damage is not attended at appropriate time. Hence an early detection of damage is essential. The proposed identification methodology requires experimentally determined sparse modal data of any particular mode as input to detect the location and extent of damage in the structure. Here, the first natural frequency and corresponding partial mode shape values are used as input to the model and results are compared by changing the sensor placement locations at different floors to conclude the best location of sensors for accurate damage identification. Initially experimental data are simulated numerically by solving eigen value problem of the damaged structure with inclusion of random noise on the vibration characteristics. Reliability of the procedure has been demonstrated through a few examples of multi storeyed shear structure with different damage scenarios and various noise levels. Validation of the methodology has also been done using dynamic data obtained through experiment conducted on a laboratory scale steel structure.

Experimental Study of System Identification for Seismic Response of Building Structure (건축구조물의 지진응답제어를 위한 시스템 식별의 실험적 연구)

  • 주석준;박지훈;민경원;홍성목
    • Journal of the Earthquake Engineering Society of Korea
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    • v.3 no.4
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    • pp.47-60
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    • 1999
  • The stability and efficiency of structural control systems depend on the accuracy of mathematical model of the system to be controlled. In this study, state equation models of a small scale test structure and an AMD(active mass damper) are obtained separately using OKID(observer/Kalman filter identification) which is a time domain system identification method. The test structure with each floor acceleration as outputs is identified for two inputs - the ground acceleration and the acceleration of the moving mass of AMD relative to the installation floor - individually and the two identified state equation models are integrated into one by model reduction method. The AMD is identified with the motor control signal as an input and the relative acceleration of the moving mass as an output, and it is shown that the identified model has large damping ratio and phase shift. The transfer functions and the time histories reconstructed from the identified models of the test model and the AMD match well with those measured from the experiment.

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Joint parameter identification of a cantilever beam using sub-structure synthesis and multi-linear regression

  • Ingole, Sanjay B.;Chatterjee, Animesh
    • Structural Engineering and Mechanics
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    • v.45 no.4
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    • pp.423-437
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    • 2013
  • Complex structures are usually assembled from several substructures with joints connecting them together. These joints have significant effects on the dynamic behavior of the assembled structure and must be accurately modeled. In structural analysis, these joints are often simplified by assuming ideal boundary conditions. However, the dynamic behavior predicted on the basis of the simplified model may have significant errors. This has prompted the researchers to include the effect of joint stiffness in the structural model and to estimate the stiffness parameters using inverse dynamics. In the present work, structural joints have been modeled as a pair of translational and rotational springs and frequency equation of the overall system has been developed using sub-structure synthesis. It is shown that using first few natural frequencies of the system, one can obtain a set of over-determined system of equations involving the unknown stiffness parameters. Method of multi-linear regression is then applied to obtain the best estimate of the unknown stiffness parameters. The estimation procedure has been developed for a two parameter joint stiffness matrix.

Experimental Method of a Super Structure (선체 상부구조물의 실험적 해석)

  • 박석주;박성현;오창근;제해광
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2001.05a
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    • pp.328-334
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    • 2001
  • Up to now. vibration analysis and vibration engineering have been developed, encompassing the aspects of both experimental and analytical techniques. Using experimental modal analysis or modal testing, the mode shapes and frequencies of practical structure can be measured accurately. Curve-Fitting Method is realized through experimental modal identification. In the experimental modal parameter estimation, the estimation of modal damping factor is difficult for complicated and large structure. Also numbers of Selected mode are determined before the procedure. This paper describes the vibration shape of the super-structure model of ship through experimental modal analysis.

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An Effective Retinal Vessel and Landmark Detection Algorithm in RGB images

  • Jung Eun-Hwa
    • International Journal of Contents
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    • v.2 no.3
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    • pp.27-32
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    • 2006
  • We present an effective algorithm for automatic tracing of retinal vessel structure and vascular landmark extraction of bifurcations and ending points. In this paper we deal with vascular patterns from RGB images for personal identification. Vessel tracing algorithms are of interest in a variety of biometric and medical application such as personal identification, biometrics, and ophthalmic disorders like vessel change detection. However eye surface vasculature tracing in RGB images has many problems which are subject to improper illumination, glare, fade-out, shadow and artifacts arising from reflection, refraction, and dispersion. The proposed algorithm on vascular tracing employs multi-stage processing of ten-layers as followings: Image Acquisition, Image Enhancement by gray scale retinal image enhancement, reducing background artifact and illuminations and removing interlacing minute characteristics of vessels, Vascular Structure Extraction by connecting broken vessels, extracting vascular structure using eight directional information, and extracting retinal vascular structure, and Vascular Landmark Extraction by extracting bifurcations and ending points. The results of automatic retinal vessel extraction using jive different thresholds applied 34 eye images are presented. The results of vasculature tracing algorithm shows that the suggested algorithm can obtain not only robust and accurate vessel tracing but also vascular landmarks according to thresholds.

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Correlations between Sperm Motility, SCSA (Sperm Chromatin Structure Assay), Reproductive Performance and Heterospermic Fertility in Boars

  • Kim, In-Cheul;Ryu, Jae-Weon;Cho, Kyu-Ho;Hong, Joon-Ki;Choi, Eun-Ji;Choi, Bong-Hwan;Park, Jun-Cheol;Moon, Hong-Kil;Son, Jung-Ho
    • Reproductive and Developmental Biology
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    • v.32 no.2
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    • pp.127-133
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    • 2008
  • The objective of this study was two folds: to investigate the relationship between paternal identification rate and sperm quality parameters such as motility and sperm chromatin structure assay after heterospermic insemination; to see if mutual complement between tests and development of useful technique to enhance the fertility in artificial insemination. In individual boar's fertilizing ability, 3 high fertility boars showed significantly high fertility (p<0.05) compared to 3 low fertility boars, but there was no difference in litter size between two groups. Sperm motility test in pooled and individual semen using computer assisted sperm analysis (CASA) revealed that no significant difference among boars. The high fertile boar showed tendency of low %Red (High red fluorescence/green+red fluorescence) in sperm chromatin structure assay (SCSA) but paternal identification rate from piglets did not differ after heterospermic insemination. The correlation coefficient between individual or pooled semen function test and farrowing rates were well correlated as follows: %Red with litter size (r= - 0.53, p=0.03); %Red with paternal identification rates (r=-0.51, p=0.03); paternal identification rates with litter size (r=0.57, p=0.02). These results indicate that sperm chromatin structure assay and sperm quality parameter test in pooled semen are useful method to predict and evaluate the fertilizing capacity after heterospermic insemination in boars.

Design of Learning Fuzzy Controller by the Self-Tuning Algorithm for Equipment Systems (설비시스템을 위한 자기동조기법에 의한 학습 FUZZY 제어기 설계)

  • Lee, Seung
    • The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.9 no.6
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    • pp.71-77
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    • 1995
  • This paper deals with design method of learning fuzzy controller for control of an unknown nonlinear plant using the self-tuning algorithm of fuzzy inference rules. In this method the fuzzy identification model obtained that the joined identification model of nonlinear part and linear identification model of linear part by fuzzy inference systems. This fuzzy identification model ordered self-tuning by Decent method so as to be servile to nonlinear plant. A the end, designed learning fuzzy controller of fuzzy identification model have learning structure to model reference adaptive system. The simulation results show that th suggested identification and learning control schemes are practically feasible and effective.

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Comparative study on modal identification methods using output-only information

  • Yi, Jin-Hak;Yun, Chung-Bang
    • Structural Engineering and Mechanics
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    • v.17 no.3_4
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    • pp.445-466
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    • 2004
  • In this paper, several modal identification techniques for output-only structural systems are extensively investigated. The methods considered are the power spectral method, the frequency domain decomposition method, the Ibrahim time domain method, the eigensystem realization algorithm, and the stochastic subspace identification method. Generally, the power spectral method is most widely used in practical area, however, the other methods may give better estimates particularly for the cases with closed modes and/or with large measurement noise. Example analyses were carried out on typical structural systems under three different loading cases, and the identification performances were examined throught the comparisons between the estimates by various methods.