• Title/Summary/Keyword: Structure identification

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Preparation and Determination of Structure of L-3-Deoxymimosine-containing Peptides

  • Chae, Whi-Gun;Lee, Eung-Seok
    • Archives of Pharmacal Research
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    • v.23 no.3
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    • pp.211-221
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    • 2000
  • L-3-Deoxymimosine-containing decapeptides were prepared for the development of protein tyrosine kinase (PTK) inhibitors. During the preparation of peptides, several side products were formed. identification and determination of major peptides generated were reported.

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An Adaptive Algorithm Using A Polyphase Subband Decomposition (다위상 서브밴드 분해를 이용한 적응 알고리즘)

  • 주상영;이동규;이두수
    • Proceedings of the IEEK Conference
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    • 2000.06d
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    • pp.182-185
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    • 2000
  • In this paper, we present a new adaptive filter structure which is based on polyphase decomposition of the filter to be adapted. This structure uses wavelet transform to acquire transform-domain coefficients of the input signal. With this coefficients RLS algorithm is used for adaptation. Particularly, using the polyphase parallel structure, we can trace the system which has very long impulse response with only increasing the subband, and show that computational savings can be achieved. The proposed structure was applied to system identification for performance estimation and compared with fullband adaptive filter.

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Experimental axial force identification based on modified Timoshenko beam theory

  • Li, Dong-sheng;Yuan, Yong-qiang;Li, Kun-peng;Li, Hong-nan
    • Structural Monitoring and Maintenance
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    • v.4 no.2
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    • pp.153-173
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    • 2017
  • An improved method is presented to estimate the axial force of a bar member with vibrational measurements based on modified Timoshenko beam theory. Bending stiffness effects, rotational inertia, shear deformation, rotational inertia caused by shear deformation are all taken into account. Axial forces are estimated with certain natural frequency and corresponding mode shape, which are acquired from dynamic tests with five accelerometers. In the paper, modified Timoshenko beam theory is first presented with the inclusion of axial force and rotational inertia effects. Consistent mass and stiffness matrices for the modified Timoshenko beam theory are derived and then used in finite element simulations to investigate force identification accuracy under different boundary conditions and the influence of critical axial force ratio. The deformation coefficient which accounts for rotational inertia effects of the shearing deformation is discussed, and the relationship between the changing wave speed and the frequency is comprehensively examined to improve accuracy of the deformation coefficient. Finally, dynamic tests are conducted in our laboratory to identify progressive axial forces of a steel plate and a truss structure respectively. And the axial forces identified by the proposed method are in good agreement with the forces measured by FBG sensors and strain gauges. A significant advantage of this axial force identification method is that no assumption on boundary conditions is needed and excellent force identification accuracy can be achieved.

Identification of Fuzzy Inference System Based on Information Granulation

  • Huang, Wei;Ding, Lixin;Oh, Sung-Kwun;Jeong, Chang-Won;Joo, Su-Chong
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.4 no.4
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    • pp.575-594
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    • 2010
  • In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with "conventional" evolutionary algorithms (such as PSO), SSA leads no.t only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems comes in the form of two optimization 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 polyno.mial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using four representative numerical examples such as No.n-linear function, gas furnace, NO.x emission process data, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some "conventional" fuzzy models already encountered in the literature.

A hybrid identification method on butterfly optimization and differential evolution algorithm

  • Zhou, Hongyuan;Zhang, Guangcai;Wang, Xiaojuan;Ni, Pinghe;Zhang, Jian
    • Smart Structures and Systems
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    • v.26 no.3
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    • pp.345-360
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    • 2020
  • Modern swarm intelligence heuristic search methods are widely applied in the field of structural health monitoring due to their advantages of excellent global search capacity, loose requirement of initial guess and ease of computational implementation etc. To this end, a hybrid strategy is proposed based on butterfly optimization algorithm (BOA) and differential evolution (DE) with purpose of effective combination of their merits. In the proposed identification strategy, two improvements including mutation and crossover operations of DE, and dynamic adaptive operators are introduced into original BOA to reduce the risk to be trapped in local optimum and increase global search capability. The performance of the proposed algorithm, hybrid butterfly optimization and differential evolution algorithm (HBODEA) is evaluated by two numerical examples of a simply supported beam and a 37-bar truss structure, as well as an experimental test of 8-story shear-type steel frame structure in the laboratory. Compared with BOA and DE, the numerical and experimental results show that the proposed HBODEA is more robust to detect the reduction of stiffness with limited sensors and contaminated measurements. In addition, the effect of search space, two dynamic operators, population size on identification accuracy and efficiency of the proposed identification strategy are further investigated.

Damage Estimation of Bridge Structures Using System Identification (동특성추정법을 이용한 교량구조물의 손상도 추정)

  • 김원종;강용중
    • Computational Structural Engineering
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    • v.6 no.2
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    • pp.71-78
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    • 1993
  • A method to estimate damage of bridge structures is developed using system identification approach. Dynamic behavior of damaged structures is represented by a non-linear hysteretic moment model. Structural properties can be evaluated through system identification. To incorporate variability of the structural properties and uncertainties of structural response, damage is represented as random quantities. Numerical example is shown for the bridge structure under different ground excitation.

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System Identification of a Building Structure Using Wireless MEMS System (무선 MEMS 시스템을 이용한 구조물 식별)

  • Kim, Hong-Jin
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.18 no.4
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    • pp.458-464
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    • 2008
  • The structural health monitoring has been gaining more importance in civil engineering areas such as earthquake and wind engineering. The use of health monitoring system can also provide tools for the validation of structural analytical model. However, only few structures such as historical buildings and some important long bridges have been instrumented with structural monitoring system due to high cost of installation, long and complicated installation of system wires. In this paper, the structural monitoring system based on cheap and wireless monitoring system is investigated. The use of advanced technology of micro-electro-mechanical system(MEMS) and wireless communication can reduce system cost and simplify the installation. Further the application of wireless MEMS system can provide enhanced system functionality and due to low noise densities. Identification results are compared to ones using data measured from traditional accelerometers and results indicate that the system identification using wireless MEMS system estimates system parameters accurately.