Joint stiffness identification using FRF measurements

In this research, an identification method for joint parameters has been developed by using the substructure synthesis method and frequency response functions (FRFs). A matrix form for the joint model was proposed. Identifying the joint parameters of a beam screwed down at one of its end was conducted. Only two measurement points were used in the experiment. The simulated results of the FRFs of the constrained beam with the properly identified joint parameters agree with the experimental results very satisfactorily. The accuracy of the identified results is strongly sensitive to the parameters. The rotational stiffness of the screw joint is identified to be located in the sensitive region, so the identified result of the rotational stiffness is very good. However, the less accurate results for the translational stiffness located in the insensitive region do not affect apparently the accuracy of the simulated results. The cross-coupled term between the translational and rotational stiffness in the joint model was identified, which greatly improved the accuracy of the simulated FRFs in higher frequency regions.     

Writer identification using directional ink-trace width measurements

As suggested by modern paleography, the width of ink traces is a powerful source of information for off-line writer identification, particularly if combined with its direction. Such measurements can be computed using simple, fast and accurate methods based on pixel contours, the combination of which forms a powerful feature for writer identification: the Quill feature. It is a probability distribution of the relation between the ink direction and the ink width. It was tested in writer identification experiments on two datasets of challenging medieval handwriting and two datasets of modern handwriting. The feature achieved a nearest-neighbor accuracy in the range of 63–95%, which even approaches the performance of two state-of-the-art features in contemporary-writer identification (Hinge and Fraglets). The feature is intuitive and explainable and its principle is supported by a model of trace production by a quill. It illustrates that ink width patterns are valuable. A slightly more complex variant of Quill, QuillHinge, scored 70–97% writer identification accuracy. The features are already being used by domain experts using a graphical interface.     

Model reduction by power decomposition and frequency response matching

A mixed method of linear system model reduction, retaining the advantages of the power decomposition method and the frequency response matching method, is proposed. From the viewpoint of energy contributions to the system output, the dynamic modes with dominant energy contributions instead of those with dominant eigenvalues will be preserved. Having determined the denominator of the reduced model, the parameters of the numerator are found by using the frequency response matching technique.     

Model uncertainty in frequency response based system identification

Determination of plant nominal model error bound is one of the most essential topics in robust control oriented identification. The objective of this paper is to investigate the structure of plant model uncertainties when a set of corrupted plant frequency response samples are supplied. It is shown that the plant model uncertainties, which result from the “partialness” and “corruptedness” of the provided plant information, can be represented by structure-fixed, norm-bounded but uncertain matrix-valued functions that perturb a plant nominal model through a linear fractional transformation. These results are very similar to those when plant time domain identification experiment data have been provided.     

Small hydro power plant identification using NNARX structure

A feedforward multi-layer perceptron neural network structure is developed to model the nonlinear dynamic relationship between input and output of a hydro power plant connected as single machine infinite bus system. Two independent second-order neural network nonlinear auto-regressive with exogenous signal models are used in the study. The structure selection of each independent model is based on various validation tests. The optimal brain surgeon pruning strategy adopted for optimizing the neural network structure. The network performance is studied for fixed and change in operating point.     

Model identification for adaptive control of nuclear power plants

The problem being considered is the identification and adaptation of a stochastic model of a nuclear power plant, and the uses of the stochastic model in the control of large and fast load changes. In order to make such load changes, a control strategy is designed to coordinate the major plant inputs. However, to be viable the control must overcome several kinds of variation and uncertainty in plant behavior.The control developed in this study makes use of a stochastic model as a reduced order representation of the plant. The parameters of this model are identified from measurement data records, using a maximum likelihood identification technique. The stochastic model is then used in a state estimator which gives estimates of important unmeasured plant variables. The stochastic model is also used to adjust control constraints and to predict the plant performance during the load change for display and monitoring purposes. If the plant behavior deviates from the model predictions, the model is adapted to be consistent with the plant performance. These techniques together overcome the difficulties of variation and uncertainty in the nuclear plant behavior, thus permitting large, fast load changes to be made without upsetting the plant operations.     

Frequency domain subspace-based identification of discrete-time power spectra from uniformly spaced measurements

In this paper, we revisit the problem of identifying multi-input/multi-output linear-time-invariant discrete-time systems from measured power spectrum data on uniform grids of frequencies studied by Van Overschee, De Moor, Dehandschutter, and Swevers (1997). We show that the algorithm proposed by these authors is not consistent. Then, we propose an interpolatory identification algorithm which is strongly consistent when the corruptions in the spectrum measurements have a bounded covariance function. The performance of the proposed algorithm is demonstrated in a simulation example.     

Frequency domain subspace-based identification of discrete-time power spectra from nonuniformly spaced measurements

In this paper, we present a new subspace-based algorithm for the identification of multi-input/multi-output, square, discrete-time, linear-time invariant systems from nonuniformly spaced power spectrum measurements. The algorithm is strongly consistent and it is illustrated with one practical example that solves a stochastic road modeling problem.     

Range measurements by a mobile robot using a navigation line

A continuous, straight-edged line is used for the visual navigation of an autonomous mobile robot in a factor environment. This line, which resides on the floor and contrasts with background, may also be used to determine range information. Two methods are developed for determining the range of an object in the sensor's field of view. The effects of various error conditions in the system geometry on each ranging method are determined. Equations are derived which yield the percent error in calculating ranges given estimates of these error conditions. Numerical examples using typical sensor parameters are given.     

Systematic investigation of biomolecular interactions using combined frequency and motional resistance measurements

The resonance frequency of acoustic biosensors is today used as a label-free technique for detecting mass changes on sensor surfaces. In combination with an appropriate continuous flow system it has earlier been used for affinity and kinetic rate determination. Here, we assess the potential of a modified acoustic biosensor, monitoring also the real-time dissipation through the resistance of the sensor, to obtain additional kinetic information related to the structure and conformation of the molecules on the surface. Actual interaction studies, including an attempt to determine avidity, are presented along with thorough verification of the experimental setup utilizing true viscous load exposure together with protein and DNA immobilizations.True viscous loads show a linear relationship between resistance and frequency as expected. However, in the interaction studies between antibodies and proteins, as well as in the immobilization of DNA and proteins, higher surface concentrations of interacting molecules led to a decrease (i.e. deviation from the linear trend) in the differential resistance to frequency ratio. This is interpreted as increased surface rigidity at higher surface concentrations of immobilized molecules. Consequently, studies that aim at obtaining biological binding information, such as avidity, from real-time resistance and dissipation data should be conducted at low surface concentrations. In addition, the differential resistance to frequency relationship was found to be highly dependent on the rigidity of the preceding layer(s) of immobilized molecules. This dependence can be utilized to obtain a higher signal-to-noise ratio for resistance measurement by using low surface densities of immobilized interaction partners.     

Model variables identification of a gas turbine using a subspace approach based on input/output data measurements

This paper deals with a state model identification of a gas turbine used for gas transport, using a subspace approach of the state space model. This method provides a reliable and robust state representation of the model, taking advantage of its benefits in the control, monitoring, and supervision of this machine. The model for each variable is set so that the state matrices associated with the gas turbine model are determined from their real input/output data. The comparison of the obtained identification results with those of the actual turbine operation serves to validate the proposed model in this work. This numerical algorithm of the subspace identification method is full of information and more accurate in terms of residual modeling error, and expresses a very high level of confidence in the identified turbine system dynamics. Hence, the controllability and observability tests of turbine operation for different input/output variables allowed to validate the real-time operating stability of the turbine.     

Life-cycle data management of engineered-to-order components using radio frequency identification

Management of engineered-to-order (ETO) components and their related information is a challenging task due to the complexity of information and its flow. Different information items are generated, accessed and exchanged between different organizations and they must continually flow through design, production, construction, and operations and maintenance. Current manual and labor-intensive methods are inefficient; as a result, information is frequently incomplete, inaccurate or unavailable during the life-cycle of a facility. This paper provides a vision of intelligent components, which know their identities, locations and history, and communicate this information to their environments. It proposes streamlining information flow through supply chains by utilizing radio frequency identification (RFID) technology. To explore the technical feasibility of intelligent components, component-related information flow patterns in ETO supply chains were identified and analyzed. Requirements analysis and corresponding technology deployment and testing were performed for three types of ETO components through different life-cycle phases. These experiments demonstrated that it is technically feasible to have intelligent components in construction supply chains by using RFID technology; that status information can be collected automatically; and that maintenance information can be stored and retrieved during the service life of a facility.     

利用弯矩影响线的移动荷载识别方法

基于模态叠加理论,通过桥梁多个截面处加速度响应数据,计算得到桥梁受移动荷载作用下的模态加速度.根据d'Alembertian原理,桥梁截面任意时刻的动弯矩可看作是任意时刻受惯性分布力和移动荷载作用下的静弯矩.利用影响线,建立起移动荷载与弯矩之间的关系,提出了一种利用弯矩影响线识别移动荷载的方法.算例表明,当荷载只有一个时,可由单点弯矩直接识别,当有多个移动荷载时,可基于多个截面的弯矩数据,利用最小二乘法可以有效的识别出任意时刻作用于桥梁上的移动荷载值.该方法避免了求解桥梁的动力学微分方程,识别精度高且过程简单,适合于工程应用.     

A subspace algorithm for the identification of discrete time frequency domain power spectra

In this paper we present a new subspace algorithm for the identification of multi-input multi-output linear discrete time systems from measured power spectrum data. We show how the state space system matrices can be determined by taking the inverse discrete Fourier transform of the given data and applying the result to a new realization algorithm. Special attention is paid to ensure the positive realness of the identified power spectrum. The computational speed is improved by applying a Lanczos algorithm. The algorithm is illustrated with two practical examples.     

A spatial power limiter using a nonlinear frequency selective surface

A novel spatial power limiter based on nonlinear frequency selective surface (FSS) is presented for high power electromagnetic (HPEM) wave protection. Embedded with Schottky diodes, the nonlinear FSS not only reflects out‐of‐band electromagnetic incidence like a filter, but also exhibits a power‐limiting characteristic, allowing low‐loss transmission for an in‐band low‐power incidence while rejecting a high‐power one. Such a FSS with 4 × 4 unit cells is designed, fabricated and measured. Results demonstrate its pass‐band centering at 2.5 GHz, power density threshold of about 0.27 W/m² and shielding effectiveness (SE) up to 20 dB at 2.5 GHz.     

Modeling of wideband radio frequency power amplifiers using Zernike polynomials

In this article, we suggest a new set of basis functions that are based on Zernike polynomials for the behavioral modeling of radio frequency power amplifiers (PAs). The modeling of highly nonlinear PAs exhibits numerical instability that degrades the accuracy of the model parameters and predistorter modeling efficiency. Simulation results show that the proposed polynomial model is more suitable to resolve the numerical instability problem and proves to have greater accuracy with reduced complexity. A Doherty PA driven by a multicarrier wideband code division multiple access signal was used for validation; and, the obtained results show that the new model exhibits superior numerical stability as the nonlinearity order and memory depth of the model increase. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.     

Model-based tool condition prognosis using power consumption and scarce surface roughness measurements

In machining processes, underusing and overusing cutting tools directly affect part quality, entailing economic and environmental impacts. In this paper, we propose and compare different strategies for tool replacement before processed parts exceed surface roughness specifications without underusing the tool. The proposed strategies are based on an online part quality monitoring system and apply a model-based algorithm that updates their parameters using adaptive recursive least squares (ARLS) over polynomial models whose generalization capabilities have been validated after generating a dataset using theoretical models from the bibliography. These strategies assume that there is a continuous measurement of power consumption and a periodic measurement of surface roughness from the quality department (scarce measurements). The proposed strategies are compared with other straightforward tool replacement strategies in terms of required previous experimentation, algorithm simplicity and self-adaptability to disturbances (such as changes in machining conditions). Furthermore, the cost of each strategy is analyzed for a given benchmark and with a given batch size in terms of needed tools, consumed energy and parts out of specifications (i.e., rejected). Among the analyzed strategies, the proposed model-based algorithm that detects in real-time the optimal instant for tool change presents the best results.     

基于频域辨识模型的新型幂次趋近律滑模控制

针对刚柔耦合系统传统理论建模过程复杂、模型不准确问题,采用频域子空间方法来辨识实验系统中的电机模型和压电片模型。同时针对传统趋近律的到达时间较长以及抖振问题,提出一种新型幂次趋近律的滑模控制。理论分析表明在不影响抖振情况下可缩短到达时间和扩大传统幂次趋近律参数α的范围。考虑柔性梁振动特性对系统性能的影响,采用子滑模面的方法来设计滑模控制器。最终,实验结果表明所设计控制器能够实现对中心刚体角度的快速跟踪,并且能够快速抑制柔性梁的振动。     

Test feature selection for a complex electro-hydraulic servo system using frequency response measurements

A technique for the selection of the best set of test features for checkout or go-no-go test of a complex electro-hydraulic servo system from input-output measurements is presented. The first step is to establish checkout tolerance bands on the system response. Then the measurement set based on gain and phase which would best discriminate between the ‘healthy’ and ‘sick’ systems is selected from an initially large set of sampled frequencies via an optimization procedure in which the feature efficiency vector is introduced to add or discard features until a satisfactory set is obtained. Finally the selected feature sets are assessed for effectiveness via a ‘goodness’ criterion.     

Robust control design of power system stabilizers using multivariable frequency domain techniques

H_∞ and structured singular value optimization techniques are used to design robust power system stabilizers (PSS) for a single-machine and a two-machine system with varying operating conditions. Realistic uncertainty models to represent the possible operating conditions as perturbations from a nominal operating condition are developed. System experience is used to select weighting functions to provide adequate damping and shape the controller frequency response. Computer simulations show that the PSS designed using the proposed technique provides improved damping compared to a conventional PSS.