Along with the progress of rating and signal processing technology, because of its advantages and wide application, grating measuring technology has become a research hotspot in the precision measuring field. Nanometer measuring has become urgently to solve problem with the need of industrial development and scientific research from sub micrometer to nanometer precision measurement. This paper systematically discusses its research status, existing problems, developing trends and other issues about grating nanometer measuring technology. It provides references to grating nanometer measuring technology researches and its development.
A signal processing method for reflective fiber optic displacement sensor is presented by means of a differential evolution optimized extreme learning machine (DE-ELM). The sensing head of the sensor is a combination of an illuminating fiber bundle transmitting incoming light beam and two receiving fiber bundles employed to collect the reflected beam from the reflector. Three fiber bundles with same type are put together and arranged side by side, but the two receiving fiber bundles enfaces have different distances from the reflector surface. The DE-ELM is used for extending the measuring the range of reflective fiber optic displacement sensor. A simulation experiment has been illustrated. The experimental results show that the measuring range can be extended to the whole response characteristics of the fiber optic displacement sensor and a high measuring accuracy can be obtained by the proposed method.
Main error source, error characteristics and its correction method have a detailed analysis of the grating interference displacement measuring system in this paper. Through the error separation and compensation method, its system error is modified. Random error is separated through frequency spectrum analysis method. Experimental results show that, the system accuracy can be improved from micron or submicron magnitude to nanometer level by these error correction methods. This system can realize nanometer level measurement.
Study of the dynamic characteristics and accuracy theory about measuring system must build its mathematic model. Aim
at the limitations of traditional modeling methods, studying the whole-system dynamic error modeling theory, this
modeling method and theory consider sufficiently the information about insider buildup units of measurement system, so
the built model can reflect the change of the transfer behaviors of system's inside structural units to influence the actual
measuring system along with time. The modeling theory can be applied widespread; using this modeling theory can build
all system's whole-error modeling. The whole-error modeling of the hundredth meter is built using this theory.
An intelligent optimization technique based on particle swarm optimization is proposed to identify parameters of chaotic
optical systems. The feasibility of this approach was demonstrated with the computer simulation through identifying the
parameters of Bragg acoustic-optical bistable system. The performance of the particle swarm optimization technique was
compared with the more common genetic algorithm in terms of parameter accuracy and computation time. Simulation
results demonstrated that the particle swarm optimization has better performance than the genetic algorithm in solving
the parameter identification problem of chaotic optical systems.
The least squares support vector machines (LS-SVMs) are proposed for nonlinear dynamic modeling of wrist force sensors. The LS-SVMs are established based on the structural risk minimization principle rather than minimize the empirical error commonly implemented in the neural networks, the LS-SVMs can achieve higher generalization performance. Also, local minima and over fitting are unlikely to occur. Therefore, the LS-SVMs can overcome the shortcoming of neural networks in dynamic modeling of wrist force sensors. The effectiveness and reliability of the method are demonstrated by applying it to the examples. The experimental results show that the method is still effective even if the sensor dynamic model is nonlinear.
A least squares support vector machine (LS-SVM) based signal processing approach of reflective fiber optic displacement sensor is presented. The example for extending measuring range of the sensor using LS-SVM has been illustrated. From the experimental results, it can be clearly seen that not only the measuring range can be extended to the whole response characteristics of the fiber optics displacement sensor effectively, but also a desired linear relationship between the actual displacement and the LS-SVM predicted output can be obtained. This means the method proposed is very effective for the signal processing of the sensor.
A new fast algorithm to segment man-made target from infrared image is given employing Bezier histogram and edge information. In order to reduce computation burden, an efficient approach to select region of interest (ROI) is proposed based on prior-information. Thus a piece of region, which contains a target to be segmented, is extracted from original image. The gray level histogram of ROI is smoothed by Bezier curve to restrain noise in the ROI. Thus Bezier histogram is obtained. Peaks of curvature curve in the Bezier histogram are detected to obtained segmentation thresholds. An optimal segmentation threshold is selected with a new criterion. The optimal threshold segments the ROI well. In order to obtain better segmentation result, firstly a new algorithm, which bases on discrete stationary wavelet transform and non-linear gain operator is proposed to enhance the detail of target in the ROI. Canny operator is used to extract the edge information of target in the enhanced ROI. Finally, excellent segmentation result is obtained by combining Bezier histogram threshold method with edge information of target. Experimental results show that a man-made target can be segmented effectively from complex background in infrared image by the new algorithm.
An identification approach of nonlinear optical dynamic systems, based on adaptive kernel methods which are modified version of least squares support vector machine (LS-SVM), is presented in order to obtain the reference dynamic model for solving real time applications such as adaptive signal processing of the optical systems. The feasibility of this approach is demonstrated with the computer simulation through identifying a Bragg acoustic-optical bistable system. Unlike artificial neural networks, the adaptive kernel methods possess prominent advantages: over fitting is unlikely to occur by employing structural risk minimization criterion, the global optimal solution can be uniquely obtained owing to that its training is performed through the solution of a set of linear equations. Also, the adaptive kernel methods are still effective for the nonlinear optical systems with a variation of the system parameter. This method is robust with respect to noise, and it constitutes another powerful tool for the identification of nonlinear optical systems.
A new method for position error correction of position-sensitive detector (PSD) using least squares support vector machine (LS-SVM) is presented. The LS-SVM is established based on the structural risk minimization principle rather than minimize the empirical error commonly implemented in the neural networks, LS-SVM achieves higher generalization performance than the MLP and RBF neural networks in solving these machine learning problems. Another key property is that unlike MLP’ training that requires non-linear optimization with the danger of getting stuck into local minima, training LS-SVM is equivalent to solving a set of linear equations. Consequently, the solution of LS-SVM is always unique and globally optimal. A difference with the RBF neural networks is that no center parameter vectors of the Gaussians have to be specified and no number of hidden units has to be defined because of Mercer's condition. The position error correction procedure has been illustrated using 2D PSD as example. The results indicate that this approach is effective, and the position detection errors can be reduced from ±300μm to ±10μm.
A modeling and non-linear correction method for the two-dimension photoelectric position sensitive detector (PSD) is presented by means of a radial basis function (RBF) neural network. Utilizing its powerful ability in function approximation, the RBF network can perform the mapping between the PSD's readings and the light spot actual position. In order to obtain the mapping, the RBF network is trained by learning algorithm with the input/output data pairs of the PSD. The mapping is used as an inverse model of the PSD from the readings to the light spot actual position or as a forward model of it from the light spot actual position to the readings. The inverse model based on RBF network is used as a corrector. This model provides a linear response when the PSD's readings applied to the inputs of the RBF network during operation. The example shows that the measuring system with a proper RBF network correction can provide a high linearity over a wide position range. Furthermore, the forward model that expresses the characteristics of the PSD will be beneficial to provide the theoretical instruction for the analysis, design and application of the PSD.
This paper discusses the working principle of reflective fiber optic sensor with intensity compensation for simultaneous measurement of displacement and surface roughness. The sensor with a single sensing head is a combination of an illuminating fiber bundle transmitting incoming light beam and two receiving fiber bundles employed to collect the reflected beam from the workpiece surface. Three fiber bundles with same type are put together and arranged side by side, but the two receiving fiber bundles endface have different distances from the workpiece surface. The output characteristics of the sensor are analyzed. Although the sensor can simultaneously output two different readings containing displacement and surface roughness information using such a sensing head, it suffers from the influence of the cross-sensitivity and non-linearity. An intelligent signal processing method based on artificial neural network for the sensor output is proposed. The artificial neural network is trained to properly represent the complex nonlinear mapping between the sensor outputs information and the actual sensed measuring values. The example shows that the method can be used for both separation and linearization of the sensor output signals. Wide measuring range and high measuring precision of the two parameters are also obtained by means of the artificial neural network.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.