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The geometric errors parameters are important for machining accuracy of five-axis machine tool. However, most of the error identification methods only based on the detection instruments to achieve identification of single error parameter, without considering the coupling relationship of these errors between five axes. This paper measures and identifies the position-dependent error parameters and verticality errors of non-orthogonal five-axis machine tools. First, position-dependent error parameters related to the translational axis are measured by the laser interferometer. Next, based on its identification method, the rotary axis is measured in the axial, radial and tangential measurement modes by using a double ball bar. With the measured result, 18 position-dependent errors which related to the motion of linear axis, 6 position-dependent error parameters and 2 verticality errors of axis C and 4 position-dependent error parameters and 1 verticality error of axis B are identified. Finally, the identification results are verified by the compensation of an actual process on the sample of S-shaped piece, whose precise results show that the machining precision increased by 84.2% on average after compensation. Consequently, the measurement and identification method as well as the effectiveness of the compensation method of this paper are verified.

To realize the error parameter estimation of strap-down inertial navigation system (SINS) and improve the navigation accuracy for aircraft, a hybrid improved restricted Boltzmann machine BP neural network (IRBM-BPNN) approach, which combines restricted Boltzmann machine (RBM) and BP neural network (BPNN), is proposed to forecast the inertial measurement unit (IMU) instrument errors and initial alignment errors of SINS. Firstly, the error generation mechanism of SINS is analyzed, and initial alignment error model and IMU instrument error model are established. Secondly, an unsupervised RBM method is introduced to initialize BPNN to improve the forecast performance of the neural network. The RBM-BPNN model is constructed through the information fusion of SINS/GPS/CNS integrated navigation system by using the sum of position deviation, the sum of velocity deviation and the sum of attitude deviation as the inputs and by using the error parameters of SINS as the outputs. The RBM-BPNN structure is improved to enhance its forecast accuracy, and the pulse signal is increased as the input of the neural network. Finally, we conduct simulation experiments to forecast and compensate the error parameters of the proposed IRBM-BPNN method. Simulation results show that the artificial neural network method is feasible and effective in forecasting SINS error parameters, and the forecast accuracy of SINS error parameters can be effectively improved by combining RBM and BPNN methods and improving the neural network structure. The proposed IRBM-BPNN method has the optimal forecast accuracy of SINS error parameters and navigation accuracy of aircraft compared with the radial basis function neural network method and BPNN method.

A Crimp Force Monitor (CFM) is equipment for detecting crimp errors by analyzing crimp signals obtained from force and strain sensors. The analysis is commonly performed by aligning a measured crimp signal with a reference signal and comparing their difference. Current analysis methods often suffer from wrong alignments that result in false negative detections. This paper presents a new crimp signal analysis method in CFM. First, a falling edge alignment is proposed that matches falling edges of the measured and the reference signals by minimizing the absolute difference summation. Second, a signal parameter error is introduced to evaluate the crimp quality difference between the measured signal and the reference. For calculating the signal parameter error, part of a signal is identified and divided into several regions to maximize the signal parameter errors. Experiments showed that the proposed method can improve the signal alignment and accurately detect bad crimps especially with the strain sensor.

A model presented in current paper designed for dynamic classifying of real time cases received in a stream of big sensing data. The model comprises multiple remote autonomous sensing systems; each generates a classification scheme comprising a plurality of parameters. The classification engine of each sensing system is based on small data buffers, which include a limited set of “representative” cases for each class (case-buffers). Upon receiving a new case, the sensing system determines whether it may be classified into an existing class or it should evoke a change in the classification scheme. Based on a threshold of segmentation error parameter, one or more case-buffers are dynamically regrouped into a new composition of buffers, according to a criterion of segmentation quality.

It is inevitable that the mechanical part has error when machining. To increasing the machining precision and decreasing the shape and position error, error compensation is one way. The geometry error parameter is necessary when the error compensation model is founded and the compensation data is calculated. The common measuring and identifying methods are introduced in this article. Their mathematic models and solutions are listed and their merits and drawbacks are compared. This method has been applied in CNC error compensation and the effect is advisable.

During the construction control in order to better control the elevation, the need for sensitivity analysis of the design parameters, the sensitivity analysis of design parameter adjustment principle, so as to adjust the measured deflection and deflection calculation error of judgment, and for the cross section should be the main design parameters of stress sensitivity. This method can identify accurately fast parameter error during the bridge construction, and ensure the smooth and long construction Liang Qiao. To provide reference for construction control of this kind of bridge deflection.

This study investigates organic consumers’ preferences for local production of apples. The analysis is based on a choice experiment among 637 Danish consumers used in combination with a principal component analysis of a set of opinion questions. The principal component analysis identifies two components of questions. Component 1 concerns benefits related to organic products while component 2 relates to positive features of locally produced products. When the components are included in analysis of data from the choice experiment a random parameter error component model suggests that respondents who recognize the benefits of organic products have relatively high preferences for both organically and locally produced apples. Respondents who, on the other hand, recognize the benefits of locally produced products have high preferences for domestically and locally produced apples but not for organic apples.

In order to improve the prediction accuracy of stock index, eliminate of the blindness of parameters selection for support vector machine, a stock index prediction method combined the genetic simulated annealing algorithm (GASA) which integrated the parallel search of genetic algorithm with the probabilistic sudden jumping characteristics of simulated annealing algorithm, with support vector machine (SVM) is proposed. Using daily data of Shanghai stock index opening quotation which is normalization processed, the stock index prediction model based on GASA-SVM is established. Optimal parameter error penalty parameter c=1 and Gaussian kernel parameter g=1.625 are obtained. Compared the result with GA-SVM prediction model, the comparative analysis shows that GASA-SVM(MSE= 0.000191111) model prediction capabilities are superior to GA-SVM(MSE=0.000018825) prediction model. It can provide valuable references for the investors.

Previous work on parameter error identification based on normalized Lagrange multipliers has shown that the errors in parameters of branches constituting single- or double-edge cutsets could not be detected or identified without incorporating phasor measurements. In order to address this deficiency, a computationally efficient algorithm has been developed to identify all such cutsets and documented in a recent paper. This paper builds on that work and formulates a strategic phasor measurement placement problem whose objective is to ensure detectability and identifiability of all network parameters in a given system. A practical solution is developed for this problem and simulation results for IEEE 14-, 30- and 57-bus test systems are provided to illustrate its implementation.

The paper discusses the optimization technology and algorithm features in the hydraulic system simulation. It presents the realization in the package. Detailed description of the principle and development is shown, focused on parameter optimization by parameter error integration and genetic algorithm which are realized in the optimization module. The application is an example of hydraulic system of injection molding machine which showed the efficiency and capacity of the module. The comparison between curves of simulation and experiment indicated the achievements, to find optimized hydraulic parameters efficiently and quickly so as to shorten the design cycle.

The Normalized Lagrange Multiplier (NLM) method was shown to be useful in network parameter error detection, but suffered from occasional failure in certain cases. This paper exploits the advantages of PMUs and proposes a solution to the detection failure issue. The relationship between NLMs and parameter errors is first derived then used to identify insensitive NLMs which account for the issue of missing parameter error. Subsequently, a PMU placement strategy is proposed to improve the performance of the NLM method. Simulation results in the IEEE 14-bus test system show that the increased redundancy and accuracy gained by the incorporation of PMUs significantly facilitate the detection of parameter errors which was not possible by using only SCADA measurements.

Rigid registration is an important step in 3D scanning and modeling of manufactured objects that are generally composed of a combination of basic geometric primitives such as planes, spheres, cylinders, etc. In this paper, an efficient and robust method is proposed to align two basic geometric primitives. The transformation between two primitives is found by minimizing the parameter error between primitive correspondences. The approach applies an interior-point method and a new objective function to achieve good results. Compared to previous primitive-based alignment approach proposed by Rabbani et al. (Rabbani et al., 2007), the presented approach achieves better results in terms of convergence and accuracy. Finally, the proposed method is used in various applications such as data completion and primitive-based registration for quality control and inspection.

Machine spiral bevel gear tooth surface will be associated with the machine error in the event of a failure, the mathematical model with the use of the new method of calculation error based on better understanding of spiral bevel gears, the error theory and the law of error flank tooth surfaces calculated. And the Machine Tool and Tool error parameter adjustment errors can be introduced into the study of the influence of different error understand the correlation between the sizes of the error value to provide guidance and basis for the production and processing.

The analysis of amplified stress interaction in cortical bone is relatively new since most previous research have neglected the error parameter of fracture without realizing the accuracy of prediction has been violated, numerically. Indeed, the related analytical and empirical formulation for stress interaction analysis for example Kachanov theory is relatively accurate for very specific case. Thus, an approach that would produce less error for better prediction is paramount. The crack interaction parameter using the stress singularity element is one of alternative approach. This simulation is based on the changes of stress amplification in different crack intervals. Stress amplification in a solid, with the existence of crack is the amount of load that exhibits onto the solid perpendicularly. This research aims is to simulate stress amplification induced by crack interaction in cortical bone. Hence, the average error of the numerical method has been discussed and validated.

Image restoration plays an important role in transportation applications. This paper studies a motion blurred image processing method, which has good recovery effect. In this method, first the wiener filter is used for image restoration. Then, based on the error parameter analysis, the parameters of point spread function are estimated, and the noise parameter is estimated by using the probability and statistics method. Furthermore, the ringing effect is processed by using the smooth boundary method. Finally, experimental results show that the proposed method can restore the motion blurred images effectively and has strong robustness for the noise.

BACKGROUND: When using a target-controlled infusion of propofol to produce sedation, the operator assumes that the individual patient's pharmacokinetic parameters match those in the control system so that the specified effect-site target is achieved, and that the specified target is appropriate for the individual patient's sensitivity. These inaccuracies cascade, and this produces error in the desired level of sedation, termed "target error." To address this issue, we designed a control system that incorporates the operator's observation of loss of responsiveness to determine patient sensitivity. Our hypothesis was that this control system would reduce the impact of pharmacokinetic parameter error and uncertainty in sensitivity on the system's target error. METHODS: A novel control system was implemented that produces a slow transition in the probability of loss of responsiveness, providing the operator with greater resolution to observe the time of this transition. The system uses the time of this transition to infer the effect-site concentration associated with loss of responsiveness, and the infusion sequence necessary to maintain this concentration. We used computer simulation to generate a population of 10,000 patients with randomly distributed pharmacokinetic parameters and sensitivity to propofol, and compared the target error of our system with that of a target-controlled infusion system targeting the effect-site concentration associated with 50% probability of loss of responsiveness. RESULTS: Our system exhibited a target error of -0.75% ± 8.96%, compared with 0% ± 27.6% for target-controlled infusion, reducing the variability in achieving the specified target by a factor of 3.1 compared with target-controlled infusion, which was significant at P < 0.0001. CONCLUSIONS: Our system reduces the impact of biological variability by including the operator in the control loop. The utility of this approach in clinical practice will require further evaluation.

As a tool of complex shape, the manufacturing error of hobs can be classed into three kinds: the datum level error, the tooth thickness error and the parameter error of helicoidal surface. The parameter error of helicoidal surface, which is composed of tooth error and helix error, is the most importance among them. For precision of hob and convenience of measurement, several individual errors are instead of the helix error. How to set reasonable individual errors to guarantee the helix error within tolerance, the relationship between them in gear hob measurements is analyzed, and the approach to decompose the helix error into individual error has been discussed in the paper. On the basis of above theory, a high precision mode can be easily established using the soft ware.

For the high-resolution airborne synthetic aperture radar (SAR) stereo geolocation application, the final geolocation accuracy is influenced by various error parameter sources. In this paper, an airborne SAR stereo geolocation parameter error model, involving the parameter errors derived from the navigation system on the flight platform, has been put forward. Moreover, a kind of near-direct method for modeling and sensitivity analysis of navigation parameter errors is also given. This method directly uses the ground reference to calculate the covariance matrix relationship between the parameter errors and the eventual geolocation errors for ground target points. In addition, utilizing true flight track parameters’ errors, this paper gave a verification of the method and a corresponding sensitivity analysis for airborne SAR stereo geolocation model and proved its efficiency.

Methods for power network parameter estimation and correction are discussed in this paper. A sequential-type method for real-time processing of network parameters is proposed. The method exploits the information contained in the measurement residuals to estimate a better network parameter; it uses a linearized sensitivity model to relate the measurement residuals to the parameter error. The proposed method has been coded in FORTRAN using several short-cuts and has been thoroughly tested in the estimation of transformer ratios in the Belgian HV system.

A sensorless driving method of brushless DC motors (BLDCM) using neural networks has been studied in this paper. Considering the nonlinear characteristics and the parameter error of the modeling, neural networks are introduced to estimate the electromotive force (EMF). The results of simulation and experiment using offline trained neural networks show that the proposed method is useful. In addition, the robustness about the parameters is discussed. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 162(4): 64–71, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20240

The paper proposes a robust three-view matching method to produce quasi-dense 3D points in multi-view stereo. In order to overcome the limitations of traditional wide baseline match propagation methods, the proposed method simultaneously models illumination variations and perspective distortions in images, and performs an effective affine parameter error detection and rectification mechanism in the process of match propagation. Experiments show the proposed method can effectively enhance the stability and robustness of match propagation, and achieve better performance in both accuracy and quantity of the quasi-dense 3D points.

In PBN flight procedure design, in order to ensure the design quality, we need to evaluation the error of route parameters. Actual test is a good way, but it often requires large amounts of human, financial and other resources. In this paper, we use ArcGIS which has powerful data analysis function to assess the route parameter error, by comparative analysis of reverser's data and design data. The results show that the error assessment methods and conclusions of the final assessment of the actual test error coincide, the method can be used to evaluation the error of route parameters in PBN flight procedure design.

Recently a practical and efficient off-line approach for network branch parameter (series and shunt admittances) errors detection, identification and correction was developed. In comparison with other methodologies already proposed for branch parameters validation, the novel features of this off-line approach are the identification procedure of suspicious branches and the way the augmented state-parameter estimation problem is solved. Several simulation results (with IEEE bus systems) have demonstrated the high correctness and reliability of that off-line approach to deal with single and multiple parameter errors in adjacent (those having a terminal bus in common) and non-adjacent branches. Moreover the approach was also demonstrated on tests performed on the Hydro-Quebec Trans-Energie networks. One of these tests shows that approach enables the validation of series and shunt admittances of parallel transmission lines with series compensation. However, that offline approach does not have the desired property of distinguishing between bad data (bad analog measurements) and branch parameter errors. This paper presents a simple yet effective improvement by creating new indexes that enable the distinction between bad data and branch parameter errors, even when both appear simultaneously. Simulation results have sown the effectiveness of including the new indexes in that off-line approach.

This paper combines with Kafang’s engineering practice of Xinshan mining area, makes crater tests, and then determines the blasting parameters under experimental conditions. Train the key stakeholders blasting parameters both at home and abroad based on the BP artificial neural network (ANN) model. On the basis that the best charge depth is 1.09m which under the experimental conditions of blasting crater test. Conduct optimizing calculation of blasting parameters by using EasyNN-plus software. Through a comprehensive analysis of optimization ways and parameter error, recommend blasting parameters under experimental conditions: charge depth L=1.09m, the best crater radius Rj=0.77-0.79m, the best crater volume Vj=0.5-0.6m3, and explosive consumption 1.0-1.1kg/t.

In this paper, the quantization error and parameters of the single-lens prism-based stereovision system are studied in detail. Each image captured using this system can be divided into two subimages on the left and right, and these subimages are generated by two virtual cameras that are produced by the biprism. This stereovision system is equivalent to the conventional two-camera system and the two subimages captured have disparity that can be used to reconstruct the 3D scene. The relative range error of this system is studied with respect to the system parameters, which gives us a better way to quantify the range resolution of the system and enables the selection of system parameters to become easier.

A novel error parameter analysis approach for synthesis of an artificial transmission line (ATL) from the ABCD parameters of a uniform transmission line section is presented. An MMIC ATL delay line with 180deg phase shift is synthesized to demonstrate this approach. The MMIC is fabricated using a conventional GaAs process and test results are validated with those of the simulated results.

*† Standard sequential loop closure design based on Evans root migration concepts is a well accepted, insightful and effective technique for meeting nominal performance objectives and traditional gain-phase stability margin requirements. Unfortunately, assessment of closedloop variations resulting from parameter error is a post-process, implying an iterative design-assess-redesign procedure is necessary if variations are found unacceptable. Further, the modern parameter uncertainty assessment framework is often inconsistent, both quantitatively and qualitatively, with the redesign task. Specifically, the parameter uncertainty assessment variables are not readily apparent in the sequential loop design framework, and these variables provide little guidance for the redesign strategy. Therefore, expanding the root migration sequential loop design methodology to directly incorporate the parameter uncertainty framework is explored. A sequential loop closure design procedure is enhanced to include closed-loop transfer function polynomials as a function of parameter uncertainty in such away that allows uncertainty root locus plots to be carried along in successive design steps. This graphical information facilitates insight into key closed-loop poles and zeros and system variables that influence parameter robustness properties. The engineer can evaluate and tune traditional stability and performance metrics simultaneously with newer parameter uncertainty metrics at each design step leading to the final overall multivariable control system.

The development of the XCS Learning Classifier System [26] has produced a stable implementation, able to consistently identify the accurate and optimally general population of classifiers mapping a given reward landscape [15,16,29]. XCS is particularly powerful within direct-reward environments, and notably within problems suitable for commercial application [3]. The application of XCS within delayed reward environments has also shown promise, although early investigations were within enviroments with a comparatively short delay to reward (e.g. [28, 19]). Subsequent systematic investigation [19,20,1,2] have suggested that XCS has difficulty accurately mapping and exploiting even simple environments with moderate reward delays. This paper summarises these results and presents new results that identify some limits and their implications. A modification to the error computation within XCS is introduced that allows the minimum error parameter to be applied relative to the magnitude of the payoff to each classifier. First results demonstrate that this modification enables XCS to successfully map longer delayed-reward enviroments.

A new adaptation algorithm is presented for adaptive control of robot manipulators. The passivity property of the proposed algorithm is first established, then the stability is proved based on the passivity properties of the plant and those of the proposed algorithm. Because of the use of the past information and averaging effect, this algorithm gives a smoother tracking and parameter error in the presence of disturbances, and a parameter convergence under a weaker excitation condition. Computer simulations are presented to illustrate this effect. >

An identification method for dynamic models of robots that improves on the sequential estimation method of Mayeda, K. Osuka and A. Kangawa (1984) is proposed. This method has the following advantages over the sequential estimation method: the parameter error propagation is reduced, because parameters directly estimated from the data are used instead of parameters indirectly calculated; and analysis calculation for identification is reduced, because at most four degrees of freedom of the robot to be identified are considered. The method is applied to a 6 d.o.f. direct drive robot. >

This article deals with the algorithm of measurement and design of probe sensors of conductivity for determination of parameters of plasma and melt of the heat-protecting antenna material within the framework of solving the problem of maintenance of steady communication with a satellite on a flight descent segment. The problem of the measured parameter error is considered separately.

Many parameters influence the quality of machined parts on five-axis machine tools. One important family of parameters are the geometric inaccuracies in the relative location of the machine axes. A method is proposed which uses a telescoping magnetic ball bar to identify these error sources. The methodology is applied to position independent geometric error parameters such as joint misalignments, angular offset and axes separation. Simulation results support the effectiveness of the approach.

This paper presents a new automation algorithm for parameter commissioning of AC motors, which is based upon the method using the characteristics of the controller and Walsh function analysis. By investigating the distortion of controller output resulting from parameter error and evaluating through Walsh function analysis, the proposed adaptation process can achieve the commissioning of AC motors automatically. In this paper, a waveform distortion index (WDI) according to type of AC motors and their parameters are decided. To prove the effectiveness of the proposed automatic parameter commissioning algorithm to real industrial fields experimental verifications are achieved.

Purpose: The purpose of this study is to evaluate delivery parameter errors (DPEs) and their impact on clinical dose variation with the Varian RapidArc technique. Methods: The dynalog files of 16 head-and-neck patients were retrospectively analyzed to characterize three RapidArc DPEs: dose MU, gantry angle, and MLC gap errors. A total of 64 reconstructed plans were created by creating four variants of each of the original 16 plans (three with the DPEs applied individually and one with the three DPEs combined). These reconstructed plans were compared to the original plans to evaluate the impact of the DPEs on the clinical dose distribution. Results: The mean dose MU, gantry angle, and MLC gap error for all patients were 0.00 ± 0.00 MU, −0.36 ± 0.03°, and 0.00 ± 0.01 mm, respectively. The DPEs had no obvious dosimetric impact on any of the studied dosimetric endpoints except the parotid dose. The gantry angle error, MLC gap error, and combined DPEs changed the parotid Dmean (mean dose) and parotid V30 (volume receiving at least 30 Gy) by 1%–2%. Conclusions: It is feasible to use dose distributions reconstructed from dynalog file data as a quality assurance tool. The dose MU, gantry angle, and MLC errors have only minor effects on the accuracy of the delivered dose.

The effects of random errors in positioning the elements of uniform phased arrays on the scanning pattern are investigated. Expressions for the mean and variance of the element pattern and the complete array pattern are given and a typical computer-simulated array is also studied. The curves show that the sharp dips which occur at some scan angles in the case of the hypothetical error-free array are filled in, and the blindness is completely removed for large values of the error parameter \sigma . A simple feeding system is presented to partially correct the element phasing for the element positioning errors.

The paper discusses the use of a modulus error parameter tracker for estimating two unknown parameters in a simple dynamic system. The tracker dynamics are analysed in detail and the tracker is shown to be stable, but not asymptotically stable, and the existence of a circular terminal orbit is demonstrated. It is destabilised by amplifier offset, but this implementation error may be reduced by application of a high frequency perturbation. The response to certain useful test inputs is determined, and by use of involute geometry the effects of time-varying plant parameters analysed. A brief account of the tracking of lung resistance and lung elastance of artificially ventilated subjects is given.

This paper deals with the estimation of parameters involved in the classical steady-state modelling of network elements within the context of power system state estimation. A general method is proposed for the identification and correction of the real-time data, based on the sensitivity relationship between the measurement residuals and the parameter error; this relationship is treated as an estimation problem, yielding a parameter error estimate. Special emphasis is put on the real-time correction of erroneous transformer tap positions, the most frequent parameter errors during the real-time operation of state estimators. The proposed method is very simple and efficient, since it merely requires a single substitution in the state estimation linear system. Relationships with other methods are also discussed. Simulation results on a real-life system are reported.