双转子陀螺罗经产生不定误差的原因与对策

双转子液体支承陀螺罗经是目前船用罗经中使用较多的一类罗经,使用过程中经常会发生因罗经产生不定误差而无法继续使用。修理时又往往因为一时找不到产生误差的真正原因而无法对症下药,迅速排除故障。现从理论上对故障的原因进行分析,并介绍一些实践经验。

ICP-AES测定植株样品中不确定误差的估算

为了探讨植株样品中多种大量、微量元素的测定方法,本文采用湿法硝酸-高氯酸消解,用电感耦合等离子发射光谱法(ICP-AES)同时测定试样中K、Ca、Mg、Cu、Zn、Fe、Mn、Al、Cd、Cr、Mo、P、Pb、S等元素,研究了样品的处理、标准样品的配制等过程不确定误差的问题。结果表明,元素的检出限为0.025-0.064μg/mL,变异系数为0.2%-5.3%,标准样品测定的绝对偏差为1.35%-4.50%(除Pb以外,为-8.33%)。由于测定中变异过大,所以B、Si不宜采用多元素法同时测定。在所有考虑到的不确定误差中,标准样品的制备和消解样品的转移是最重要的两个误差源。ICP-AES法同时测定植株体大量、微量元素是一种简便、快速、准确、精密度好的方法。

An Uncertainty Assessment Approach for Measuring Flatness Error in Close Way

It is necessary for precise measurement to estimate the uncertainty of measurement result. When measuring flatness error in close way by pitch, usually the uncertainty of measurement result is independently estimated according to pitch points. By analyzing a concrete example, this paper proposed that the uncertainty should be evaluated by the correlation calculating method. This approach greatly improved the deficiencies of the assessment method according to independent measurement and enhanced measurement precision. It provides a reference value for uncertainty assessment in leveling a flat.

Clearance Effects in Parallel Manipulators： Position Error Discontinuities and Inertial Effects Influence

Clearances at joints cause an uncertainty in the actual posture of the end-effector of any mechanism. This uncertainty relays on the clearance dimension and the way these clearances are taken up by the mechanism under the load and the inertial effects at every instant. As a matter of fact, the actual measure of the pose error is often replaced by an uncertainty measure. However, a side effect of the existence of clearances is that they can cause sudden changes in the posture of the mechanism as a motion is performed. Such discontinuities in the position produce task defects and impacts. In this work a tool to determine the pose error due to clearances is presented together with a discontinuity analysis. In addition, effects of mass distribution and inertial effects on such discontinuities are expounded, taking a 3-PRS robot as example.

Grid convergence study in the resistance calculation of a trimaran

As a new type of hull form,trimaran has remarkable excellent performances and has drawn more and more attention.When the viscous CFD technology now available is applied to the research of resistance performance of trimaran,the spatial discretization would usually result in the grid error and uncertainty,and thus the considerable discrepancy between the numerical results and the experimental data.In order to ascertain how much the grid would affect the calculation,the grid convergence should be studied.A mathematical trimaran was chosen as an example,with the commercial code CFX for the simulation,VOF for surface treatment,and the grid study was carried out based on two different turbulence models.It was concluded that carrying out grid study is helpful in estimating the grid error and uncertainty,and indicating the direction of improving the credibility of the numerical calculation, and,in addition to grid errors and uncertainties,the turbulence modeling errors and uncertainties contribute to the simulation errors.

An Approach to Reasoning Topological Relations Between Areal Objects Under Randomness

This paper describes the geometric and statistical properties of areal object under randomness. In order to describe formally such a uncertain topological relation, a new formal model (i.e. 4ID model) is proposed. On the basis of this, the effects of positional uncertainty on topological relations between areal objects are investigated in detail. Some possibility functions for the determination of relations are constructed based on the assumption that randomness of point location complies with a normal distribution, and the concept of uncertain sets of topological relations under randomness is introduced.

Generalized Normal Derivatives and Their Applications in DDMs with Nonmatching Grids and DG Methods

A class of normal-like derivatives for functions with low regularity defined on Lipschitz domains are introduced and studied.It is shown that the new normal-like derivatives,which are called the generalized normal derivatives,preserve the major prop- erties of the existing standard normal derivatives.The generalized normal derivatives are then applied to analyze the convergence of domain decomposition methods (DDMs) with nonmatching grids and discontinuous Galerkin (DG) methods for second-order el- liptic problems.The approximate solutions generated by these methods still possess the optimal energy-norm error estimates,even if the exact solutions to the underlying elliptic problems admit very low regularities.

The Combined Effect of Initial Error and Model Error on ENSO Prediction Uncertainty Generated by the Zebiak-Cane Model

Initial errors and model errors are the source of prediction errors. In this study, the authors compute the conditional nonlinear optimal perturbation （CNOP）-type initial errors and nonlinear forcing singular vector （NFSV）- type tendency errors of the Zebiak-Cane model with respect to El Nifio events and analyze their combined effect on the prediction errors for E1 Nino events. The CNOP- type initial error （NFSV-type tendency error） represents the initial errors （model errors） that have the largest effect on prediction uncertainties for E1 Nifio events under the perfect model （perfect initial conditions） scenario. How- ever, when the CNOP-type initial errors and the NFSV- type tendency errors are simultaneously considered in the model, the prediction errors caused by them are not am- plified as the authors expected. Specifically, the predic- tion errors caused by the combined mode of CNOP-type initial errors and NFSV-type tendency errors are a little larger than those caused by the NFSV-type tendency er- rors. This fact emphasizes a need to investigate the opti- mal combined mode of initial errors and tendency errors that cause the largest prediction error for E1 Nifio events.

Interval of effective time-step size for the numerical computation of nonlinear ordinary differential equations

The computational uncertainty principle states that the numerical computation of nonlinear ordinary differential equations（ODEs） should use appropriately sized time steps to obtain reliable solutions.However,the interval of effective step size（IES） has not been thoroughly explored theoretically.In this paper,by using a general estimation for the total error of the numerical solutions of ODEs,a method is proposed for determining an approximate IES by translating the functions for truncation and rounding errors.It also illustrates this process with an example.Moreover,the relationship between the IES and its approximation is found,and the relative error of the approximation with respect to the IES is given.In addition,variation in the IES with increasing integration time is studied,which can provide an explanation for the observed numerical results.The findings contribute to computational step-size choice for reliable numerical solutions.

Some Peculiarities of the Preprocessing of Spectral Data and Images

Remotely sensed spectral data and images are acquired under significant additional effects accompanying their major formation process, which greatly determine measurement accuracy. In order to be used in subsequent quantitative analysis and assessment, this data should be subject to preliminary processing aiming to improve its accuracy and credibility. The paper considers some major problems related with preliminary processing of remotely sensed spectral data and images. The major factors are analyzed, which affect the occurrence of data noise or uncertainties and the methods for reduction or removal thereof. Assessment is made of the extent to which available equipment and technologies may help reduce measurement errors.

Influence of the instability of angular velocity of the rotating carrier itself on the stability of silicon micromachined gyroscope

In order to find out the influence of the instability of angular velocity of the rotating carrier itself on the stability of silicon micromachined gyroscope, the digital models for relative error of the high and low damping gyroscope's output signal are given respectively, based on the motion equations of the silicon micromachined gyroscope. Theory proves that the output signal error of the silicon micromachined sensor is mainly caused by the instability of damping factor and the angular velocity of the rotating carrier itself. The experiment result indicates that the error of proportionality coefficient of output voltage which is caused by the instability of the angular velocity of the rotating carrier itself reaches to 4.1 %. Theoretical demonstration and experimental verification show that the instability of angular velocity of the rotating carrier itself has an important effect on the stability of low damping silicon micromachined gyroscope.

ROBUST KALMAN FILTERING FOR SYSTEMS UNDER NORM BOUNDED UNCERTAINTIES IN ALL SYSTEM MATRICES AND ERROR COVARIANCE CONSTRAINTS

This paper concerns robust Kalman filtering for systems under norm bounded uncertainties in all the system matrices and error covariance constraints. Sufficient conditions are given for the existence of such filters in terms of Riccati equations. The solutions to the conditions can be used to design the filters. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed design procedure.

Discriminant Models for Uncertainty Characterization in Area Class Change Categorization

Discriminant space defining area classes is an important conceptual construct for uncertainty characterization in area-class maps.Discriminant models were promoted as they can enhance consistency in area-class mapping and replicability in error modeling.As area classes are rarely completely separable in empirically realized discriminant space,where class inseparabil-ity becomes more complicated for change categorization,we seek to quantify uncertainty in area classes(and change classes)due to measurement errors and semantic discrepancy separately and hence assess their relative margins objectively.Experiments using real datasets were carried out,and a Bayesian method was used to obtain change maps.We found that there are large differences be-tween uncertainty statistics referring to data classes and information classes.Therefore,uncertainty characterization in change categorization should be based on discriminant modeling of measurement errors and semantic mismatch analysis,enabling quanti-fication of uncertainty due to partially random measurement errors,and systematic categorical discrepancies,respectively.