On the optimality of two-stage state estimation in the presence ofrandom bias

Sufficient conditions for the optimality of a two-stage state estimator in the presence of random bias are derived. Under an algebraic constraint on the correlation between the state and bias process noises, the optimal estimate of the system state can be obtained as a linear combination of the output of the first stage (a bias-free filter) and the second stage (a bias filter). Because the algebraic constraint is restrictive in practice, the results indirectly indicate that for most practical systems the proposed solution to the two-stage estimation problem will be suboptimal     

微电热驱动器中偏置层的分析

设计了一种电热微驱动器,根据几何关系、泰勒公式和材料力学求得偏置层结构末端的位移公式,并验证了采用镍作为偏置层材料的合理性.通过Coventorware软件中的有限元模块进行仿真分析,得出施加驱动电压为5 V,响应时间为5 ms,驱动器的初始温度为300 K时,得出偏置层宽度W1与驱动器位移d的曲线关系.通过验证驱动器的最大应力为235 MPa,小于镍的许用应力,确定驱动器在W1=20μm可以进行可靠的工作.分析偏置层厚度和宽度的加工误差对驱动器末端位移的影响,可得在对偏置层进行加工时要严格控制偏置层厚度H1的加工误差.     

输入输出端存在噪声时系统的无偏辨识

在实际辨识中，观测到的系统输入数据往往被噪声所污染，这给无偏辨识系统带来困难，基于和文［６］中相同的原理，本文提出了一种递推的偏差补偿最小二乘法。它通过在系统输入端已知滤波器，将已知零点嵌入被辨识系统中，然后利用这些零点所提供的信息在线估计辨识偏差，并将偏差加以补偿，从而实现系统的无偏估计。     

Output Correlation Function of a Biased Full-wave Limiter†

This paper derives the output autocorrelation function of a biased full-wave limiter, which is obtained by introducing the bias into Baum'g error-function limiter. The input to the limiter is assumed to be a gaussian noise. Using the characteristic function method, two different solutions are obtained:

(1)a series expansion in powers of bios. and

(2)an integral solution for an arbitrary bias.

In both cases, the expressions reduce to Baum'B result as the bias approaches zero.     

Endowment heterogeneity and identifiability in the information-exchange dilemma

In a laboratory experiment providing an information-exchange dilemma we obtained evidence that people in an asymmetric dilemma situation apply a general cooperation norm as well as a norm of proportionality. The results showed that for privileged people the significance of the norm of proportionality is reduced. This egocentric bias allows them to justify that their privilege does not obligate them to contribute more than others. However, this bias is not strong enough to totally invalidate the norm of proportionality. Even with this bias privileged people contributed more than unprivileged. In addition, we found people to be more cooperative if their behavior in the information exchange is identifiable, whereas identifiability does not influence people’s general cooperation norm nor their fairness concept.     

Increasing the Accuracy of Solving Discrete Ill-Posed Problems by the Random Projection Method

To solve discrete ill-posed problems by the random projection method, the error bias and variance that arise from averaging over the random matrix realizations are studied. An estimate for the input vector is obtained that makes possible to significantly improve the accuracy of solving such problems using the random projection method.     

基于DSP的雪崩光电二极管数控偏压源设计

为保证雪崩光电二极管(APD)增益恒定,不受温度变化的影响而处于最佳工作状态,通过分析APD的增益和温漂特性,设计了一种APD偏压随温度按一定规律变化的数控偏压电路.采用DSP芯片TMS320F2812为主控制器,启动A/D转换对温度传感模块输出的电压信号进行采集,经计算处理得到APD的温度,然后由DSP输出相应的PWM信号来调节APD的偏压,从而保持APD增益恒定.该电路通用性好、可靠性高、操作性强,适合于高频连续信号检测的光电系统.     

Error growth in position estimation from noisy relative pose measurements

We examine how the estimation error grows with time when a mobile robot estimates its location from relative pose measurements without global position or orientation sensors. We show that, in both two-dimensional and three-dimensional space, both the bias and the variance of the position estimation error grows at most linearly with time asymptotically. Non-asymptotic bounds on the bias and variance are obtained, which provide insight into the mechanism of error growth. The bias is crucially dependent on the trajectory of the robot. Conclusions on the asymptotic growth rate of the bias continue to hold even with unbiased measurements or error-free translation measurements. Exact formulas for the bias and the variance of the position estimation error are provided for two specific two-dimensional trajectories–straight line and periodic. Experiments with a P3-DX wheeled robot and Monte Carlo simulations are provided to verify the theoretical predictions. A method to reduce the bias is proposed based on the lessons learned.     

UKF based in‐flight calibration of magnetometers and rate gyros for pico satellite attitude determination

In this paper an unscented Kalman filter based procedure for the bias estimation of both the magnetometers and the gyros carried onboard a pico satellite, is proposed. At the initial phase, biases of three orthogonally located magnetometers are estimated as well as the attitude and attitude rates of the satellite. During the initial period after the orbit injection, gyro measurements are accepted as bias free since the precise gyros are working accurately and the accumulated gyro biases are negligible. At the second phase estimated constant magnetometer bias components are taken into account and the algorithm is run for the estimation of the gyro biases that are cumulatively increased by time. As a result, six different bias terms for two different sensors are obtained in two stages, where attitude and attitude rates are estimated regularly. For both estimation phases of the procedure an unscented Kalman filter is used as the estimation algorithm. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Effect of symmetry bias on linguistic evolution

As described in this paper, we investigated the effect of the symmetry bias on linguistic evolution. We specifically examined symmetry bias, which indicates the meaning in a state of environment. For this task, we constructed a meaning selection iterated learning model based on Simon Kirby’s iterated learning Model, and used it for simulation with three strategies: perfect matching symmetry bias, imperfect matching symmetry bias, and random strategy. Results of applying imperfect matching symmetry bias show that the language of the agent evolved into more compositional language. The agent acquired a more expressive, and a more similar language to the parent’s language than with the Random strategy agent. However, application of perfect matching symmetry bias showed that the language of the agent did not evolve. The agent acquired a less expressive and a more different language to the parent’s language than with Random strategy agent. Our experimentally obtained results demonstrate that the effect of imperfect matching symmetry bias accelerates linguistic evolution into compositional language, whereas perfect matching symmetry bias disturbs linguistic evolution.     

一类线性最优滤波的多级分块分解算法

本文将B.Friedland的递推滤波器中的常值偏差处理法推广为一般时变偏差情况下的 多级分块分解算法,研究了偏差状态维数变化时实现多级分块分解的可能性,得到了相应的结 果,并说明了多级分块分解算法的工程意义.     

The accuracy of the AATSR sea surface temperatures in the Caribbean

The Along-Track Scanning Radiometer (ATSR) series of instruments provides the means to obtain accurate measurements of sea surface temperature (SST), with a target total uncertainty of ± 0.3 K, 1σ. In this paper, we present validation results from 1 year of comparisons between 1 km resolution SSTs derived from the third instrument in the series, the Advanced ATSR (AATSR), and in situ measurements obtained during 2003 from the Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) over the Caribbean.Our dataset consists of 99 cloud-free matchups, 25 of which are two-channel SST retrievals (daytime) and 74 three-channel SST retrievals (nighttime). Validation results for both dual- and nadir-view SSTs for these matchups are presented. We demonstrate that the dual-nadir SST measurement difference (D-N) can be used to classify matchups and attribute the likely cause of a particularly high D-N to outbreaks of Saharan dust. Under conditions of ‘normal’ D-N, the bias and standard deviation of the two- and three-channel dual-view retrievals is − 0.05 K and 0.26 K, and 0.02 K and 0.25 K, respectively. Dual-view SSTs obtained when the D-N is high over the Caribbean exhibit significant warm biases of 0.60 K and 0.32 K for two- and three-channel retrievals, respectively. Cool biases, with respect to the bias for ‘normal’ D-N, are observed in the nadir three-channel (N3) SSTs; for matchups with high D-N, the bias and standard deviation are − 0.16 K and 0.31 K, compared with 0.14 K and 0.24 K for ‘normal’ D-N. The distribution of the nadir two-channel retrievals is non-Gaussian and the apparent accuracy is comparatively poor, demonstrating clearly the advantages of using a sensor with dual-viewing capabilities to obtain a superior atmospheric correction, particularly when data from an additional short-wave infrared channel (e.g. at 3.7 μm) is not available.     

Sensor bias fault isolation in a class of nonlinear systems

This note presents a robust fault isolation scheme for a class of nonlinear systems with sensor bias type of faults. The proposed fault diagnosis architecture consists of a fault detection estimator and a bank of isolation estimators, each corresponding to a particular output sensor. Based on the class of nonlinear systems and sensor bias faults under consideration, the stability and learning properties of the fault isolation estimators are obtained, adaptive thresholds are derived for the isolation estimators, and fault isolability conditions are rigorously investigated, characterizing the class of nonlinear faults that are isolable by the proposed scheme. A simulation example is used to illustrate the effectiveness of the sensor bias fault isolation methodology.     

具有非参数化不确定系统的无偏辨识

本文研究了具有参数和非参数不确定性系统的集员辨识问题：分析表明利用我们在文（５）中提出的ＢＥＬＳ方法可以消除集员辨识中观测噪声引起的偏差，文中通过对系统输入数据的预滤波将已知零点嵌入系统，利用这些零点提供的信息将观测噪声引起的辨识偏差予以消除。     

GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems

A new support vector machine, SVM, is introduced, called GSVM, which is specially designed for bi-classification problems where balanced accuracy between classes is the objective. Starting from a standard SVM, the GSVM is obtained from a low-cost post-processing strategy by modifying the initial bias. Thus, the bias for GSVM is calculated by moving the original bias in the SVM to improve the geometric mean between the true positive rate and the true negative rate. The proposed solution neither modifies the original optimization problem for SVM training, nor introduces new hyper-parameters. Experimentation carried out on a high number of databases (23) shows GSVM obtaining the desired balanced accuracy between classes. Furthermore, its performance improves well-known cost-sensitive schemes for SVM, without adding complexity or computational cost.     

Bias of nearest neighbor error estimates

The bias of the finite-sample nearest neighbor (NN) error from its asymptotic value is examined. Expressions are obtained which relate the bias of the NN and 2-NN errors to sample size, dimensionality, metric, and distributions. These expressions isolate the effect of sample size from that of the distributions, giving an explicit relation showing how the bias changes as the sample size is increased. Experimental results are given which suggest that the expressions accurately predict the bias. It is shown that when the dimensionality of the data is high, it may not be possible to estimate the asymptotic error simply by increasing the sample size. A new procedure is suggested to alleviate this problem. This procedure involves measuring the mean NN errors at several sample sizes and using our derived relationship between the bias and the sample size to extrapolate an estimate of the asymptotic NN error. The results are extended to the multiclass problem. The choice of an optimal metric to minimize the bias is also discussed.     

Classification Bias of the k-Nearest Neighbor Algorithm

The k-nearest neighbor classifier has been used extensively in pattern analysis applications. This classifier can, however, have substantial bias when there is little class separation and the sample sizes are unequal. This classification bias is examined for the two-class situation and formulas presented that allows selection of values of k that yields minimum bias.     

Radial basis functions as surrogate models with a priori bias in comparison with a posteriori bias

In order to obtain a robust performance, the established approach when using radial basis function networks (RBF) as metamodels is to add a posteriori bias which is defined by extra orthogonality constraints. We mean that this is not needed, instead the bias can simply be set a priori by using the normal equation, i.e. the bias becomes the corresponding regression model. In this paper we demonstrate that the performance of our suggested approach with a priori bias is in general as good as, or even for many test examples better than, the performance of RBF with a posteriori bias. Using our approach, it is clear that the global response is modelled with the bias and that the details are captured with radial basis functions. The accuracy of the two approaches are investigated by using multiple test functions with different degrees of dimensionality. Furthermore, several modeling criteria, such as the type of radial basis functions used in the RBFs, dimension of the test functions, sampling techniques and size of samples, are considered to study their affect on the performance of the approaches. The power of RBF with a priori bias for surrogate based design optimization is also demonstrated by solving an established engineering benchmark of a welded beam and another benchmark for different sampling sets generated by successive screening, random, Latin hypercube and Hammersley sampling, respectively. The results obtained by evaluation of the performance metrics, the modeling criteria and the presented optimal solutions, demonstrate promising potentials of our RBF with a priori bias, in addition to the simplicity and straight-forward use of the approach.     

基于六维力传感器的工业机器人末端负载受力感知研究

针对工业机器人末端负载与外界环境接触力的感知需求,在机器人法兰与负载之间设置六维力传感器,并研究一套标定与计算方法,综合考虑负载重力作用、传感器零点、机器人安装倾角等因素,利用不少于3个机器人姿态下的力传感器数据,可求得传感器零点、机器人安装倾角、负载重力大小、负载重心坐标等参数,进一步可消除传感器零点及负载重力对受力感知的影响,精确得到机器人末端负载所受的外部作用力与力矩.实验得到对于重量从320N到1917N的负载,在静态条件下,感知外力的误差在负载重力的0.28%以内,感知外力矩的误差在负载对传感器力矩的0.59%以内.     

On the convergence and origin bias of the Teaching-Learning-Based-Optimization algorithm

Teaching-Learning-Based-Optimization (TLBO) is a population-based Evolutionary Algorithm which uses an analogy of the influence of a teacher on the output of learners in a class. TLBO has been reported to obtain very good results for many constrained and unconstrained benchmark functions and engineering problems. The choice for TLBO by many researchers is partially based on the study of TLBO's performance on standard benchmark functions. In this paper, we explore the performance on several of these benchmark functions, which reveals an inherent origin bias within the Teacher Phase of TLBO. This previously unexplored origin bias allows the TLBO algorithm to more easily solve benchmark functions with higher success rates when the objective function has its optimal solution as the origin. The performance on such problems must be studied to understand the performance effects of the origin bias. A geometric interpretation is applied to the Teaching and Learning Phases of TLBO. From this interpretation, the spatial convergence of the population is described, where it is shown that the origin bias is directly tied to spatial convergence of the population. The origin bias is then explored by examining the performance effect due to: the origin location within the objective function, and the rate of convergence. It is concluded that, although the algorithm is successful in many engineering problems, TLBO does indeed have an origin bias affecting the population convergence and success rates of objective functions with origin solutions. This paper aims to inform researchers using TLBO of the performance effects of the origin bias and the importance of discussing its effects when evaluating TLBO.