光纤陀螺温度漂移的多变量模型

针对光纤陀螺温度漂移的补偿问题,本文提出一种线性多变量光纤陀螺温度漂移建模方法.建立的模型由两部分组成:陀螺输出的自回归项和温度梯度的多项式分布滞后项(PDL).自回归项描述光纤陀螺历史输出对当前输出的影响,PDL项描述由温度变化引起的陀螺漂移.根据模型的线性特性,采用最小二乘法确定模型参数.用实测的光纤陀螺温度漂移数据进行了模型的有效性验证.实验结果表明,提出的线性多变量模型能有效补偿光纤陀螺的温度漂移,补偿后光纤陀螺的精度提高50%以上.     

基于多项式模型的半球谐振陀螺温度漂移建模

在半球谐振陀螺工作过程中,环境温度的变化是不可避免的.温度的变化影响陀螺的结构和谐振频率,导致陀螺产生漂移,为了提高半球谐振陀螺的精度,根据温度漂移和温度变化的相关性,本文利用回归理论对温度漂移数据进行分析,建立了半球谐振陀螺温度偏置漂移的多项式数学模型,实验表明,多项式模型能够有效的补偿半球谐振陀螺的温度偏置漂移.     

光纤陀螺的温度试验与误差补偿

分析了光纤陀螺的温度特性及非线性特性,并在组建光纤陀螺温度试验系统的基础上,进行了全温度范围下的位置试验和角速率试验,研究不同的温度及输入角速率对光纤陀螺输出的影响.根据试验结果,分别建立了光纤陀螺零偏的温度模型以及标度因数的温度和非线性模型,并采用最小二乘法拟合模型的参数.通过实测数据进行仿真验证,结果表明,建立的模型能够较好地描述光纤陀螺的温度及非线性特性,利用该模型进行光纤陀螺的温度和非线性误差补偿,取得了较好的效果,光纤陀螺的测试精度得到了较大程度的提高.     

OBD支持下公交车到达时间的回归预测方法

公交车到达时间的精准预测是共享公交专用道研究的基础,为此基于车辆车载自动诊断系统(OBD)数据提出一种基于证据理论优化高斯过程回归(DS-GPR)的公交车到达时间预测方法。首先分析影响公交车到达时间的影响因素,随后通过历史融合数据训练得到模型的超参数,并且最终通过D-S证据理论为高斯过程的回归输出分布分配全局信任度,得到当前时刻信任度最优的输出值。最后通过实例对DS-GPR模型的预测性能进行对比分析,证明了 DS-GPR模型对公交车到达时间的良好预测性能。     

基于MEMS惯性传感器的微型姿态测量系统

提出了一种基于低成本MEMS惯性传感器的微型姿态测量系统,包括MEMS速率陀螺、MEMS磁强计、单轴MEMS加速度传感器.重点研究了基于扩展Kalman滤波（EKF）的姿态估计创新算法,通过速率陀螺更新误差状态四元数计算姿态角,并通过飞行方向的加速度传感器和三轴磁强计来补偿陀螺漂移和姿态角误差,利用扩展卡尔曼滤波方程消除瞬时干扰,实现高动态姿态测量.系统的仿真和高动态实验表明,姿态测量动态精度低于5°,静态精度低于0.7°.     

硅微陀螺信号处理方法——基于前向线性预测小波变换方法

为补偿漂移误差对硅微陀螺的测量精度造成的损失,针对漂移误差易受外部环境噪声影响的特点,提出了一种基于前向线性预测（FLP）的小波变换（WT）处理方法——DWT-FLP算法,并通过硅微陀螺试验对其进行了验证。该方法利用快速小波变换算法进行信号的小波分解和小波重构,并将FLP方法用于小波分解系数的重构,比较显著地提高了重构信号的精度。对于4尺度的db4小波变换,40阶FLP的滤波方法可以将硅微陀螺静态漂移的标准差提高4.8倍,动态测量过程信噪比可以提高6.5dB,并且该算法的实时性也可以满足实际工程的需要。     

光纤陀螺在摇摆状态下的误差建模与补偿

光纤陀螺在摇摆运动条件下存在不可忽略的角速率测量误差,该项误差制约了光纤陀螺捷联系统在恶劣角动态应用环境下的精度。针对这种情况,基于自动控制理论和光纤陀螺闭环控制方案,分析了闭环光纤陀螺摇摆误差的产生机理,指出角加速度是导致摇摆误差的主要因素。随后建立了光纤陀螺摇摆误差的简化模型,并提出了摇摆误差补偿算法。最后采用基于等效输入原理的动态特性测试方法,在不同的摇摆频率下对光纤陀螺进行了摇摆误差测试和补偿试验。试验结果表明,补偿后摇摆误差减小了一个数量级,验证了理论模型的准确性和补偿算法的有效性。     

基于SVM的电子分析天平温度漂移补偿方法

电磁力平衡传感器等关键部件的温敏特性是引起电子分析天平温度漂移的主要因素。针对电子分析天平温度漂移问题提出了基于支持向量机的补偿方法。通过分析引起电子分析天平温度漂移误差的原因,将温度敏感部件的温升和电子分析天平的温度漂移数据作为模型输入,运用自适应参数优化方法寻找最优参数,建立电子分析天平温度漂移误差模型并进行温度漂移补偿。通过对量程200g、分辨力0.1mg的电子分析天平进行补偿检验,结果表明全量程内的示值误差绝对值≤0.3mg,优于国家标准GB/T 26497-2011《电子天平》规定的I级天平对温度漂移指标的要求。     

柔索驱动并联机器人定位误差的柔性补偿法

提出了用人工神经元网络(ANN)补偿大射电望远镜(LT)中柔索驱动并联机器人(WDPR)系统动平台位姿误差的方法.为了提高WDPR的运动精度,建立了3种可行的误差补偿方案,并运用Levenberg-Marquart(L-M)算法训练了相应的3个神经元网络.标定仿真显示,基于索长补偿的柔性标定方案比基于动平台位姿补偿的标定方案好.研究结果为提高LT舱索系统的控制精度奠定了理论基础.     

光纤陀螺组件误差标定ARLS算法

捷联惯性导航系统误差参数标定的准确程度对于系统的导航和定位精度具有重要影响.针对常规速率标定法不能辩识陀螺零偏,未充分预热时光纤陀螺的误差标定易受温度变化影响这两个问题,提出了一种用于光纤陀螺捷联惯性导航系统的新标定算法--自适应递推最小二乘法(ARLS).在建立光纤陀螺误差及其补偿模型的基础上,通过大量温度实验研究了自适应遗忘因子的求取方法,详细推导了ARLS算法及其实现思路.最后通过算法仿真和速率试验证明了在器件特性不稳定条件下,ARLS算法能有效辨识陀螺的误差参数及减小温度变化对光纤陀螺误差标定的影响.     

Adsorption behaviors of volatile organic compounds (VOCs) on porous clay heterostructures (PCH)

Porous clay heterostructures (PCHs) are capable of adsorbing volatile organic compounds (VOCs). In this study, PCH was synthesized by modifying bentonite (Bent) with cetyltrimethylammonium bromide (CTMAB) and dodecylamine (DDA). Adsorption of six volatile organic compounds (VOCs) including acetone, toluene, ethylbenzene, o-xylene, m-xylene and p-xylene by PCH was investigated. It was observed that adsorption capacities of VOCs were strongly dependent on their properties including cross-sectional area, polarizability, enthalpy of vaporization and critical volume by the multiple linear regression (MLR) approach. Furthermore, PCH had higher adsorption affinity for the aliphatic hydrocarbon compound (acetone) than that for aromatic compounds, which could be attributed to the HOMO energy effects of VOCs. Therefore, PCH could be attractive candidate adsorbents for VOC removal.     

Development and analyses of data-driven models for predicting the bed depth profile of solids flowing in a rotary kiln

Soft computing data-driven modeling (DDM) techniques have attracted the attention of many researchers across the globe as they do not require deep knowledge of the complex physical process. In the present research, data-driven based models have been developed using support vector regression (SVR), multilayer perceptron neural network (MLP), radial basis function neural network (RBFNN) and general regression neural networks (GRNN) techniques for predicting the bed depth profile of solids flowing in a rotary kiln. The performances of the developed models were compared and evaluated against the experimental results in terms of statistical measures such as coefficient of determination (R²), and average absolute relative error (AARE). The obtained results and findings from this research have revealed that data-driven models can predict the bed depth profile of solids flowing in a rotary kiln quite accurately. The SVR-based model exhibited the lowest AARE value of 1.72% and highest R² value of 0.9981 while GRNN, RBFNN, and MLP models gave corresponding values of AARE as 3.69%, 55.13%, 98.15% and those of R² as 0.9898, 0.0052 and 0.0081, respectively. Moreover, the developed DDM-based models i.e. GRNN, RBFNN, and MLP models overcame the limitations of the existing solutions which involved iterative numerical procedure entailing high degree of computational complexity.     

Response surface approach to aerodynamic optimization design of helicopter rotor blade

This paper describes a hovering rotor blade design through the suitable combination of flow analysis and optimization technique. It includes a parametric study concerned with the influence of design variables and different design conditions such as objective functions and constraints on the rotor performance. Navier–Stokes analysis is employed to compute the hovering rotor performance in subsonic and transonic operating conditions. Response surface method based on D‐optimal 3‐level factorial design and genetic algorithm are applied to obtain the optimum solution of a defined objective function including the penalty terms of constraints. The designs of the rotor airfoil geometry and the rotor tip shape are performed in subsonic and transonic conditions, and it is observed that the new rotor blades optimized by various objective functions and constraints have better aerodynamic characteristics than the baseline rotor blade. The influence of design variables and their mutual interactions on the rotor performance is also examined through the optimization process. Copyright © 2005 John Wiley & Sons, Ltd.     

A semiparametric technique for the multi‐response optimization problem

Multi‐response optimization (MRO) in response surface methodology is quite common in applications. Before the optimization phase, appropriate fitted models for each response are required. A common problem is model misspecification and occurs when any of the models built for the responses are misspecified resulting in an erroneous optimal solution. The model robust regression (MRR) technique, a semiparametric method, has been shown to be more robust to misspecification than either parametric or nonparametric methods. In this study, we propose the use of MRR to improve the quality of model estimation and adapt its fits of each response to the desirability function approach, one of the most popular MRO techniques. A case study and simulation studies are presented to illustrate the procedure and to compare the semiparametric method with the parametric and nonparametric methods. The results show that MRR performs much better than the other two methods in terms of model comparison criteria in most situations during the modeling stage. In addition, the simulated optimization results for MRR are more reliable during the optimization stage. Copyright © 2010 John Wiley & Sons, Ltd.     

基于广义回归神经网络的工程公司风险评估

针对工程公司日常管理中由于缺乏风险评估工具,经常造成公司资源的浪费,甚至造成大量损失的现状,结合其行业特点,构建风险评价体系模型。在模型的基础上,提出一种基于广义回归神经网络(GRNN)的工程公司风险评估方法,通过矩阵实验室中的神经网络工具对其进行仿真计算,以某安防系统工程公司的实例证明了其有效性。该研究为同类型公司的风险评估提供了一种有效的管理工具。     

Potential of airborne hyperspectral imagery to estimate fruit yield in citrus

Alternate bearing is a well-marked yield variability phenomenon that occurs in almost all tree-fruit crops. The potential benefits of applying various alternate bearing control measures on alternate bearing crops can only be realized when yield information on individual trees of particular crops is obtained. The objective of this study was to examine the potential of airborne hyperspectral imagery to estimate the fruit yield in citrus. Hyperspectral images in 72 visible and near-infrared (NIR) wavelengths (from 407 to 898 nm) were acquired over a citrus orchard in Japan by an Airborne Imaging Spectrometer for Applications (AISA) Eagle system. The canopy features of individual trees were identified using pixel-based average spectral reflectance values at various wavelengths from the acquired images, which were then used to develop yield prediction models. Yield prediction models were developed using five different techniques — (i) several vegetation indices (VIs), (ii) key wavelengths determined by simple correlation analysis (SCA), (iii) principal components (PCs) based on principal component regression (PCR), and (iv) PLS factors as well as (v) important wavelengths determined by B-matrix based on partial least squares (PLS) regression. The results indicated that the VIs used in this study were poorly correlated with fruit yield on individual trees. The key or important wavelengths determined by the two methods proposed in this study could provide reasonable prediction of fruit yield. Comparatively, the B-matrix method based on the PLS regression was superior to the simple correlation analysis in determining the key or importance wavelengths that are correlated to the fruit yield. However, the PCs extracted from the hyperspectral data were weak predictors of citrus yield. Greater prediction accuracy was obtained with the model based on PLS factors than with the models based on the key or important wavelengths. These results confirmed the hypothesized correlation between canopy features and citrus yield. The methods proposed in this study have considerable promise in estimating fruit yield on individual citrus trees. The yield information is valuable for planning harvest schedules and developing programs for application of tree-specific alternate bearing control measures and other management practices.     

A two-stage regression approach for spectroscopic quantitative analysis

In this paper, we propose a two-stage regression approach, which is based on the residual correction concept. Its underlying idea is to correct any given regressor by analyzing and modeling its residual errors in the input space. We report and discuss results of experiments conducted on three different datasets in infrared spectroscopy and designed in such a way to test the proposed approach by: 1) varying the kind of adopted regression method used to approximate the chemical parameter of interest. Partial least squares regression (PLSR), support vector machines (SVM) and radial basis function neural network (RBF) methods are considered; 2) adopting or not a feature selection strategy to reduce the dimension of the space where to perform the regression task. A comparative study with another approach which exploits differently estimation errors, namely adaptive boosting for regression (AdaBoost.R), is also included. The obtained results point out that the residual-based correction approach (RBC) can improve the accuracy of the estimation process. Not all the improvements are statistically significant but, at the same time, no case of accuracy decrease has been observed.     

基于EEMD和SVR的单自由度结构状态趋势预测

为了解决结构早期损伤难以正确识别的问题,本文结合聚类经验模式分解（EEMD）解决随机不确定性问题和支持向量机（SVM）解决预测问题这两者的优势,提出了一种基于EEMD特征提取的支持向量机回归（SVR）结构状态趋势预测方法。先对单自由度结构渐进损伤的加速度振动信号进行EEMD,再进行希尔伯特变换（HT）,计算瞬时频率,然后用回归支持向量机对反映结构健康状态的瞬时频率进行趋势预测。研究表明：对于渐变损伤该方法可以准确地、高精度地预测结构状态趋势。     

Modified robust continuum regression by net analyte signal to improve prediction performance for data with outliers

Contaminated data exist in diverse situations, even in high quality surveys and experiments. If classical statistic models are blindly applied to data containing outliers, the results can be misleading at best. In this paper, a modified robust continuum regression (mRCR) method is proposed to improve prediction performance for data with outliers. The mRCR method constructs projection pursuit directions by using projection matrix for computing the net analyte signal (NAS) of the target analyte. This paper examines applications to the determination of glucose concentration by near-infrared (NIR) spectrometry, including aqueous solution with glucose experiment, plasma experiment in vitro, oral glucose tolerance test (OGTT) in vivo, to illustrate the advantages of mRCR for various kinds of outliers depending on the way of contamination. The results indicate that the mRCR method is entirely robust with respect to any type of outlying observations, and it yields smaller prediction errors for normal samples than other calibration methods.     

Generalized nonlinear models for rear-end crash risk analysis

A generalized nonlinear model (GNM)-based approach for modeling highway rear-end crash risk is formulated using Washington State traffic safety data. Previous studies majorly focused on causal factor identification and crash risk modeling using Generalized linear Models (GLMs), such as Poisson regression, Logistic regression, etc. However, their basic assumption of a generalized linear relationship between the dependent variable (for example, crash rate) and independent variables (for example, contribute factors to crashes) established via a link function can be often violated in reality. Consequently, the GLM-based modeling results could provide biased findings and conclusions. In this research, a GNM-based approach is developed to utilize a nonlinear regression function to better elaborate non-monotonic relationships between the independent and dependent variables using the rear end accident data collected from 10 highway routes from 2002 through 2006. The results show for example that truck percentage and grade have a parabolic impact: they increase crash risks initially, but decrease them after the certain thresholds. Such non-monotonic relationships cannot be captured by regular GLMs which further demonstrate the flexibility of GNM-based approaches in the nonlinear relationship among data and providing more reasonable explanations. The superior GNM-based model interpretations help better understand the parabolic impacts of some specific contributing factors for selecting and evaluating rear-end crash safety improvement plans.