基于改进的脉冲电压注入永磁同步电机转子初始位置检测方法

由于永磁同步电机存在凸极性,定子电感随气隙磁场饱和程度的不同而变化,电感值中包含了转子位置信息。根据这个原理,研究了通过注入脉冲电压矢量来检测永磁同步电机转子初始位置的方法,分析了不同位置的电压矢量对电感饱和程度的影响,以及对转子位置估测精度的影响,得出了能获得最佳估测精度的优化电压矢量,并提出了改进的五电压矢量注入方式。根据转子所在区域选择优化的电压矢量进行二次计算,进一步提高了转子位置估算的精度。实验结果表明,该方法能够可靠而有效的检测出永磁同步电机的转子初始位置,而且实施简单、估测精度高。

区域尺度杉木生物量估计的不确定性度量

基于系统抽样体系江西省固定样地连续观测数据,以杉木立木生物量为估测对象,采用异速生长模型建立杉木单木地上生物量和各组分生物量估测模型,结合抽样理论和泰勒级数原理,以均方根误差为不确定性度量指标,分别测算由抽样误差和模型估测误差引起的生物量估计不确定性。结果显示:2009年江西省杉木地上生物量为19.34 t·hm-2,不确定性为0.92 t·hm-2,树干、树皮、树枝和树叶生物量分别为11.87,1.95,3.15,2.62 t·hm-2,其中地上总生物量和各组分(树干、树皮、树枝和树叶)生物量估计中模型不确定性分别占估计量的2.48%,3.67%,3.43%,7.27%和6.33%。胸径对树枝、树叶的解释能力低于树干和树皮,抽样误差对生物量估计准确度的影响明显大于模型估测误差。研究方法适用于基于森林资源连续清查数据的生物量和碳储量估测。

雨区衰减对双线偏振雷达测雨的影响研究

以滴谱理论为基础,给出了双线偏振雷达各测雨式的雨区衰减订正公式和方法,并利用模拟的滴谱分布资料,分析了各波段双线偏振雷达各测雨式受雨区衰减的影响情况以及做衰减订正后的改进情况,提出了双线偏振雷达各测雨式在估测降雨中使用的建议。

Semiparametric Regression and Model Refining

This paper presents a semiparametric adjustment method suitable for general cases.Assuming that the regularizer matrix is positive definite,the calculation method is discussed and the corresponding formulae are presented.Finally,a simulated adjustment problem is constructed to explain the method given in this paper.The results from the semiparametric model and G_M model are compared.The results demonstrate that the model errors or the systematic errors of the observations can be detected correctly with the semiparametric estimate method.

Influence of age in estimating maximal oxygen uptake

Objective To assess the influence of age on the error of estimate （EE） of maximal oxygen uptake （VO2max） using sex and population specific-equations in cycle ergometer exercise testing, since estimated VO2 max is associated with a substantial EE, often exceeding 20%, possibly due to intrinsic variability of mechanical efficiency. Methods 1850 adults （68% men）, aged 18 to 91 years, underwent maximal cycle ergometer cardiopulmonary exercise testing. Cardiorespiratory fitness （CRF） was assessed relative to sex and age [younger （18 to 35 years）, middle-aged （36 to 60 years） and older （〉 60 years）]. VO2max [mL.（kg.min）-1] was directly measured by assessment of gas exchange and estimated using sex and population specific-equations. Measured and estimated values of VO2max and related EE were compared among the three age- and sex-specific groups. Results Directly measured VO2max of men and women were 29.5 ± 10.5 mL.（kg.min）-1 and 24.2 ± 9.0 mL.（kg·min） -1 （P 〈 0.01）. EE [mL·（kg·min）-1] and percent errors （%E） for men and women had similar values, 0.5 ± 3.2 and 0.4 ± 2.9 mL·（kg·min）-1, and -0.8 ± 13.1% and -1.7 ± 15.4% （P 〉 0.05）, respectively. EE and %E for each age-group were, respectively, for men： younger = 1.9 ± 4.1 mL·（kg·min）-1 and 3.8 ± 10.5%, middle-aged = 0.6 ± 3.1 mL.（kg·min）-1 and 0.4 ± 10.3%, older = -0.2 ± 2.7 mL·（kg·min） -1 and -4.2 ± 16.6% （P 〈 0.01）; and for women： younger = 1.2 ± 3.1 mL.（kg.min）-1 and 2.7 ±10.0%, middle-aged = 0.7 ± 2.8 mL·（kg·min）-1 and 0.5 ± 11.1%, older = -0.8 ± 2.3 mL-（kg·min）-1 and -9.5 ± 22.4% （P 〈 0.01）. Conclusion VO2max were underestimated in younger age-groups and were overestimated in older age groups. Age significantly influences the magnitude of the EE of VO2max in both men and women and should be considered when CRF is estimated using population specific equations, rather than directly measured.

A Dataset of Global Ocean Surface Currents for 1999-2007 Derived from Argo Float Trajectories: A Comparison with Surface Drifter and TAO Measurements

A dataset of surface current vectors with error estimate from 1999 to 2007 is derived from the trajectories of the Array for Real-time Geostrophic Oceanography (Argo) drifting on surface over the global ocean. The error of the estimated surface currents is about 4.7 cm s-1 which is equivalent to the accuracy of the currents determined from the surface drifters. Geographically, the Argo-derived surface currents can fill many gaps left by the Global Drifter Program due to the greater number of floats, and can provide a complementary in situ observational system for monitoring global ocean surface currents. The surface currents from the Argo floats are compared with the surface drifter-derived currents and the Tropical Atmosphere Ocean program (TAO) measurements. The comparisons show good agreement for both the current amplitude and the direction of surface currents. Results indicate the feasibility of obtaining ocean surface currents from the Argo array and of combining the surface currents from Argo and the ocean surface drifters for in situ mapping of the global surface currents. The authors also make the dataset available to users of interest for many types of applications.

Residual life estimation based on bivariate Wiener degradation process with measurement errors

An adaptive method of residual life estimation for deteriorated products with two performance characteristics (PCs) was proposed, which was sharply different from existing work that only utilized one-dimensional degradation data. Once new degradation information was available, the residual life of the product being monitored could be estimated in an adaptive manner. Here, it was assumed that the degradation of each PC over time was governed by a Wiener degradation process and the dependency between them was characterized by the Frank copula function. A bivariate Wiener process model with measurement errors was used to model the degradation measurements. A two-stage method and the Markov chain Monte Carlo (MCMC) method were combined to estimate the unknown parameters in sequence. Results from a numerical example about fatigue cracks show that the proposed method is valid as the relative error is small.

A novel SINR and mutual information based radar jamming technique

The improvements of anti-jamming performance of modem radar seeker are great threats to military targets. To protect the target from detection and estimation, the novel signal-to-interference-plus-noise ratio （SINR）-based and mutual information （Ml）-based jamming design techniques were proposed. To interfere with the target detection, the jamming was designed to minimize the S1NR of the radar seeker. To impair the estimation performance, the mutual information between the radar echo and the random target impulse response was used as the criterion. The spectral of optimal jamming under the two criteria were achieved with the power constraints. Simulation results show the effectiveness of the jamming techniques. SINR and MI of the SINR-based jamming, the MI-based jamming as well as the predefined jamming under the same power constraints were compared. Furthermore, the probability of detection and minimum mean-square error （MMSE） were also utilized to validate the jamming performance. Under the jamming power constraint of I W, the relative decrease of the probability of detection using S1NR-based optimal jamming is about 47%, and the relative increase of MMSE using Ml-based optimal jamming is about 8%. Besides, two useful jamming design principles are concluded which can be used in limited jamming power situations.

Online process monitoring for complex systems with dynamic weighted principal component analysis

Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivated to propose a new monitoring method by compensating the principal component analysis with a weight approach.The proposed monitor consists of two tiers. The first tier uses the principal component analysis method to extract cross-correlation structure among process data, expressed by independent components. The second tier estimates auto-correlation structure among the extracted components as auto-regressive models. It is therefore named a dynamic weighted principal component analysis with hybrid correlation structure. The essential of the proposed method is to incorporate a weight approach into principal component analysis to construct two new subspaces, namely the important component subspace and the residual subspace, and two new statistics are defined to monitor them respectively. Through computing the weight values upon a new observation, the proposed method increases the weights along directions of components that have large estimation errors while reduces the influences of other directions. The rationale behind comes from the observations that the fault information is associated with online estimation errors of auto-regressive models. The proposed monitoring method is exemplified by the Tennessee Eastman process. The monitoring results show that the proposed method outperforms conventional principal component analysis, dynamic principal component analysis and dynamic latent variable.

Methods, current status, and prospect of targeted observation

Targeted observation is an observation strategy by which the concerned phenomenon is observed. In geoscienee, targeted ob- servation is mainly related to the forecasts of weather events or predictions of climate events. This paper will first review the history of targeted observation, and then introduce the main methods used in targeted observation. The discussion on the theo- retical basis of targeted observation includes its advantages and limitations. After presenting the current situation of domestic and international targeted observations in atmospheric and oceanic sciences, the methods used for targeted observation, and their effect evaluation and testing are mainly discussed here. Finally, the author presents his suggestion about the prospect of further development in the field, and how to extend the method of targeted observation to deal with numerical model errors.

X-ray pulsar-based navigation using pulse phase and Doppler frequency measurements

In order to eliminate the impact of the Doppler effects caused by the motion of the spacecraft on the X-ray pulsar-based navigation, an innovative navigation method using the pulse phase and Doppler frequency measurements of the X-ray pulsars is proposed. Given the initial estimate of the spacecraft's state,the real-time photon arrival model is established at the spacecraft with respect to the spacecraft's position and velocity predicted by the orbit dynamic model and their estimation errors. On this basis, a maximum likelihood estimation algorithm directly using the observed photon event timestamps is developed to extract a single pair of pulse phase and Doppler frequency measurements caused by the spacecraft's state estimation error. Since the phase estimation error increases as the observation time increases, we propose a new measurement updating scheme of referring the measurements to the middle time of an observation interval. By using the ground-based simulation system of X-ray pulsar signals, a series of photon-level simulations are performed. The results testify to the feasibility and real-timeliness of the proposed navigation method, and show that the incorporation of the Doppler measurement as well as the pulse phase into the navigation filter can improve the navigation accuracy.

Asymptotic Normality of LS Estimate in Simple Linear EV Regression Model

Though EV model is theoretically more appropriate for applications in which measurement errors exist, people are still more inclined to use the ordinary regression models and the traditional LS method owing to the difficulties of statistical inference and computation. So it is meaningful to study the performance of LS estimate in EV model. In this article we obtain general conditions guaranteeing the asymptotic normality of the estimates of regression coefficients in the linear EV model. It is noticeable that the result is in some way different from the corresponding result in the ordinary regression model.

Interobserver error in grassland vegetation surveys:sources and implications

Aims Observer error is an unavoidable aspect of vegetation surveys involving human observers.We quantified four components of interobserver error associated with long-term monitoring of prairie vegetation:overlooking error,misidentification error,cautious error and estimation error.We also evaluated the association of plot size with pseudoturnover due to observer error,and how documented pseudochanges in species composition and abundance compared with recorded changes in the vegetation over a 4-year interval.Methods This study was conducted at Tallgrass Prairie National Preserve,Kansas.Monitoring sites contained 10 plots;each plot consisted of a series of four nested frames(0.01,0.1,1 and 10 m^(2)).The herbaceous species present were recorded in each of the nested frames,and foliar cover was visually estimated within seven cover categories at the 10 m^(2)spatial scale only.Three hundred total plots(30 sites)were surveyed,and 28 plots selected at random were resurveyed to assess observer error.Four surveyors worked in teams of two.Important Findings At the 10 m^(2)spatial scale,pseudoturnover resulting from overlooking error averaged 18.6%,compared with 1.4%resulting from misidentification error and 0.6%resulting from cautious error.Pseudoturnover resulting from overlooking error increased as plot size decreased,although relocation error likely played a role.Recorded change in species composition over a 4-year interval(excluding potential misidentification error and cautious error)was 30.7%,which encompassed both pseudoturnover due to overlooking error and actual change.Given a documented overlooking error rate of 18.6%,this suggests the actual change for the 4-year period was only 12.1%.For estimation error,26.2%of the time a different cover class was recorded.Over the 4-year interval,46.9%of all records revealed different cover classes,suggesting that 56%of the records of change in cover between the two time periods were due to observer error.

Partial functional linear quantile regression

This paper studies estimation in partial functional linear quantile regression in which the dependent variable is related to both a vector of finite length and a function-valued random variable as predictor variables. The slope function is estimated by the functional principal component basis. The asymptotic distribution of the estimator of the vector of slope parameters is derived and the global convergence rate of the quantile estimator of unknown slope function is established under suitable norm. It is showed that this rate is optirnal in a minimax sense under some smoothness assumptions on the covariance kernel of the covariate and the slope function. The convergence rate of the mean squared prediction error for the proposed estimators is also established. Finite sample properties of our procedures are studied through Monte Carlo simulations. A real data example about Berkeley growth data is used to illustrate our proposed methodology.