A LARGE SAMPLE ESTIMATE IN MEDIAN LINEAR REGRESSION MODEL Ⅰ: NONTRUNCATED CASE

This paper uses a grouping-adjusting procedure to the data from a median linear regression model, and estimtes the regression coefficients by the method of weighted least squares. This method simplifies computation and in the meantime, preserves the same asymptotic normal distribution for the estimator, as in the ordinary minimum L_1-norm estimates.

THE COMPRESSION LS ESTIMATE OF REGRESSION COEFFICIENT IN MULTIVARIATE LINEAR MODEL

In this paper, compression LS estimate (k) of the regression coefficient B isconsidered when the design matrix present ill-condition in multivariate linear model.The MSE (mean square error)of the estimate(k)=Vec( (k))is less than theMSE of LS estimate β￣* of the regression coefficient β= Vec(B) by choosing the pa-rameter k. Admissibility , numerical stability and relative efficiency of (k)are proved. The method of determining k value for practical use is also suggested

Robust design of sliding mode control for airship trajectory tracking with uncertainty and disturbance estimation

The robotic airship can provide a promising aerostatic platform for many potential applications.These applications require a precise autonomous trajectory tracking control for airship.Airship has a nonlinear and uncertain dynamics.It is prone to wind disturbances that offer a challenge for a trajectory tracking control design.This paper addresses the airship trajectory tracking problem having time varying reference path.A lumped parameter estimation approach under model uncertainties and wind disturbances is opted against distributed parameters.It uses extended Kalman filter(EKF)for uncertainty and disturbance estimation.The estimated parameters are used by sliding mode controller(SMC)for ultimate control of airship trajectory tracking.This comprehensive algorithm,EKF based SMC(ESMC),is used as a robust solution to track airship trajectory.The proposed estimator provides the estimates of wind disturbances as well as model uncertainty due to the mass matrix variations and aerodynamic model inaccuracies.The stability and convergence of the proposed method are investigated using the Lyapunov stability analysis.The simulation results show that the proposed method efficiently tracks the desired trajectory.The method solves the stability,convergence,and chattering problem of SMC under model uncertainties and wind disturbances.

Estimating Aboveground Fresh Biomass of Different Cotton Canopy Types with Homogeneity Models Based on Hyper Spectrum Parameters

AGB （aboveground fresh biomass） is one of the most important parameters of the crop condition monitored with remote sensing. Hyper spectrum remote sensing with the fine spectrum information becomes the efficient method estimating the vegetation AGB. The research was conducted in Xinjiang, the largest cotton planting region of China. The paper analyzed the correlation between the cotton AGB and reflective spectrum and the first derivative spectrum, and the variation coefficient of the waveband reflectance. According to the analysis above, all of 23 parameters, including the hyper spectrum reflectance, the first derivative spectrum parameters and normalization vegetation indexes, were established. And then the estimation models on cotton AGB of relaxing and compact canopy type were established and tested respectively. The tested results showed that Fgo1, [901,502], [901,629], [901,672] among the reflective spectral parameters and D525, D956, D1019, D1751 among the first derivative spectral parameters had the homogenous effect on different cotton canopy types, and the determination coefficients of the models above all arrive at the significant level of 0.99 confidence interval. At last, the tested results of the homogeneity models for different canopy types indicated the parameters of [901, 502], [901,629], [901,672] have more satisfying veracity than others, and the relative errors are as low as 17.0, 16.3 and 16.7% correspondingly; in contrast, the estimation veracity of the first derivative spectrum parameters of single waveband is low.

Evaluating the grassland net primary productivity of southern China from 2000 to 2011 using a new climate productivity model

Grassland is the important component of the terrestrial ecosystems. Estimating net primary productivity （NPP） of grassland ecosystem has been a central focus in global climate change researches. To simulate the grassland NPP in southern China, we built a new climate productivity model, and validated the model with the measured data from different years in the past. The results showed that there was a logarithmic correlation between the grassland NPP and the mean annual temperature, and there was a linear positive correlation between the grassland NPP and the annual precipitation in southern China. Al these results reached a very signiifcant level （P〈0.01）. There was a good correlation between the simulated and the measured NPP, withR2 of 0.8027, reaching the very signiifcant level. Meanwhile, both root mean square errors （RMSE） and relative root-mean-square errors （RRMSE） stayed at a relatively low level, showing that the simulation results of the model were reliable. The NPP values in the study area had a decreasing trend from east to west and from south to north, and the mean NPP was 471.62 g C m？2 from 2000 to 2011. Additionaly, there was a rising trend year by year for the mean annual NPP of southern grassland and the tilt rate of the mean annual NPP was 3.49 g C m？2 yr？1 in recent 12 years. The above results provided a new method for grassland NPP estimation in southern China.

Airship aerodynamic model estimation using unscented Kalman filter

An airship model is made-up of aerostatic,aerodynamic,dynamic,and propulsive forces and torques.Besides others,the computation of aerodynamic forces and torques is difficult.Usually,wind tunnel experimentation and potential flow theory are used for their calculations.However,the limitations of these methods pose difficulties in their accurate calculation.In this work,an online estimation scheme based on unscented Kalman filter(UKF)is proposed for their calculation.The proposed method introduces six auxiliary states for the complete aerodynamic model.UKF uses an extended model and provides an estimate of a complete state vector along with auxiliary states.The proposed method uses the minimum auxiliary state variables for the approximation of the complete aerodynamic model that makes it computationally less intensive.UKF estimation performance is evaluated by developing a nonlinear simulation environment for University of Engineering and Technology,Taxila(UETT)airship.Estimator performance is validated by performing the error analysis based on estimation error and 2-σ uncertainty bound.For the same problem,the extended Kalman filter(EKF)is also implemented and its results are compared with UKF.The simulation results show that UKF successfully estimates the forces and torques due to the aerodynamic model with small estimation error and the comparative analysis with EKF shows that UKF improves the estimation results and also it is more suitable for the under-consideration problem.

Path Classification and Estimation Model Based Prognosis of Pneumatic Cylinder Lifetime

Prognosis is a key technology to improve reliability,safety and maintainability of products,a lot of researchers have been devoted to this technology.But to improve the predict accuracy of remaining life of products has been difficult.To predict the lifetime specification of pneumatic cylinders with high reliability and long lifetime and small specimen,this paper put forward the prognosis algorithm based on the path classification and estimation（PACE） model.PACE model is based entirely on failure data instead of failure threshold.Pneumatic cylinders normally characterize with failure mechanism wear and tear.Since the minimum working pressure increases with the number of working cycles,the minimum working pressure is chosen as degradation signal.PACE model is fundamentally composed of two operations：path classification and remaining useful life（RUL） estimation.Path classification is to classify a current degradation path as belonging to one or more of previously collected exemplary degradation paths.RUL estimation is to use the resulting memberships to estimate the remaining useful life.In order for verification and validation of PACE prognostic method,six pneumatic cylinders are tested.The test data is analyzed by PACE prognostics.It is found that the PACE based prognosis method has higher prediction accuracy and smaller variance and PACE model is significantly outperform population based prognostics especially for small specimen condition.PACE model based method solved the problem of prediction accuracy for small specimen pneumatic cylinders＇ prognosis.

Cooperative interception with fast multiple model adaptive estimation

For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang-bang evasive maneuver with a random switching time.Combined Fast multiple model adaptive estimation(Fast MMAE)algorithm,the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration.Introduced the detection error model related to the line-of-sight(LOS)separation angle of two interceptors,an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance.Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion,Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately.The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.

TYRE DYNAMICS MODELLING OF VEHICLE BASED ON SUPPORT VECTOR MACHINES

Various methods of tyre modelling are implemented from pure theoretical to empirical or semi-empirical models based on experimental results. A new way of representing tyre data obtained from measurements is presented via support vector machines （SVMs）. The feasibility of applying SVMs to steady-state tyre modelling is investigated by comparison with three-layer backpropagation （BP） neural network at pure slip and combined slip. The results indicate SVMs outperform the BP neural network in modelling the tyre characteristics with better generalization performance. The SVMsqyre is implemented in 8-DOF vehicle model for vehicle dynamics simulation by means of the PAC 2002 Magic Formula as reference. The SVMs-tyre can be a competitive and accurate method to model a tyre for vehicle dynamics simuLation.

Crosstalk Model and Estimation Formula for VLSI Interconnect Wires

We develop an interconnect crosstalk estimation model on the assumption of linearity for CMOS device. First, we analyze the terminal response of RC model on the worst condition from theS field to the time domain. The exact 3 order coefficients inS field are obtained due to the interconnect tree model. Based on this, a crosstalk peak estimation formula is presented. Unlike other crosstalk equations in the literature, this formula is only used coupled capacitance and grand capacitance as parameter. Experimental results show that, compared with the SPICE results, the estimation formulae are simple and accurate. So the model is expected to be used in such fields as layout-driven logic and high level synthesis, performance-driven floorplanning and interconnect planning.

Fault detection and identification based on combining logic and model in a wall-climbing robot

A combined logic- and model-based approach to fault detection and identification （FDI） in a suction foot control system of a wall-climbing robot is presented in this paper. For the control system, some fault models are derived by kinematics analysis. Moreover, the logic relations of the system states are known in advance. First, a fault tree is used to analyze the system by evaluating the basic events （elementary causes）, which can lead to a root event （a particular fault）. Then, a multiple-model adaptive estimation algorithm is used to detect and identify the model-known faults. Finally, based on the system states of the robot and the results of the estimation, the model-unknown faults are also identified using logical reasoning. Experiments show that the proposed approach based on the combination of logical reasoning and model estimating is efficient in the FDI of the robot.

PARAMETER ESTIMATION OF MULTI-CONSTITUENT WATER QUALITY MODEL FOR THE LIANGXI RIVER BY MARQUARDT METHOD

A multi-constituent water quality model is presented,Which relates carbonaceous biochemical oxygen demand (CBOD),amonia (NH3-N), nitrite(NO2-N), nitrate(NO3-N) and dissolvedoxygen(DO). The parameters are solved by Marquardt Method (i. e.,Dampled Least Square Method) while initial values inoptimization are produced by Monte-Carlo Method. The Potential ofthe method as a parameter estimation aid is demonstrated for theapplication to the Liangyi Rver, JiangSu Province of China and by aspecial comparison with Gauss Method.

Model Reduction by Generalized Falk Method for Efficient Dedicated to Professor Karl Stark Pister for his 95th birthday Field-Circuit Simulations

The Generalized Falk Method(GFM)for coordinate transformation,together with two model-reduction strategies based on this method,are presented for efficient coupled field-circuit simulations.Each model-reduction strategy is based on a decision to retain specific linearly-independent vectors,called trial vectors,to construct a vector basis for coordinate transformation.The reduced-order models are guaranteed to be stable and passive since the GFM is a congruence transformation of originally symmetric positive definite systems.We also show that,unlike the Pade-via-Lanczos(PVL)method,the GFM does not generate unstable positive poles while reducing the order´of circuit problems.Further,the proposed GFM is also faster when compared to methods of the type Lanczos(or Krylov)that are already widely used in circuit simulations for electrothermal and electromagnetic problems.The concept of response participation factors is introduced for the selection of the trial vectors in the proposed model-reduction methods.Further,we present methods to develop simple equivalent circuit networks for the field component of the overall field-circuit system.The implementation of these equivalent circuit networks in circuit simulators is discussed.With the proposed model-reduction strategies,significant improvement on the efficiency of the generalized Falk method is illustrated for coupled field-circuit problems.

On the Stability of the Solution to a Gonorrhea Diserete Mathematical Model

In this paper, the author studies the stability of the solution to a three-dimension-al gonorrhea discrete mathematical model by Liapunoy method. The parameter es-timator of the slability domain is obtained and the rationality of the model is ex-plained in a theoretic way.

Letter to the Editor Re “Fractional Modeling and SOC Estimation of Lithium-ion Battery”

A Recent paper by Ma et al.,claims to estimate the state of charge of Lithium-ion batteries with a fractionalorder impedance model including a Warburg and a constant phase element(CPE)with a maximum error of 0.5%[1].The proposed equivalent circuit model from[1]is reproduced in Fig.1.

OPERATOR-SPLITTING GALERKIN METHOD FOR ONE KIND OF OIN REACTION MODEL FOR THE POLLUTION IN GROUNDWATER

The operator-splitting methods for the mathematic model of one kind of oin reactions for the problem of groundwater are considered.Optimal error estimates in L 2 and H 1 norm are obtained for the approximation solution.

Global parameter estimation of the Cochlodinium polykrikoides model using bioassay data

Cochlodinium polykrikoides is a notoriously harmful algal species that inflicts severe damage on the aquacultures of the coastal seas of Korea and Japan. Information on their expected movement tracks and boundaries of influence is very useful and important for the effective establishment of a reduction plan. In general, the information is supported by a red-tide（a.k.a algal bloom） model. The performance of the model is highly dependent on the accuracy of parameters, which are the coefficients of functions approximating the biological growth and loss patterns of the C. polykrikoides. These parameters have been estimated using the bioassay data composed of growth-limiting factor and net growth rate value pairs. In the case of the C. polykrikoides, the parameters are different from each other in accordance with the used data because the bioassay data are sufficient compared to the other algal species. The parameters estimated by one specific dataset can be viewed as locally-optimized because they are adjusted only by that dataset. In cases where the other one data set is used, the estimation error might be considerable. In this study, the parameters are estimated by all available data sets without the use of only one specific data set and thus can be considered globally optimized. The cost function for the optimization is defined as the integrated mean squared estimation error, i.e., the difference between the values of the experimental and estimated rates. Based on quantitative error analysis, the root-mean squared errors of the global parameters show smaller values, approximately 25%–50%, than the values of the local parameters. In addition, bias is removed completely in the case of the globally estimated parameters. The parameter sets can be used as the reference default values of a red-tide model because they are optimal and representative. However, additional tuning of the parameters using the in-situ monitoring data is highly required.As opposed to the bioassay data, it is necessary because the bioassay data have limitations in terms of the in-situ coastal conditions.

Partial Linear Model Averaging Prediction for Longitudinal Data

Prediction plays an important role in data analysis.Model averaging method generally provides better prediction than using any of its components.Even though model averaging has been extensively investigated under independent errors,few authors have considered model averaging for semiparametric models with correlated errors.In this paper,the authors offer an optimal model averaging method to improve the prediction in partially linear model for longitudinal data.The model averaging weights are obtained by minimizing criterion,which is an unbiased estimator of the expected in-sample squared error loss plus a constant.Asymptotic properties,including asymptotic optimality and consistency of averaging weights,are established under two scenarios:(i)All candidate models are misspecified;(ii)Correct models are available in the candidate set.Simulation studies and an empirical example show that the promise of the proposed procedure over other competitive methods.

落叶果树需冷量及其估算模型研究进展

落叶果树的需冷量得到满足,才能顺利完成自然休眠,进行下一个生长发育循环(尤其是正常开花结果),否则其他环境条件再适宜,果树也不萌芽开花,即使萌芽也不整齐,生长结果不良,达不到促成栽培的目的[1].因此,对落叶果树需冷量及其估算的研究至关重要,成为落叶果树设施栽培成败的关键.本文仅就需冷量估算模型进行介绍,以期为设施栽培和更完善的需冷量估算提供理论依据.……