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The accurate estimation of road friction coeffi- cient in the active safety control system has become increasingly prominent. Most previous studies on road friction estimation have only used vehicle longitudinal or lateral dynamics and often ignored the load transfer, which tends to cause inaccurate of the actual road friction coef- ficient. A novel method considering load transfer of front and rear axles is proposed to estimate road friction coef- ficient based on braking dynamic model of two-wheeled vehicle. Sliding mode control technique is used to build the ideal braking torque controller, which control target is to control the actual wheel slip ratio of front and rear wheels tracking the ideal wheel slip ratio. In order to eliminate the chattering problem of the sliding mode controller, integral switching surface is used to design the sliding mode sur- face. A second order linear extended state observer is designed to observe road friction coefficient based on wheel speed and braking torque of front and rear wheels. The proposed road friction coefficient estimation schemes are evaluated by simulation in ADAMS/Car. The results show that the estimated values can well agree with the actual values in different road conditions. The observer can estimate road friction coefficient exactly in real-time andresist external disturbance. The proposed research provides a novel method to estimate road friction coefficient with strong robustness and more accurate.

The estimation of the signal variance is a critical challenge in wavelet-domain minimum mean square error（MMSE） based image denoising.In contrast to the conventional approaches that treat the neighboring wavelet coefficients equally to estimate the signal variance at each coefficient position,here an adaptive approach is proposed that utilizes a bilateral statistical scheme adaptively adjusting the contributions of neighboring wavelet coefficients to provide an accurate estimation of the signal variance.Experimental results are presented to demonstrate the superior performance of the proposed approach.

A simple and accurate method to estimate evapotranspiration （ET） is essential for dynamic monitor- ing of the Earth system at a large scale. In this paper, we developed an artificial neural network （ANN） model forced by remote sensing and AmeriFlux data to estimate ET. First, the ANN was trained with ET measurements made at 13 AmeriFlux sites and land surface products derived from satellite remotely sensed data （normalized difference vegetation index, land surface temperature and surface net radiation） for the period 2002-2006. ET estimated with the ANN was then validated by ET observed at five AmeriFlux sites during the same period. The validation sites covered five different vegetation types and were not involved in the ANN training. The coefficient of determination （RE） value for comparison between estimated and measured ET was 0.77, the root-mean- square error was 0.62 mm/d, and the mean residual was - 0.28. The simple model developed in this paper captured the seasonal and interannual variation features of ET on the whole. However, the accuracy of estimated ET depended on the vegetation types, among which estimated ET showed the best result for deciduous broadleaf forest compared to the other four vegetation types.

In multilevel modeling, contextual effects are defined as the pure effects of contextual variables on the outcomes after the impact of explanative variable at individual level been removed. The multilevel model with contextual effect is frequently of interest in education and psychological research since the group means of the explanative variable at individual level reflected the situational influence have both methodological an substantive meanings. In the present study, a Monte Carlo simulation along with an empirical data contained 38 companies and 1,200 employees are adapted to explore the influences of intra-class correlation (ICC) of predictor and outcome on the estimation of contextual effects. The Bayesian estimation was applied in this present study in order to compare with the traditional maximum likelihood method. Results of simulation study revealed that, in the cases of small sample size, a smaller ICCx combined with a higher ICCy has better efficient for the parameter estimation; in contrast, a