数控机床空间误差的无模测量与补偿

提出了用激光球杆仪直接测量数控机床刀具的定位误差 ,实现空间误差无模识别的方法 .建立了误差预报的三维网格模型 ,利用有限元方法实现空间任意一点误差的预报 .在XK713数控加工中心上进行了本方法的误差测量、预报和补偿实验 .结果表明所提出的误差测量方法具有操作方便、成本底、精度高的特点 。

An Approach to Unsupervised Character Classification Based on Similarity Measure in Fuzzy Model

This paper presents a fuzzy logic approach to efficiently perform unsupervised character classification for improvement in robustness, correctness and speed of a character recognition system. The characters are first split into eight typographical categories. The classification scheme uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity function. The fuzzy unsupervised character classification, which is natural in the repre...

Path loss modelling and comparison based on the radio propagation measurement at 3.5GHz

Wideband IMT-Advanced mobile communication systems tend to operate in the high frequency bands due to a relatively large capacity available. Thus, Measurement and modelling methods of radio propaga- tion eharaeteristics are proposed for the field test of Chinese 4th generation （4G） trial system. The mea- surement system is established for 3.5GHz based on the sophistieated measurement instruments and the virtual instrument teehnology. The characteristic parameters of radio propagation sueh as path loss （PL） exponent and shadow fading standard deviation are extracted from measurement data, which result in the path loss model finally. The comparisons with other existing international models results validate our mea- surement in terms of path loss model. Based on the analysis of the existing extension model assumed for the microwave frequency at 3.5GHz, we find that the Stanford University Interim （SUI） model fits very well with the measurement result in the hotspot scenario, while the COST 231 model is closer to the mea- surement result in the suburban scenario. This result provides a measurement-based channel referenee for the development of the future IMT-Advanced systems in China.

Spectrum Sensing Model for Wireless Internet of Things

In the last few years, the number of devices operating in wireless Internet of Things （IoT） has experienced tremendous growth. On the other hand, the growth results in spectrum scarcity. Cog- nitive Radio （CR） systems have been proposed to efficiently exploit the spectra that have been assigned but are underutilized. In this paper, a spectrum sensing model based on Markov chain is proposed to predict the spectrum hole for CR in wireless IoT. Theoretical analysis and simulation results have been evaluated that a Markov model with two- state or four-state works well enough in wireless loT whereas a model with more states is not necessary for it is complex.

Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.

An Algorithm for Parameter Identification of UAS from Flight Data

The aim of the present work is to realize an identification algorithm especially devoted to UAS （unmanned aerial systems）. Because UAS employ low cost sensor, very high measurement noise has to be taken into account. Therefore, due to both modelling errors and atmospheric turbulence, noticeable system noise has also to be considered. To cope with both the measurement and system noise, the identification problem addressed in this work is solved by using the FEM （filter error method） approach. A nonlinear mathematical model of the subject aircraft longitudinal dynamics has been tuned up through semi-empirical methods, numerical simulations and ground tests. To take into account model nonlinearities, an EKF （extended Kalman filter） has been implemented to propagate the state. A procedure has been tuned up to determine either aircraft parameters or the process noise. It is noticeable that, because the system noise is treated as unknown parameter, it is possible to identify system affected by noticeable modelling errors. Therefore, the obtained values of process noise covariance matrix can be used to highlight system failure. The obtained results show that the algorithm requires a short computation time to determine aircraft parameter with noticeable precision by using low computation power. The present procedure could be employed to determine the system noise for various mechanical systems, since it is particularly devoted to systems which present dynamics that are difficult to model. Finally, the tuned up off-line EKF should be employed to on-line estimation of either state or unmeasurable inputs like atmospheric turbulence.

Searching for sterile neutrinos in dynamical dark energy cosmologies

We investigate how the dark energy properties change the cosmological limits on sterile neutrino parameters by using recent cosmological observations. We consider the simplest dynamical dark energy models, the wCDM model and the holographic dark energy（HDE） model, to make an analysis. The cosmological observations used in this work include the Planck 2015 CMB temperature and polarization data, the baryon acoustic oscillation data, the type Ia supernova data, the Hubble constant direct measurement data, and the Planck CMB lensing data. We find that, mν,sterileff〈 0.2675 eV and Neff〈 3.5718 for ΛCDM cosmology, mν,sterileff〈 0.5313 eV and Neff〈 3.5008 for wCDM cosmology, and mν,sterileff〈 0.1989 eV and Neff〈 3.6701 for HDE cosmology, from the constraints of the combination of these data. Thus, without the addition of measurements of growth of structure, only upper limits on both mν,sterileff and Neff can be derived, indicating that no evidence of the existence of a sterile neutrino species with e V-scale mass is found in this analysis. Moreover, compared to the ΛCDM model, in the wCDM model the limit on mν,sterileff becomes much looser, but in the HDE model the limit becomes much tighter. Therefore, the dark energy properties could significantly influence the constraint limits of sterile neutrino parameters.