两相非等节距时栅位移传感器

在分析时栅位移传感器的一般信号模型的基础上,深入探讨了三相、两相时栅位移传感器的测量机理,进而提出了一种新型位移传感器--两相非等节距时栅位移传感器,然后详细介绍了它的结构和原理.通过和其它两种传感器的对比表明,该传感器可以在不增加加工难度的情况下,增加传感器的极对数.最后通过实验表明,该传感器的精度比其它两种传感器得到了提高.

Abstract：

On the basis of analyzing general signal modes of time grating displacement sensors, the measuring mechanism of three-phase and two-phase time grating displacement sensors are explored. Then a new displacement sensor, i.e. a two-phase unequal-pitch displacement sensor, is proposed and its structure and theory are introduced. A comparison with other two types of sensor shows that its pole-pair number has increased while its processing difficulty has not been increased. Finally, experiments show that the precision of this sensor is higher than the other two types of sensor.     

IN-FLIGHT ALIGNMENT OF INERTIAL NAVIGATION SYSTEM BY CELESTIAL OBSERVATION TECHNIQUE

This paper presents an in-flight alignment technique for a strapdown inertial navigation system (SINS) and employs a star pattern recognition procedure for identifying stars sensed by a CCD electrooptical star sensor.Collinearity equations are used to estimate sensor frame star coordinates and the conventional least square differential correction method is used to estimate the unknown orientation angles. A comparison of this attitude with the attitude estimated by the SINS provides axis misalignment angles. Simulations using a Kalman filter are carried out for an SINS and the system employs a local level navigation frame. The space stabilized SINS is discussed in conjunction with the celestial aiding. Based on the observation of the Kalman filter, the estimating and compensating gyro errors, as well as the position and velocity errors caused by the SINS misalignments are calibrated by celestial attitute information.     

IONOSPHERIC CORRECTION METHOD FOR PRECISE POSITIONING WITH GPS ACTIVE NETWORK

The ionospheric delay error is a major error source which degrades the positioning accuracy in network real time kinematic (RTK) positioning over a long distance. Different approaches are proposed to estimate GPS errors based on GPS reference network, such as virtual reference stations (VRSs) and network corrections. A new method is used to model the ionospheric total electronic content (TEC) distribution in space. Unlike most ionospheric models, only the ionospheric delays along the satellite tracks are modelled. Therefore, the models are of high precise resolution of the ionospheric TEC distribution in both spatial and temporal scales. A new algorithm is used to solve the equation singularity problem. Experiments demonstrate that the new ionospheric correction method can be used to describe the ionospheric variation at a low latitude area where ionospheric activities are strong. Also, the accuracy of the ionospheric model is enough to support centimeter-level positioning within the network. As ionospheric models are satellite-based models (each satellite has one model), the model parameters can be easily incorporated with the existing differential GPS Radio Technical Commission for Maritime Service (DGPS RTCM) 104 format.     

Spatial Interpolation of Soil Texture Using Compositional Kriging and Regression Kriging with Consideration of the Characteristics of Compositional Data and Environment Variables

The spatial interpolation for soil texture does not necessarily satisfy the constant sum and nonnegativity constraints. Meanwhile, although numeric and categorical variables have been used as auxiliary variables to improve prediction accuracy of soil attributes such as soil organic matter, they (especially the categorical variables) are rarely used in spatial prediction of soil texture. The objective of our study was to comparing the performance of the methods for spatial prediction of soil texture with consideration of the characteristics of compositional data and auxiliary variables. These methods include the ordinary kriging with the symmetry logratio transform, regression kriging with the symmetry logratio transform, and compositional kriging (CK) approaches. The root mean squared error (RMSE), the relative improvement value of RMSE and Aitchison’s distance (D_A) were all utilized to assess the accuracy of prediction and the mean squared deviation ratio was used to evaluate the goodness of fit of the theoretical estimate of error. The results showed that the prediction methods utilized in this paper could enable interpolation results of soil texture to satisfy the constant sum and nonnegativity constraints. Prediction accuracy and model fitting effect of the CK approach were better, suggesting that the CK method was more appropriate for predicting soil texture. The CK method is directly interpolated on soil texture, which ensures that it is optimal unbiased estimator. If the environment variables are appropriately selected as auxiliary variables, spatial variability of soil texture can be predicted reasonably and accordingly the predicted results will be satisfied.     

流域不透水面及其变化信息提取(英文)

Impervious surface is one of the important parameters of valley water circular simulation,scientific estimation for which has significant and practical value for the urban water quantity and process simulation,diffuse pollution estimating and the forecast of climate changes.The objective of this research is to get the information of impervious surface and its dynamic change.Through the computer-assisted field method,the technologies of decision tree and data mining were applied to withdraw the impervious surface information in research region by the Landsat TM data in 1988,1994 and 2002.The results suggested that the accuracy of impervious surface information extraction in the study area arrived above 94.4% in 2002 image.On this basis,the mixed method was used to extract the location and the types of the impervious surface change.The overall accuracy of monitoring reached 89%,which meets the demand of the hydrological models.     

Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation

To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations,winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index(LAI) values. The performances of the calibrated crop environment resource synthesis for wheat(CERES-Wheat) model for two different assimilation scenarios were compared by employing ensemble Kalman filter(EnKF)-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors,assimilation stages and temporal-spatial scales. Overall,the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehensively consider several factors in the initial conditions and observations. When using this strategy,an adjusted coefficients of determination(R~2) of 0.84,a root mean square error(RMSE) of 323 kg ha~(–1),and a relative errors(RE) of 4.15% were obtained at the field plot scale and an R~2 of 0.81,an RMSE of 362 kg ha~(–1),and an RE of 4.52% were obtained at the pixel scale of 30 m×30 m. With increasing observation errors,the accuracy of the yield estimates obviously decreased,but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution,the accuracy of the crop yield estimates decreased; however,the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time,especially in operational systems used for regional crop yield estimates.     

Classification Methods Based on Pattern Discrimination Models for Web-Based Diagnosis of Rice Diseases

Two classification and identification methods based on pattern discrimination models and the majority-vote technique were investigated for implementing a World Wide Web-based system for the identification of rice diseases. The experiment was carried out using color and shape patterns in 425 images of three rice diseases, which were classified into four classes： two classes of leaf blast, and one class each of sheath blight and brown spot. A method consisting of two discrimination steps involving application of multiple discrimination models of a support vector machine gave the best result because of its capacity to evaluate the similarity of disease types. This accuracy of the method was 88% for leaf blast （A-type）, 94% for sheath blight, and 80% for leaf blast （B-type） and brown spot; on average, the accuracy of this method was 5% greater than that of the other method when three classes were used in the model. Although the accuracy of both methods was inadequate, the results of this study show that it is possible to estimate the least number of possible or similar diseases from a large number of diseases. Therefore, we conclude that there is merit in grouping classes into subgroups rather than attempting to discriminate between all classes simultaneously and that these methods are effective in identifying diseases for web-based diagnosis.     

泡塑富集ICP-MS测定土壤中痕量金(英文)

The aim of this study is to determine the trace amount of gold in soil sample using plastic foam enrichment-ICP-MS method.The national standard reference materials including GAu-2,GAu-10,GAu-11 and GAu-13 were used for parallel determination by ICPMS.The results showed that the method performed well in detection limit,precision and accuracy,indicating that the plastic foam enrichment-ICP-MS method could be used to determine the trace amounts of gold in soil sample.     

Determination of Trace Amounts of Gold in Soil Sample Using Plastic Foam Enrichment-ICP-MS Method

The aim of this study is to determine the trace amount of gold in soil sample using plastic foam enrichment-ICP-MS method.The national standard reference materials including GAu-2,GAu-10,GAu-11 and GAu-13 were used for parallel determination by ICPMS.The results showed that the method performed well in detection limit,precision and accuracy,indicating that the plastic foam enrichment-ICP-MS method could be used to determine the trace amounts of gold in soil sample.     

Back Ground Segmentation of Cucumber Target Based on DSP

In order to realize automatic and accurate grading of cucumber, the first thing is to make sure the high accuracy and integrity in cucumber shape segmentation. As the core processor of this dissertation, DSP TMS320DM6437 acquired and processed digital image, it solved the common shadowing problem associated with the natural light. Ultimately, the background subtraction was proposed. Compared with the result of above-mentioned image data processing, the error rate of classic background subtraction method was often high. The result of optimization showed that the improved background subtraction method worked well, and it could meet an accurate segmentation of the fruit in comparison with the original methods.