基于北斗星间链路闭环残差检测的星间钟差平差改正

利用“闭环检测”思想检测和修正系统测量累积误差,是工程科学中的常用和有效手段。本文指出了北斗三号系统全球星间链路中所存在的有利闭环条件,并提出一种利用其进行闭合残差检测与分析的方法。在此基础上,构建了闭合残差整网平差模型,实现了对北斗三号卫星星间相对钟差的误差修正。基于在轨实测数据的计算表明,北斗三号系统全球星间链路中确实存在着明显的常数性或周期性非零闭合残差。通过对星间链路闭环残差的平差修正,基本消除了卫星星间钟差不闭合的现象,减少星间钟差随机噪声30%~50%,有效提高了星间相对钟差测定的精度,有利于提升北斗系统服务能力。

便携式激光盘煤系统原理及应用

在分析以往各种盘煤技术优缺点的基础上,提出一种新型的便携式激光盘煤系统。通过采用无棱镜激光测距仪及数字角度编码器技术,更适合燃煤电厂具有强磁场干扰的场合。本系统数据采集软件基于WindowsCE掌上电脑软件,更适合现场使用,并使用了网络平差技术,确保了盘煤结果的准确性。通过掌上电脑的数据传输,PC室内处理软件可计算面积、体积等参数,同时可形成3D立体图及三角网图,此外本系统煤质信息与空间坐标的耦合,可快速显示煤种在煤堆中的分布状况。

海上多源多缆地震采集综合导航定位数据处理技术

目前海上三维地震勘探主要以多缆采集为主,对地震船、震源和拖缆等设备进行全方位的精确定位是确保高精度地震采集资料品质的关键所在。介绍了海上多源多缆地震采集综合导航定位网络的配置及其质量评价标准;根据现场工作经验总结提出了一套海上拖缆地震采集综合导航定位数据实时处理和质量控制流程;详细论述了定位数据预处理和网络平差等关键技术环节以及必须把握的准则;进一步讨论了综合导航定位数据现场实时处理可以同时起到的定位参数设置检查修正和水下设备故障分析判断作用。

拖缆综合导航数据后处理质量控制

在海上拖缆采集作业中,电缆形状和位置的计算有着重要的意义。在实时导航时,需要它显示共中心点位置、控制面元覆盖和判断电缆水下状态;在导航数据后处理时,需要依靠它计算电缆各检波点的位置。海上施工中,由于天气状况不佳、定位信号不稳、设备通讯问题和躲避障碍物等原因,原始的定位导航数据会记录一些突变的或者错误的数据,不能直接使用。必须对原始数据进行后处理,得到更加合理准确的定位数据,并根据产生的相关质控数据对施工质量进行监控。本文结合海上多个拖缆采集作业实例,深入分析了综合导航后处理的流程和参数选择原则,提出了质量控制的关键点,具有一定的借鉴和推广价值。

Adaptive control of machining process based on extended entropy square error and wavelet neural network

Combining information entropy and wavelet analysis with neural network,an adaptive control system and an adaptive control algorithm are presented for machining process based on extended entropy square error(EESE)and wavelet neural network(WNN).Extended entropy square error function is defined and its availability is proved theoretically.Replacing the mean square error criterion of BP algorithm with the EESE criterion,the proposed system is then applied to the on-line control of the cutting force with variable cutting parameters by searching adaptively wavelet base function and self adjusting scaling parameter,translating parameter of the wavelet and neural network weights.Simulation results show that the designed system is of fast response,non-overshoot and it is more effective than the conventional adaptive control of machining process based on the neural network.The suggested algorithm can adaptively adjust the feed rate on-line till achieving a constant cutting force approaching the reference force in varied cutting conditions,thus improving the machining efficiency and protecting the tool.

Comprehensive Analysis and Artificial Intelligent Simulation of Land Subsidence of Beijing, China

Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy.

Flow stress behavior and constitutive modeling of 20MnNiMo low carbon alloy

The hot deformation behavior of 20 Mn Ni Mo low carbon alloy was investigated by isothermal compression tests over wide ranges of temperature(1223-1523 K) and strain rate(0.01-10 s^(-1)). According to the experimental true stress-true strain data, the constitutive relationships were comparatively studied based on the Arrhenius-type model, Johnson-Cook(JC) model and artificial neural network(ANN), respectively. Furthermore, the predictability of the developed models was evaluated by calculating the correlation coefficient(R) and mean absolute relative error(AARE). The results indicate that the flow stress behavior of 20 Mn NiM o low carbon alloy is significantly influenced by the strain rate and deformation temperature. Compared with the Arrhenius-type model and Johnson-Cook(JC) model, the ANN model is more efficient and has much higher accuracy in describing the flow stress behavior during hot compressing deformation for 20 Mn Ni Mo low carbon alloy.

The study of estimation method of broadband emissivity from EOS/MODIS data

The broadband emissivity is an important parameter for estimating the energy balance of the Earth. This study focuses on estimating the window （8 -12 μm） emissivity from the MODIS （mod- erate-resolution imaging spectroradiometer） data, and two methods are built. The regression method obtains the broadband emissivity from MODllB1 - 5KM product, whose coefficient is developed by using 128 spectra, and the standard deviation of error is about 0.0118 and the mean error is about O. 0084. Although the estimation accuracy is very high while the broadband emissivity is estimated from the emissivity of bands 29, 31 and 32 obtained from MOD11B1 _ 5KM product, the standard deviations of errors of single emissivity in bands 29, 31, 32 are about 0.009 for MOD11B1 5KM product, so the total error is about O. 02 and resolution is about 5km × 5km. A combined radiative transfer model with dynamic learning neural network method is used to estimate the broadband emis- sivity from MODIS 1B data. The standard deviation of error is about 0.016, the mean error is about 0.01, and the resolution is about 1 km x 1 km. The validation and application analysis indicates that the regression is simpler and more practical, and estimation accuracy of the dynamic learning neural network method is higher. Considering the needs for accuracy and practicalities in application, one of them can be chosen to estimate the broadband emissivity from MODIS data.

Artificial neural network approach for rheological characteristics of coal-water slurry using microwave pre-treatment

Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheology characteristic for microwave pre-treatment of coal-water slurry(CWS) was performed in an online Bohlin viscometer. The non-Newtonian character of the slurry follows the rheological model of Ostwald de Waele. The values of n and k vary from 0.31 to 0.64 and 0.19 to 0.81 Pa·sn,respectively. This paper presents an artificial neural network(ANN) model to predict the effects of operational parameters on apparent viscosity of CWS. A 4-2-1 topology with Levenberg-Marquardt training algorithm(trainlm) was selected as the controlled ANN. Mean squared error(MSE) of 0.002 and coefficient of multiple determinations(R^2) of 0.99 were obtained for the outperforming model. The promising values of correlation coefficient further confirm the robustness and satisfactory performance of the proposed ANN model.

A new group contribution-based method for estimation of flash point temperature of alkanes

Flash point is a primary property used to determine the fire and explosion hazards of a liquid. New group contribution-based models were presented for estimation of the flash point of alkanes by the use of multiple linear regression(MLR)and artificial neural network(ANN). This simple linear model shows a low average relative deviation(AARD) of 2.8% for a data set including 50(40 for training set and 10 for validation set) flash points. Furthermore, the predictive ability of the model was evaluated using LOO cross validation. The results demonstrate ANN model is clearly superior both in fitness and in prediction performance.ANN model has only the average absolute deviation of 2.9 K and the average relative deviation of 0.72%.

Simultaneous determination of brilliant blue and indigotine by derivative fluorescence spectrometry combined with WT-RBFNN

The mixed solutions of brilliant blue and indigotine are prepared and the fluorescence spectra of them are experimentally measured. The serious overlapping spectra of brilliant blue and indigotine are solved by means of the first-derivative fluorescence spectrometry. The wavelet coefficients, obtained by compressing the spectral data using wavelet transformation （WT）, are taken as inputs to establish the radial basis function neural network （RBFNN）. The neural network model can realize simultaneous determination of brilliant bFue and indigotine, and the mean relative errors of both compounds are 1.84% and 1.26%, respectively