模拟表示、混合推理与战场态势评估

在战场态势评估领域,战场环境的知识表示和相关的推理方法是一个急需解决的难题。推广的模拟表示及混合推理框架采用了多层模拟结构来表示战场环境及相关的军事知识,对图模型进行了扩展,在其语句子系统融入了包括一阶谓词逻辑、非单调逻辑、模糊逻辑、Demster-Shafer证据方法及其相应的证明理论,使其具备了表示多种不确定性知识与现象的能力,以及利用这些知识对不确定现象进行推理的能力。关于战场态势评估的应用实例简要地说明了所述方法的应用步骤及其作用。

模拟式功率表示值误差测量结果的不确定度评定

本文详细介绍了用 71 51数字多用表对模拟式功率表直流示值误差测量结果的不确定度评定的方法。

Digital simulation of 3D turbulence wind field of Sutong Bridge based on measured wind spectra

Time domain analysis is an essential implement to study the buffeting behavior of long-span bridges for it can consider the non-linear effect which is significant in long-span bridges. The prerequisite of time domain analysis is the accurate description of 3D turbulence winds. In this paper, some hypotheses for simplifying the 3D turbulence simulation of long-span cable-stayed bridges are conducted, considering the structural characteristics. The turbulence wind which is a 3D multivariate stochastic vector process is converted into four independent 1D univariate stochastic processes. Based on recorded wind data from structural health monitoring system (SHMS) of the Sutong Bridge, China, the measured spectra expressions are then presented using the nonlinear least-squares fitting method. Turbulence winds at the Sutong Bridge site are simulated based on the spectral representation method and the Fast Fourier transform (FFT) technique, and the relevant results derived from target spectra including measured spectra and recommended spectra are compared. The reliability and accuracy of the presented turbulence simulation method are validated through comparisons between simulated and target spectra (measured and recommended spectra). The obtained turbulence si-mulations can not only serve further analysis of the buffeting behavior of the Sutong Bridge, but references for structural anti-wind design in adjacent regions.

An upgraded scheme of surface physics for Antarctic ice sheet and its implementation in the WRF model

Regional climate models often lack detailed description of ice sheet surface and, as a result, are limited in their capability to provide useful information for Antarctic climate research and field campaigns. In this study, an upgraded scheme of surface physics for Antarctic ice sheet(IST) is developed to improve the surface temperature simulations in Antarctica. Through stand-alone simulations, IST shows advantages over the Noah glacial module, a commonly utilized scheme in the widely used Weather Research and Forecasting(WRF) model. These improvements are mainly attributed to the incorporation of detailed snow physics and optimized surface layer parameterization, which results in better simulations of both the surface albedo in summer and the turbulent sensible heat flux in winter. When coupled with IST instead of Noah,WRF models show improved simulation of surface temperatures throughout the year. The bias and root-meansquare-error of annual mean surface temperatures are reduced from 5.7 and 6.0 to 0.2 and 2.7 K.

Range Profile Target Recognition Using Sparse Representation Based on Feature Space

A novel method is presented to improve the recognition rate of warhead in this paper. Firstly, a tool for electromagnetic calculation, like CST Microwave Studio, is used to simulate the frequency response of the electromagnetic scattering. Secondly, the echo and further the range profile are acquired from the frequency response by further processing. Thirdly, a set of discriminative features is extracted from the range profiles of the target. Fourthly, these features are used to construct a dictionary for the sparse representation classifier. Finally, the sample of the target can be classified by solving the sparsest coefficients. Since the reconstruction result is determined by a linear combination of the training samples, this method has a good robustness for the variable features. By formulating the problem within a feature-based sparse representation framework, the presented method combines the discriminative features of each sample during the sparse recovery process rather than in a postprocessing manner. Moreover, based on the feature representation space rather than a single feature or image pixel, the constructed dictionary exhibits both strong expressive and discriminative powers that can enhance the classification performance of the test sample. A series of test results based on the simulated data demonstrates the effectiveness of our method. © 2017, Shanghai Jiaotong University and Springer-Verlag GmbH Germany.