ON THE VERTICAL DISCRETIZATION OF ATMOSPHERIC MODELS-PART Ⅰ:EXISTING PROBLEMS,DATA PROCESSING AND CHOICE OF VERTICAL COORDINATE AND BASIS FUNCTIONS

Discussions are carried out on the vertical discretization of current atmospheric models.It is pointed out that there exist problems in the integration of the hydrostatic equation and the computation of vertical advection,vertical diffusion and so on.Then some possible ways for solving or alleviating them are suggested.Finally,the choice of vertical coordinate and basis functions is discussed.     

GrADS绘图汉字显示的技术研究与实现

气象常用绘图软件GrADS中不能用汉字设置标题和题注,给国内的业务和科研工作带来不便。根据GrADS软件的特点,在其已有功能的基础上,对GrADS软件功能进行扩充,发展了一些关键技术,实现了GrADS汉字显示功能。其中,发展的关键技术包括:自定义汉字库创建、汉字库索引创建、中英文混合显示、快速查找自定义汉字库、解码自定义汉字库和汉字绘制。实现的汉字显示技术具有使用方便、速度快、可移植、可中英文混合显示等特点。     

基于采样分布式估计器的多无人艇轨迹跟踪控制

提出了一种级联控制算法解决多无人艇（USVs）系统的分布式轨迹跟踪问题.这种控制算法可以分为两层：第一层是基于采样信息的分布式估计器,主要用于估计领航者的期望轨迹;第二层是每个无人艇的本地控制器,主要是结合滑模控制与神经网络径向基函数,在系统具有欠驱动、参数不确定性和扰动等因素的情况下,使其状态跟踪期望轨迹的本地估计值.为了求解上述跟踪控制问题,基于李雅普诺夫理论与级联系统理论,推导得到了所有无人艇位置状态收敛到期望轨迹的充分条件,并通过仿真结果验证了所提出控制方法的有效性与正确性.     

径向基函数算法中插值参数对DEM精度的影响

以多重对数径向基函数插值算法中的插值参数为研究对象,选取了5种不同地貌类型的离散点数据,使用中误差度量指标,研究光滑因子、搜索点数、搜索方向对DEM插值精度的影响,给出插值参数的“最优”取值区间,消除插值参数选择的随意性,更好地指导DEM建模的运用。     

中国古代地图的数学基础与地理空间维度认知

从几个典型的中国古地图的数学基础出发,结合相关的数学方法,探讨了中国古人在地理空间维度认知方面走过的历程。认为,当中国古代地图发展到以"计里画方"进行地图绘制时,已经实现了3维地理空间的科学认知。这也是"计里画方"的制图方法可以持续1 000多年的理论依据。     

RTK技术在沛县新城区图根控制测量中的应用

叙述了RTK技术在沛县新城区图根控制测量中的应用,通过比较分析说明：RTK的测量精度完全能够满足1：500地形测图图根控制的精度要求。     

径向基神经网络GPS高程转换方法

针对GPS高程转换问题,给出了基于径向基神经网络转换GPS高程的模型。用实际观测数据对该模型进行了试验,结果表明,用径向基神经网络转换GPS高程精度高于二次拟合法和BP神经网络法。径向基神经网络能够有效克服BP神经网络局部极小值的缺点,并且具有较高的收敛速度,在GPS高程转换方面具有广阔应用前景。     

Optimal design of structures for earthquake loads by a hybrid RBF-BPSO method

The optimal seismic design of structures requires that time history analyses （THA） be carried out repeatedly. This makes the optimal design process inefficient, in particular, if an evolutionary algorithm is used. To reduce the overall time required for structural optimization, two artificial intelligence strategies are employed. In the first strategy, radial basis function （RBF） neural networks are used to predict the time history responses of structures in the optimization flow. In the second strategy, a binary particle swarm optimization （BPSO） is used to find the optimum design. Combining the RBF and BPSO, a hybrid RBF-BPSO optimization method is proposed in this paper, which achieves fast optimization with high computational performance. Two examples are presented and compared to determine the optimal weight of structures under earthquake loadings using both exact and approximate analyses. The numerical results demonstrate the computational advantages and effectiveness of the proposed hybrid RBF-BPSO optimization method for the seismic design of structures.     

Determination of the degree 120 time-variable gravity field in the Sichuan-Yunnan region using Slepian functions and terrestrial measurements

The terrestrial time-variable gravity measurements are characterized by a high signal-to-noise ratio and sensitivity to the sources of mass change in the Earth's crust. These gravity data have many applications, such as surface deformation, groundwater storage changes, and mass migration before and after earthquakes. Based on repeated terrestrial gravity measurements at 198 gravity stations in the Sichuan-Yunnan region (SYR) from 2015 to 2017, we determine a time series of degree 120 gravity fields using the localized spherical harmonic (Slepian) basis functions. Our results show that adopting the first 6 Slepian basis functions is sufficient for effective localized Slepian modeling in the SYR. The differences between two gravity campaigns at the same time of year show an obvious correlation with tectonic features. The degree 120 timevariable gravity models presented in this paper will benefit the study of the regional mass migration inside the crust of the SYR and supplement the existing geophysical models for the China Seismic Experimental Site.     

Reservoir induced earthquakes analyzed via radial basis function networks

Estimation of the magnitude of reservoir induced seismicity is essential for seismic risk analysis of dam sites. Different geological and empirical methods dealing with the mechanism or magnitude of such earthquakes are available in the literature. In this study, a method based on an artificial neural network utilizing radial basis functions (RBF network) was employed to analyze the problem. The network has only two input neurons, one representing the maximum depth of the reservoir and the other being a comprehensive parameter representing reservoir geometry. Magnitudes of the induced earthquakes predicted using the RBF network were compared with the actual recorded data. Compared with the conventional statistical approach, the proposed method gives a better prediction, both in terms of coefficients of correlation and error rates.