定点潮汐形变观测与GPS大地测量

论述了我国定点潮汐形变观测与GPS在观测周期，物理量，频域，实测精度，监测优势者方面的异同，认为定点潮汐形变观测是测震学与大地测量（含GPS）的必要补充，介绍了我国定点潮汐形变观测技术的发展与台网观测技术，以及它在我国10多次中强震测报中的应用。     

控制网优化设计中的蒙特卡罗法

介绍了蒙特卡罗法的基本步骤以及伪随机数的检验方法；结合工程控制网的设计给出了一个实际算例，算例的结果表明该方法切实有效。     

我国电子政务建设的机遇挑战及其对策

通过对电子政务发展战略的深入分析 ,指出我国电子政务的建设正面临四个挑战 ,包括重复建设、无序操作、忽视应用、管理滞后等。面对重复建设、建设无序、忽视应用、管理滞后等问题 ,提出了迎接四个挑战的具体对策     

动态城市交通网络优化模型研究及算法设计

在城市交通网络中,行车路线所耗时间是用户关心的关键因素之一.本文构建了基于时间因素的城市交通网络模型,为反映交通网络随时间动态变化的特性,网络模型中弧的权重是和时间统计变量相关.本文设计了交通网络模型算法,并对算法进行了详细的论述.针对实际交通网络的行驶线路选择人群中乐观型、保守型和折中型,给出了路线选择的简化模型.文...     

IDENTIFICATION AND ANALYSIS OF NATURAL CHANNEL NETWORKS FROM DIGITAL ELEVATION MODELS

The identification and analysis of natural channel networks from digital elevation models are discussed from the point of view of their environmental applications. An interactive, graphical software package that implements some of the most widely used techniques for the automatic recognition of channel networks and for the computation of some useful geomorphologic indices and functions is presented.     

改进BP神经网络在岩质边坡稳定性分析中的应用

使用MATLAB软件中的NNtool工具箱建立了一个通过添加动量改进的BP神经网络模型,用以分析岩质边坡的稳定性。通过对天津某工程的边坡进行分析来检验这个模型的可靠度。根据现场调查的实际情况分析,该边坡岩体比较破碎,因此文中选择了几个有代表性的岩石完整性参数作为输入层参数,通过收集的一些类似边坡的参数对所建立的神经网络模型进行训练,以保证此模型适合被用来分析该工程。结果证明,所建立的模型是可靠的,此种方法在分析岩质边坡稳定性的的实践中具有广泛的使用性。     

浅析山西长治数字测震台网维护工作

文章概述了山西长治数字测震台网的系统构成,并对台网的维护工作从维修次数、故障类型等方面进行分析,认为,要保证观测质量必须做好五方面的维护工作。     

Neural network modelling of planform geometry of headland-bay beaches

The shoreline of beaches in the lee of coastal salients or man-made structures, usually known as headland-bay beaches, has a distinctive curvature; wave fronts curve as a result of wave diffraction at the headland and in turn cause the shoreline to bend. The ensuing curved planform is of great interest both as a peculiar landform and in the context of engineering projects in which it is necessary to predict how a coastal structure will affect the sandy shoreline in its lee. A number of empirical models have been put forward, each based on a specific equation. A novel approach, based on the application of artificial neural networks, is presented in this work. Unlike the conventional method, no particular equation of the planform is embedded in the model. Instead, it is the model itself that learns about the problem from a series of examples of headland-bay beaches (the training set) and thereafter applies this self-acquired knowledge to other cases (the test set) for validation. Twenty-three headland-bay beaches from around the world were selected, of which sixteen and seven make up the training and test sets, respectively. As there is no well-developed theory for deciding upon the most convenient neural network architecture to deal with a particular data set, an experimental study was conducted in which ten different architectures with one and two hidden neuron layers and five training algorithms – 50 different options combining network architecture and training algorithm – were compared. Each of these options was implemented, trained and tested in order to find the best-performing approach for modelling the planform of headland-bay beaches. Finally, the selected neural network model was compared with a state-of-the-art planform model and was shown to outperform it.     

基于Householder矩阵和Butterworth滤波器组的反馈延迟网络人工混响

针对现有人工混响方法中反馈延迟网络方法存在延迟线的个数选择受限和无法设定子带混响时间的不足,本文设计了一种基于Householder反馈矩阵和Butterworth滤波器组的反馈延迟网络人工混响器.Householder反馈矩阵有助于最大化回声密度,延迟线个数的选择相对自由,Butterworth滤波器组便于设定子带混响时间.仿真实验中分别利用语谱图、语音质量感知评价和主观评价验证了本文方法的有效性.