A comparison of single‐run pushover analysis techniques for seismic assessment of bridges

Traditional pushover analysis is performed subjecting the structure to monotonically increasing lateral forces with invariant distribution until a target displacement is reached; both the force distribution and target displacement are hence based on the assumption that the response is controlled by a fundamental mode, that remains unchanged throughout. However, such invariant force distributions cannot account for the redistribution of inertia forces caused by structural yielding and the associated changes in the vibration properties, including the increase of higher‐mode participation. In order to overcome such drawbacks, but still keep the simplicity of using single‐run pushover analysis, as opposed to multiple‐analyses schemes, adaptive pushover techniques have recently been proposed. In order to investigate the effectiveness of such new pushover schemes in assessing bridges subjected to seismic action, so far object of only limited scrutiny, an analytical parametric study, conducted on a suite of continuous multi‐span bridges, is carried out. The study seems to show that, with respect to conventional pushover methods, these novel single‐run approaches can lead to the attainment of improved predictions. Copyright © 2007 John Wiley & Sons, Ltd.     

基于启发式图搜索的遥感影像道路半自动提取

启发式图搜索法用于线状目标识别的原理是：用图结构表示边缘点和边缘段，根据启发函数计算顶点权值，在图的路径上建立相应的代价函数，通过在图中搜索对应的最小代价的通道以找到最优路径。图搜索法是一种全局最优方法，它在受噪声影响较大时效果仍然较好。文中使用了启发式图搜索法（A*算法）实现了道路的半自动跟踪。它的基本思路是：首先利用自适应平滑滤波算子进行道路信息增强，然后对传统的道路数学模型进行了进一步的扩展，突出了对道路几何特性和辐射特性的描述，并依此构建图搜索的代价函数，实现了基于启发式图搜索法A*算法的道路半自动跟踪。经实验证明，该方法进行遥感影像的道路半自动提取效果较好。     

基于自适应四叉树分割的遥感图像压缩算法

遥感图像具有不同于一般图像的特点,针对遥感图像对压缩算法的特殊要求,提出了自适应四叉树分割算法实现图像的分块,以各像块的均值近似表示该像块的灰度值,从而以尺量少的数值获得对原始图像的最佳逼近,再对辐近误差进行自适应分割和基于像块方差的自适应量化,最后对各分量进行Huffman编码.实验表明,本算法能表现出良好的实用性能,比JPEG算法更适用于遥感图像的压缩.     

Deriving rules from activity diary data: A learning algorithm and results of computer experiments

 Activity-based models consider travel as a derived demand from the activities households need to conduct in space and time. Over the last 15 years, computational or rule-based models of activity scheduling have gained increasing interest in time-geography and transportation research. This paper argues that a lack of techniques for deriving rules from empirical data hinders the further development of rule-based systems in this area. To overcome this problem, this paper develops and tests an algorithm for inductively deriving rules from activity-diary data. The decision table formalism is used to exhaustively represent the theoretically possible decision rules that individuals may use in sequencing a given set of activities. Actual activity patterns of individuals are supplied to the system as examples. In an incremental learning process, the system progressively improves on the selection of rules used for reproducing the examples. Computer experiments based on simulated data are performed to fine-tune rule selection and rule value update functions. The results suggest that the system is effective and fairly robust for parameter settings. It is concluded, therefore, that the proposed approach opens up possibilities to derive empirically tested rule-based models of activity scheduling. Follow-up research will be concerned with testing the system on empirical data. Received: 31 January 2001 / Accepted: 13 September 2001     

局地自适应多级模式降水预报

近年来，我国对西北太平洋上的热带气旋的实时业务定位精度有了很大的提高，平均定位误差均在20－30km，美国在同一海区的定位误差也在这一精度水平上。值得注意的是，计算业务定位误差的标准是各国确定的所谓“最佳位置”，然而，该“最佳位置”并不一定能代表实况。事实上，我国和美国的“最佳位置”是有差别的，平均达54．4km，且存在明显的年际变化和地理分布特征。这意味着，我们可能过小地估计了业务定位误差。     

生态区评价研究的兴起与发展——以美国森林生态系统管理评价(FEMAT)研究为例

生态区评价是近年来出现在美国的一种新型区域生态系统管理方法的理论与实践科学研究,起始于美国总统森林会议(1993)后成立的森林生态系统管理评价组(FEMAT),目标是实现地区生态系统的科学管理.本文评介了生态区评价首项案例--美国西北太平洋沿岸区原始森林及其他各类森林经营管理方案的生态、经济、社会影响综合评价的科学与方法实践意义.指出FEMAT的生态区评价:1)迈出了生态区评价历史上坚实的第一步,实现了区域土地利用规划与生态系统管理方案从以行政疆域为准划分向以生态区域为准划定边界的转变;2)方案优化过程充分考虑区域生态、经济、社会的历史、现状与未来趋势等各方面,将科学、政策、管理融为一体,使之易于在实践中贯彻;3)促进了不同政府机构和利益团体间为实现资源开发与生态保育双重目标而共同协作.     

水下机动目标轨迹跟踪的自适应估计方法

研究了用ＧＰＳ测定基阵坐标，分析了水听器基阵所接收到的声脉冲信号的处理。通过模拟和实测数据的试算，证明了本文提出的模型及方法不但能处理多系统含有粗差的观测值，而且具有良好的跟踪处理机动目标的性能。     

Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series

ABSTRACT

Accurate runoff forecasting plays a key role in catchment water management and water resources system planning. To improve the prediction accuracy, one needs to strive to develop a reliable and accurate forecasting model for streamflow. In this study, the novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog-leaping algorithm (SFLA) is proposed. Historical streamflow data of two different rivers were collected to examine the performance of the proposed model. To evaluate the performance of the proposed ANFIS-SFLA model, six different scenarios for the model input–output architecture were investigated. The results show that the proposed ANFIS-SFLA model (R² = 0.88; NS = 0.88; RMSE = 142.30 (m³/s); MAE = 88.94 (m³/s); MAPE = 35.19%) significantly improved the forecasting accuracy and outperformed the classic ANFIS model (R² = 0.83; NS = 0.83; RMSE = 167.81; MAE = 115.83 (m³/s); MAPE = 45.97%). The proposed model could be generalized and applied in different rivers worldwide.     

MIDAS-W: a workstation-based incoherent scatter radar data acquisition system

The Millstone Hill Incoherent Scatter Data Acquisition System (MIDAS) is based on an abstract model of an incoherent scatter radar. This model is implemented in a hierarchical software system, which serves to isolate hardware and low-level software implementation details from higher levels of the system. Inherent in this is the idea that implementation details can easily be changed in response to technological advances. MIDAS is an evolutionary system, and the MIDAS hardware has, in fact, evolved while the basic software model has remained unchanged. From the earliest days of MIDAS, it was realized that some functions implemented in specialized hardware might eventually be implemented by software in a general-purpose computer. MIDAS-W is the realization of this concept. The core component of MIDAS-W is a Sun Microsystems UltraSparc 10 workstation equipped with an Ultrarad 1280 PCI bus analog to digital (A/D) converter board. In the current implementation, a 2.25 MHz intermediate frequency (IF) is bandpass sampled at 1 s intervals and these samples are multicast over a high-speed Ethernet which serves as a raw data bus. A second workstation receives the samples, converts them to filtered, decimated, complex baseband samples and computes the lag-profile matrix of the decimated samples. Overall performance is approximately ten times better than the previous MIDAS system, which utilizes a custom digital filtering module and array processor based correlator. A major advantage of MIDAS-W is its flexibility. A portable, single-workstation data acquisition system can be implemented by moving the software receiver and correlator programs to the workstation with the A/D converter. When the data samples are multicast, additional data processing systems, for example for raw data recording, can be implemented simply by adding another workstation with suitable software to the high-speed network. Testing of new data processing software is also greatly simplified, because a workstation with the new software can be added to the network without impacting the production system. MIDAS-W has been operated in parallel with the existing MIDAS-1 system to verify that incoherent scatter measurements by the two systems agree. MIDAS-W has also been used in a high-bandwidth mode to collect data on the November, 1999, Leonid meteor shower.     

联邦滤波的改进算法

分析并综述了联邦滤波算法,以及该算法存在的问题.针对观测信息不足的情况下,结合抗差估计和自适应估计理论对联邦滤波算法进行了改进,给出了联邦滤波在局部传感器观测信息不足情况下的改进算法.由模拟计算结果可知,在局部传感器观测信息不足的情况下,改进的联邦滤波器能较好地抑制载体观测异常和状态扰动异常对动态系统参数估值的影响,具有很强的的抗差性和自适应性.