Rating the Aggressivity of Australian Passenger Vehicles Towards Other Vehicle Occupants and Unprotected Road Users

Two measures of aggressivity of Australian passenger vehicles have been developed. The first measures the aggressivity to occupants of other cars. This type of aggressivity rating is based on two-car crashes between passenger vehicles and measures the injury risk each make/model in the collisions poses to the drivers of the other vehicles. The second measures aggressivity to unprotected road users. These aggressivity ratings reflect the threat of severe injury to pedestrians, bicyclists and motorcyclists by die make/model of vehicle colliding with them. This analysis was based on nearly 102,000 drivers involved in tow-away crashes with the makes/models which were the focus of the study and on nearly 22,000 injured pedestrians, bicyclists, and motorcyclists. The results suggest that crasbworthiness and aggressivity are two different aspects of a vehicle's safety performance, with good performance on one dimension not necessarily being associated with good performance on the other.     

论中国生态农业建设的五个基本问题

在最近的10年间,中国生态农业建设迅速发展,现已成为中国农村发展中一个不可分割的重要部分。生态农业建设利用生态经济学原理及系统工程学方法进行农业规划、设计和管理,寻求农村持续、稳定、协调的发展。本文从资源管理的角度讨论了中国生态农业建设的5个基本问题:经济规模、农民接受程度、市场、资金和可持续发展的方向。     

基于自适应网络模糊推理方法在河道洪水预报中的应用

对于易受洪灾的地区而言,快速而准确的洪水预报非常重要,能够为洪水预警消息的发布提供更长的先导时间,从而为可能受灾地区的人们提供更充足的时间以采取相应的防洪措施或安全转移。 常用的预报模型包括基于物理性模型和基于系统技术模型。尽管物理性模型能对洪水形成的物理过程提供很好的解释, 水文学家并不愿意使用它们,因为模型中参数的率定是比较复杂的。因此,一种基于纯数据集的黑箱技术已被广泛采纳。常用的黑箱模型包括线性模型(LR)、自回归移动平均模型(ARMA)和人工神经网络模型（ANN）等。 在当前的研究中,一个相对新颖的黑箱模型--基于自适应网络的模糊推理系统（ANFIS）被用来对长江某河段的洪水进行预报。与此同时,一个线性回归模型（LR）用来作为ANFIS模型的对照。在构建ANFIS中,混合学习算法 (即误差反衍（BP）耦合最小二乘法（LSE）) 用来训练模型的参数。此外,为避免出现过度训练现象,原始数据集基于统计特征值划分成3个子集：训练集、测试集和校正集。当对ANFIS模型训练时,测试集用来帮助控制训练代数。结果表明,ANFIS的预报效果优于LR模型。分析认为ANFIS能够提供预报精度是因为其采用了局部拟合技术,通常它会优于LR模型所采用的全局拟合技术。最后,对本研究而言,最适合的ANFIS模型是输入量为梯形的成员度函数。     

Short-Term Hydroscheduling with Discrepant Objectives Using Multi-Step Progressive Optimality Algorithm1

Cheng, Chuntian, Jianjian Shen, Xinyu Wu, and Kwok-wing Chau, 2012. Short-Term Hydroscheduling with Discrepant Objectives Using Multi-step Progressive Optimality Algorithm. Journal of the American Water Resources Association (JAWRA) 48(3): 464-479. DOI: 10.1111/j.1752-1688.2011.00628.x Abstract: With increase in the number and total capacity of hydropower plants in power systems, optimality algorithms with a single objective are not suitable for optimizing the operation of complex hydropower systems to meet complex demands. Hydropower plants should prioritize discrepant objectives, such as peak regulation and maximizing generation during solving of optimal operation problems of hydropower systems. In this article, we present a multi-step progressive optimality algorithm (MSPOA) for the short-term hydroscheduling (STHS) problem to improve the quality of optimal solutions and enhance the convergence speed of progressive optimality algorithm (POA). In MSPOA, the original problem is first decomposed into a sequence of problems with the longer time steps. Next, the problem with the longest time step is solved, and the optimal solution is used as the initial solution for the problem with the second longest time step. This process proceeds until the original problem with the shortest time step is solved. The proposed discrepant-objective method and solution technique are tested for two types of hydroelectric systems. The results show that MSPOA can give better solutions and cost less time than POA due to enlarging feasible range of decision variables and reducing the number of computational stages. Discrepant objectives among hydropower plants can express the operation characteristics of complex hydropower systems more accurately than unique objective or multiple objectives.     

软泥夹层对香港软土场地地震反应的影响

选择了香港有代表性的８个典型场地,通过钻探、现场剪切波速测量和土样动力实验建立了场地的分析模型,用２１次地震的１２８条基岩地震动记录作为,计算了场地土层地震反应,结果表明,对带海泥软夹层的砂质回填土场地,地震动的长周期分量（１．２～１．５s）被显著放大了,现有抗震规范的设计反应谱不能反映这类场地的反应特性,基于计算结果,提出了分别适合有、无软夹层场地的归－化设计反应谱。