泡沫沥青混合料物理力学特性的试验研究

泡沫沥青混合料是一种全新的道路材料，其有关特性尚不完全清楚。本文以泡沫沥青稳定2种骨料(新砂石料和回收料)为基础，从材料的选取与级配的组成、沥青的最佳发泡条件、混合料拌和方法及养护方案等几个方面提出一种泡沫沥青混合料的研究方法，并对该材料的物理性质和强度特性进行了研究。结果表明泡沫沥青混合料密度较小，空隙率较大，其强度能够满足路面设计规范关于基层设计参数的要求。水泥作为填料对泡沫沥青混合料强度影响不大，其主要作用是能显著改善水稳性。     

求解二层规划的一种模糊交互式方法

主要讨论上下层具有共同决策变量的一类二层规划问题的求解方法．在引入目标函数的模糊目标，并充分考虑上层与下层决策者的满意度后，构造了一个模糊交互式决策方法，从而使得上下层决策者对最后的求解结果都能满意．数值结果表明此法可行有效．     

空间体J积分表达式及其守恒性

受Rice的J积分的启发文中定义了空间体J积分,从三维角度定义了J积分的空间表达方式,并证明了文中定义的由闭合曲面表达的空间J积分具有守恒性,是对Rice的二维J积分理论的拓展和延伸,为研究求解含裂纹的弹塑性断裂问题提出了新思路.     

蛋白激酶C活性在脑血管痉挛过程中的变化

目的探讨蛋白激酶C(PKC)活性在脑血管痉挛(CVS)过程中的变化及其意义。方法日本大耳白兔25只,随机分为对照组(5只)与模型组(CVS组,20只),CVS组又分为1、3、5、8 d组,每组5只。采用枕大池2次注血法建立脑血管痉挛动物模型,血管造影观察基底动脉(BA)痉挛,放射免疫法和放射性同位素标记蛋白激酶测定法动态测定血浆内皮素和兔BA管壁PKC活性。结果枕大池2次注血法可以成功构建脑血管痉挛模型,是一种简便、可靠的方法;模型组血浆中内皮素含量和BA壁内PKC的活性均明显升高,且在5 d组达到峰值,与对照组相比差异有统计学意义(P<0.05)。结论PKC活性与CVS的严重程度呈正相关。     

舰船装备健康状态评估及其应用研究

文章分析了装备健康状态评估的国内外研究现状,对装备健康状态及其与技术状态之间的关系进行了较为深入的探讨,明确了舰船装备健康状态与技术状态之间的区别和联系,以及利用健康状态和技术状态进行装备状态评估的优缺点。最后根据舰船装备的实际需求,将舰船健康状态的概念引入到舰船的使用和维修过程中,提出了健康状态在舰船使用、维修和训练方面的应用设想。     

金属基复合材料热机械疲劳寿命影响参数研究

基于细观损伤机理,通过对金属基复合材料在热/机械循环载荷下疲劳破坏的数值模拟,研究了六种参数对疲劳寿命的影响.结果表明:(1)疲劳寿命随着试样尺寸的增大而缩短,但达到临界尺寸,即纤维规模超过25根或者长度超过30倍纤维直径后,疲劳寿命将不受尺寸变化的影响;(2)纤维体积含量影响了材料抗疲劳破坏的能力,疲劳寿命随着纤维体积百分含量的提高而延长;(3)疲劳寿命依赖于纤维强度Weibull分布中形状参数的变化,疲劳寿命随着形状参数的增大而延长;(4)疲劳寿命随着疲劳延性系数的增大而延长,但随着疲劳延性指数绝对值的增大而缩短.     

水下潜器改进S面控制及控制系统仿真(英文)

S surface controllers have been proven to provide effective motion control for an autonomous underwater vehicle (AUV). However, it is difficult to adjust their control parameters manually. Choosing the optimum parameters for the controller of a particular AUV is a significant challenge. To automate the process, a modified particle swarm optimization (MPSO) algorithm was proposed. It was based on immune theory, and used a nonlinear regression strategy for inertia weight to optimize AUV control parameters. A semi-physical simulation system for the AUV was developed as a platform to verify the proposed control method, and its structure was considered. The simulation results indicated that the semi-physical simulation platform was helpful, the optimization algorithm has good local and global searching abilities, and the method can be reliably used for an AUV.     

基于蚁群优化算法与出租车GPS数据的公众出行路径优化

以出租车GPS采集的浮动车数据为依据,研究出租车驾驶员路径选择的认知及类蚂蚁的行为特征。根据城市道路功能等级与出租车的通行频率等信息素,建立出租车驾驶员路径选择信息素等级路网,并以此作为路网初始信息素,综合考虑路径通行时间、通行距离、路径信息素等级等多个因素,提出了基于蚁群优化算法的公众出行路径规划优化算法。以武汉市路网和浮动车为试验数据,将模型规划的道路与浮动车数据库中的轨迹进行了比较。结果表明:基于蚁群优化算法与出租车GPS数据的公众出行路径同出租车驾驶员选择的出行路径相似度很高,能为公众出行提供出租车驾驶员选择的行车路径。     

Hierarchical ordering of nests in a joint mode and destination choice model

This paper seeks to explore the relationship between mode and destination choice in an integrated nested choice model. A fundamental argument can be made that in certain circumstances, the ordering of choices should be reversed from the usual sequence of destination choice preceding mode choice. This results in a travel demand model where travelers are more likely to change destinations than to change transportation modes. For small and medium size urban areas, particularly in the United States, with less well developed public transit systems that draw few choice riders, this assumption makes much more sense than the traditional modeling assumptions. The models used in the new travel modeling system developed for Knoxville, Tennessee utilize this reversed ordering, with generally good results, which required no external tinkering in the logsum parameters.     

Improving the robustness of an MPC-based obstacle avoidance algorithm to parametric uncertainty using worst-case scenarios

Previous work by the authors focused on obstacle avoidance in large, high-speed autonomous ground vehicles within unknown and unstructured environments. This work resulted in a nonlinear model predictive control based algorithm that simultaneously optimises both the speed and steering commands. The algorithm can exploit the dynamic limits of the vehicle to navigate it to a target position as quickly as possible without compromising safety. In the algorithm, a model of the vehicle is used explicitly to predict and optimise future actions, but in practice the model parameter values are not known exactly. Thus, in this paper, the robustness of the algorithm to parametric uncertainty is evaluated. It is first demonstrated that using nominal parameter values in the algorithm leads to safety issues in 24% of the evaluated scenarios with the considered parametric uncertainty distributions. To improve the algorithm's robustness, a novel double-worst-case formulation is developed that simultaneously accounts for the robust satisfaction of the two safety requirements of high-speed obstacle avoidance: collision-free and no-wheel-lift-off. Results from simulations with stratified random scenarios and worst-case scenarios show that the double-worst-case formulation renders the algorithm robust to all uncertainty realisations tested. The trade-off between robustness and the task completion performance is also quantified.