基于多传感器信息融合的移动机器人避障

为了更好地解决移动机器人在未知环境下的自主避障问题,采用多传感器信息融合的方法,通过多个超声传感器对障碍物信息进行采集。合理确立模糊控制器的输入输出,通过模糊推理将障碍物距离信息模糊化,建立模糊规则并解模糊,以达到对移动机器人的安全避障的控制。通过建立移动机器人运动模型,设计了仿真平台,得到实验结果表明:该算法具有良好的可行性。     

一种路点受限的移动传感器网络移动模型

提出了一种基于随机路点移动模型的路点受限移动模型。在模型设计中,对节点的路点选择和暂停时间进行了设置,从而保持节点移动的规律性,满足特定场景的需要。利用仿真工具模拟模型的节点移动轨迹与其它移动模型进行比较,并从路由协议方面进行评估。仿真结果证明:该模型对集体搜索等特定场景具有较好的适用性。     

基于移动锚节点与多级通信的三维传感器网络节点自定位算法研究

在基于移动锚节点的三维传感器网络节点自定位算法SNLSFA(Sensor Node Localization Scheme based on Flying Anchors)的基础上,提出了一种新的基于移动锚节点与多级通信的三维传感器网络节点自定位算法SNLSFAMC(Sensor Node Localization Scheme based on Flying Anchors and Multi-level Communication)。首先由移动锚节点提供3个或4个辅助定位点,再由辅助定位点得到两条非平行线段,然后过线段中点分别做垂直于线段的平面,经两平面相交后得到一条经过待定位节点的直线,最后利用辅助定位点与待定位节点之间的距离作为通信半径即可得到待定位节点的位置。仿真结果表明,与SNLSFA相比,在相同锚节点数量下,SNLSFAMC提高了定位精度,且在相同定位精度下,SNLSFAMC降低了对锚节点数量的需求,提高了算法的响应时间。     

基于预测与优先级缓存的MIPv6切换算法

为了提升移动IPv6的切换性能,保证实时性要求高的音频、视频等业务的服务质量,针对去除DAD过程的RDMIPv6方案未考虑在传输速率相对较慢的无线信道中,移动节点向NAR本地注册所产生的时延以及切换过程中引入的丢包问题,提出一种优化方案PCRD-MIPv6。PCRD-MIPv6方案结合基于L2触发的切换预测和数据包优先级缓存机制,在L2切换完成之前向移动节点MN维护的接入路由器缓存列表T_AR中的所有AR发送MOA实现本地注册,与此同时,将切换过程中的数据包按优先级缓存在PAR中,在切换完成后发往移动节点,从而减少切换过程中的丢包率。NS-2仿真结果表明,PCRD-MIPv6方案有效地减少了切换时延,降低了切换过程中的丢包率。     

一种优化内容适配技术的设计与实现

针对传统内容适配技术的特点和目前手机浏览器的发展现状,本文提出优先适配浏览器的内容适配技术。在传统内容适配技术上增加uA特征串和Accept特征串作为内容适配参数,并设计浏览器优先终端、终端优先Accept的适配规则。综合网络侧适配和终端侧适配优点,有效优化终端上的内容适配率,使得终端能够动态地、智能地针对不同的手机浏览器适配最佳的浏览版本。     

一种基于移动P2P平台的无线传感器网络体系结构

借助P2P思想,构建一个基于移动网络基础设施提供对等服务的无线传感器网络体系结构。移动网络充当移动P2P平台,提供一种普适的传感器组网。代表传感器网络的网关节点隐藏了其实现细节,在P2P模式工作下向用户提供更加优质的个人服务。此外,还讨论了服务平台构建过程中的一般性问题。     

NEURAL-NETWORK-AIDED DESIGN OF AUTOMOBILE EXHAUST CATALYSTS

A priori design of catalysts is not yet possible. Such task would demand unavailable scientific knowledge of the correlations among synthesis parameters and resulting solid state and surface structures, on the one hand, and among those atomic-level structural details and their catalytic functions, on the other hand. To avoid testing every possible combination, therefore, the applied chemist or chemical engineer must identify empirical correlations underlying the existing experimental data base.

The ability of artificial neural networks to identify complex correlations and to predict the result of experiments has recently generated considerable interest in various areas of science and engineering. In this paper, neural networks are used to identify composition-performance relationships in automobile exhaust catalysts.

This work employs an artificial neural network technique to do a sensitivity analysis of the conversions of pollutant gases as a function of the catalyst composition and the operating conditions. This approach converges on the optimum catalyst composition and operating condition in order to produce specified conversions of carbon monoxide, hydrocarbons and nitrogen oxides, to carbon dioxide, water and di-nitrogen respectively.     

基于改进自适应遗传算法的移动WSN覆盖方法

针对传统的WSN覆盖模型的弊端,尤其是如果一个传感器失效,K-覆盖模型需要至少k个传感器节点监测其范围内是否有目标需要覆盖,提出了一种基于改进自适应遗传算法的移动WSN覆盖方法,在能量资源有限的前提下,尽可能长时间的对指定的目标进行连续监测。该算法考虑到了移动传感器是可以连续和变速运动的,从而能够保证所有目标都在它们的覆盖范围内。仿真结果表明,在使用移动节点的情况下,与其他常用模型相比,改进方法的生存周期和数据包数量都有明显提高。     

An immune orthogonal learning particle swarm optimisation algorithm for routing recovery of wireless sensor networks with mobile sink

The growth of mobile handheld devices promotes sink mobility in an increasing number of wireless sensor networks (WSNs) applications. The movement of the sink may lead to the breakage of existing routes of WSNs, thus the routing recovery problem is a critical challenge. In order to maintain the available route from each source node to the sink, we propose an immune orthogonal learning particle swarm optimisation algorithm (IOLPSOA) to provide fast routing recovery from path failure due to the sink movement, and construct the efficient alternative path to repair the route. Due to its efficient bio-heuristic routing recovery mechanism in the algorithm, the orthogonal learning strategy can guide particles to fly on better directions by constructing a much promising and efficient exemplar, and the immune mechanism can maintain the diversity of the particles. We discuss the implementation of the IOLPSOA-based routing protocol and present the performance evaluation through several simulation experiments. The results demonstrate that the IOLPSOA-based protocol outperforms the other three protocols, which can efficiently repair the routing topology changed by the sink movement, reduce the communication overhead and prolong the lifetime of WSNs with mobile sink.     

Adaptive recurrent neural network control of uncertain constrained nonholonomic mobile manipulators

In this article, motion/force control problem of a class of constrained mobile manipulators with unknown dynamics is considered. The system is subject to both holonomic and nonholonomic constraints. An adaptive recurrent neural network controller is proposed to deal with the unmodelled system dynamics. The proposed control strategy guarantees that the system motion asymptotically converges to the desired manifold while the constraint force remains bounded. In addition, an adaptive method is proposed to identify the contact surface. Simulation studies are carried out to verify the validation of the proposed approach.