随机非线性系统的输出反馈控制

针对满足线性增长条件的一类随机非线性系统, 本文研究了输出反馈镇定问题. 然而不同于现有的所有文献, 由于线性增长条件中含有不可量测的状态, 引入了一个待定的高增益观测器. 利用反推设计技术, 构造性地给出了一个输出反馈控制器的设计, 通过适当地选取高增益参数, 保证了闭环系统的零解是概率意义下全局渐近稳定的, 输出几乎处处调节于零.     

短期负荷预测Volterra滤波器模型

讨论依据相空间邻近轨道演化相似性的特点构造训练模式,建立短期负荷预测Ｖｏｌｔｅｒｒａ滤波器模型的问题．以距离相似度和趋势相似度来衡量电力负荷吸引子轨道的相似度,提出了电力负荷吸引子邻近轨道判别的新方法．从模型训练充分性的角度出发,探讨了模型训练集规模的选择依据．仿真结果表明该模型是有效的．     

行波型超声电机基于神经网络的逆模型辨识

行波型超声电机的动态特性受定子压电陶瓷迟滞和接触层非线性摩擦力的影响,表现出复杂的多值映射特征.通过引入动态迟滞逆算子,将存在于超声波电机逆系统中的多值映射在新的扩张输入空间上,转换为一一映射;然后使用神经网络建立超声波电机的逆模型,对迟滞和非线性摩擦力的影响进行补偿.所建立的模型结构简单,可以在线调整适应电机参数的非线性变化.实验仿真结果验证了该方法的有效性.     

超声波目标定位与跟踪系统在机器人中的应用

机器人目标定位是实现自主导航的关键问题之一。借助超声波测距原理,采用三边测量法,设计了一个超声波定位系统。该系统通过分析处理多个传感器的信息,得到目标物体的方位数据,并配合相应的控制系统和执行机构,实现机器人对单个目标物体的跟踪。同时为了修正声速,在系统中加入了温度补偿电路。     

多进制小波的遥感影像融合对比分析

提出了利用多进制小波进行融合的方案,目标是为了深度挖掘小波在遥感影像像素级融合方面的应用。采用多进制小波进行融合的优势是在减少能量损失的前提下使高频分量的分解方向更加细化,重构时频段组合更加灵活。首先从理论方面阐述了多进制小波和二进制小波(包)的异同,通过对比分析在像素级遥感影像融合实验结果,同时结合目视效果和多种评价指标,发现对于中分辨率——北京一号遥感影像,用多进制（M=4时）db5小波对全色和多光谱遥感影像进行融合的效果不亚于采用二进制小波(包)的融合效果。实验结果表明,小波融合的效果取决于小波基的选取、分解级数,也因处理对象而不同,但在采用相同的小波基的基础上,多进制小波比二进制小波(包)融合效果在目视效果和融合指标两方面都略胜一筹。其为小卫星数据在中国水资源、灾害、考古等领域的重点应用推广的数据处理方面打下基础,同时可为其他不同分辨率遥感影像的类似处理起到示范作用。     

钢铁企业全厂仪表维修设施设计初探

以我国某大型钢铁企业全厂仪表维修设施的设计方案为例,简要说明了仪修设施的设计原则、全厂仪表维修的一般解决方案,较详细地介绍了仪修设施专业划分、实验室及维修车间的设置和主要设备构成,为该类项目的设计和工程建设提供一定参考。     

基于WZ-2188超声波流量计煤气流量检测及其补偿中的应用

根据梅山钢厂石灰窑现有采用孔板流量计测量煤气流量所存在的问题,提出了一种基于WZ-2188超声波流量检测为基准,结合孔板流量计流量检测的双重检测方法,实现了石灰窑供气系统煤气流量的准确测量,并以此为基础实现煤气流量在线补偿。此补偿方法对于采用孔板流量计测量煤气流量的套筒石灰窑均适用。     

测距传感器数据在线自适应加权融合

针对移动机器人的测距系统,采用了红外线传感器与超声波传感器共同测距,避免了因使用单个传感器进行多次测量而降低系统的实时性和产生信号串扰问题;应用自适应加权数据融合估计算法对实时测量数据进行在线融合估计,只对当前采样时刻的测量数据进行自适应加权融合,而各传感器的加权因子则通过传感器的测量数据进行方差在线学习估计以自适应方式进行调整,使融合结果的均方误差始终最小,实现两种传感器在功能上的互补;实验结果表明,该方法提高了整体测距精度,得到了被测距离更加准确的估计.     

Using visual and text features for direct marketing on multimedia messaging services domain

Traditionally, direct marketing companies have relied on pre-testing to select the best offers to send to their audience. Companies systematically dispatch the offers under consideration to a limited sample of potential buyers, rank them with respect to their performance and, based on this ranking, decide which offers to send to the wider population. Though this pre-testing process is simple and widely used, recently the industry has been under increased pressure to further optimize learning, in particular when facing severe time and learning space constraints. The main contribution of the present work is to demonstrate that direct marketing firms can exploit the information on visual content to optimize the learning phase. This paper proposes a two-phase learning strategy based on a cascade of regression methods that takes advantage of the visual and text features to improve and accelerate the learning process. Experiments in the domain of a commercial Multimedia Messaging Service (MMS) show the effectiveness of the proposed methods and a significant improvement over traditional learning techniques. The proposed approach can be used in any multimedia direct marketing domain in which offers comprise both a visual and text component.

Modeling finite buffer effects on TCP traffic over an IEEE 802.11 infrastructure WLAN

The network scenario is that of an infrastructure IEEE 802.11 WLAN with a single AP with which several stations (STAs) are associated. The AP has a finite size buffer for storing packets. In this scenario, we consider TCP-controlled upload and download file transfers between the STAs and a server on the wireline LAN (e.g., 100 Mbps Ethernet) to which the AP is connected. In such a situation, it is well known that because of packet losses due to finite buffers at the AP, upload file transfers obtain larger throughputs than download transfers. We provide an analytical model for estimating the upload and download throughputs as a function of the buffer size at the AP. We provide models for the undelayed and delayed ACK cases for a TCP that performs loss recovery only by timeout, and also for TCP Reno. The models are validated in comparison with NS2 simulations.