Robust state estimation for uncertain linear systems with deterministic input signals

In this paper, we investigate state estimations of a dynamical system in which not only process and measurement noise, but also parameter uncertainties and deterministic input signals are involved. The sensitivity penalization based robust state estimation is extended to uncertain linear systems with deterministic input signals and parametric uncertainties which may nonlinearly affect a state-space plant model. The form of the derived robust estimator is similar to that of the well-known Kalman filter with a comparable computational complexity. Under a few weak assumptions, it is proved that though the derived state estimator is biased, the bound of estimation errors is finite and the covariance matrix of estimation errors is bounded. Numerical simulations show that the obtained robust filter has relatively nice estimation performances.     

Algorithm for the Removing Uniformed Motion Blur

Motion deblurring is one of the basic problems inthe field of image processing. This paper summarizes the mathematical basis of the previous work and presents a deblurringmethod that can improve the estimation of the motion blurkernel and obtain a better result than the traditional methods.Experiments show the motion blur kernel loses some important and useful properties during the estimation of the kernel which may cause a bad estimation and increase the ringingartifacts. Considering that the kernel is provided by the motion of the imaging sensor during the exposure and that the kernel shows the trace of the motion, this paper ensures the physical meaning of the kernel such as the continuity and the center of thekernel during the iterative process. By adding a post process to the estimation of the motion blur kernel, we remove some discrete points and make use of the centralizationof the kernel in order to accurate the estimation. The experiment shows the existence of the post process improves the effect of the estimation of the kernel and provides a better result with the clear edges.     

基于语言模型的有监督词义消歧模型优化研究

词义消歧是自然语言领域中重要的研究课题之一。目前,有监督词义消歧方法已经是解决该问题的有效手段。但是,由于缺乏大规模的训练语料,有监督方法还不能取得满意的效果。该文提出一种基于语言模型的词义消歧优化模型,该模型采用语言模型优化传统的有监督消歧模型,充分利用有监督和语言模型两种模型的消歧优势,共同推导歧义词的词义。该模型可以在训练语料不足的情况下,有效的提高词义消歧效果。在真实数据上表明,该方法的消歧性能超过了参加SemEval-2007:task #5评测任务的最好的有监督词义消歧系统。     

等效剪切刚度的框架结构损伤识别方法

为研究有别于现有侧重于研究结构具体构件(梁、柱及支撑等)的建筑结构损伤识别方法,提出应直接从整体开展框架结构损伤识别方法.将框架结构转换成"等效剪切型"结构,数值分析表明,结构具体构件出现损伤(即刚度发生退化)时,会引起受影响层的等效剪切刚度发生变化,因此,可以采用层等效剪切刚度变化率与层抗侧刚度作为指标来进行结构损伤定位与损伤程度评定.通过大量的框架结构数值模拟计算,归纳出了一套能将损伤定位到结构层及从整体来评定结构损伤程度的方法.     

OFDM快速频偏估计方法

提出一种基于前导符号为L等分结构的OFDM快速频偏估计的新方法,该方法只需一个OFDM符号作为前导符号,数据开销较小.接收端先用相关求相角的方法完成细频偏估计,再对经过细频偏补偿后的前导数据进行符号重构,然后对重构符号采用L点FFT运算,通过寻找频域信号峰值,对剩余频偏进行估计,从而完成粗频偏估计.为进一步降低系统的计算开销,采用了多级判决方式.计算机仿真表明,该方案具有计算复杂度低,估计性能较高的优点.     

Synchronization and channel estimation for MIMO OFDM wireless LAN systems

A new preamble structure is designed for wireless LAN based on MIMO OFDM systems, which can be used for both synchronization and channel estimation. Modulatable orthogonal polyphase sequence is utilized in training symbol design regarding its correlation properties. The time synchronization and channel estimation are achieved by measuring the correlation between the received training sequence and the locally generated training sequence. Repeated training symbols are used to get carrier frequency offset (CFO) estimation. It is shown from the analysis that the accuracy of frequency synchronization is close to the Cramér-Rao lower bound. The training sequences are optimal for channel estimation based on the minimum mean square error (MMSE).     

基于邻域风险最小化概率密度估计的自适应盲分离算法*

为实现由不同统计特性和概率分布平滑特性信号得到混合信号的盲分离,对基于支持向量机的邻域风险最小化概率密度估计算法进行研究,提出一种邻域函数的构造方法,将其与自然梯度批处理算法相结合,形成一种新的自适应盲分离算法;利用广义高斯模型分析了分离算法的精确度。通过仿真实验,验证了该算法能分离统计特性不同的混合信号,相比于基于经验风险最小化的方法,该方法在收敛速度和精度方面的性能有很大提高。     

基于模糊神经网络的大场景人群密度估计方法 *

提出了一种估计大场景下密集人群密度的方法。该方法根据人类视觉的模糊性原理,认为用模糊集来划分人群密度范围比用确定的方法更符合人眼视知觉的认知方式,利用统计的方法确定灰度共生矩阵各指标对于各个密度类别的隶属函数;设计基于误差反向传播训练算法（BP）的模糊神经网络,计算样本模式对于各个密度类别的隶属度,并根据人群密度变化的时域连续性原理对人群密度范围进行合理估计。实验表明该方法提高了估计精度。     

一种光纤陀螺随机漂移的高精度建模方法

为减小光纤陀螺随机漂移,采用时间序列分析法对其进行ARMA模型辨识.提出一种全局最优的模型阶次搜索算法,将模型适用性检验方法中的BIC(Bayesian information criterion)用于模型阶次搜索,并采用Pandit-Wu建模思想,把二维搜索化为一维搜索,得到了模型阶次的一致性估计.提出了一种改进的U-C算法,并与长自回归模型计算残差法相结合共同估计模型参数.它将非线性参数估计过程转化为线性过程,使用了正置与逆置漂移时序参与估计,以前向和后向模型的滤波误差平方和最小为参数估计的指标,在p+1维空间中求极小值.采用上述方法确定的模型其残差标准差为0.002 4°,最大预报误差为0.011 2°,能准确预报光纤陀螺随机漂移趋势.     

矿用大容量磷酸铁锂电池管理系统设计

为了保证矿用磷酸铁锂电池的安全性,结合煤矿工业实际需求,设计了8节单体60A·h磷酸铁锂电池串联、24V额定电压输出的矿用电池管理系统。该系统将微处理器配合集成芯片LTC6803组成电池保护模块,利用安时法估算电池组电量,采用电阻分流型均衡技术改善电池组性能。现场应用结果表明,该系统运行稳定可靠,保证了矿用磷酸铁锂电池的安全高效性。