基于改进HMM的文本信息抽取模型

提出一种基于改进隐马尔可夫模型(HMM)的文本信息抽取模型。给出一个新假设,使用绝对平滑算法对模型参数进行平滑,利用Viterbi算法对观察值序列进行正序和逆序解码,基于N-Gram模型对2次解码结果进行对比消歧,得到较准确的状态序列。实验结果表 明,该信息抽取模型能提高信息抽取的准确率。     

在离散观测和反馈延迟下的混杂随机系统镇定

对混杂随机系统的状态反馈控制近年来引起了广泛的关注. 一个更现实也更经济的情况是对状态的观测 不是连续时间而是离散时间的. 同时, 现实中绝大多数的观测和反馈系统都或多或少会存在时滞现象. 因此, 讨论 这种基于离散时间观测同时带有观测反馈时滞的混杂随机系统的反馈控制是很有意义的. 特别地, 通过使用一个 李雅普诺夫泛函, 不仅可以得到H无穷稳定、渐近稳定和指数稳定, 而且还能显著地改善对时滞上界的要求. 本文是 文献[20]工作的深入和推广.     

Observers and output feedback stabilising controllers for nonlinear strict-feedback systems with sampled observation

The design of observers and output feedback stabilising controllers for continuous-time strict-feedback systems with sampled observation is considered. First two types of observers are designed. One is a discrete-time semiglobal and practical reduced-order observer for the exact model and the other is a continuous-time semiglobal and practical full-order observer for continuous-time strict feedback systems with sampled observation. Then by combining the designed continuous-time observers and continuous-time state feedback laws that are continuous, zero at the origin, and uniformly globally asymptotically stabilise continuous-time systems, output feedback semiglobally practically uniformly asymptotically stabilising controllers are constructed. Numerical examples are given to illustrate the proposed design of observers and output feedback controllers.     

基于NetCDF的数据交换平台研究

针对异构海洋观测数据交互困难的问题,提出基于NetCDF的海洋观测数据交换格式,定义海洋观测数据交互规范,实现海洋观测数据灵活装载和随机访问。基于此,设计海洋观测数据交换平台框架,通过分层架构模式实现海洋观测数据用户"一站式"访问。最后,开发原型系统加以验证。     

Point Light Source Estimation from Two Images and Its Limits

In this paper, the performance of parameter estimation of a single static distant point light source from two video images is analyzed in terms of estimation theory. The illumination parameters are the intensity and direction of the light source.In the first part of this paper, estimators from the literature are reviewed. Most recent estimators evaluate as input data two video images as well as the 3D shape and the 3D motion of the visible moving objects.In the second part of the paper, the performance of these recent methods is analyzed. The input data to estimation as well as the inherent input data errors are described by a stochastic observation model. Based on this model, the performance is analyzed regarding the Cramér-Rao theoretical lower bound of estimation error variances. The bound is derived for a variety of cases of scene illumination, object motion and errors in input data. For simplification purpose, the bound is valid only for object motions with the rotation axis lying in the image plane. The analysis shows in which cases which estimation accuracy can be expected with current methods.Finally, a comparison of the bound with one of the recent estimators shows that recent estimators are suboptimal in case of errors in the 3D shape of the objects. In future work, the stochastic observation model presented in this paper can be used to improve illumination estimation.     

State Observation and Diagnosis of Discrete-Event Systems Described by Stochastic Automata

The problems ofstate observation and diagnosis are solved for discrete–eventsystems, which are described by stochastic automata. As manysystems are not observable in the sense that it is possible toreconstruct the state unambiguously, the observation problemis set up as the problem of determining the smallest possibleset of states that are compatible with the measured input andoutput sequences. The diagnostic problem is shown to be, in principle,an observation problem. Conditions for the observability anddiagnosability of stochastic automata are presented. The resultsare illustrated by examples.     

观测数据时空密度对集合卡尔曼滤波计算精度的影响

集合卡尔曼滤波（EnKF）算法在地下水数据同化领域中的应用受到了越来越广泛的关注。作为同化系统的重要组成部分,观测数据的空间/时间密度的配置直接影响滤波运算结果。本文构造了一个理想二维地下水流算例考察空间/时间密度对传统EnKF和局域化EnKF的影响。研究结果表明：随着空间密度的增大,局域化EnKF运算精度增高,而传统EnKF运算精度无此改进倾向。总体趋势上时间密度增大使EnKF运算精度增高,但对不同数目的观测井方案,这种精度增高的幅度有所变化,观测井越多,增高越不明显。由此得出结论：局域化改进EnKF能够有效同化更多的观测井数据,给出更精确的结果;模拟初期水头变化波动较大,观测数据价值较高;在一定时间密度配置下,低空间密度局域化EnKF运算精度可以接近甚至超过高空间密度配置。     

Satellite ocean color algorithms: A review of applications to the Great Lakes

We review the literature dealing with retrievals of chlorophyll concentrations in the Great Lakes from satellite observations. Most studies show that the satellite estimates of chlorophyll concentrations are linearly related to the observed concentrations, though they tend to overestimate concentrations at lower values and underestimate them at higher values. Deviations from a consistent, accurate, linear relationship can be attributed to temporal and spatial variations in the inherent optical properties of the color producing agents in the water as well as to varying concentrations of non-algal substances that interfere with the retrievals. We confirmed these results by using a simple optical model to examine the sensitivity of the retrieved chlorophyll values to the concentrations of interfering substances and to differences in model parameters. Because the spatial and temporal optical properties of the Great Lakes are unpredictable, no retrieval method is likely to produce accurate results all the time. The papers we reviewed show that simple band ratio algorithms can provide chlorophyll estimates that are proportional to in situ concentrations. The published literature suggests that the band ratio methods will be of most value in regions where the concentrations of non-algal interfering substances are minimal. Because of these limitations we recommend that future papers presenting chlorophyll analysis based on satellite data provide confirming field observations that include measurements of chlorophyll, suspended particles and dissolved organic carbon. We also recommend that Great Lakes scientists explore novel methods for retrieving chlorophyll concentrations from satellite observations that have proven useful in other optically complex waters.     

基于太阳能电源的图像采集系统

利用光伏转换与控制技术,将太阳能光伏运用于图像采集上,开发出一套基于太阳能光伏电源的图像采集系统。前级在硬件上采用BUCK型DC-DC电路,软件上提出了固定电压法结合双向扰动观察法的最大功率点跟踪算法,后级图像采集由AVR控制数字摄像头OV7620完成图像采集并传输至上位机。测试结果表明,该系统能够快速、精确、稳定地追踪到太阳能电池板的最大功率点,并且只需稍作修改便可应用于各种需要图像采集的场合。     

基于强跟踪滤波器的永磁同步电动机状态观测

针对永磁同步电动机(permanent magnet synchronous motor,PMSM)研究其在外界作用发生突变情况下的状态观测问题。首先基于理想状态下PMSM的数学模型,采用强跟踪滤波器(the strong tracking filter,STF)算法估计电动机的转速、转角、磁链和转矩,并通过仿真比较STF算法与扩展卡尔曼滤波器(extended Kalman filter,EKF)算法的估计性能,结果表明STF与EKF相比对突加给定起动以及突加扰动等因素引起的状态快速变化能够实现更快速、更准确的跟踪。最后在考虑电动机的铁耗情况下分别通过这两种方法进行状态观测,所得结果与上述结论相符,进一步表明将STF应用于实际电动机进行状态观测的可行性和优越性。