基于Kalman滤波的无源动态定位算法的研究

目前已经能够研制出测量用户至卫星伪距信号的“北斗”用户接收机，此款接收机可以工作在无源定位的情况下．由于“北斗”定位卫星数量极其有限，接收机在动态定位的精度上会出现很大的误差．文中针对这一问题进行讨论，采用“当前”统计模型来建立用户载体的状态方程，并且提出一种新的量测方程，然后将构建的Kalman滤波方程组运用于接收机的动态定位中，仿真结果表明，这种算法能很好地改善定位的精度及动态性能．     

小波域中值滤波的图像去噪方法

提出了小波域中值滤波的去噪方法,在去除高斯白噪声的同时能有效地去除脉冲噪声.此方法是在小波域内对高频子带进行中值滤波,然后选择相应的门限进行降噪处理.仿真实验表明该算法不仅能滤除图像中的脉冲噪声和高斯噪声,而且能较好的保留图像的边缘细节,其滤波效果优于小波软阀值去噪.     

基于小波和Kalman滤波的交叉口流量组合预测

为更准确地预测动态变化的道路交叉口流量，根据城市道路流量分布的特点，提出了在小波分析和离散Kalman滤波的基础上进行组合预测的方法．这种方法将路口的流量数据构造成几个相关序列，对每个序列分别进行小波变换和Kalman预测，然后将各序列的预测值组合成最终结果．这种预测方法可用于其他动态流量预测，如路段流量预测、路口流量分配预测等．实验表明，该方法可以有效地减少预测误差，并具有较好的鲁棒性。     

Gαq/11在哇巴因信号转导中的意义

目的观察不同浓度哇巴因对SD大鼠胸主动脉平滑肌细胞Gαq/11表达的影响,并探讨其在哇巴因信号转导中的作用。方法采用贴块法原代培养SD大鼠胸主动脉平滑肌细胞,进行传代和纯化。分别给予生理浓度以及病理浓度的哇巴因干预48 h,免疫细胞化学染色观察平滑肌细胞Gαq/11蛋白的表达。结果生理浓度哇巴因干预后,平滑肌细胞有增殖现象;而病理浓度组细胞发生凋亡。生理浓度组Gαq/11表达(111.21±30.47)显著高于病理浓度组(68.70±6.21)以及对照组(58.81±4.51,P<0.01);病理浓度组和对照组相比无显著变化。结论①Gαq/11介导了生理浓度哇巴因引起细胞增殖的生物学效应;②病理浓度哇巴因引起细胞凋亡的生物学效应与Gαq/11无关;③哇巴因在不同浓度下,可能通过不同的信号转导通路产生相应的药理学作用或生物学效应。     

一种基于数学形态学的灰度图象边缘检测方法

本文用数学形态学的理论，提出了一种新的灰度图像边缘检测算法。并通过选取合适的结构元素，在微机图象处理系统上，用软件的方法加以具体实现。结果表明：这种方法算法简单，运算速度快，效果好。     

智能化多功能声级计

本文介绍了以mcs—51为核心组成的声级计基本原理,并详细介绍了计权网络、倍频程滤波、有效值变换、数据采集电路以及系统软件设计。本仪器测量精度高,测量范围宽,抗扰性好,一机多用。     

Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo

This study considers advanced statistical approaches for sequential data assimilation. These are explored in the context of nowcasting and forecasting using nonlinear differential equation based marine ecosystem models assimilating sparse and noisy non-Gaussian multivariate observations. The statistical framework uses a state space model with the goal of estimating the time evolving probability distribution of the ecosystem state. Assimilation of observations relies on stochastic dynamic prediction and Bayesian principles. In this study, a new sequential data assimilation approach is introduced based on Markov Chain Monte Carlo (MCMC). The ecosystem state is represented by an ensemble, or sample, from which distributional properties, or summary statistical measures, can be derived. The Metropolis-Hastings based MCMC approach is compared and contrasted with two other sequential data assimilation approaches: sequential importance resampling, and the (approximate) ensemble Kalman filter (including computational comparisons). A simple illustrative application is provided based on a 0-D nonlinear plankton ecosystem model with multivariate non-Gaussian observations of the ecosystem state from a coastal ocean observatory. The MCMC approach is shown to be straightforward to implement and to effectively characterize the non-Gaussian ecosystem state in both nowcast and forecast experiments. Results are reported which illustrate how non-Gaussian information originates, and how it can be used to characterize ecosystem properties.     

Multigrid state vector for data assimilation in a two-way nested model of the Ligurian Sea

A system of two nested models composed by a coarse resolution model of the Mediterranean Sea, an intermediate resolution model of the Provençal Basin and a high resolution model of the Ligurian Sea is coupled with a Kalman-filter based assimilation method. The state vector for the data assimilation is composed by the temperature, salinity and elevation of the three models. The forecast error is estimated by an ensemble run of 200 members by perturbing initial condition and atmospheric forcings. The 50 dominant empirical orthogonal functions (EOF) are taken as the error covariance of the model forecast. This error covariance is assumed to be constant in time. Sea surface temperature (SST) and sea surface height (SSH) are assimilated in this system.     

Data assimilation into a Princeton Ocean Model of the Mediterranean Sea using advanced Kalman filters

This study investigates the effectiveness of the Singular Evolutive Extended Kalman filter (SEEK) and its variants (SEIK and SFEK filters) for data assimilation into a Princeton Ocean Model (POM) of the Mediterranean Sea. The SEEK filters are sub-optimal Kalman filters based on the approximation of the filter's error covariance matrices by singular low-rank matrices, reducing in this way extensive computational burden. At the initialization, the filters error covariance matrix is parameterized by a set of multivariate empirical orthogonal functions (EOFs) which describe the dominant modes of the system's variability. The Mediterranean model is implemented on a 1/4° × 1/4° horizontal grid with 25 sigma levels and is forced with 6-hour ECMWF re-analysis atmospheric data. Several twin experiments, in which pseudo-observations of altimetric data and/or data profiles were assimilated, were first performed to evaluate the filters performances and to study their sensitivities to different parameters and setups. The results of these experiments were very encouraging and helped in setting up an effective configuration for the assimilation of real data in near-real time situation. In the hindcast experiments, Topex/Poseidon and ERS weekly sea level anomaly data were first assimilated during 1993 and the filters solution was evaluated against independent Reynolds sea surface temperature (SST) analysis. The assimilation system was able to significantly enhance the consistency between the model and the assimilated data, although the improvement with respect to independent SST data was significantly less pronounced. The model SST was only improved after including SST data in the assimilation system.     

平板在组合载荷作用下的极限强度预报的一种简化解析法

船体平板强度在船舶结构极限强度分析中起到十分重要的作用。最近几年，一些作者提出了计算平板极限强度的一种简化解析方法。但绝大部分的研究只考虑了平板纵向受压这一种简单载荷的情况。在我们成功地解决了三向组合载荷(纵、横压缩和垂向均布压力)的基础上，在本文中，我们又进一步将此方法推广到包含面内剪力在内的所有组合载荷分量均存在的一般情况。通过与一些规范公式的比较表明，本文所推导的公式是可以比较精确地预报平板在一般组合载荷作用下的极限强度。这一工作一方面可以为规范中的一些经验公式提供理论依据，另一方面也许可以提供比经验公式更好的外插能力。为了简化使用本文的方法，本文也在大量参数系列计算的基础上给出了一个回归的经验公式。