ACF的结构及其滤材特性的研究

根据活性炭纤维与颗粒状活性炭的平行实验，对比了ＡＣＦ与ＧＡＣ的物化特性和基本结构，以说明ＡＣＦ作为新型环境功能材料的特点，在实验室建立的流动法测定材料吸附特性的系统中，对ＡＣＦ在不同流量下对不同上游浓度污染气体（ＮＨ３）的吸附性能进行了测定，获得了ＡＣＦ的穿透曲线，对其吸附性能与吸附床流量和上游浓度进行了相关分析，并对以ＡＣＦ为滤料的空气过滤器的除污性能进行了测试。     

提高GPS定位精度的群调度滤波算法*

为解决现行安全生产科技领域中全球定位系统（GPS）/惯性导航系统（INS）融合系统容易产生较大时变误差的关键问题,满足GPS定位信号的精度和信号数据处理时间的要求,提出1种群调度滤波算法,通过实验仿真,分别与模糊算法、机器学习算法及卡尔曼滤波网络算法进行比较研究。结果表明:群调度滤波算法兼顾了定位精度与数据处理时间,具有较大优势,可在航空安全、船舶安全、应急监测、灾后抢险等安全科技领域广泛应用。     

脉冲袋式除尘器过滤风速的确定

过滤风速的选择是设计布袋除尘器的关键。本文在对工程应用中的脉冲袋式除尘器相关参数测定后,采用数学拟合法,得出了过滤风速与烟粉尘粒径的对数成正比、与废气含尘浓度和废气温度成反比的数学关系,并建立了具体的数学模型。     

Estimating marine biogeochemical rates of the carbonate pH system—A Kalman filter tested

Oxygen (O₂), nitrate (NO₃), dissolved inorganic carbon (DIC) or pCO₂, and pH or total alkalinity (TA), are useful indices of marine chemical, physical and biological processes operating on varying time-scales. Although these properties are increasingly being monitored at high frequency, they have not been extensively used for studying ecosystem dynamics. We test whether we can estimate time-evolving biogeochemical rates (e.g. primary production, respiration, calcification and carbonate dissolution, and nitrification) from synthetic high frequency time-series of O₂, NO₃, DIC, pCO₂, TA or pH. More specifically, a Kalman filter has been implemented in a very simplified biogeochemical model describing the dynamics of O₂, NO₃, DIC and TA and linking the concentration data to biogeochemical fluxes. Different sets of concentration data are assimilated and biogeochemical rates are estimated. The frequency of assimilation required to get acceptable results is investigated and is compared with the frequency of sampling in the field or in controlled experimental settings.Smoothing of the data to remove data noise before assimilation improves the estimation of the biogeochemical rates. The best estimated rates are obtained when assimilating O₂, NO₃ and TA although the assimilation of DIC instead of TA also gives satisfactory results. In case pH or pCO₂ is assimilated rather than DIC or TA, the linearization of the (now nonlinear) observation equation introduces perturbations and the Kalman filter behaves suboptimal. We conclude that, given the resolution of data required, the tool has potential to estimate biogeochemical rates of the carbonate system under controlled settings.     

KALMAN FILTER CALCULATION OF SAMPLING FREQUENCY WHEN DETERMINING ANNUAL MEAN SOLUTE CONCENTRATIONS

ABSTRACT: A linear filter (Kalman filter) technique was used with a Streamflow-concentration model the minimize surface water quality sampling frequencies when determining annual mean solute concentrations with a predetermined allowable error. The Kalman filter technique used the stream discharge interval as a replacement for the more commonly used time interval. Using filter computations, the measurement error variance was minimized within the sample size constraints. The Kalman filter application proposed here is applicable only under several conditions including: monitoring is solely to estimate annual mean concentration; discharge measurement errors are negligible; the Streamflow-concentration model is valid; and monthly samples reflect the total variance of the solute in question.     

SHORT-TERM FORECASTING OF SNOWMELT RUNOFF USING ARMAX MODELS1

ABSTRACT: Time series models of the ARMAX class were investigated for use in forecasting daily riverflow resulting from combined snowmelt/rainfall. The Snowmelt Runoff Model (Martinec-Rango Model) is shown to have a form similar to the ARMAX model. The advantage of the ARMAX approach is that analytical model identification and parameter estimation techniques are available. In addition, previous forecast errors can be included to improve forecasts and confidence limits can be estimated for the forecasts. Diagnostic checks are available to determine if the model is performing properly. Finally, Kalman filtering can be used to allow the model parameters to vary continuously to reflect changing basin runoff conditions. The above advantages result in improved flow forecasts with fewer model parameters.     

Kalman最优平滑器在滑坡位移监测数据处理中的应用

针对传统方法中利用Kalman滤波器来处理物体的形变数据存在精度低的缺点,依据Kalman平滑比滤波的精度要高的原理,提出了将滑坡形变的估计问题视为Kalman平滑问题,而不仅仅是Kalman滤波问题;利用Kalman最优平滑器来处理滑坡位移监测数据,可使滑坡预测预报的精度得到较大提高,并在此基础上给出了一种固定区间平滑算法;最后,以龙羊峡龙西滑坡为例进行了实例分析,结果表明本文提出的方法是可行和有效的。此外,结论中针对实际运用中存在的问题提出了相应的建议。     

关联交叉口交通流模糊变权重组合预测方法

针对单一交通流预测方法存在的局限性和传统交通流组合预测模型中权重不能动态变化的问题,提出一种关联交叉口交通流模糊变权重组合预测方法。先对交叉口交通流的关联性进行分析,并给出关联交叉口的定义;再建立关联交叉口交通流模糊自适应变权重组合预测模型,该模型分别利用Kalman滤波器模型与SVM模型来预测关联交叉口交通流量,然后根据这2个模型预测的误差和交通量的变化趋势,采用模糊逻辑推理方法,对这2个预测模型分别赋予适当的权重。试验结果表明,组合预测模型的最大绝对误差、平均绝对误差和相关系数均明显好于单一的预测方法,分别为9.8%、4.63%和0.99。     

On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models

Nonlinear state-space models have been increasingly applied to study population dynamics and data assimilation in environmental sciences. State-space models can account for process error and measurement error simultaneously to correct for the bias in the estimates of system state and model parameters. However, few studies have compared the performance of different nonlinear state-space models for reconstructing the state of population dynamics from noisy time series. This study compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF) and Bayesian nonlinear state-space models (BNSSM) through simulations. Synthetic population time series were generated using the theta logistic model with known parameters, and normally distributed process and measurement errors were introduced using the Monte Carlo simulations. At higher levels of nonlinearity, the UKF and BNSSM had lower root mean square error (RMSE) than the EKF. The BNSSM performed reliably across all levels of nonlinearity, whereas increased levels of nonlinearity resulted in higher RMSE of the EKF. The Metropolis–Hastings algorithm within the Gibbs algorithm was used to fit the theta logistic model to synthetic time series to estimate model parameters. The estimated posterior distribution of the parameter θ indicated that the 95% credible intervals included the true values of θ (=0.5 and 1.5), but did not include 1.0 and 0.0. Future studies need to incorporate the adaptive Metropolis algorithm to estimate unknown model parameters for broad applications of Bayesian nonlinear state-space models in ecological studies.     

平纹滤布除尘性能的研究

从织物的结构来分析过滤的机理,推导出颗粒通过滤布的理论计算式,并用实际滤布作试验来验证该计算式的可靠性。