Inverse estimation of indoor airflow patterns using singular value decomposition

The fast pace in the development of indoor sensors and communication technologies is allowing a great amount of sensor data to be utilized in various areas of indoor air applications, such as estimating indoor airflow patterns. The development of such an inverse model and the design of a sensor system to collect appropriate data are discussed in this study. Algebraic approaches, including singular value decomposition (SVD), are evaluated as methods to inversely estimate airflow patterns given limited sensor measurements. In lieu of actual sensor data, computational fluid dynamics data are used to evaluate the accuracy of the airflow patterns estimated by the inverse models developed in this study. It was found that the airflow patterns estimated by the linear inverse SVD model were as accurate as those estimated by the nonlinear inverse-multizone model. For the zones tested, sensor measurements along on the walls and near the inlet and outlet provided the greatest improvement in the accuracy of the estimated airflow patterns when compared with the results using measurements from other locations.     

Digital stochastic control of distributed-parameter systems

This paper presents a method for discrete-time control and estimation of flexible structures in the presence of actuator and sensor noise. The approach consists of complete decoupling of the modal equations and estimator dynamics based on the independent modal-space control technique and modal spatial filtering of the system output. The solution for the Kalman filter gains reduces to that of independent second-order modal estimators, thus permitting real-time digital control of distributed-parameter systems in a noisy environment. The method can be used to control and estimate any number of modes without computational restraints and is theoretically free of observation spillover. Two examples, the first using nonlinear, quantized control and the second using linear, state feedback control are presented.This work was supported by the National Science Foundation, Grant No. PFR-80-20623.     

Integrated Motional Measurement for a Flexible Structure

Integrated navigation systems based on gyros and accelerometers are well established devices for vehicle guidance. The system design is traditionally based on the assumption that the vehicle is a rigid body. However, generalizing such integrated systems to flexible structures is possible. The example of the motion of a simple beam being considered here is meant to be a first approach to obtain sophisticated motional measurements of a wing of a large airplane during flight. The principle of integrated navigation systems consists of combining different measuring methods by using their specific advantages. Gyros and accelerometers are used to obtain reliable signals within a short period of time. On the other hand, aiding sensors like radar units and strain gauges are used because of their long-term accuracy. The kernel of the integrated system consists, however, of an extended Kalman filter that estimates the motion state of the structure. Besides the sensor signals, the basis for the filter is an additional kinematical model of the structure. By means of a model reduction, a kinematical model of the beam was developed. Based on simulation the paper presents this approach, the appropriate sensor set, and first estimated motion results. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

衰减信道下的决策融合问题

本文针对国际上近几年兴起的研究热点——无线传感器网络在信道衰减下的决策融合问题进行探讨。分析了已有传感器决策融合问题算法的结构,在已知信道传输错误概率的条件下,将信道无传输错误的分析方法推广到信道衰减的情况,对传输错误和融合律的关系进行了深入分析。对给定融合律的网络决策融合问题,获得了最优容错传感器观测量化器的必要条件并设计了迭代算法。在没有增加问题的计算复杂度的情况下,得到了最优观测量化器。与国际上现有结果不同,新结果不要求传感器决策条件独立,也不要求各传感器与融合中心之间的信道相互独立,具有更加广泛的使用范围。     

Adaptive Gaussian process emulators for efficient reliability analysis

This paper presents an approximation method for performing efficient reliability analysis with complex computer models. The computational cost of industrial-scale models can cause problems when performing sampling-based reliability analysis. This is due to the fact that the failure modes of the system typically occupy a small region of the performance space and thus require relatively large sample sizes to accurately estimate their characteristics. The sequential sampling method proposed in this article, combines Gaussian process-based optimisation and subset simulation. Gaussian process emulators construct a statistical approximation to the output of the original code, which is both affordable to use and has its own measure of predictive uncertainty. Subset simulation is used as an integral part of the algorithm to efficiently populate those regions of the surrogate which are likely to lead to the performance function exceeding a predefined critical threshold. The emulator itself is used to inform decisions about efficiently using the original code to augment its predictions. The iterative nature of the method ensures that an arbitrarily accurate approximation of the failure region is developed at a reasonable computational cost. The presented method is applied to an industrial model of a biodiesel filter.     

多雷达系统数据融合与航迹预测

为了便于准确定位传感器网络,分布式的单雷达系统首先进行各自的数据处理,包括:地理坐标换算至平面直角坐标;剔除孤立的异常点迹;采用模糊c-均值聚类方法和"动态分区",将单雷达数据中属于同目标的相似点迹归类集合;根据雷达观测和目标运动的特征,在每个点迹集合中设计门限滤波和相关矩阵检验,提取完整连续的目标运动的航迹;结合各航迹特征进行种类分析.接下来对属于不同雷达的航迹两两比较,找出有相交时间段的航迹,采用三次样条对两条航迹的进行内插和外推,再通过模糊综合函数对这两条航迹给出一个相似性度量,并取阈值为0.85.最后得出雷达间各航迹匹配关系.通过该雷达所观测到的航迹的稳定程度来近似估计其观察精度.首先对每一条航迹进行分段拟合得到其剩余方差,然后直接用每一条航迹的剩余方差来衡量雷达的观察精度,最后我们得出雷达的精度排序29107728,7724253720252539.对航迹融合,我们首先采用D-S证据理论并利用分析得到的雷达精度,对表示同一目标的航迹对进行融合.其次试图运用卡尔曼滤波对航迹进行融合:思路一是设法离线估计出噪声矩阵,得出系统噪声方差矩阵和观测噪声方差矩阵,从而用于标准卡尔曼滤波方程;思路二是探究较为实用的自适应滤波,兼顾Sage-Husa自适应滤波算法的高精度与强跟踪自适应滤波算法的可靠性,采用了一种混合算法给出收敛的估计.最终给出了雷达7728和2910的融合算例以及10秒钟的预测轨迹.最后,我们将导弹拦截飞机建模为三维的追逃问题,建立了运动学关系方程,最终归结为最小能量导引律问题.采用"模糊T-S线性模型"以及RH控制方法和伴随技术,在目标作对抗性机动条件下,获得了一个有效拦截的导引律.还对多雷达系统平均处理周期、数据融合系统的航迹处理周期进行了分析,对雷达网络实时性做了评价.     

Estimating the number of undetected software errors via the correlated capture–recapture model

Sometimes a complex software system fails because of errors undiscovered in the design stage of the development process. Detecting these errors early in the process would eliminate many downstream problems. The so-called “capture–recapture” model, initially used by biologists to estimate the size of wildlife populations, has been widely used to estimate the number of software design errors. However, one simplifying assumption in capture–recapture models is that the inspections performed by various inspectors are statistically independent from each other. In the paper, we propose a novel method that is based on the correlation matrix of multiple inspectors. In a numerical analysis, we show that our method outperforms other traditional models that are based on the independence assumption.     

Initial data truncation for multivariate output of discrete-event simulations using the Kalman filter

Data truncation is a commonly accepted method of dealing with initialization bias in discrete-event simulation. An algorithm for determining the appropriate initial-data truncation point for multivariate output is proposed. The technique entails averaging across independent replications and estimating a steady-state output model in a state-space framework. A Bayesian technique called Multiple Model Adaptive Estimation (MMAE) is applied to compute a time varying estimate of the output's steady-state mean vector. This MMAE implementation features the use, in paralle, of a bank of Kalman filters. Each filter is constructed under a different assumption concerning the output's steady-state mean vector. One of the filters assumes that the steady-state mean vector is accurately reflected by an estimate, called the assumed steady-state mean vector, taken from the last half of the simulation data. As the filters process the output through the effective transient, this particular filter becomes more likely (in a Bayesian sense) to be the best filter to represent the data and the MMAE mean estimator is influenced increasingly towards the assumed steady-state mean vector. The estimated truncation point is selected when a norm of the MMAE mean vector estimate is within a small tolerance of the assumed steady-state mean vector. A Monte Carlo analysis using data from simulations of open and closed queueing models is used to evaluate the technique. The evaluation criteria include the ability to construct accurate and reliable confidence regions for the mean response vector based on the truncated sequences.     

Fixed Rank Filtering for Spatio-Temporal Data

Datasets from remote-sensing platforms and sensor networks are often spatial, temporal, and very large. Processing massive amounts of data to provide current estimates of the (hidden) state from current and past data is challenging, even for the Kalman filter. A large number of spatial locations observed through time can quickly lead to an overwhelmingly high-dimensional statistical model. Dimension reduction without sacrificing complexity is our goal in this article. We demonstrate how a Spatio-Temporal Random Effects (STRE) component of a statistical model reduces the problem to one of fixed dimension with a very fast statistical solution, a methodology we call Fixed Rank Filtering (FRF). This is compared in a simulation experiment to successive, spatial-only predictions based on an analogous Spatial Random Effects (SRE) model, and the value of incorporating temporal dependence is quantified. A remote-sensing dataset of aerosol optical depth (AOD), from the Multi-angle Imaging SpectroRadiometer (MISR) instrument on the Terra satellite, is used to compare spatio-temporal FRF with spatial-only prediction. FRF achieves rapid production of optimally filtered AOD predictions, along with their prediction standard errors. In our case, over 100,000 spatio-temporal data were processed: Parameter estimation took 64.4 seconds and optimal predictions and their standard errors took 77.3 seconds to compute. Supplemental materials giving complete details on the design and analysis of a simulation experiment, the simulation code, and the MISR data used are available on-line.     

Methodology for determining the acceptability of system designs in uncertain environments

In practice, managers often wish to ascertain that a particular engineering design of a production system meets their requirements. The future environment of this design is likely to differ from the environment assumed during the design. Therefore it is crucial to find out which variations in that environment may make this design unacceptable (unfeasible). This article proposes a methodology for estimating which uncertain environmental parameters are important (so managers can become pro-active) and which combinations of parameter values (scenarios) make the design unacceptable. The proposed methodology combines simulation, bootstrapping, design of experiments, and linear regression metamodeling. This methodology is illustrated through a simulated manufacturing system, including fourteen uncertain parameters of the input distributions for the various arrival and service times. These parameters are investigated through the simulation of sixteen scenarios, selected through a two-level fractional–factorial statistical design. The resulting simulation Input/Output (I/O) data are analyzed through a first-order polynomial metamodel and bootstrapping. A second experiment with other scenarios gives some outputs that turn out to be unacceptable. In general, polynomials fitted to the simulation’s I/O data can estimate the border line (frontier) between acceptable and unacceptable environments.     

于改进UGF算法的线性拓扑WSN系统可靠性评估

提出了面向感知数据融合的通用发生函数(UGF)改进算法,并使用该算法对线性拓扑结构的无线传感网络(WSN)可靠性进行了评估。首先对PEGASIS协议下WSN的线性拓扑结构及数据传输过程进行抽象,建立了双向连续k/n:F系统模型。然后根据WSN感知数据传输及融合方式,在改进算法中重新定义了传感节点的UGF表达式和组合算子。最后对双向连续k/n:F模型进行单向化分解,根据得到的单向模型可靠性推导出双向模型的可靠性表达式。通过具体实例对提出的改进算法进行了验证,计算结果显示改进的算法可有效解决传感网络线性拓扑结构可靠性评估问题。     

Calibrating software reliability models when the test environment does not match the user environment

Software failures have become the major factor that brings the system down or causes a degradation in the quality of service. For many applications, estimating the software failure rate from a user's perspective helps the development team evaluate the reliability of the software and determine the release time properly. Traditionally, software reliability growth models are applied to system test data with the hope of estimating the software failure rate in the field. Given the aggressive nature by which the software is exercised during system test, as well as unavoidable differences between the test environment and the field environment, the resulting estimate of the failure rate will not typically reflect the user‐perceived failure rate in the field. The goal of this work is to quantify the mismatch between the system test environment and the field environment. A calibration factor is proposed to map the failure rate estimated from the system test data to the failure rate that will be observed in the field. Non‐homogeneous Poisson process models are utilized to estimate the software failure rate in both the system test phase and the field. For projects that have only system test data, use of the calibration factor provides an estimate of the field failure rate that would otherwise be unavailable. For projects that have both system test data and previous field data, the calibration factor can be explicitly evaluated and used to estimate the field failure rate of future releases as their system test data becomes available. Copyright © 2002 John Wiley & Sons, Ltd.     

Knowledge-Based Design and Simulation Environment (KBDSE): Foundational Concepts and Implementation

Research developments leading to implementation of an intelligent software environment supporting system design and simulation are presented. Knowledge-based system design and multifaceted simulation methodologies are a foundation for the system realization. The paper describes the major theoretical concepts and processes employed to develop and simulate design models. The environment implementing these concepts and methods consists of two basic components: one serves as a front end supporting the model construction processes; the other is an object-oriented, discrete-event simulator supporting evaluation of hierarchical, multi-component models. Current state of the system implementation and future work are discussed.     

Bivariate nonparametric estimation of the Pickands dependence function using Bernstein copula with kernel regression approach

In this study, a new nonparametric approach using Bernstein copula approximation is proposed to estimate Pickands dependence function. New data points obtained with Bernstein copula approximation serve to estimate the unknown Pickands dependence function. Kernel regression method is then used to derive an intrinsic estimator satisfying the convexity. Some extreme-value copula models are used to measure the performance of the estimator by a comprehensive simulation study. Also, a real-data example is illustrated. The proposed Pickands estimator provides a flexible way to have a better fit and has a better performance than the conventional estimators.     

On the Application of Accurate Distance Algorithms to Multibody Simulations

Distance algorithms are most frequently used in robotics to determine the distance between two obstacles in the environment of a robot or between a sensor point and an object. We extend the multibody simulation package MOBILE for an application of accurate algorithms for distance computation between objects represented by convex or non-convex polyhedra. These objects are represented by their vertices and oriented facets. As an application example, a multibody system is discussed where a sensor point moves close to a non-convex obstacle. The computed results show that the algorithms developed are suitable for accurate real-time multibody simulations. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

组网雷达系统中的数据融合

将数据融合技术应用于组网雷达系统的目标状态融合,研究多种条件下的融合算法,逐一建立组网雷达测量设备的精度已知和精度未知的线性模型,以及非线性融合模型,分别给出这些模型的理论分析结论和算法流程。分析过程表明理论推导的严谨性,仿真计算结果说明算法是实用的。     

Recursive state and parameter estimation with applications in water resources

Hydrologic models, as well as measurements of hydrologic processes, are corrupted by noise. The Kalman filter is a convenient tool to estimate the true but unknown state of a hydrologic system. It is, however, difficult to specify the necessary error covariances. A procedure is proposed to estimate the error covariances recursively in a combined state and parameter filter. Applications of the procedure yield meaningful results for two hydrologic data series of very different character. A major benefit of the proposed algorithm seems to be its robustness against instability.     

Stabilization of a coupled transport-diffusion system with boundary input

We consider the problem of stabilizing a coupled transport-diffusion system with boundary input. The system is described by two linear transport-diffusion equations and is not asymptotically stable. In order to stabilize the system with boundary input, sensor influence functions are assumed to be located at interior of the domain. First, we formulate the system as an evolution equation with unbounded output operators in a Hilbert space, using variable transformation. Next, we derive a reduced-order model with a finite-dimensional state variable for the infinite-dimensional system. Then, a stabilizing controller is constructed for the reduced-order model under an additional assumption. It is shown that the finite-dimensional controller together with a residual mode filter plays a role of a finite-dimensional stabilizing controller for the original infinite-dimensional system, if the order of the residual mode filter is chosen sufficiently large. Finally, the validity of the design method is demonstrated through a numerical simulation.     

The adaptive lasso in high-dimensional sparse heteroscedastic models

In this paper we study the asymptotic properties of the adaptive Lasso estimate in high-dimensional sparse linear regression models with heteroscedastic errors. It is demonstrated that model selection properties and asymptotic normality of the selected parameters remain valid but with a suboptimal asymptotic variance. A weighted adaptive Lasso estimate is introduced and investigated. In particular, it is shown that the new estimate performs consistent model selection and that linear combinations of the estimates corresponding to the non-vanishing components are asymptotically normally distributed with a smaller variance than those obtained by the “classical” adaptive Lasso. The results are illustrated in a data example and by means of a small simulation study.     

A novel approach using parallel Kalman filters in time and frequency estimation

In the present information based knowledge society, accurate knowledge of time and frequency plays a fundamental role. For many applications, real time estimates of the clocks time and frequency error are required. We have developed a novel approach for estimating time and frequency errors based on an assembly of clocks of varying quality. By using parallel Kalman filters we utilize all available measurements in the estimation. As these measurements are available with different delays, the Kalman filter produces estimates of different quality. One filtermay produce real-time estimates while another filter waits for delayed measurements. When new information becomes available, the parallel Kalman filters exchange information in order to keep the state matrices updated with the most recent information. In Kalman filtering, accurate modelling of the measurement system is fundamental. All the contributing clocks, as well as the time transfer methods, are modelled as stochastic processes. By using this methodology and by correctly modelling the contributing clocks, we obtain minimum mean square error estimators (MMSE) of the time and frequency errors of a specific clock at every epoch. In addition, we also determine the uncertainty of each time and frequency error estimate. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)