Optimal and self‐tuning fusion Kalman filters for discrete‐time stochastic singular systems

Based on the optimal fusion estimation algorithm weighted by scalars in the linear minimum variance sense, a distributed optimal fusion Kalman filter weighted by scalars is presented for discrete‐time stochastic singular systems with multiple sensors and correlated noises. A cross‐covariance matrix of filtering errors between any two sensors is derived. When the noise statistical information is unknown, a distributed identification approach is presented based on correlation functions and the weighted average method. Further, a distributed self‐tuning fusion filter is given, which includes two stage fusions where the first‐stage fusion is used to identify the noise covariance and the second‐stage fusion is used to obtain the fusion state filter. A simulation verifies the effectiveness of the proposed algorithm. Copyright © 2008 John Wiley & Sons, Ltd.     

Coordinating production and distribution of jobs with bundling operations

We consider an integrated scheduling and distribution model in which jobs completed by two different machines must be bundled together for delivery. The objective is to minimize the sum of the delivery cost and customers' waiting costs. Such a model not only attempts to coordinate the job schedules on both machines, but also aims to coordinate the machine schedules with the delivery plan. Polynomial-time heuristics and approximation schemes are developed for the model with only direct shipments as well as the general model with milk-run deliveries.     

WKB近似下的Fourier衍射成象方法

对于介质目标微波衍射成象，本文引入了ＷＫＢ近似来模拟目标内部总场。基于这种近似，我们导出了Ｆｏｕｒｉｅｒ衍射公式，并采用了广义滤波逆传播方法由目标空间谱实现目标特性的重建。计算机模拟结果表明采用ＷＫＢ近似重建目标特性较Ｂｏｒｎ近似有明显改善。     

Best L1 Piecewise Monotonic Data Modelling

Let us consider n data measurements of a univariate process that have been altered by random errors. We assume that an underlying model function has a substantially smaller number of turning points than the observed ones. We propose algorithms that make least the sum of the moduli of the errors by requiring k monotonic sections, alternately increasing and decreasing, in the sequence of the smoothed values. The main difficulty in this calculation is that the optimal positions of the joins of the monotonic sections have to be found automatically among so many combinations that it is impossible to test each one separately. Moreover, the calculation seems to be very intractable to general optimization techniques because O(n^k) local minima can occur. It is shown that dynamic programming can be used for separating the data into optimal disjoint sections of adjacent data, where each section requires a single L₁ monotonic calculation. This procedure is highly efficient, requiring at most O(kn²) computer operations and O(n) best L₁ monotonic calculations to subranges of data for a global minimum.     

Issues in nonlinear stochastic grey box identification

Grey box identification refers to the practice of identifying dynamical systems in model structures exploiting partial prior information. This contribution reviews a method for stochastic grey box identification and surveys experiences and lessons of applying it to a number of industrial processes. Issues to be addressed include advantages and costs of introducing stochastics into the model, the question of what contribution must be expected from the model designer as opposed to what can be formalized in computer algorithms, and an outlook on future plans to resolve present shortcomings.     

Practical method for generation expansion planning based on dynamic programming

This paper presents an efficient method which provides the optimal generation mix and the optimal generation construction process. The approximation method in which the dynamic programming technique and gradient method are combined is applied to determine the optimal generation mix with hydropower generation technologies. The successive approximations dynamic programming (SADP) technique, which is very suitable for high-dimensional multistage decision process problems, is used for obtaining the optimal generation construction process. The effectiveness and feasibility of the developed technique are demonstrated on a practical power system model which has five types of generation technologies including a hydropower generation technology.     

Extremes of random fields over arbitrary domains with application to concrete rupture stresses

To find the exact probability distribution of the global maximum or minimum of a random field within a bounded domain is a pending problem even for Gaussian fields. Except for very special examples of fields, recourse must be taken to approximate reasoning or asymptotic considerations to be judged with respect to accuracy by simulations. In this paper, the problem is addressed through a functional equation that leads to the definition of a class of distribution functions that depend solely on process or field characteristics and domain quantities that can be calculated explicitly. This distribution function class is studied for Gaussian processes in earlier works by the author and it has been obtained explicitly for Gaussian fields on rectangular domains in the plane. Simulation studies show that rather good predictions are obtained for sufficiently smooth wide band Gaussian processes and fields. In this paper, the distribution function is obtained in general for Gaussian fields over arbitrary bounded domains with piecewise continuous and differentiable boundaries, and as in earlier works the distribution function is tested against empirical distribution functions obtained by simulation of sample functions of a smooth approximately Gaussian field, herein called a broken line Hino field. For completeness this particular field type is defined in appendix a and appendix b. The paper concludes with a statistical application on data for plain concrete tensile strength.     

Thermomechanical stress fields in high-temperature fibrous composites. I: Unidirectional laminates

This two-part paper presents a closed-form procedure for evaluation of estimates of local thermomechanical stress fields in two-phase fibrous composites and laminates. The first part is concerned with a unidirectional elastic laminate subjected to uniform mechanical loads and to uniform changes in temperature. Both phases are assumed to be elastic, with temperature-dependent moduli and expansion coefficients; the solution reflects the influence of thermomechanical interactions. Exact solutions are not possible for any real system, because the local geometry is not known in detail. Instead, estimates of the fields are found from a modified Mori-Tanaka approximation. Examples are presented for two SiC/Ti---Al---Nb composites. Local stresses of interest are found after cooling from fabrication to room temperature. The presence of local yielding, and the influence of coupling terms on the local stress magnitudes are examined. Extension of the results to laminated plates is presented in Part II (Dvorak, G.J., Chen, T. & Teply, J., Composites Science and Technology, 43 (1992) 359–368, this issue).     

Convergence of iterated boolean sums of simultaneous approximants

Explicit error estimates are given for the iterated Boolean sum of a sequence of simultaneous approximants; the rate of convergence is shown to be improved for smooth functions. The general results are applied in the case of the Bernstein, Durrmeyer and Stancu operators.     

局域网服务器缓冲区容量的Petri网设计方法

本文在分析局域网服务器信息缓冲区、数据包接受和发送缓冲区的相互关系基础上提出一种新的局域网服务器缓冲区设计方法─—广义随机Ｐｅｔｒｉ网方法，并给出了局域网服务器信息缓冲区的广义随机Ｐｅｔｒｉ网模型。该设计方法理论完备、严谨实用，且不再局限于追求某一边界值（上限或下限），并且避免了排队理论处理多级服务的繁琐公式。最后本文用一例题说明了这种方法的应用。