Heat-integrated reactor concepts for catalytic reforming and automotive exhaust purification

Optimal solutions in environmental catalysis require a well-coordinated development of catalysts and of process design. This contribution is devoted to energy integrated design concepts for fuel reforming and for automotive exhaust purification. The examples presented demonstrate the importance of an innovative process design for optimal utilization of existing catalysts and show the potential of future developments.

New concepts for steam reforming through the efficient coupling of the endothermic reforming reaction with an exothermic combustion reaction are discussed in the first part. These concepts have been implemented for methanol steam reforming in a counter-current reactor with distributed side feed of burner gas and for methane steam reforming in a modular reactor with a co-current reaction section for the endothermic and the combustion reaction and attached counter-current heat exchangers. Both applications employ the so-called folded sheet reactor design, which ensures an excellent heat transfer between the reforming and combustion channels and efficient heat recovery.

A similar design solution is introduced for the apparently different case of automotive exhaust purification. The proposed concept aims at decoupling exhaust after-treatment from engine control. Its main component is a counter-current heat exchanger with integrated purification stages for HC-oxidation, NO_X storage and reduction and soot filtering. A small catalytic burner at the hot end of the heat exchanger provides both heat and oxidizing or reducing agents on demand. A new soot filter design allows for safe soot filter regeneration.     

Nonlinear filtering structure for image smoothing in mixed-noise environments

This paper introduces a new nonlinear filtering structure for filtering image data that have been corrupted by both impulsive and nonimpulsive additive noise. Like other nonlinear filters, the proposed filtering structure uses order-statistic operations to remove the effects of the impulsive noise. Unlike other filters, however, nonimpulsive noise is smoothed by using a maximum a posteriori estimation criterion. The prior model for the image is a novel Markov random-field model that models image edges so that they are accurately estimated while additive Gaussian noise is smoothed. The Markov random-field-based prior is chosen such that the filter has desirable analytical and computational properties. The estimate of the signal value is obtained at the unique minimum of the a posteriori log likelihood function. This function is convex so that the output of the filter can be easily computed by using either digital or analog computational methods. The effects of the various parameters of the model will be discussed, and the choice of the predetection order statistic filter will also be examined. Example outputs under various noise conditions will be given.     

Discrete quadratic curvature energies

We present a family of discrete isometric bending models (IBMs) for triangulated surfaces in 3-space. These models are derived from an axiomatic treatment of discrete Laplace operators, using these operators to obtain linear models for discrete mean curvature from which bending energies are assembled. Under the assumption of isometric surface deformations we show that these energies are quadratic in surface positions. The corresponding linear energy gradients and constant energy Hessians constitute an efficient model for computing bending forces and their derivatives, enabling fast time-integration of cloth dynamics with a two- to three-fold net speedup over existing nonlinear methods, and near-interactive rates for Willmore smoothing of large meshes.     

Application of stochastic filtering for lifetime prediction

This paper introduces a stochastic filtering modeling approach for predicting the remaining lifetime of a component based on information on the stochastic degradation process and uncertain condition monitoring measurements. The model is illustrated by a case study, where the degradation is assumed to be a simplified fatigue crack growth process. The model accounts for uncertainties in both degradation process and condition measurements in a sound way. If completed with information on costs of monitoring, failure and replacement, such model could be used in optimizing both the condition monitoring intervals and, e.g. the replacement time for the component.     

动态信号分析系统的进展及应用

本文综述了动态信号分析仪器与系统的发展,并综合介绍了双通道动态信号分析仪的构成、性能与指标,以及由动态测量、数据采集、信号处理等硬件与相应软件组成的多通道动态信号分析系统的进展.     

On-Line Shape recognition with incremental training using binary synaptic weights algorithm

Recognition of hand drawn shapes is beneficial in drawing packages and automated sketch entry in handheld computers. In this paper, we propose a new approach to on-line geometric shape recognition with incremental training function, which utilizes a heuristic function to reduce noise and a neural network for classification and on-line training. Instead of recognizing segments of a drawing and then performing syntactical analysis to match with a predefined shape, which is weak in terms of generalization and dealing with noise, we examine the shape as a whole. The main concept of the recognition method is derived from the fact that internal angles are very important in the perceived shape of outlines. Our application's aim is to recognize elliptic, rectangular, and triangular shapes in a way similar to human cognition of these shapes. Human beings recognize such basic shapes regardless of the variations in size, noise on the shape border, rotation and in the case of triangles, regardless of the type of the triangle. The key concept is that the neural network learns the relationships between the internal angles of a shape and its classification, therefore only a few training samples which represent the class of the shape are sufficient. Fast meremental training, which is performed on-line, is accomplished by the use of the Binary Synaptic Weights algorithm, a one pass, feedforward neural network training algorithm. Incremental training offers the advantage of adjusting the recognition capability of the system to the user's drawings. the results are very successful, such that the neural network correctly classified shapes that did not have any resemblance to the shapes in the initial training set.     

A note on a local twist estimator

For the construction of composite surfaces interpolating a given curve net a new local twist estimator was proposed recently (Farin and Hagen, 1992). Some properties of this estimator—including an explicit representation for Gaussian curvature K at the knots of the curve net—are presented in this note.     

Steady-state weights solution to affine projection algorithm

A new expression of the weights update equation for the affine projection algorithm (APA) is proposed that improves the convergence rate of an adaptive filter,particularly for highly colored input sign...     

基于GPS/DR的GPS滞后时间自适应检测算法

针对GPS输出信息的随机延迟特性,基于航位推算(DR)系统惯性器件的实时性与短时高精度,提出了航向差和速度自适应GPS滞后时间检测方法,在此基础上,对GPS和DR数据进行数据对准,提出了自适应GPS滞后时间处理算法。在GPS/DR实际系统中的应用结果表明,本算法能够准确检测出GPS的滞后时间,提高车辆的定位精度。     

抗几何失真的视频Hashing算法研究

文中提出了一种基于视频灰度直方图形状的Hashing算法,能有效抵抗各种常见的几何失真和视频处理操作.算法的鲁棒性原理如下:1)由于直方图的形状与像素位置无关,故基于直方图的视频Hashing算法能有效抵抗各种常见的几何攻击;2)由于在计算Hashing前对视频帧进行了平滑预处理,故算法对加噪攻击、模糊滤波、有损压缩等处理操作有很好的鲁棒性;3)由于计算Hashing前在时间轴上进行了低通滤波预处理,故算法能抵抗帧率变化、帧丢失等时域同步攻击.实验结果表明,所提出的Hashing算法有良好的唯一性和鲁棒性能.