Sediment Concentration and Its Prediction by Perceptron Kalman Filtering Procedure

Predictions of the discharge and the associated sediment concentration are very useful ingredients in any water resources reservoir design, planning, maintenance, and operation. Although there are many empirical relationships between the discharge and sediment concentration amounts, they need estimation of model parameters. Generally, parameter estimations are achieved through the regression method (RM), which has several restrictive assumptions. Such models are locally valid and their structures and parameter values are questionable from region to others. This paper proposes a new approach for sediment concentration prediction provided that there are measurements of discharge and sediment concentration. The basis of the methodology is a dynamic transitional model between successive time instances based on two variables, namely, discharge and sediment concentration measurements. The transition matrix elements are estimated from the measurements through a special form of the artificial neural networks as perceptrons. The sediment concentration predictions from discharge measurements are achieved through a perceptron Kalman filtering (PKF) technique. In the meantime, this technique also provides temporal predictions. A certain portion of the measurement sequence is employed for the model parameter estimations through training and the remaining part is used for the model verification. Detailed comparisons between RM and PKF approaches are presented and, finally, it is shown that the latter model works dynamically by simulating the observation scatter diagram in the best possible manner with smaller prediction errors. The application of the methodology is performed for the discharge and sediment concentration measurements obtained from the Mississippi River basin at St. Louis, Missouri. It is found that the PKF methodology has smaller average relative, root-mean-square, and absolute errors than RM. Furthermore, graphical representation, such as the scatter and frequency diagrams, indicated that the PKF approach has superiority over the RM.     

Observer-based monitoring of thermal runaway in microwaves food defrosting

Despite its energetic interest, the use of microwaves for the thawing or temperage of food products presents a major drawback called thermal runaway. This is mainly due to the important gap between the dielectric properties of water in solid and liquid phases. A monitoring approach of the temperatures inside a foodstuff during microwave defrosting is therefore proposed to, in a first step, detect the critical phenomena of thermal runaway, and, in further work, to control it. An observer allowing to estimate the internal temperatures from the temperature of the surface has been developed. An Extended Kalman Filter, because of its simplicity of design, has been chosen, but this choice is not exclusive. The heat transfer with phase change and microwave effects is represented by a nonlinear distributed parameters system, and the observer is based on a standard finite volume scheme. The approach is experimentally carried out and validated during the defrosting of a block of tylose.     

Bayesian and Dempster-Shafer fusion

The Kalman Filter is traditionally viewed as a prediction-correction filtering algorithm. In this work we show that it can be viewed as a Bayesian fusion algorithm and derive it using Bayesian arguments. We begin with an outline of Bayes theory, using it to discuss well-known quantities such as priors, likelihood and posteriors, and we provide the basic Bayesian fusion equation. We derive the Kalman Filter from this equation using a novel method to evaluate the Chapman-Kolmogorov prediction integral. We then use the theory to fuse data from multiple sensors. Vying with this approach is the Dempster-Shafer theory, which deals with measures of “belief”, and is based on the nonclassical idea of “mass” as opposed to probability. Although these two measures look very similar, there are some differences. We point them out through outlining the ideas of the Dempster-Shafer theory and presenting the basic Dempster-Shafer fusion equation. Finally we compare the two methods, and discuss the relative merits and demerits using an illustrative example.     

被动累积~(222)Rn-~(222)Rn探测器

本文采用 2只扩散杯 ,一只选择渗透率较大的滤膜 ,使2 2 2 Rn和2 2 0 Rn能很容易的进入。另一只选择渗透率较小的滤膜 ,该滤膜只允许2 2 2 Rn进入 ,半衰期较短的2 2 0 Rn被阻挡在外面。通过不同滤膜扩散率的差异 ,改变探测器的空气交换率 ,从而达到同时测量2 2 2 Rn和2 2 0 Rn的目的     

Accurate estimation of environment parameters from ultrasonic data

This paper describes a robust and accurate ultrasonic sensing system for a mobile robot. The system continuously updates a local map of the environment in which obstacles are represented by straight lines or points in a robot centered coordinate frame. The presented algorithms use a Kalman filter for the reduction of the noise in the ultrasonic data and use a systematical error correction (‘bundle correction’) to reduce the uncertainty in obstacle direction. Experiments are carried out in simulation and with a real mobile robot system. Results show that the accuracy with which line parameters can be estimated is in the order of 1 degree for the orientation and about 2 cm for the position. The effect of the bundle correction is significant and maximal when the robot approaches walls under a small angle.     

Conditions for stability of the extended Kalman filter and their application to the frequency tracking problem

The error dynamics of the extended Kalman filter (EKF), employed as an observer for a general nonlinear, stochastic discrete time system, are analyzed. Sufficient conditions for the boundedness of the errors of the EKF are determined. An expression for the bound on the errors is given in terms of the size of the nonlinearities of the system and the error covariance matrices used in the design of the EKF. The results are applied to the design of a stable EKF frequency tracker for a signal with time-varying frequency.This research was supported by the Co-operative Research Centre for Robust and Adaptive Systems ((CR)² ASys). The authors wish to acknowledge the funding of the activities of (CR)² ASys by the Australian Commonwealth Government under the Co-operative Research Centre Program.     

复合轴控制系统应用研究

复合轴控制是提高光电跟踪系统精度和控制带宽的一种有效手段，尤其在光束定向器系统中，不仅要求系统要稳定地跟踪上动态目标，而且必须将光束涣定在目标的某一点上，这对控制系统的要求是非常高的，必须采用双或三复合轴控制方式。     

四方向上的任意角度扇形数字滤波器的设计

具有任意角度（扇形所围成的角度）和任意方向（扇形所取方向）的扇形数字滤波器在图像数据压缩和地质、地震等方面的数据处理中有着广泛的应用。目前，大多数的工作多集中于水平及垂直方向的９０°扇形数字滤波器的设计，因而有必要研究任意方向上的任意角度的扇形数字滤波器的设计。本文介绍利用ＭｃＣｌｅｌｌａｎ交换法设计具有垂直，水平及±４５°方向上的任意角度的扇形数字滤波器的设计方法，垂直水平方向上的扇形滤波器的设     

Recursive solution of inverse natural convection problems by means of mode reduction

 A recursive method based on the Kalman filtering is developed to solve inverse natural convection problems of estimating the unsteady nonuniform wall heat flux from temperature measurements in the flow. By employing the Karhunen–Loève Galerkin procedure that reduces the Boussinesq equation to a small set of ordinary differential equations, the computational difficulties associated with the Kalman filtering for the partial differential equations are overcome. The present method is assessed through several numerical experiments, and is found to yield satisfactory results. Received 20 January 2001 / Accepted 31 May 2001     

A novel approach to detecting DDoS Attacks at an Early Stage

Distributed Denial-of-Service (DDoS) attacks pose a serious threat to Internet security. Most current research focuses on detection and prevention methods on the victim server or source side. To date, there has been no work on defenses using valuable information from the innocent client whose IP has been used in attacking packets. In this paper, we propose a novel cooperative system for producing warning of a DDoS attack. The system consists of a client detector and a server detector. The client detector is placed on the innocent client side and uses a Bloom filter-based detection scheme to generate accurate detection results yet consumes minimal storage and computational resources. The server detector can actively assist the warning process by sending requests to innocent hosts. Simulation results show that the cooperative technique presented in this paper can yield accurate DDoS alarms at an early stage. We theoretically show the false alarm probability of the detection scheme, which is insensitive to false alarms when using specially designed evaluation functions. This work is partially supported by HK Polyu ICRG A-PF86 and CERG Polyu 5196/04E, and by the National Natural Science Foundation of China under Grant No. 90104005.