https://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Vehicular ad hoc network (VANET) is an emerging new technology integrating ad hoc network, wireless LAN (WLAN) and cellular technology to achieve intelligent inter-vehicle communications and improve road traffic safety and efficiency. VANETs are distinguished from other kinds of ad hoc networks by their hybrid network architectures, node movement characteristics, and new application scenarios. Therefore, VANETs pose many unique networking research challenges, and the design of an efficient routing protocol for VANETs is very crucial. In this article, we discuss the research challenge of routing in VANETs and survey recent routing protocols and related mobility models for VANETs.

/pdf/routing-in-vehicular-ad-hoc-networks-a-survey-2fwcriwix4.pdf

Routing in vehicular ad hoc networks: A survey

Classical Sets and Fuzzy Sets.- Classical and Fuzzy Relations.- Membership Functions.- Defuzzification.- Fuzzy Rule-Based System.- Fuzzy Decision Making.- Applications of Fuzzy Logic.- Fuzzy Logic Projects with Matlab.

Introduction to Fuzzy Logic using MATLAB

A new control concept, state feedback assisted classical control, is described. The concept incorporates a classical output feedback control system as an easily understood inner control loop and ca...

State Feedback Assisted Classical Control: An Incremental Approach to Control Modernization of Existing and Future Nuclear Reactors and Power Plants

This paper presents a reactive location routing algorithm that uses cluster-based flooding for Vehicular Ad-hoc Networks (VANET). We compare both position-based and non-position-based routing strategies in typical urban and motorway traffic scenarios. A microscopic traffic model, developed in OPNET, is used to evaluate the performance of the Location Routing Algorithm with Cluster-Based Flooding (LORA_CBF), Ad-Hoc On-demand Distance Vector (AODV) and Dynamic Source Routing (DSR) in terms of average Route Discovery (RD) time, End-to-End Delay (EED), Routing Load, Routing Overhead, Overhead, and Delivery Ratio.

Performance evaluation of routing protocols in vehicular ad-hoc networks

A novel approach to wide-range optimal reactor temperature control using diagonal recurrent neural networks (DRNNs) with an adaptive learning rate scheme is presented. The drawback of the usual feedforward neural network (FNN) is that it is a static mapping and requires a large number of neurons and takes a long training time. The usual fixed learning rate based on an empirical trial and error scheme is slow and does not guarantee convergence. The DRNN is for dynamic mapping and requires much fewer neurons and weights, and thus converges faster than FNN. A dynamic backpropagation algorithm coupled with an adaptive learning rate guarantees even faster convergence. The DRNN controller described includes both a neurocontroller and a neuroidentifier. A reference model which incorporates an optimal control law with improved reactor temperature response is used for training of the neurocontroller and neuroidentifier. Rapid convergence of this DRNN-based control system is demonstrated when used to improve reactor temperature performance. >

Improved nuclear reactor temperature control using diagonal recurrent neural networks

The authors present the design of a robust controller using the linear quadratic Gaussian with loop transfer recovery (LQG/LTR) for nuclear reactors with the objective of maintaining a desirable performance for reactor fuel temperature and the temperature of the coolant leaving the reactor for a wide range of reactor powers The results obtained are compared to those for an observer-based state feedback optimal reactor temperature controller Sensitivity analysis of the dominant closed-loop eigenvalues and nonlinear simulation are used to demonstrate and compare the performance and robustness of the two controllers The LQG/LTR approach is systematic, methodical, and easy to design and can give improved temperature performance over a wide range of reactor operation >

/pdf/lqg-ltr-robust-control-of-nuclear-reactors-with-improved-30rfssmrfp.pdf

LQG/LTR robust control of nuclear reactors with improved temperature performance

The design and evaluation by simulation of an automatically tuned fuzzy logic controller is presented. Typically, fuzzy logic controllers are designed based on an expert's knowledge of the process. However, this approach has its limitations in the fact that the controller is hard to optimize or tune to get the desired control action. A method to automate the tuning process using a simplified Kalman filter approach is presented for the fuzzy logic controller to track a suitable reference trajectory. Here, for purposes of illustration an optimal controller's response is used as a reference trajectory to determine automatically the rules for the fuzzy logic controller. To demonstrate the robustness of this design approach, a nonlinear six-delayed neutron group plant is controlled using a fuzzy logic controller that utilizes estimated reactor temperatures from a one-delayed neutron group observer. The fuzzy logic controller displayed good stability and performance robustness characteristics for a wide range of operation.

Robert M. Edwards

Papers

State Feedback Assisted Classical Control: An Incremental Approach to Control Modernization of Existing and Future Nuclear Reactors and Power Plants

Performance evaluation of routing protocols in vehicular ad-hoc networks

Improved nuclear reactor temperature control using diagonal recurrent neural networks

LQG/LTR robust control of nuclear reactors with improved temperature performance

An automatic tuning method of a fuzzy logic controller for nuclear reactors