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

In this paper, a simplified model with a lower order is first developed for a nuclear steam generator system and verified against realistic environments. Based on this simplified model, a hybrid multi-input and multi-out (MIMO) control system, consisting of feedforward control (FFC) and feedback control (PEC), is designed for wide range conditions by using the genetic algorithm (GA) technique. The FFC control, obtained by the GA optimization method, injects an a priori command input into the system to achieve an optimal performance for the designed system, while the GA-based FBC control provides the necessary compensation for any disturbances or uncertainties in a real steam generator. The FBC control is an optimal design of a PI-based control system which would be more acceptable for industrial practices and nuclear power plant control system upgrades. The designed hybrid MIMO FFC/FBC control system is first applied to the simplified model and then to a more complicated model with a higher order which is used as a substitute of the real system to test the efficacy of the designed control system. Results from computer simulations show that the designed GA-based hybrid MIMO FFC/FBC control can achieve good responses and robust performances. Hence, it can be considered as a viable alternative to the current control system upgrades.

Hybrid feedforward and feedback controller design for nuclear steam generators over wide range operation using genetic algorithm

This paper investigates a method of facilitating specific absorption rate (SAR) measurements in the head when using a 1800 MHz radiation source placed in front of the face. A specific anthropomorphic mannequin (SAM) head phantom is modified by removing the rear most part to enable fully-automated scanning of the face region by a DASY4 electric-field probe. Prior to the modification, simulations were carried out in FDTD to establish the optimum area to be removed. This paper compares predicted local SAR values behind the face with actual measurements carried out using the new modified SAM phantom head. Measurements show good agreement with simulations, indicating that the modified SAM head is suitable for SAR measurements when the source is placed in front of the face.

/pdf/experimental-verification-of-a-modified-specific-1fz4a05emy.pdf

Experimental Verification of a Modified Specific Anthropomorphic Mannequin (SAM) Head used for SAR Measurements

A deaerating feedwater heater, equipped with a water level controller and a pressure controller, is used to investigate the feasibility of a reconfigurable control scheme for power plants by incorporating the concept of learning automata. The approach uses stochastic automata to learn the current operating status of the plant by dynamically monitoring the system performance and then switching to the appropriate controller on the basis of the observed performance. Simulation results based on a model of the experimental breeder reactor (EBR-II) at the Argonne National Laboratory site in Idaho are presented to demonstrate the efficacy of the scheme. The results show that it is capable of providing a sufficient margin for the net positive suction head at the feedwater pumps under loss of steam flow into the deaerator. Under similar circumstances, the existing controller in the deaerator would be incapable of maintaining the pressure and its decay rate within the safe margins, and so would oblige the plant operator to take additional measures to protect the feedwater pumps. >

Reconfigurable control of power plants using learning automata

The design of a robust controller for the deaerator of the Experimental Breeder Reactor-II (EBR-II) that uses the linear quadratic Gaussian with loop transfer recovery (LQG/LTR) procedure is described. At present, classical proportional-integral (PI) controllers are used to control the deaerator. When the operating condition changes, the system is disturbed, or a fault occurs, and the PI controllers may fail to maintain the desired performance. A robust controller that can accommodate system faults and obtain a reasonable behavior for a wide range of model uncertainty was designed. The controller provides the desired performance despite a considerable change in the operating condition, accommodates some of the failures that can occur, and provides the choice of penalizing one variable over another. The design is tested for robustness by varying the system operating conditions and simulating a steam valve failure. The set of nonlinear simulations using the modular modeling system and the advanced continuous simulation language is included. >

Robert M. Edwards

Papers

Hybrid feedforward and feedback controller design for nuclear steam generators over wide range operation using genetic algorithm

Experimental Verification of a Modified Specific Anthropomorphic Mannequin (SAM) Head used for SAR Measurements

Reconfigurable control of power plants using learning automata

Multivariable robust control of a power plant deaerator

A modal-based reduced-order model of bwr out-of-phase instabilities