Mohamed Abdelrahman

Two Fundamental Representations of Localized Pulse Solutions to the Scalar Wave Equation

A study is undertaken of two fundamental representations suitable for the derivation of localized pulse (LW) solutions to the scalar wave equation. The first one uses superpositions over products of plane waves moving in opposite directions along the characteristic variables z - ct and Z + ct. This bidirectional representation, introduced in an earlier publication, has proved instrumental in advancing our understanding of Focus Wave Mode (FWM)-like pulses. The second representation, based on the Lorentz invariance of the scalar wave equation, uses products of plane waves propagating along the subluminal and superluminal boost variables. This representation is suitable for the derivation of X-wave-type solutions. Subluminal and superluminal Lorentz transformations are used to derive closed-form LW solutions to the scalar wave equation by boosting known solutions of other equations, e.g., the 2-D scalar wave equation, the Helmholtz equation and Laplaces equation. Several of these LW solutions are deduced in this manner and their properties are discussed. Of particular interest is the derivation of a novel finite energy LW solution, named the Modified Focus X-Wave pulse. It is characterized by low sidelobe levels, a desirable property for applications, e.g., in pulse echo techniques used in medical imaging.

A confidence-based approach to the self-validation, fusion and reconstruction of quasi-redundant sensor data

Often is the case in industrial applications that multiple sensors are used to measure similar quantities. These sensors may not be truly redundant in that they are not placed to measure exactly the same parameter. However, these parameters may be very well correlated. In this paper, we address three aspects of dealing with data from such quasi-redundant sensors. Specifically, we (1) employ fuzzy logic rules for self-validation and self-confidence; (2) exploit the near linear relationship between sensors for reconstructing missing or low-confidence data; and (3) fuse this data to determine a single measure and a qualitative value for its reliability. The methodology presented is illustrated on experimental temperature data from a cupola iron-melting furnace.

A Complete Multiagent Framework for Robust and Adaptable Dynamic Job Shop Scheduling

This paper presents a complete multiagent framework for dynamic job shop scheduling, with an emphasis on robustness and adaptability. It provides both a theoretical basis and some experimental justifications for such a framework: a job dispatching procedure for a completely reactive scheduling approach, combining real-time and predictive decision making. It resolves various disruptions as flexibly as dispatching rules while providing more stability. It is ready to be implemented in a distributed environment where agents have minimum global information thereby improving system fault tolerance. Computational experiments on dynamic job arrivals provide the experimental justification of the framework. First, a comparison of computational results on unpredictable job arrivals among the presented framework and commonly used dispatching rules is presented to show the effectiveness and robustness of the developed framework. Then, a comparison of the computational results among four cases of dynamic job arrivals is presented to demonstrate the effects of making full use of available uncertain information about disruptions using this framework for the enhancement of scheduling robustness.

Feedback linearization control of current and arc length in GMAW systems

In this paper we design a model based nonlinear controller for a GMAW system. The controller uses nonlinear state feedback to exactly linearize and decouple the GMAW system. The linearized system is then controlled using 2-PI controllers. The effect of parameters uncertainty over the closed loop system performance is investigated and simulations that show the system performance are presented.

Contingency selection and static security enhancement in power systems using heuristics-based genetic algorithms

This paper proposes a technique to identify critical double-line outages and enhance static security by optimal placement of FACTS devices using heuristics-based genetic algorithm (GA). Two indices have been used for contingency screening: double-line contingency index (DCI), based on line overflows, and voltage sensitivity index (VSI), based on bus voltage violations. Contingency screening is treated as the primary optimization problem with an objective of finding all double-line outages with maximal DCI and VSI. Security enhancement is treated as a constrained secondary optimization problem. The objective is to reduce transmission losses through the branches of the system and reduce or remove the overflows and voltage violations present. The voltage magnitude at each bus and the line flow through each branch for all the buses and branches have been considered as inequality and equality constraints. The technique is demonstrated using IEEE 14 bus and IEEE 30 bus systems.

Aperture synthesis of time-limited X waves and analysis of their propagation characteristics

The feasibility of exciting a localized X-wave pulse from a finite aperture is addressed. Also, the possibility of using a finite-time excitation of a dynamic aperture to generate a finite-energy approximation to an X-wave pulse is explored. The analysis is carried out by using a Gaussian time window to time limit the infinite X-wave initial excitation. Huygens’ construction is used to calculate the amplitude of the radiated wave field away from the finite-time source. The decay rate of the peak of the X wave is compared to that of a quasi-monochromatic signal. It is shown that the finite-time X-wave propagates to much farther distances without significant decay. Furthermore, the decay pattern of the radiated X-wave pulse is derived for a source consisting of an array of concentric annular sections. The decay behavior of the radiated pulse is similar to that of an X-wave launched from a finite-time aperture. This confirms the fact that time windowing the infinite energy X-wave excitation is a viable schem...

A nonlinear fuzzy assisted image reconstruction algorithm for electrical capacitance tomography.

A nonlinear method based on a Fuzzy Inference System (FIS) to improve the images obtained from Electrical Capacitance Tomography (ECT) is proposed. Estimation of the molten metal characteristic in the Lost Foam Casting (LFC) process is a novel application in the area of the tomography process. The convergence rate of iterative image reconstruction techniques is dependent on the accuracy of the first image. The possibility of the existence of metal in the first image is computed by the proposed fuzzy system. This first image is passed to an iterative image reconstruction technique to get more precise images and to speed up the convergence rate. The proposed technique is able to detect the position of the metal on the periphery of the imaging area by using just eight capacitive sensors. The final results demonstrate the advantage of using the FIS compared to the performance of the iterative back projection image reconstruction technique.

Integration of multiple sensor fusion in controller design

In this paper we present a methodology for integrating multiple sensor fusion into the controller design. The sensor fusion algorithm produces in addition to the estimate of the measurand, a parameter that measures the confidence in the estimated value. This confidence is integrated as a parameter into the controller to produce fast system response when the confidence in the estimate is high, and a slow response when the confidence in the estimate is low. Conditions for the stability of the system with the developed controller are discussed. This methodology is demonstrated on a cupola furnace model. The simulations illustrate the advantages of the new methodology.

An adaptive neuro fuzzy power system stabilizer for damping inter-area oscillations in power systems

An adaptive neuro-fuzzy inference system (ANFIS) based PSS is proposed in this paper. The controller is essentially divided into two sub-systems, a recursive least square identifier for the generator and an adaptive neuro fuzzy PSS to damp the oscillations. The PSS is coupled to a single machine in every area and the parameters of this PSS are tuned online in order to minimize a cost function. The cost function consists of a summation of terms, in which each term is made up of the square of the difference in speed between the machine to which the PSS is connected and another machine in that same area (the number of terms equal the number of machines in that area excluding the machine installed with a PSS). The PSS is trained to reduce the speed difference between machines in every area while helping to reduce inter area oscillations. The proposed technique is illustrated on a 2 area 4-machine 13 bus system. This ANFIS PSS showed satisfactory performance under severe faulting conditions, where a three-phase fault applied to a line, was cleared after a extended period of time. The conventional PSS and the ANFIS using the original cost function (consisting of just the square of the speed difference of the generator installed with the PSS) failed to perform under such conditions.

Experimental control of a cupola furnace

We present some final results from a research project focused on introducing automatic control to the operation of cupola iron furnaces. The main aim of this research is to improve the operational efficiency and performance of the cupola furnace. Experimental data are used to calibrate the model, which is taken as a first-order multivariable system with time delay. Then relative gain analysis is used to select loop pairings to be used in a multiloop controller. The resulting controller pairs melt-rate with blast volume, iron temperature with oxygen addition, and carbon composition with metal-to-coke ratio. Special (nonlinear) filters are used to compute the melt-rate from actual scale readings of the amount of iron produced and to smooth the temperature measurement. The temperature and melt-rate loops use single-loop PI control. The composition loop uses a Smith predictor to discount the deadtime associated with mass transport through the furnace. Experiments results validate the conceptual controller design.