Engineering Commons^™

Correlation Between Delay Time And Measured Concentration And Concentration Uncertainty By Neutron Activation Analysis, James Thomas Seman

Doctoral Dissertations

"For the last several decades, it has been apparent that new methods of identifying explosives can help investigators trace their origins. One way to identify an explosive is through the use of taggants: materials added to a product that encodes information about the product such as when it was manufactured.

This research investigates the survivability of a new identification taggant called the Nuclear Barcode that overcomes some of the downfalls that have been identified in prior taggants. The Nuclear Barcode encodes information as a unique combination of concentrations of rare earths (Ho, Eu, Sm, Lu, and Dy) and precious metals …

Detection, Identification And Localization Of R/C Electronic Devices Through Their Unintended Emissions, Vivek Thotla

Doctoral Dissertations

"The accurate and reliable detection of unintended emissions from radio receivers has a broad range of commercial and security applications. This thesis presents detection, identification, and localization methods for multiple RC electronic devices in a realistic environment. First, a Hurst parameter based detection method for super-regenerative receivers (SRR) has been used for detection. Hurst parameter based detection method exploits a self-similarity property of the SRR receiver emissions to distinguish it from background noise. Second paper presents a novel detection and localization scheme of multiple RC electronic devices called Edge-Synthetic Aperture Radar (Edge-SAR). It employs cost-effective, mobile antenna-array detectors. Two types …

State-Of-The-Art And Evolution In Public Data Sets And Competitions For System Identification, Time Series Prediction And Pattern Recognition, Joos Vandewalle, Johan Suykens, Bart De Moor, Amaury Lendasse

Engineering Management and Systems Engineering Faculty Research & Creative Works

It is the Aim of Reproducible Research to Provide Mechanisms for Objective Comparison of Methods, Algorithms, Software and Procedures in Various Research Topics. in This Paper, We Discuss the Role of Data Sets, Benchmarks and Competitions in the Fields of System Identification, Time Series Prediction, Classification, and Pattern Recognition in View of Creating an Environment of Reproducible Research. Important Elements Are the Data Sets, their Origin, and the Comparison Measures that Will Be Used to Rank the Performance of the Methods. the Issues Are Discussed, a Comparison is Made and Recommendations Are Given. © 2007 IEEE.

Detection And Identification Of Vehicles Based On Their Unintended Electromagnetic Emissions, Xiaopeng Dong, Haixiao Weng, Daryl G. Beetner, Todd H. Hubing, Donald C. Wunsch, Michael Noll, Huseyin Goksu, Benjamin Moss

Electrical and Computer Engineering Faculty Research & Creative Works

When running, vehicles with internal combustion engines radiate electromagnetic emissions that are characteristic of the vehicle. Emissions depend on the electronics, harness wiring, body type, and many other features. Since emissions are unique to each vehicle, these may be used for identification purposes. This paper investigates a procedure for detecting and identifying vehicles based on their RF emissions. Parameters like the average magnitude or standard deviation of magnitude within a frequency band were extracted from measured emission data. These parameters were used as inputs to an artificial neural network (ANN) that was trained to identify the vehicle that produced the …

Development And Implementation Of New Nonlinear Control Concepts For A Ua, Vijayakumar Janardhan, Derek Schmitz, S. N. Balakrishnan

Mechanical and Aerospace Engineering Faculty Research & Creative Works

A reconfigurable flight control method is developed to be implemented on an Unmanned Aircraft (UA), a thirty percent scale model of the Cessna 150. This paper presents the details of the UAV platform, system identification, reconfigurable controller design, development, and implementation on the UA to analyze the performance metrics. A Crossbow Inertial Measurement Unit provides the roll, pitch and yaw accelerations and rates along with the roll and pitch. The 100400 mini-air data boom from spaceage control provides the airspeed, altitude, angle of attack and the side slip angles. System identification is accomplished by commanding preprogrammed inputs to the control …

An Adaptive Neural Network Identifier For Effective Control Of A Static Compensator Connected To A Power System, Salman Mohagheghi, Jung-Wook Park, Ganesh K. Venayagamoorthy, Mariesa Crow, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

A novel method for nonlinear identification of a static compensator connected to a power system using continually online trained (COT) artificial neural networks (ANNs) is presented in this paper. The identifier is successfully trained online to track the dynamics of the power network without any need for offline data and can be used in designing an adaptive neurocontroller for a static compensator connected to such system.

An Extended Kalman Filter (Ekf) Approach On Fuzzy System Optimization Problem, Nian Zhang, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Optimizing the membership functions of a fuzzy system can be viewed as a system identification problem for a nonlinear dynamic system. Basically, we can view the optimization of fuzzy membership functions as a weighted least-squares minimization problem, where the error vector is the difference between the fuzzy system outputs and the target values for those outputs. The extended Kalman filter algorithm is a good choice to solve this system identification problem, not only because it is a derivative-based algorithm that is suitable to solve the weighted least-squares minimization problem, but also because of its appealing predictor-corrector feature for nonlinear system …

Comparison Of Mlp And Rbf Neural Networks Using Deviation Signals For On-Line Identification Of A Synchronous Generator, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper compares the performances of a multilayer perceptron network (MLPN) and a radial basis function network (RBFN) for the online identification of the nonlinear dynamics of a synchronous generator. Deviations of signals from their steady state values are used. The computational complexity required to process the data for online training, generalization and online global minimum testing are investigated by time-domain simulations. The simulation results show that, compared to the MLPN, the RBFN is simpler to implement, needs less computational memory, converges faster and global minimum convergence is achieved even when operating conditions change.

Experimental Studies With Continually Online Trained Artificial Neural Network Identifiers For Multiple Turbogenerators On The Electric Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing complexity of a modern power grid highlights the need for advanced system identification techniques for effective control of power systems. This paper provides a new method for nonlinear identification of turbogenerators in a 3-machine 6-bus power system using online trained feedforward neural networks. Each turbogenerator in the power system is equipped with a neuro-identifier, which is able to identify its particular turbogenerator and the rest of the network to which it is connected from moment to moment, based on only local measurements. Each neuro-identifier can then be used in the design of a nonlinear neurocontroller for each turbogenerator …

Implementation Of An Adaptive Neural Network Identifier For Effective Control Of Turbogenerators, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper describes an on-line identification technique for modelling a turbogenerator system. The dynamics of a single turbogenerator infinite bus system are modelled using an adaptive artificial neural network identifier (AANNI) based on continual online training (COT). This paper goes further to show that multilayered perceptrons with deviation signals as inputs and outputs trained using the standard backpropagation algorithm retain past learned information despite COT. Simulation and practical results are presented.

System Modeling And Control Of Smart Structures, Frank J. Kern, Leslie Robert Koval, K. Chandrashekhara, Vittal S. Rao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents multidisciplinary research and curriculum efforts at the University of Missouri-Rolla in the smart structures area. The primary objective of our project is to integrate research results with curriculum development for the benefit of students in electrical, and mechanical and aerospace engineering and engineering mechanics. The approach to the accomplishment of curriculum objectives is the development of a two-course sequence in the smart structures area with an integrated laboratory. The research portion of the project addresses structural identification and robust control methods for smart structures. A brief summary of the research results and a description of curriculum development …