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Full-Text Articles in Mechanical Engineering

Integration Of Microwave And Thermographic Ndt Methods For Corrosion Detection, Dustin F. Pieper, Kristen M. Donnell, Mohammad Tayeb Ahmad Ghasr, Edward C. Kinzel Jul 2014

Integration Of Microwave And Thermographic Ndt Methods For Corrosion Detection, Dustin F. Pieper, Kristen M. Donnell, Mohammad Tayeb Ahmad Ghasr, Edward C. Kinzel

Electrical and Computer Engineering Faculty Research & Creative Works

Infrastructure health monitoring is an important issue in the transportation industry. For the case of cement-based structures in particular, detection of corrosion on reinforcing steel bars (rebar) is an ongoing problem for aging infrastructure. There have been a number of techniques that have shown promise in this area including microwave nondestructive testing (NDT) and thermography. Thermography is quite advantageous as it is an established method, and can be utilized for large inspection areas with intuitive results. Typical heat sources include induction heating and flash lamps, but these are not without drawbacks. Microwave nondestructive testing has also been successful at detecting ...


Organizing A Student Poster Session In An Asee Section Conference, Steve Eugene Watkins, Les Kinsler, Julia L. Morse, Douglas R. Carroll Jan 2014

Organizing A Student Poster Session In An Asee Section Conference, Steve Eugene Watkins, Les Kinsler, Julia L. Morse, Douglas R. Carroll

Electrical and Computer Engineering Faculty Research & Creative Works

Student poster sessions at conferences can be valuable experiences for undergraduate and graduate students and can enrich the conference program for all participants. Student poster presentations beyond the local campus can provide additional experience in professional communication (especially in preparing succinct abstracts and in effective visual design), can prepare students for future conference participations, and can facilitate student-faculty interaction. Several issues exist when including student poster sessions in engineering education conferences. How can the content of posters be related to an engineering education theme? How are communication principles of audience and purpose incorporated into the session guidelines and review process ...


Computational Modeling And Experimental Study On Optical Microresonators Using Optimal Spherical Structure For Chemical Sensing, Hanzheng Wang, Lei Yuan, Jie Huang, Xinwei Lan, Cheol-Woon Kim, Lan Jiang, Hai Xiao Sep 2013

Computational Modeling And Experimental Study On Optical Microresonators Using Optimal Spherical Structure For Chemical Sensing, Hanzheng Wang, Lei Yuan, Jie Huang, Xinwei Lan, Cheol-Woon Kim, Lan Jiang, Hai Xiao

Electrical and Computer Engineering Faculty Research & Creative Works

Chemical sensors based on optical microresonators have been demonstrated highly sensitive by monitoring the refractive index (RI) changes in the surrounding area near the resonator surface. In an optical resonator, the Whispering Gallery Modes (WGMs) with high quality (Q) factor supported by the spherical symmetric structure interacts with the contiguous background through evanescent field. Highly sensitive detection can be realized because of the long lifetime of the photons. The computational models of solid glass microspheres and hollow glass spheres with porous wall (PW-HGM) were established. These two types of microresonators were studied through simulations. The PWHGM resonator was proved as ...


Ultra-Abrupt Tapered Fiber Mach-Zehnder Interferometer Sensors, B. Li, J. Lan, W. Sumei, L. Zhou, Hai Xiao, Hai-Lung Tsai Jan 2011

Ultra-Abrupt Tapered Fiber Mach-Zehnder Interferometer Sensors, B. Li, J. Lan, W. Sumei, L. Zhou, Hai Xiao, Hai-Lung Tsai

Electrical and Computer Engineering Faculty Research & Creative Works

A fiber inline Mach-Zehnder interferometer (MZI) consisting of ultra-abrupt fiber tapers was fabricated through a new fusion-splicing method. By fusion-splicing, the taper diameter-length ratio is around 1:1, which is much greater than those (1:10) made by stretching. The proposed fabrication method is very low cost, 1/20-1/50 of those of LPFG pair MZI sensors. The fabricated MZIs are applied to measure refractive index, temperature and rotation angle changes. The temperature sensitivity of the MZI at a length of 30 mm is 0.061 nm/°C from 30-350 °C. The proposed MZI is also used to measure rotation ...


Reinforcement-Learning-Based Output-Feedback Control Of Nonstrict Nonlinear Discrete-Time Systems With Application To Engine Emission Control, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier Oct 2009

Reinforcement-Learning-Based Output-Feedback Control Of Nonstrict Nonlinear Discrete-Time Systems With Application To Engine Emission Control, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

A novel reinforcement-learning-based output adaptive neural network (NN) controller, which is also referred to as the adaptive-critic NN controller, is developed to deliver the desired tracking performance for a class of nonlinear discrete-time systems expressed in nonstrict feedback form in the presence of bounded and unknown disturbances. The adaptive-critic NN controller consists of an observer, a critic, and two action NNs. The observer estimates the states and output, and the two action NNs provide virtual and actual control inputs to the nonlinear discrete-time system. The critic approximates a certain strategic utility function, and the action NNs minimize the strategic utility ...


Reinforcement Learning Based Dual-Control Methodology For Complex Nonlinear Discrete-Time Systems With Application To Spark Engine Egr Operation, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier Aug 2008

Reinforcement Learning Based Dual-Control Methodology For Complex Nonlinear Discrete-Time Systems With Application To Spark Engine Egr Operation, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

A novel reinforcement-learning-based dual-control methodology adaptive neural network (NN) controller is developed to deliver a desired tracking performance for a class of complex feedback nonlinear discrete-time systems, which consists of a second-order nonlinear discrete-time system in nonstrict feedback form and an affine nonlinear discrete-time system, in the presence of bounded and unknown disturbances. For example, the exhaust gas recirculation (EGR) operation of a spark ignition (SI) engine is modeled by using such a complex nonlinear discrete-time system. A dual-controller approach is undertaken where primary adaptive critic NN controller is designed for the nonstrict feedback nonlinear discrete-time system whereas the secondary ...


Output Feedback Controller For Operation Of Spark Ignition Engines At Lean Conditions Using Neural Networks, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier Mar 2008

Output Feedback Controller For Operation Of Spark Ignition Engines At Lean Conditions Using Neural Networks, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

Spark ignition (SI) engines operating at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle bifurcation of heat release. Past literature suggests that operating an engine under such lean conditions can significantly reduce NO emissions by as much as 30% and improve fuel efficiency by as much as 5%-10%. At lean conditions, the heat release per engine cycle is not close to constant, as it is when these engines operate under stoichiometric conditions where the equivalence ratio is 1.0. A neural network controller employing output feedback has shown ability in simulation to reduce the nonlinear cyclic dispersion ...


Reinforcement Learning Based Output-Feedback Control Of Nonlinear Nonstrict Feedback Discrete-Time Systems With Application To Engines, Peter Shih, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier Jul 2007

Reinforcement Learning Based Output-Feedback Control Of Nonlinear Nonstrict Feedback Discrete-Time Systems With Application To Engines, Peter Shih, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

A novel reinforcement-learning based output-adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The controller includes an observer for estimating states and the outputs, critic, and two action NNs for generating virtual, and actual control inputs. The critic approximates certain strategic utility function and the action NNs are used to minimize both the strategic utility function and their outputs. All NN weights adapt online towards minimization of a performance index, utilizing gradient-descent based rule ...


Near Optimal Output-Feedback Control Of Nonlinear Discrete-Time Systems In Nonstrict Feedback Form With Application To Engines, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier Jan 2007

Near Optimal Output-Feedback Control Of Nonlinear Discrete-Time Systems In Nonstrict Feedback Form With Application To Engines, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

A novel reinforcement-learning based output-adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The controller includes an observer for estimating states and the outputs, critic, and two action NNs for generating virtual, and actual control inputs. The critic approximates certain strategic utility function and the action NNs are used to minimize both the strategic utility function and their outputs. All NN weights adapt online towards minimization of a performance index, utilizing gradient-descent based rule ...


Neural Network Controller Development And Implementation For Spark Ignition Engines With High Egr Levels, Jonathan B. Vance, Atmika Singh, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier Jan 2007

Neural Network Controller Development And Implementation For Spark Ignition Engines With High Egr Levels, Jonathan B. Vance, Atmika Singh, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier

Electrical and Computer Engineering Faculty Research & Creative Works

Past research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% -25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to operate with high EGR levels. A neural network (NN)-based output feedback controller is developed to reduce cyclic variation in the heat release under high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A ...


Optimal Control Of Class Of Non-Linear Plants Using Artificial Immune Systems: Application Of The Clonal Selection Algorithm, S. A. Panimadai Ramaswamy, Ganesh K. Venayagamoorthy, S. N. Balakrishnan Jan 2007

Optimal Control Of Class Of Non-Linear Plants Using Artificial Immune Systems: Application Of The Clonal Selection Algorithm, S. A. Panimadai Ramaswamy, Ganesh K. Venayagamoorthy, S. N. Balakrishnan

Electrical and Computer Engineering Faculty Research & Creative Works

The function of natural immune system is to protect the living organisms against invaders/pathogens. Artificial Immune System (AIS) is a computational intelligence paradigm inspired by the natural immune system. Diverse engineering problems have been solved in the recent past using AIS. Clonal selection is one of the few algorithms that belong to the family of AIS techniques. Clonal selection algorithm is the computational implementation of the clonal selection principle. The process of affinity maturation of the immune system is explicitly incorporated in this algorithm. This paper presents the application of AIS for the optimal control of a class of ...


Incorporating The Effects Of Magnetic Saturation In A Coupled-Circuit Model Of A Claw-Pole Alternator, Hua Bai, Steven Pekarek, Jerry L. Tichenor, Walter Eversman, Duane J. Buening, Gregory R. Holbrook, Ronald J. Krefta Jan 2007

Incorporating The Effects Of Magnetic Saturation In A Coupled-Circuit Model Of A Claw-Pole Alternator, Hua Bai, Steven Pekarek, Jerry L. Tichenor, Walter Eversman, Duane J. Buening, Gregory R. Holbrook, Ronald J. Krefta

Electrical and Computer Engineering Faculty Research & Creative Works

A method of representing the effects of magnetic saturation in a coupled-circuit model of a claw-pole alternator is presented. In the approach considered, the airgap flux density produced by each winding is expressed as a function of magnetic operating point. A challenge in the implementation is that the airgap flux densities consist of several significant harmonics, each of which changes at a distinct rate as iron saturates. Despite this complication, it is shown that relatively simple measurements can be used to determine model parameters. The model is implemented in the analysis of several alternator/rectifier systems using a commercial state-model-based ...


Neural Network-Based Output Feedback Controller For Lean Operation Of Spark Ignition Engines, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier, Jonathan B. Vance, Pingan He Jan 2006

Neural Network-Based Output Feedback Controller For Lean Operation Of Spark Ignition Engines, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier, Jonathan B. Vance, Pingan He

Electrical and Computer Engineering Faculty Research & Creative Works

Spark ignition (SI) engines running at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle dispersion of heat release even though such operation can significantly reduce NOx emissions and improve fuel efficiency by as much as 5-10%. A suite of neural network (NN) controller without and with reinforcement learning employing output feedback has shown ability to reduce the nonlinear cyclic dispersion observed under lean operating conditions. The neural network controllers consists of three NN: a) A NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input ...


Intelligent Strain Sensing On A Smart Composite Wing Using Extrinsic Fabry-Perot Interferometric Sensors And Neural Networks, Kakkattukuzhy M. Isaac, Donald C. Wunsch, Steve Eugene Watkins, Rohit Dua, V. M. Eller Jan 2003

Intelligent Strain Sensing On A Smart Composite Wing Using Extrinsic Fabry-Perot Interferometric Sensors And Neural Networks, Kakkattukuzhy M. Isaac, Donald C. Wunsch, Steve Eugene Watkins, Rohit Dua, V. M. Eller

Electrical and Computer Engineering Faculty Research & Creative Works

Strain prediction at various locations on a smart composite wing can provide useful information on its aerodynamic condition. The smart wing consisted of a glass/epoxy composite beam with three extrinsic Fabry-Perot interferometric (EFPI) sensors mounted at three different locations near the wing root. Strain acting on the three sensors at different air speeds and angles-of-attack were experimentally obtained in a closed circuit wind tunnel under normal conditions of operation. A function mapping the angle of attack and air speed to the strains on the three sensors was simulated using feedforward neural networks trained using a backpropagation training algorithm. This ...


Interdisciplinary Graduate Experience: Lessons Learned, Steve Eugene Watkins, V. M. Eller, Josh Corra, M. J. Molander, Bethany Konz, Richard H. Hall, K. Chandrashekhara, Abdeldjelil Belarbi Jan 2002

Interdisciplinary Graduate Experience: Lessons Learned, Steve Eugene Watkins, V. M. Eller, Josh Corra, M. J. Molander, Bethany Konz, Richard H. Hall, K. Chandrashekhara, Abdeldjelil Belarbi

Electrical and Computer Engineering Faculty Research & Creative Works

Engineers interact in the workplace with technical peers in other disciplines at all stages of design, development, and application. Awareness of the constraints and needs of the other disciplines can be key in many situations. Such interdisciplinary activity and the associated communication are facilitated if the all participants have a solid knowledge of discipline-specific terminology and an understanding of connecting concepts. Consequently, experience relating to interdisciplinary teamwork is a necessary component of engineering education. The Smart Engineering Group at the University of Missouri-Rolla was established to conduct interdisciplinary research and to create interdisciplinary educational resources. The topical interest area is ...


Adaptive Critic-Based Neural Network Controller For Uncertain Nonlinear Systems With Unknown Deadzones, Pingan He, Jagannathan Sarangapani, S. N. Balakrishnan Jan 2002

Adaptive Critic-Based Neural Network Controller For Uncertain Nonlinear Systems With Unknown Deadzones, Pingan He, Jagannathan Sarangapani, S. N. Balakrishnan

Electrical and Computer Engineering Faculty Research & Creative Works

A multilayer neural network (NN) controller in discrete-time is designed to deliver a desired tracking performance for a class of nonlinear systems with input deadzones. This multilayer NN controller has an adaptive critic NN architecture with two NNs for compensating the deadzone nonlinearity and a third NN for approximating the dynamics of the nonlinear system. A reinforcement learning scheme in discrete-time is proposed for the adaptive critic NN deadzone compensator, where the learning is performed based on a certain performance measure, which is supplied from a critic. The adaptive generating NN rejects the errors induced by the deadzone whereas a ...


Detection And Classification Of Impact-Induced Damage In Composite Plates Using Neural Networks, Rohit Dua, Steve Eugene Watkins, Donald C. Wunsch, K. Chandrashekhara, Farhad Akhavan Jan 2001

Detection And Classification Of Impact-Induced Damage In Composite Plates Using Neural Networks, Rohit Dua, Steve Eugene Watkins, Donald C. Wunsch, K. Chandrashekhara, Farhad Akhavan

Electrical and Computer Engineering Faculty Research & Creative Works

Artificial neutral networks (ANN) can be used as an online health monitoring systems (involving damage assessment, fatigue monitoring and delamination detection) for composite structures owing to their inherent fast computing speeds, parallel processing and ability to learn and adapt to the experimental data. The amount of impact-induced strain on a composite structure can be found using strain sensors attached to composite structures. Prior work has shown that strain-based ANN can characterize impact energy on composite plates and that strain signatures can be associated with damage types and severity. This paper reports the extension of this approach for damage classification using ...


System Modeling And Control Of Smart Structures, Frank J. Kern, Leslie Robert Koval, K. Chandrashekhara, Vittal S. Rao Jan 1995

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 ...