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Full-Text Articles in Engineering
Dynamic Maximum Power Point Tracking Of Photovoltaic Arrays Using Ripple Correlation Control, Trishan Esram, Jonathan W. Kimball, Philip T. Krein, Patrick L. Chapman, Pallab Midya
Dynamic Maximum Power Point Tracking Of Photovoltaic Arrays Using Ripple Correlation Control, Trishan Esram, Jonathan W. Kimball, Philip T. Krein, Patrick L. Chapman, Pallab Midya
Electrical and Computer Engineering Faculty Research & Creative Works
A dynamically rapid method used for tracking the maximum power point of photovoltaic arrays, known as ripple correlation control, is presented and verified against experiment. The technique takes advantage of the signal ripple, which is automatically present in power converters. The ripple is interpreted as a perturbation from which a gradient ascent optimization can be realized. The technique converges asymptotically at maximum speed to the maximum power point without the benefit of any array parameters or measurements. The technique has simple circuit implementations.
Power Transmission Control Using Distributed Max-Flow, Bruce M. Mcmillin, Austin Armbruster, Mariesa Crow, Michael R. Gosnell
Power Transmission Control Using Distributed Max-Flow, Bruce M. Mcmillin, Austin Armbruster, Mariesa Crow, Michael R. Gosnell
Computer Science Faculty Research & Creative Works
Existing maximum flow algorithms use one processor for all calculations or one processor per vertex in a graph to calculate the maximum possible flow through a graph's vertices. This is not suitable for practical implementation. We extend the max-flow work of Goldberg and Tarjan to a distributed algorithm to calculate maximum flow where the number of processors is less than the number of vertices in a graph. Our algorithm is applied to maximizing electrical flow within a power network where the power grid is modeled as a graph. Error detection measures are included to detect problems in a simulated power …
Optimal Control Of A Class Of One-Dimensional Nonlinear Distributed Parameter Systems With Discrete Actuators, Radhakant Padhi, S. N. Balakrishnan
Optimal Control Of A Class Of One-Dimensional Nonlinear Distributed Parameter Systems With Discrete Actuators, Radhakant Padhi, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Combining the principles of dynamic inversion and optimization theory, a new approach is presented for stable control of a class of one-dimensional nonlinear distributed parameter systems with a finite number of actuators in the spatial domain. Unlike the existing ''approximate-then-design'' and ''design-then-approximate'' techniques, this approach does not use any approximation either of the system dynamics or of the resulting controller. The formulation has more practical significance because one can implement a set of discrete controllers with relative ease. To demonstrate the potential of the proposed technique, a real-life temperature control problem for a heat transfer application is solved through simulations. …
Energy-Efficient Rate Adaptation Mac Protocol For Ad Hoc Wireless Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani
Energy-Efficient Rate Adaptation Mac Protocol For Ad Hoc Wireless Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Resource constraints in ad hoc wireless networks require that they are energy efficient during both transmission and rate adaptation. In this paper, we propose a novel energy-efficient rate adaptation protocol that selects modulation schemes online to maximize throughput based on channel state while saving energy. This protocol uses the distributed power control (DPC) algorithm (M. Zawodniok et al., 2004) to accurately determine the necessary transmission power and to reduce the energy consumption. Additionally, the transmission rate is altered using energy efficiency as a constraint to meet the required throughput, which is estimated with queue fill ratio. Moreover, back-off scheme is …
Probabilistic Balancing Of Fault Coverage And Test Cost In Combined Built-In Self-Test/Automated Test Equipment Testing Environment, Shanrui Zhang, Minsu Choi, Nohpill Park, Fabrizio Lombardi
Probabilistic Balancing Of Fault Coverage And Test Cost In Combined Built-In Self-Test/Automated Test Equipment Testing Environment, Shanrui Zhang, Minsu Choi, Nohpill Park, Fabrizio Lombardi
Electrical and Computer Engineering Faculty Research & Creative Works
As design and test complexities of SoCs ever intensify, the balanced utilization of combined built-in self-test (BIST) and automated test equipment (ATE) testing becomes desirable to meet the required minimum-fault-coverage while maintaining an acceptable cost overhead. The cost associated with combined BIST/ATE testing of such systems mainly consists of 1) the cost induced by the BIST area overhead and 2) the cost induced by the overall testing time. In general, BIST is significantly faster than ATE, while it can provide only limited fault-coverage, and driving higher fault-coverage from BIST means additional area cost overhead. On the other hand, higher fault-coverage …
Extraction Of Spice-Type Equivalent Circuits Of Signal Via Transitions Using The Peec Method, Jingkun Mao, James L. Drewniak, Giulio Antonini, Antonio Orlandi
Extraction Of Spice-Type Equivalent Circuits Of Signal Via Transitions Using The Peec Method, Jingkun Mao, James L. Drewniak, Giulio Antonini, Antonio Orlandi
Electrical and Computer Engineering Faculty Research & Creative Works
Digital devices and discontinuities are typically analyzed by inserting their equivalent circuits into SPICE-type simulators. The partial element equivalent circuit method has been proven to be very useful for electromagnetic modeling. It can be used in both the time and the frequency domain. In this paper, the PEEC technique is employed as an efficient full-wave modeling tool to derive SPICE-type equivalent circuits of signal via transition structures. A nodal analysis technique is utilized in conjunction with the optimization algorithm to extract the equivalent circuits, whose component values are the parameters optimized. The good agreement between different approaches demonstrates that the …
Cost-Driven Optimization Of Fault Coverage In Combined Built-In Self-Test/Automated Test Equipment Testing, Shanrui Zhang, Minsu Choi, Nohpill Park, Fabrizio Lombardi
Cost-Driven Optimization Of Fault Coverage In Combined Built-In Self-Test/Automated Test Equipment Testing, Shanrui Zhang, Minsu Choi, Nohpill Park, Fabrizio Lombardi
Electrical and Computer Engineering Faculty Research & Creative Works
As the design and fabrication complexities for the instrumentation-on-silicon systems intensify, optimization of combined Built-In Self-Test (BIST) and Automated Test Equipment (ATE) testing becomes more desirable to meet the required fault-coverage while maintaining acceptable cost overhead. The cost associated with combined BIST/ATE testing of such systems mainly consists of the following; (1) the cost induced by the BIST area overhead and (2) the cost induced by the overall testing time. In general, BIST has faster testing speed than ATE, while it can provide only limited fault-coverage and driving higher fault-coverage from BIST means additional area cost overhead. On the other …
Unmanned Vehicle Navigation Using Swarm Intelligence, Sheetal Doctor, Ganesh K. Venayagamoorthy
Unmanned Vehicle Navigation Using Swarm Intelligence, Sheetal Doctor, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Unmanned vehicles are used to explore physical areas where humans are unable to go due to different constraints. There have been various algorithms that have been used to perform this task. This paper explores swarm intelligence for searching a given problem space for a particular target(s). The work in this paper has two parts. In the first part, a set of randomized unmanned vehicles are deployed to locate a single target. In the second part, the randomized unmanned vehicles are deployed to locate various targets and are then converged at one of targets of a particular interest. Each of the …
Navigation Of Mobile Sensors Using Pso And Embedded Pso In A Fuzzy Logic Controller, Ganesh K. Venayagamoorthy, Sheetal Doctor
Navigation Of Mobile Sensors Using Pso And Embedded Pso In A Fuzzy Logic Controller, Ganesh K. Venayagamoorthy, Sheetal Doctor
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents novel structures for optimization and communication of a swarm of mobile sensors or robots for maximizing local and global tasks such as firefighting, landmine detection, radioactivity detection, etc. The navigation of the sensors is carried out using two strategies. The first strategy is based on particle swarm optimization (PSO) and the second strategy is based on a swarm of fuzzy logic based controllers. In addition, the membership functions and the rules of the fuzzy logic controller (FLC) are optimized using the PSO algorithm. Navigation of mobile sensors is considered in this paper to locate desirable target sources …
Time Series Prediction With Recurrent Neural Networks Using A Hybrid Pso-Ea Algorithm, Nian Zhang, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Xindi Cai
Time Series Prediction With Recurrent Neural Networks Using A Hybrid Pso-Ea Algorithm, Nian Zhang, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Xindi Cai
Electrical and Computer Engineering Faculty Research & Creative Works
To predict the 100 missing values from the time series consisting of 5000 data given for the IJCNN 2004 time series prediction competition, we applied an architecture which automates the design of recurrent neural networks using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA). By combining the searching abilities of these two global optimization methods, the evolution of individuals is no longer restricted to be in the same generation, and better performed individuals may produce offspring to replace those with poor performance. The novel …
Optimal Pso For Collective Robotic Search Applications, Ganesh K. Venayagamoorthy, Venu Gopal Gudise, Sheetal Doctor
Optimal Pso For Collective Robotic Search Applications, Ganesh K. Venayagamoorthy, Venu Gopal Gudise, Sheetal Doctor
Electrical and Computer Engineering Faculty Research & Creative Works
Unmanned vehicles/mobile robots are of particular interest in target tracing applications since there are many areas where a human cannot explore. Different means of control have been investigated for unmanned vehicles with various algorithms like genetic algorithms, evolutionary computations, neural networks etc. This work presents the application of particle swarm optimization (PSO) for collective robotic search. The performance of the PSO algorithm depends on various parameters called quality factors and these parameters are determined using a secondary PSO. Results are presented to show that the performance of PSO algorithm and search is improved for a single and multiple target searches.
Adaptive Critics For Dynamic Particle Swarm Optimization, Ganesh K. Venayagamoorthy
Adaptive Critics For Dynamic Particle Swarm Optimization, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This work introduces a novel technique for dynamic particle swarm optimization (DPSO) using adaptive critic designs. The adaptation between global and local search in an optimization algorithm is critical for optimization problems especially in a dynamically changing environment or process over time. The inertia weight in particle swarm optimization (PSO) is dynamically adjusted in this paper in order to provide a nonlinear search capability for the PSO algorithm. Results on benchmark functions in the literature are provided.
Dynamic Optimization Of A Multimachine Power System With A Facts Device Using Identification And Control Objectnets, Ganesh K. Venayagamoorthy
Dynamic Optimization Of A Multimachine Power System With A Facts Device Using Identification And Control Objectnets, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This work presents a novel technique for dynamic optimization of the electric power grid using brain-like stochastic identifiers and controllers called ObjectNets based on neural network architectures with recurrence. ObjectNets are neural network architectures developed to identify/control a particular object with a specific objective in hand. The IEEE 14 bus multimachine power system with a FACTS device is considered in this paper. The paper focuses on the combined minimization of the terminal voltage deviations and speed deviations at the generator terminals and the bus voltage deviation at the point of contact of the FACTS device to the power network. Simulation …
Fpga Placement And Routing Using Particle Swarm Optimization, Ganesh K. Venayagamoorthy, Venu Gopal Gudise
Fpga Placement And Routing Using Particle Swarm Optimization, Ganesh K. Venayagamoorthy, Venu Gopal Gudise
Electrical and Computer Engineering Faculty Research & Creative Works
Field programmable gate arrays (FPGAs) are becoming increasingly important implementation platforms for digital circuits. One of the necessary requirements to effectively utilize the FPGA's fixed resources is an efficient placement and routing mechanism. This paper presents particle swarm optimization (PSO) for FPGA placement and routing. Preliminary results for the implementation of an arithmetic logic unit on a Xilinx FPGA show that PSO is a potential technique for solving the placement and routing problem.
A Comparison Of Dual Heuristic Programming (Dhp) And Neural Network Based Stochastic Optimization Approach On Collective Robotic Search Problem, Nian Zhang, Donald C. Wunsch
A Comparison Of Dual Heuristic Programming (Dhp) And Neural Network Based Stochastic Optimization Approach On Collective Robotic Search Problem, Nian Zhang, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
An important application of mobile robots is searching a region to locate the origin of a specific phenomenon. A variety of optimization algorithms can be employed to locate the target source, which has the maximum intensity of the distribution of some detected function. We propose two neural network algorithms: stochastic optimization algorithm and dual heuristic programming (DHP) to solve the collective robotic search problem. Experiments were carried out to investigate the effect of noise and the number of robots on the task performance, as well as the expenses. The experimental results showed that the performance of the dual heuristic programming …
Comparison Of Particle Swarm Optimization And Backpropagation As Training Algorithms For Neural Networks, Ganesh K. Venayagamoorthy, Venu Gopal Gudise
Comparison Of Particle Swarm Optimization And Backpropagation As Training Algorithms For Neural Networks, Ganesh K. Venayagamoorthy, Venu Gopal Gudise
Electrical and Computer Engineering Faculty Research & Creative Works
Particle swarm optimization (PSO) motivated by the social behavior of organisms, is a step up to existing evolutionary algorithms for optimization of continuous nonlinear functions. Backpropagation (BP) is generally used for neural network training. Choosing a proper algorithm for training a neural network is very important. In this paper, a comparative study is made on the computational requirements of the PSO and BP as training algorithms for neural networks. Results are presented for a feedforward neural network learning a nonlinear function and these results show that the feedforward neural network weights converge faster with the PSO than with the BP …
Excitation And Turbine Neurocontrol With Derivative Adaptive Critics Of Multiple Generators On The Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Excitation And Turbine Neurocontrol With Derivative Adaptive Critics Of Multiple Generators On The Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
Based on derivative adaptive critics, neurocontrollers for excitation and turbine control of multiple generators on the electric power grid are presented. The feedback variables are completely based on local measurements. Simulations on a three-machine power system demonstrate that the neurocontrollers are much more effective than conventional PID controllers, the automatic voltage regulators and the governors, for improving the dynamic performance and stability under small and large disturbances
Adaptive Critic Based Neural Networks For Control-Constrained Agile Missile Control, Dongchen Han, S. N. Balakrishnan
Adaptive Critic Based Neural Networks For Control-Constrained Agile Missile Control, Dongchen Han, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
We investigate the use of an `adaptive critic' based controller to steer an agile missile with a constraint on the angle of attack from various initial Mach numbers to a given final Mach number in minimum time while completely reversing its flightpath angle. We use neural networks with a two-network structure called `adaptive critic' to carry out the optimization process. This structure obtains an optimal controller through solving Hamiltonian equations. This approach needs no external training; each network along with the optimality equations generates the output for the other network. When the outputs are mutually consistent, the controller output is …
Short-Term Resource Scheduling With Ramp Constraints [Power Generation Scheduling], Chung-Li Tseng, Chao-An Li, A. J. Svoboda, R. B. Johnson
Short-Term Resource Scheduling With Ramp Constraints [Power Generation Scheduling], Chung-Li Tseng, Chao-An Li, A. J. Svoboda, R. B. Johnson
Engineering Management and Systems Engineering Faculty Research & Creative Works
This paper describes a Lagrangian relaxation-based method to solve the short-term resource scheduling (STRS) problem with ramp constraints. Instead of discretizing the generation levels, the ramp rate constraints are relaxed with the system demand constraints using Lagrange multipliers. Three kinds of ramp constraints, startup, operating and shutdown ramp constraints are considered. The proposed method has been applied to solve the hydro-thermal generation scheduling problem at PG&E. An example alone with numerical results is also presented
Measurement Parameter Optimization For Surface Crack Detection In Metals Using An Open-Ended Waveguide Probe, R. Zoughi, Stoyan I. Ganchev, Christian J. Huber
Measurement Parameter Optimization For Surface Crack Detection In Metals Using An Open-Ended Waveguide Probe, R. Zoughi, Stoyan I. Ganchev, Christian J. Huber
Electrical and Computer Engineering Faculty Research & Creative Works
Fatigue and stress induced surface crack detection in metals is an important practical issue. A newly developed microwave inspection approach, using an open-ended rectangular waveguide, has proved to be an effective tool for detecting such cracks. This novel microwave approach overcomes some of the limitations associated with the standard detection methods for surface crack detection. In addition, this approach is applicable to exposed, filled (with a dielectric such as dirt, rust, etc.) and cracks under dielectric coatings such as paint. This paper presents the basic foundation of this surface crack detection methodology along with the ways by which measurement parameters …
Detection Optimization Of Disbond In Layered Composites With Varying Thicknesses Using An Open-Ended Rectangular Waveguide, Stoyan I. Ganchev, Nasser N. Qaddoumi, R. Zoughi
Detection Optimization Of Disbond In Layered Composites With Varying Thicknesses Using An Open-Ended Rectangular Waveguide, Stoyan I. Ganchev, Nasser N. Qaddoumi, R. Zoughi
Electrical and Computer Engineering Faculty Research & Creative Works
The detection of air disbond in layered dielectric composite, which is an important practical issue in many industries, is studied both theoretically and experimentally. Sensitivity of disbond detection depends on certain parameters, like the frequency of operation, the distance between the sensor and the first dielectric layer, and the layered composite geometry (conductor backed or terminated by an infinite half-space of air). The impact of all these parameters is investigated theoretically and then verified experimentally.
Parallel Implementation Of A Recursive Least Squares Neural Network Training Method On The Intel Ipsc/2, James Edward Steck, Bruce M. Mcmillin, K. Krishnamurthy, M. Reza Ashouri, Gary G. Leininger
Parallel Implementation Of A Recursive Least Squares Neural Network Training Method On The Intel Ipsc/2, James Edward Steck, Bruce M. Mcmillin, K. Krishnamurthy, M. Reza Ashouri, Gary G. Leininger
Computer Science Faculty Research & Creative Works
An algorithm based on the Marquardt-Levenberg least-square optimization method has been shown by S. Kollias and D. Anastassiou (IEEE Trans. on Circuits Syst. vol.36, no.8, p.1092-101, Aug. 1989) to be a much more efficient training method than gradient descent, when applied to some small feedforward neural networks. Yet, for many applications, the increase in computational complexity of the method outweighs any gain in learning rate obtained over current training methods. However, the least-squares method can be more efficiently implemented on parallel architectures than standard methods. This is demonstrated by comparing computation times and learning rates for the least-squares method implemented …
Planning Optimal Robot Trajectories By Cell Mapping, W. H. Zhu, Ming-Chuan Leu
Planning Optimal Robot Trajectories By Cell Mapping, W. H. Zhu, Ming-Chuan Leu
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A cell-mapping method is introduced for planning global trajectories of robotic manipulators in cases where the cell space is composed of combination pairs of plane cells. With the proposed method, optimal trajectory problems in the free field and in the obstacle-constrained field are studied. Two numerical examples are given to show the obtained optimal trajectories and controls.
Application Of Distributed Knowledge Bases In Intelligent Manufacturing, Cihan H. Dagli, Gerald E. Hoffman
Application Of Distributed Knowledge Bases In Intelligent Manufacturing, Cihan H. Dagli, Gerald E. Hoffman
Engineering Management and Systems Engineering Faculty Research & Creative Works
The authors consider independent knowledge bases operating on separate work stations networked together within the domain of knowledge-based scheduling. The scheduling problem is addressed through distributed knowledge bases that have an ability to pass information back and forth between small knowledge bases functioning at different decision-making levels. A small manufacturing plant is conceptualized in order to experiment with this process. The general outline and areas of the manufacturing shop are shown. The domain specific area for the knowledge bases is to optimize the scheduling of work at each work station in order to meet a weekly quota. Automatic guided vehicle, …
Characteristics And Optimal Design Of Variable Airgap Linear Force Motors, Ming-Chuan Leu, E. V. Scorza, D. L. Bartel
Characteristics And Optimal Design Of Variable Airgap Linear Force Motors, Ming-Chuan Leu, E. V. Scorza, D. L. Bartel
Mechanical and Aerospace Engineering Faculty Research & Creative Works
An analytical model for predicting the characteristics of variable airgap linear force motors is developed. the model takes into account magnetic losses including the leakage and fringing effects and the reluctance existing at the contacts between permanent magnets and pole pieces. the model is validated by comparing its predicted characteristics with the results obtained from experiments and a finite element program. with the use of the modelled characteristics, computer programs based on the method of constrained steepest descent with state equations are developed for automating and optimising the design of linear force motors. Numerical studies are made for both minimisation …
A Practical C Language Compiler/Optimizer For Real-Time Implementations On A Family Of Floating Point Dsps, J. Hartung, Steven L. Grant, S. G. Haigh
A Practical C Language Compiler/Optimizer For Real-Time Implementations On A Family Of Floating Point Dsps, J. Hartung, Steven L. Grant, S. G. Haigh
Electrical and Computer Engineering Faculty Research & Creative Works
Digital signal processors (DSPs) have traditionally been used in real-time applications with very high data throughput. For this reason, system designers have been reluctant to accept the degradation in performance inherent in machine code compiled from high-level languages such as C. The problem is compounded by the fact that DSPs use pipelined architectures to achieve their high data throughput, resulting in hazards and latencies between instructions. Simple compiler implementation cannot take advantage of latent instructions, resulting in a conservative and inefficient executable program. This problem has been addressed in the C compiler package for the AT&T WE DSP32 by the …