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Electrical and Computer Engineering

Cleveland State University

Series

2010

Articles 1 - 9 of 9

Full-Text Articles in Engineering

Compensation Of Torque Ripple In High Performance Bldc Motor Drives, Ilhwan Kim, Nobuaki Nakazawa, Sungsoo Kim, Chanwon Park, Chansu Yu Oct 2010

Compensation Of Torque Ripple In High Performance Bldc Motor Drives, Ilhwan Kim, Nobuaki Nakazawa, Sungsoo Kim, Chanwon Park, Chansu Yu

Electrical and Computer Engineering Faculty Publications

Brushless DC motor drives (BLDC) are finding expanded use in high performance applications where torque smoothness is essential. The nature of the square-wave current excitation waveforms in BLDC motor drives permits some important system simplifications compared to sinusoidal permanent magnet AC (PMAC) machines. However, it is the simplicity of the BLDC motor drive that is responsible for causing an additional source of ripple torque commonly known as commutation torque to develop. In this paper, a compensation technique for reducing the commutation torque ripple is proposed. With the experimental results, the proposed method demonstrates the effectiveness for a control …

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Kalman Filtering With State Constraints: A Survey Of Linear And Nonlinear Algorithms, Daniel J. Simon Aug 2010

Kalman Filtering With State Constraints: A Survey Of Linear And Nonlinear Algorithms, Daniel J. Simon

Electrical and Computer Engineering Faculty Publications

The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications include the extended Kalman filter, the unscented Kalman filter, and the particle filter. Although the Kalman filter and its modifications are powerful tools for state estimation, we might have information about a system that the Kalman filter …

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Biogeography-Based Optimization Of Neuro-Fuzzy System Parameters For Diagnosis Of Cardiac Disease, Mirela Ovreiu, Daniel J. Simon Jul 2010

Biogeography-Based Optimization Of Neuro-Fuzzy System Parameters For Diagnosis Of Cardiac Disease, Mirela Ovreiu, Daniel J. Simon

Electrical and Computer Engineering Faculty Publications

Cardiomyopathy refers to diseases of the heart muscle that becomes enlarged, thick, or rigid. These changes affect the electrical stability of the myocardial cells, which in turn predisposes the heart to failure or arrhythmias. Cardiomyopathy in its two common forms, dilated and hypertrophic, implies enlargement of the atria; therefore, we investigate its diagnosis through P wave features. In particular, we design a neuro-fuzzy network trained with a new evolutionary algorithm called biogeography-based optimization (BBO). The neuro-fuzzy network recognizes and classifies P wave features for the diagnosis of cardiomyopathy. In addition, we incorporate opposition-based learning in the BBO algorithm for improved …

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Biogeography-Based Optimization With Blended Migration For Constrained Optimization Problems, Haiping Ma, Daniel J. Simon Jul 2010

Biogeography-Based Optimization With Blended Migration For Constrained Optimization Problems, Haiping Ma, Daniel J. Simon

Electrical and Computer Engineering Faculty Publications

Biogeography-based optimization (BBO) is a new evolutionary algorithm based on the science of biogeography. We propose two extensions to BBO. First, we propose blended migration. Second, we modify BBO to solve constrained optimization problems. The constrained BBO algorithm is compared with solutions based on a genetic algorithm (GA) and particle swarm optimization (PSO). Numerical results indicate that BBO generally performs better than GA and PSO in handling constrained single-objective optimization problems.

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Analytic Confusion Matrix Bounds For Fault Detection And Isolation Using A Sum-Of-Squared-Residuals Approach, Daniel J. Simon, Donald L. Simon Jun 2010

Analytic Confusion Matrix Bounds For Fault Detection And Isolation Using A Sum-Of-Squared-Residuals Approach, Daniel J. Simon, Donald L. Simon

Electrical and Computer Engineering Faculty Publications

Given a system which can fail in 1 of n different ways, a fault detection and isolation (FDI) algorithm uses sensor data to determine which fault is the most likely to have occurred. The effectiveness of an FDI algorithm can be quantified by a confusion matrix, also called a diagnosis probability matrix, which indicates the probability that each fault is isolated given that each fault has occurred. Confusion matrices are often generated with simulation data, particularly for complex systems. In this paper, we perform FDI using sum-of-squared residuals (SSRs). We assume that the sensor residuals are s-independent and Gaussian, which …

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A Realistic Mobility Model For Wireless Networks Of Scale-Free Node Connectivity, Sunho Lim, Chansu Yu, Chita R. Das May 2010

A Realistic Mobility Model For Wireless Networks Of Scale-Free Node Connectivity, Sunho Lim, Chansu Yu, Chita R. Das

Electrical and Computer Engineering Faculty Publications

Recent studies discovered that many of social, natural and biological networks are characterised by scale-free power-law connectivity distribution. We envision that wireless networks are directly deployed over such real-world networks to facilitate communication among participating entities. This paper proposes Clustered Mobility Model (CMM), in which nodes do not move randomly but are attracted more to more populated areas. Unlike most of prior mobility models, CMM is shown to exhibit scale-free connectivity distribution. Extensive simulation study has been conducted to highlight the difference between Random WayPoint (RWP) and CMM by measuring network capacities at the physical, link and network layers.

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Classificiation Of Atrial Fibrillation Prone Patients Using Electrocardiographic Parameters In Neuro-Fuzzy Modeling,, Mirela Ovreiu, Marc Petre, Daniel J. Simon, Daniel Sessler, C Allen Bashour Mar 2010

Classificiation Of Atrial Fibrillation Prone Patients Using Electrocardiographic Parameters In Neuro-Fuzzy Modeling,, Mirela Ovreiu, Marc Petre, Daniel J. Simon, Daniel Sessler, C Allen Bashour

Electrical and Computer Engineering Faculty Publications

Atrial Fibrillation (AF) is a significant clinical problem and the complications of cardiovascular postoperative AF often lead to longer hospital stays and higher heath care costs. The literature showed that AF may be preceded by changes in electrocardiogram (ECG) characteristics such as premature atrial activity, heart rate variability (HRV), and P-wave morphology. We hypothesize that the limitations of statistics-based attempts to predict AF occurrence may be overcome using a hybrid neuro-fuzzy prediction model that is better capable of uncovering complex, non-linear interactions between ECG parameters. We created a neuro-fuzzy network that was able to classify the patients into the control …

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Constrained Kalman Filtering Via Density Function Truncation For Turbofan Engine Health Estimation, Daniel J. Simon, Donald L. Simon Feb 2010

Constrained Kalman Filtering Via Density Function Truncation For Turbofan Engine Health Estimation, Daniel J. Simon, Donald L. Simon

Electrical and Computer Engineering Faculty Publications

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This article develops an analytic method of incorporating state variable inequality constraints in the Kalman filter. The resultant filter truncates the probability density function (PDF) of the Kalman filter estimate at the known constraints and then computes the constrained …

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A Majorization Algorithm For Constrained Correlation Matrix Approximation, Daniel J. Simon, Jeff Abell Feb 2010

A Majorization Algorithm For Constrained Correlation Matrix Approximation, Daniel J. Simon, Jeff Abell

Electrical and Computer Engineering Faculty Publications

We desire to find a correlation matrix of a given rank that is as close as possible to an input matrix R, subject to the constraint that specified elements in must be zero. Our optimality criterion is the weighted Frobenius norm of the approximation error, and we use a constrained majorization algorithm to solve the problem. Although many correlation matrix approximation approaches have been proposed, this specific problem, with the rank specification and the constraints, has not been studied until now. We discuss solution feasibility, convergence, and computational effort. We also present several examples.

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