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Articles 61 - 74 of 74
Full-Text Articles in Engineering
Hybrid Committee Classifier For A Computerized Colonic Polyp Detection System, Jiang Li, Jianhua Yao, Nicholas Petrick, Ronald M. Summers, Amy K. Hara, Joseph M. Reinhardt (Ed.), Josien P.W. Pluim (Ed.)
Hybrid Committee Classifier For A Computerized Colonic Polyp Detection System, Jiang Li, Jianhua Yao, Nicholas Petrick, Ronald M. Summers, Amy K. Hara, Joseph M. Reinhardt (Ed.), Josien P.W. Pluim (Ed.)
Electrical & Computer Engineering Faculty Publications
We present a hybrid committee classifier for computer-aided detection (CAD) of colonic polyps in CT colonography (CTC). The classifier involved an ensemble of support vector machines (SVM) and neural networks (NN) for classification, a progressive search algorithm for selecting a set of features used by the SVMs and a floating search algorithm for selecting features used by the NNs. A total of 102 quantitative features were calculated for each polyp candidate found by a prototype CAD system. 3 features were selected for each of 7 SVM classifiers which were then combined to form a committee of SVMs classifier. Similarly, features …
Neural Network Detection And Identification Of Electronic Devices Based On Their Unintended Emissions, Haixiao Weng, Xiaopeng Dong, Xiao Hu, Daryl G. Beetner, Todd H. Hubing, Donald C. Wunsch
Neural Network Detection And Identification Of Electronic Devices Based On Their Unintended Emissions, Haixiao Weng, Xiaopeng Dong, Xiao Hu, Daryl G. Beetner, Todd H. Hubing, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Electromagnetic emissions were measured from several radio receivers to demonstrate the possibility of detecting and identifying these devices based on their unintended emissions. Radiated fields from the different radio receivers have unique characteristics that can be used to identify these devices by analyzing time-frequency plots of measured radiation. A neural network was also developed for automated device detection.
A Generalized Neuron Based Adaptive Power System Stabilizer For Multimachine Environment, D. K. Chaturvedi, O. P. Malik
A Generalized Neuron Based Adaptive Power System Stabilizer For Multimachine Environment, D. K. Chaturvedi, O. P. Malik
D. K. Chaturvedi Dr.
Artificial neural networks can be used as intelligent controllers to control nonlinear, dynamic systems through learning, which can easily accommodate the nonlinearities and time dependencies. Taking advantage of the characteristics of a generalized neuron (GN), that requires much smaller training data and shorter training time, a GN-based adaptive power system stabilizer (GNAPSS) is proposed. It consists of a GN as an identifier, which predicts the plant dynamics one step ahead, and a GN as a controller to damp low frequency oscillations. Results of studies with a GN-based PSS on a five-machine power system show that it can provide good damping …
A Generalized Neuron Based Pss In A Multi-Machine Power System, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
A Generalized Neuron Based Pss In A Multi-Machine Power System, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
D. K. Chaturvedi Dr.
An artificial neural network can work as an intelligent controller for nonlinear dynamic systems through learning, as it can easily accommodate the nonlinearities and time dependencies. In dealing with complex problems, most common neural networks have some drawbacks of large training time, large number of neurons and hidden layers. These drawbacks can be overcome by a nonlinear controller based on a generalized neuron (GN) which retains the quick response of neural net. Results of studies with a GN-based power system stabilizer on a five-machine power system show that it can provide good damping over a wide operating range and significantly …
Experimental Studies Of Generalized Neuron Based Power System Stabilizer, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
Experimental Studies Of Generalized Neuron Based Power System Stabilizer, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
D. K. Chaturvedi Dr.
Artificial neural networks (ANNs) can be used as intelligent controllers to control nonlinear, dynamic systems through learning, which can easily accommodate the nonlinearities and time dependencies. However, they require large training time and large number of neurons to deal with complex problems. To overcome these drawbacks, a generalized neuron (GN) has been developed that requires much smaller training data and shorter training time. Taking benefit of these characteristics of the GN, a new power system stabilizer (PSS) is proposed. Results show that the proposed GN-based PSS can provide a consistently good dynamic performance of the system over a wide range …
Synthesis Of Electromagnetic Devices With A Novel Neural Network, Heriberto Jose Delgado, Michael Thursby, Fredric M. Ham
Synthesis Of Electromagnetic Devices With A Novel Neural Network, Heriberto Jose Delgado, Michael Thursby, Fredric M. Ham
Electrical Engineering and Computer Science Faculty Publications
A novel Artificial Neural Network (ANN) is presented, which has been designed for computationally intensive problems, and applied to the optimization of electromagnetic devices such as antennas and microwave devices. The ANN exploits a unique number representation in conjunction with a more standard neural network architecture. An ANN consisting of a hetero-associative memory provided a very efficient method of computing the necessary geometrical values for the devices, when used in conjunction with a new randomization process. The number representation used provides significant insight into this new method of fault-tolerant computing. Further work is needed to evaluate the potential of this …
Modifications To The Fuzzy-Artmap Algorithm For Distributed Learning In Large Data Sets, Jose R. Castro
Modifications To The Fuzzy-Artmap Algorithm For Distributed Learning In Large Data Sets, Jose R. Castro
Electronic Theses and Dissertations
The Fuzzy–ARTMAP (FAM) algorithm has been proven to be one of the premier neural network architectures for classification problems. FAM can learn on line and is usually faster than other neural network approaches. Nevertheless the learning time of FAM can slow down considerably when the size of the training set increases into the hundreds of thousands. In this dissertation we apply data partitioning and network partitioning to the FAM algorithm in a sequential and parallel setting to achieve better convergence time and to efficiently train with large databases (hundreds of thousands of patterns). We implement our parallelization on a Beowulf …
Model Predictive Control Of Cstr Based On Local Model Networks, Ruiyao Gao, Aidan O'Dwyer, Eugene Coyle
Model Predictive Control Of Cstr Based On Local Model Networks, Ruiyao Gao, Aidan O'Dwyer, Eugene Coyle
Conference papers
A non-linear predictive controller is presented. It judiciously combines predictive controllers with a local model network utilizing a neural-network-like gating system. It avoids the time consuming quadratic optimization calculation, which is normally necessary in non-linear predictive control. A controller simulation on a Continuous Stirred Tank Reactor (CSTR) case study was shown to be satisfactory both in terms of set point tracking and regulation performance over the entire operating range. Moreover, the inherent integration action in the local predictive controller provides zero static offsets.
Separation Of Infrasound Signals Using Independent Component Analysis, Fredric M. Ham, Sungin Park, Joseph C. Wheeler
Separation Of Infrasound Signals Using Independent Component Analysis, Fredric M. Ham, Sungin Park, Joseph C. Wheeler
Electrical Engineering and Computer Science Faculty Publications
An important element of monitoring compliance of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) is an infrasound network. For reliable monitoring, it is important to distinguish between nuclear explosions and other sources of infrasound. This will require signal (event) classification after a detection is made. We have demonstrated the feasibility of using neural networks to classify various infrasonic events. However, classification of these events can be made more reliably with enhanced quality of the recorded infrasonic signals. One means of improving the quality of the infrasound signals is to remove background noise. This can be carried out by performing signal separation using …
Bottom-Up Design Of Artificial Neural Network For Single-Lead Electrocardiogram Beat And Rhythm Classification, Srikanth Thiagarajan
Bottom-Up Design Of Artificial Neural Network For Single-Lead Electrocardiogram Beat And Rhythm Classification, Srikanth Thiagarajan
Doctoral Dissertations
Performance improvement in computerized Electrocardiogram (ECG) classification is vital to improve reliability in this life-saving technology. The non-linearly overlapping nature of the ECG classification task prevents the statistical and the syntactic procedures from reaching the maximum performance. A new approach, a neural network-based classification scheme, has been implemented in clinical ECG problems with much success. The focus, however, has been on narrow clinical problem domains and the implementations lacked engineering precision. An optimal utilization of frequency information was missing. This dissertation attempts to improve the accuracy of neural network-based single-lead (lead-II) ECG beat and rhythm classification. A bottom-up approach defined …
Discrimination Of Volcano Activity And Mountain-Associated Waves Using Infrasonic Data And A Backpropagation Neural Network, Fredric M. Ham, Thomas A. Leeney, Heather M. Canady, Joseph C. Wheeler
Discrimination Of Volcano Activity And Mountain-Associated Waves Using Infrasonic Data And A Backpropagation Neural Network, Fredric M. Ham, Thomas A. Leeney, Heather M. Canady, Joseph C. Wheeler
Electrical Engineering and Computer Science Faculty Publications
An integral part of the Comprehensive Nuclear Test Ban Treaty monitoring is an international infrasonic monitoring network that is capable of detecting and verifying nuclear explosions. Reliable detection of such events must be made from data that may contain other sources of infrasonic phenomena. Infrasonic waves can also result from volcanic eruptions, mountain associated waves, auroral waves, earthquakes, meteors, avalanches, severe weather, quarry blasting, high-speed aircraft, gravity waves, and microbaroms. This paper shows that a feedforward multi-layer neural network discriminator, trained by backpropagation, is capable of distinguishing between two unique infrasonic events recorded from single station recordings with a relatively …
Extension Of The Generalized Hebbian Algorithm For Principal Component Extraction, Fredric M. Ham, Inho Kim
Extension Of The Generalized Hebbian Algorithm For Principal Component Extraction, Fredric M. Ham, Inho Kim
Electrical Engineering and Computer Science Faculty Publications
Principal component analysis (PCA) plays an important role in various areas. In many applications it is necessary to adaptively compute the principal components of the input data. Over the past several years, there have been numerous neural network approaches to adaptively extract principal components for PCA. One of he most popular learning rules for training a single-layer linear network for principal component extraction is Sanger's generalized Hebbian algorithm (GHA). We have extended the GHA (EGHA) by including a positive-definite symmetric weighting matrix in the representation error-cost function that is used to derive the learning rule to train the network. The …
Estimation Of Surface Snow Properties Using Combined Millimeter-Wave Backscatter And Near-Infrared Reflectance Measurements, Ram M. Narayanan, Sandy R. Jackson
Estimation Of Surface Snow Properties Using Combined Millimeter-Wave Backscatter And Near-Infrared Reflectance Measurements, Ram M. Narayanan, Sandy R. Jackson
Department of Electrical and Computer Engineering: Faculty Publications
Knowledge of surficial snow properties such as grain size, surface roughness, and free-water content provides clues to the metamorphic state of snow on the ground, which in turn yields information on weathering processes and climatic activity. Remote sensing techniques using combined concurrent measurements of near-infrared passive reflectance and millimeter-wave radar backscatter show promise in estimating the above snow parameters. Near-infrared reflectance is strongly dependent on snow grain size and free-water content, while millimeter-wave backscatter is primarily dependent on free-water content and, to some extent, on the surface roughness. A neural-network based inversion algorithm has been developed that optimally combines near-infrared …
The Application Of Neural Networks To Optimal Robot Trajectory Planning, Daniel J. Simon
The Application Of Neural Networks To Optimal Robot Trajectory Planning, Daniel J. Simon
Electrical and Computer Engineering Faculty Publications
Interpolation of minimum jerk robot joint trajectories through an arbitrary number of knots is realized using a hardwired neural network. Minimum jerk joint trajectories are desirable for their similarity to human joint movements and their amenability to accurate tracking. The resultant trajectories are numerical rather than analytic functions of time. This application formulates the interpolation problem as a constrained quadratic minimization problem over a continuous joint angle domain and a discrete time domain. Time is discretized according to the robot controller rate. The neuron outputs define the joint angles (one neuron for each discrete value of time) and the Lagrange …