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Numerical Analysis and Scientific Computing
Electrical and Computer Engineering Faculty Research & Creative Works
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Full-Text Articles in Engineering
Real Time Mission Planning, Emad William Saad, Stefan Richard Bieniawski, Paul Edward Riley Pigg, John Lyle Vian, Paul Michael Robinette, Donald C. Wunsch
Real Time Mission Planning, Emad William Saad, Stefan Richard Bieniawski, Paul Edward Riley Pigg, John Lyle Vian, Paul Michael Robinette, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
The different advantageous embodiments provide a system comprising a number of computers, a graphical user interface, first program code stored on the computer, and second program code stored on the computer. The graphical user interface is executed by a computer in the number of computers. The computer is configured to run the first program code to define a mission using a number of mission elements. The computer is configured to run the second program code to generate instructions for a number of assets to execute the mission and monitor the number of assets during execution of the mission.
Preface, Gennady Fridman, Jeremy Levesley, Ivan Tyukin, Donald C. Wunsch
Preface, Gennady Fridman, Jeremy Levesley, Ivan Tyukin, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
In August 2014 a conference on “Model reduction across disciplines” was held in Leicester, UK. As a scientific field, model reduction is an important part of mathematical modelling and data analysis with very wide areas of applications. The main scientific goal of the conference was to facilitate interdisciplinary discussion of model reduction and coarse-graining methodologies in order to reveal their general mathematical nature. This time, however, the conference had an additional personal and more profound mission – it was dedicated to the 60th birthday of Professor Alexander Gorban (albeit with some delay) whose fantastic achievements in applying model reduction techniques …
Methods And Systems For Biclustering Algorithm, Donald C. Wunsch, Rui Xu, Sejun Kim
Methods And Systems For Biclustering Algorithm, Donald C. Wunsch, Rui Xu, Sejun Kim
Electrical and Computer Engineering Faculty Research & Creative Works
Methods and systems for improved unsupervised learning are described. The unsupervised learning can consist of biclustering a data set, e.g., by biclustering subsets of the entire data set. In an example, the biclustering does not include feeding know and proven results into the biclustering methodology or system. A hierarchical approach can be used that feeds proven clusters back into the biclustering methodology or system as the input. Data that does not cluster may be discarded. Thus, a very large unknown data set can be acted on to learn about the data. The system is also amenable to parallelization.
Systems, Methods And Devices For Vector Control Of Permanent Magnet Synchronous Machines Using Artificial Neural Networks, Shuhui Li, Michael Fairbank, Xingang Fu, Donald C. Wunsch, Eduardo Alonso
Systems, Methods And Devices For Vector Control Of Permanent Magnet Synchronous Machines Using Artificial Neural Networks, Shuhui Li, Michael Fairbank, Xingang Fu, Donald C. Wunsch, Eduardo Alonso
Electrical and Computer Engineering Faculty Research & Creative Works
An example method for controlling an AC electrical machine can include providing a PWM converter operably connected between an electrical power source and the AC electrical machine and providing a neural network vector control system operably connected to the PWM converter. The control system can include a current-loop neural network configured to receive a plurality of inputs. The current-loop neural network can be configured to optimize the compensating dq-control voltage. The inputs can be d- and q-axis currents, d- and q-axis error signals, predicted d- and q-axis current signals, and a feedback compensating dq-control voltage. The d- and q-axis error …
Big Data -- A 21st Century Science Maginot Line? No-Boundary Thinking: Shifting From The Big Data Paradigm, Xiuzhen Huang, Steven F. Jennings, Barry Bruce, Alison Buchan, Liming Cai, Pengyin Chen, Carole Cramer, Weihua Guan, Uwe Kk Hilgert, Hongmei Jiang, Zenglu Li, Gail Mcclure, Donald F. Mcmullen, Bindu Nanduri, Andy Perkins, Bhanu Rekepalli, Saeed Salem, Jennifer Specker, Karl Walker, Donald C. Wunsch, Donghai Xiong, Shuzhong Zhang, Yu Zhang, Zhongming Zhao, Jason H. Moore
Big Data -- A 21st Century Science Maginot Line? No-Boundary Thinking: Shifting From The Big Data Paradigm, Xiuzhen Huang, Steven F. Jennings, Barry Bruce, Alison Buchan, Liming Cai, Pengyin Chen, Carole Cramer, Weihua Guan, Uwe Kk Hilgert, Hongmei Jiang, Zenglu Li, Gail Mcclure, Donald F. Mcmullen, Bindu Nanduri, Andy Perkins, Bhanu Rekepalli, Saeed Salem, Jennifer Specker, Karl Walker, Donald C. Wunsch, Donghai Xiong, Shuzhong Zhang, Yu Zhang, Zhongming Zhao, Jason H. Moore
Electrical and Computer Engineering Faculty Research & Creative Works
Whether your interests lie in scientific arenas, the corporate world, or in government, you have certainly heard the praises of big data: Big data will give you new insights, allow you to become more efficient, and/or will solve your problems. While big data has had some outstanding successes, many are now beginning to see that it is not the Silver Bullet that it has been touted to be. Here our main concern is the overall impact of big data; the current manifestation of big data is constructing a Maginot Line in science in the 21st century. Big data is not …
Vehicle Base Station, Emad William Saad, John L. Vian, Matthew A. Vavrina, Jared A. Nisbett, Donald C. Wunsch
Vehicle Base Station, Emad William Saad, John L. Vian, Matthew A. Vavrina, Jared A. Nisbett, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
A system to load and unload material from a vehicle comprises a vehicle base station and an assembly to autonomously load and unload material from the vehicle.
Hidden Markov Model With Information Criteria Clustering And Extreme Learning Machine Regression For Wind Forecasting, Dao Lam, Shuhui Li, Donald C. Wunsch
Hidden Markov Model With Information Criteria Clustering And Extreme Learning Machine Regression For Wind Forecasting, Dao Lam, Shuhui Li, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
This paper proposes a procedural pipeline for wind forecasting based on clustering and regression. First, the data are clustered into groups sharing similar dynamic properties. Then, data in the same cluster are used to train the neural network that predicts wind speed. For clustering, a hidden Markov model (HMM) and the modified Bayesian information criteria (BIC) are incorporated in a new method of clustering time series data. to forecast wind, a new method for wind time series data forecasting is developed based on the extreme learning machine (ELM). the clustering results improve the accuracy of the proposed method of wind …
Adaptive Resonance Theory And Diffusion Maps For Clustering Applications In Pattern Analysis, Donald C. Wunsch, David J. Morris, Rui Xu
Adaptive Resonance Theory And Diffusion Maps For Clustering Applications In Pattern Analysis, Donald C. Wunsch, David J. Morris, Rui Xu
Electrical and Computer Engineering Faculty Research & Creative Works
Adaptive Resonance is primarily a theory that learning is regulated by resonance phenomena in neural circuits. Diffusion maps are a class of kernel methods on edge-weighted graphs. While either of these approaches have demonstrated success in image analysis, their combination is particularly effective. These techniques are reviewed and some example applications are given.