The Experts below are selected from a list of 9279 Experts worldwide ranked by ideXlab platform
Dong-choon Lee - One of the best experts on this subject based on the ideXlab platform.
-
MPPT Control of Wind Generation Systems Based on Estimated Wind Speed Using SVR
IEEE Transactions on Industrial Electronics, 2008Co-Authors: Ahmed G. Abo-khalil, Dong-choon LeeAbstract:In this paper, a novel algorithm for wind-speed estimation in wind-power generation systems is proposed, which is based on the theory of support-vector regression (SVR). The inputs of the SVR wind-speed estimator are chosen as the wind-turbine power and rotational speed. During the Offline Training, a specified model, which relates the inputs to the output, is obtained. Then, the wind speed is determined online from the instantaneous inputs. The experimental results have verified the validity of the proposed estimation algorithm.
Kyle Chand - One of the best experts on this subject based on the ideXlab platform.
-
limited memory adaptive snapshot selection for proper orthogonal decomposition
International Journal for Numerical Methods in Engineering, 2017Co-Authors: Geoffrey Oxberry, William Arrighi, Tanya Kostovavassilevska, Kyle ChandAbstract:Summary Reduced order models are useful for accelerating simulations in many-query contexts, such as optimization, uncertainty quantification, and sensitivity analysis. However, Offline Training of reduced order models (ROMs) can have prohibitively expensive memory and floating-point operation costs in high-performance computing applications, where memory per core is limited. To overcome this limitation for proper orthogonal decomposition, we propose a novel adaptive selection method for snapshots in time that limits Offline Training costs by selecting snapshots according an error control mechanism similar to that found in adaptive time-stepping ordinary differential equation solvers. The error estimator used in this work is related to theory bounding the approximation error in time of proper orthogonal decomposition-based ROMs, and memory usage is minimized by computing the singular value decomposition using a single-pass incremental algorithm. Results for a viscous Burgers' test problem demonstrate convergence in the limit as the algorithm error tolerances go to zero; in this limit, the full-order model is recovered to within discretization error. A parallel version of the resulting method can be used on supercomputers to generate proper orthogonal decomposition-based ROMs, or as a subroutine within hyperreduction algorithms that require taking snapshots in time, or within greedy algorithms for sampling parameter space. Copyright © 2016 John Wiley & Sons, Ltd.
-
Limited‐memory adaptive snapshot selection for proper orthogonal decomposition
International Journal for Numerical Methods in Engineering, 2016Co-Authors: Geoffrey Oxberry, Tanya Kostova-vassilevska, William Arrighi, Kyle ChandAbstract:Summary Reduced order models are useful for accelerating simulations in many-query contexts, such as optimization, uncertainty quantification, and sensitivity analysis. However, Offline Training of reduced order models (ROMs) can have prohibitively expensive memory and floating-point operation costs in high-performance computing applications, where memory per core is limited. To overcome this limitation for proper orthogonal decomposition, we propose a novel adaptive selection method for snapshots in time that limits Offline Training costs by selecting snapshots according an error control mechanism similar to that found in adaptive time-stepping ordinary differential equation solvers. The error estimator used in this work is related to theory bounding the approximation error in time of proper orthogonal decomposition-based ROMs, and memory usage is minimized by computing the singular value decomposition using a single-pass incremental algorithm. Results for a viscous Burgers' test problem demonstrate convergence in the limit as the algorithm error tolerances go to zero; in this limit, the full-order model is recovered to within discretization error. A parallel version of the resulting method can be used on supercomputers to generate proper orthogonal decomposition-based ROMs, or as a subroutine within hyperreduction algorithms that require taking snapshots in time, or within greedy algorithms for sampling parameter space. Copyright © 2016 John Wiley & Sons, Ltd.
M.a. Rahman - One of the best experts on this subject based on the ideXlab platform.
-
Untrained Artificial Neuron-Based Speed Control of Interior Permanent-Magnet Motor Drives Over Extended Operating Speed Range
IEEE Transactions on Industry Applications, 2013Co-Authors: C. B. Butt, M.a. RahmanAbstract:This paper presents an intelligent speed controller for the interior permanent-magnet synchronous motor, based on a single artificial neuron. Traditional artificial neural network-based motor controllers require extensive Offline Training, which is both time consuming and requires extensive knowledge of motor behavior for the specific drive system. In addition, drive behavior is unpredictable when parameters outside the Training set are encountered. The proposed drive system overcomes these limitations by requiring no Offline Training, is robust under varying operating parameters, and is easily adaptable to various drive systems. Drive efficacy is verified in simulation as well as experimentally.
-
Intelligent Speed Control of Interior Permanent Magnet Motor Drives Using a Single Untrained Artificial Neuron
IEEE Transactions on Industry Applications, 2013Co-Authors: Casey Butt, M.a. RahmanAbstract:This paper presents an intelligent speed controller for the interior permanent magnet synchronous motor based on a single artificial neuron. Traditional artificial neural network-based motor controllers require extensive Offline Training, which is both time consuming and requires extensive knowledge of motor behavior for the specific drive system. In addition, drive behavior is unpredictable when parameters outside the Training set are encountered. The proposed drive system overcomes these limitations by requiring no Offline Training, is robust under varying operating parameters, and is easily adaptable to various drive systems. Drive efficacy is verified in simulation as well as experimentally.
-
IAS Annual Meeting - Untrained artificial neuron based speed control of interior permanent magnet motor drives over full operating speed range
2011 IEEE Industry Applications Society Annual Meeting, 2011Co-Authors: Casey Butt, M.a. RahmanAbstract:This paper presents an intelligent speed controller for the interior permanent magnet synchronous motor (IPMSM), based on a single artificial neuron (SAN). Traditional artificial neural network-based motor controllers require extensive Offline Training, which is both time consuming and requires extensive knowledge of motor behaviour for the specific drive system. In addition, drive behaviour is unpredictable when parameters outside the Training set are encountered. The proposed drive system overcomes these limitations by requiring no Offline Training, is robust under varying operating parameters and is easily adaptable to various drive systems. Drive efficacy is verified in simulation as well as experimentally.
-
Intelligent Speed Control of Interior Permanent Magnet Motor Drives
IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics, 2006Co-Authors: C. B. Butt, M.a. RahmanAbstract:This paper presents an intelligent speed controller for the interior permanent magnet synchronous motor (IPMSM), based on a single artificial neuron (SAN). Traditional artificial neural network-based motor controllers require extensive Offline Training, which is both time consuming and requires extensive knowledge of motor behaviour for the specific drive system. In addition, drive behaviour is unpredictable when parameters outside the Training set are encountered. The proposed drive system overcomes these limitations by requiring no Offline Training, is robust under varying operating parameters and is easily adaptable to various drive systems. Drive efficacy is verified in simulation as well as experimentally.
A. Aydin Alatan - One of the best experts on this subject based on the ideXlab platform.
-
3-D Rigid Body Tracking Using Vision and Depth Sensors
IEEE transactions on cybernetics, 2013Co-Authors: O. Serdar Gedik, A. Aydin AlatanAbstract:In robotics and augmented reality applications, model-based 3-D tracking of rigid objects is generally required. With the help of accurate pose estimates, it is required to increase reliability and decrease jitter in total. Among many solutions of pose estimation in the literature, pure vision-based 3-D trackers require either manual initializations or Offline Training stages. On the other hand, trackers relying on pure depth sensors are not suitable for AR applications. An automated 3-D tracking algorithm, which is based on fusion of vision and depth sensors via extended Kalman filter, is proposed in this paper. A novel measurement-tracking scheme, which is based on estimation of optical flow using intensity and shape index map data of 3-D point cloud, increases 2-D, as well as 3-D, tracking performance significantly. The proposed method requires neither manual initialization of pose nor Offline Training, while enabling highly accurate 3-D tracking. The accuracy of the proposed method is tested against a number of conventional techniques, and a superior performance is clearly observed in terms of both objectively via error metrics and subjectively for the rendered scenes.
-
FUSION - Fusing 2D and 3D clues for 3D tracking using visual and range data
2013Co-Authors: O. Serdar Gedik, A. Aydin AlatanAbstract:3D tracking of rigid objects is required in many applications, such as robotics or augmented reality (AR). The availability of accurate pose estimates increases reliability in robotic applications and decreases jitter in AR scenarios. Pure vision-based 3D trackers require either manual initializations or Offline Training stages, whereas trackers relying on pure depth sensors are not suitable for AR applications. In this paper, an automated 3D tracking algorithm, which is based on fusion of vision and depth sensors via Extended Kalman Filter (EKF), which inherits a novel observation weighting method, is proposed. Moreover, novel feature selection and tracking schemes based on intensity and shape index map (SIM) data of 3D point cloud, increases 2D and 3D tracking performance significantly. The proposed method requires neither manual initialization of pose nor Offline Training, while enabling highly accurate 3D tracking. The accuracy of the proposed method is tested against a number of conventional techniques and superior performance is observed.
Ahmed G. Abo-khalil - One of the best experts on this subject based on the ideXlab platform.
-
MPPT Control of Wind Generation Systems Based on Estimated Wind Speed Using SVR
IEEE Transactions on Industrial Electronics, 2008Co-Authors: Ahmed G. Abo-khalil, Dong-choon LeeAbstract:In this paper, a novel algorithm for wind-speed estimation in wind-power generation systems is proposed, which is based on the theory of support-vector regression (SVR). The inputs of the SVR wind-speed estimator are chosen as the wind-turbine power and rotational speed. During the Offline Training, a specified model, which relates the inputs to the output, is obtained. Then, the wind speed is determined online from the instantaneous inputs. The experimental results have verified the validity of the proposed estimation algorithm.
