The Experts below are selected from a list of 851100 Experts worldwide ranked by ideXlab platform
Mu Seong Mun - One of the best experts on this subject based on the ideXlab platform.
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A Real-Time EMG pattern recognition System based on linear-nonlinear feature projection for a multifunction myoelectric hand
IEEE Transactions on Biomedical Engineering, 2006Co-Authors: Jun-uk Chu, Inhyuk Moon, Mu Seong MunAbstract:This paper proposes a novel Real-Time electromyogram (EMG) pattern recognition for the Control of a multifunction myoelectric hand from four channel EMG signals. To extract a feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of principal components analysis (PCA) and a self-organizing feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features into a new feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by feature projections, we show that the recognition accuracy depends more on the class separability of the projected features than on the MLP's class separation ability. Consequently, the proposed linear-nonlinear projection method improves class separability and recognition accuracy. We implement a Real-Time Control System for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand Control, are completed within 125 ms, and the proposed method is applicable to Real-Time myoelectric hand Control without an operational time delay
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Control of multifunction myoelectric hand using a real time emg pattern recognition
Intelligent Robots and Systems, 2005Co-Authors: Jun-uk Chu, Inhyuk Moon, Shinki Kim, Mu Seong MunAbstract:This paper proposes a novel Real-Time EMG pattern recognition for the Control of a multifunction myoelectric hand from four channel EMG signals. To cope with the nonstationary signal property of the EMG, features are extracted by wavelet packet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features to a new feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. We implement a Real-Time Control System for a multifunction myoelectric hand. From experimental results, we show that all processes, including myoelectric hand Control, are completed within 125 msec, and the proposed method is applicable to Real-Time myoelectric hand Control without an operation time delay.
Jun-uk Chu - One of the best experts on this subject based on the ideXlab platform.
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A Real-Time EMG pattern recognition System based on linear-nonlinear feature projection for a multifunction myoelectric hand
IEEE Transactions on Biomedical Engineering, 2006Co-Authors: Jun-uk Chu, Inhyuk Moon, Mu Seong MunAbstract:This paper proposes a novel Real-Time electromyogram (EMG) pattern recognition for the Control of a multifunction myoelectric hand from four channel EMG signals. To extract a feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of principal components analysis (PCA) and a self-organizing feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features into a new feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by feature projections, we show that the recognition accuracy depends more on the class separability of the projected features than on the MLP's class separation ability. Consequently, the proposed linear-nonlinear projection method improves class separability and recognition accuracy. We implement a Real-Time Control System for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand Control, are completed within 125 ms, and the proposed method is applicable to Real-Time myoelectric hand Control without an operational time delay
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Control of multifunction myoelectric hand using a real time emg pattern recognition
Intelligent Robots and Systems, 2005Co-Authors: Jun-uk Chu, Inhyuk Moon, Shinki Kim, Mu Seong MunAbstract:This paper proposes a novel Real-Time EMG pattern recognition for the Control of a multifunction myoelectric hand from four channel EMG signals. To cope with the nonstationary signal property of the EMG, features are extracted by wavelet packet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features to a new feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. We implement a Real-Time Control System for a multifunction myoelectric hand. From experimental results, we show that all processes, including myoelectric hand Control, are completed within 125 msec, and the proposed method is applicable to Real-Time myoelectric hand Control without an operation time delay.
Inhyuk Moon - One of the best experts on this subject based on the ideXlab platform.
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A Real-Time EMG pattern recognition System based on linear-nonlinear feature projection for a multifunction myoelectric hand
IEEE Transactions on Biomedical Engineering, 2006Co-Authors: Jun-uk Chu, Inhyuk Moon, Mu Seong MunAbstract:This paper proposes a novel Real-Time electromyogram (EMG) pattern recognition for the Control of a multifunction myoelectric hand from four channel EMG signals. To extract a feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of principal components analysis (PCA) and a self-organizing feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features into a new feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by feature projections, we show that the recognition accuracy depends more on the class separability of the projected features than on the MLP's class separation ability. Consequently, the proposed linear-nonlinear projection method improves class separability and recognition accuracy. We implement a Real-Time Control System for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand Control, are completed within 125 ms, and the proposed method is applicable to Real-Time myoelectric hand Control without an operational time delay
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Control of multifunction myoelectric hand using a real time emg pattern recognition
Intelligent Robots and Systems, 2005Co-Authors: Jun-uk Chu, Inhyuk Moon, Shinki Kim, Mu Seong MunAbstract:This paper proposes a novel Real-Time EMG pattern recognition for the Control of a multifunction myoelectric hand from four channel EMG signals. To cope with the nonstationary signal property of the EMG, features are extracted by wavelet packet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features to a new feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. We implement a Real-Time Control System for a multifunction myoelectric hand. From experimental results, we show that all processes, including myoelectric hand Control, are completed within 125 msec, and the proposed method is applicable to Real-Time myoelectric hand Control without an operation time delay.
Zhaosyong Zeng - One of the best experts on this subject based on the ideXlab platform.
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improvement of the twin rotor mimo System tracking and transient response using fuzzy Control technology
Conference on Industrial Electronics and Applications, 2006Co-Authors: Liangrui Chen, Bingze Li, Shihkai Chen, Zhaosyong ZengAbstract:In this paper, a method to obtain the optimal parameters of PID(proportional, integral and derivative) Controllers for the twin rotor multi-input multi-output System (TRMS) by using the optimal method , combined with the model reduced method, is proposed. The fuzzy Control method with the multi-section gains and derivatives is also presented for the improvement of the tracking performance and the disturbance problem from the air flow. Both methods are implemented by the new Real-Time Control System. The simulation results and the experiment verification show the effectiveness of the proposed methods
M A Elsharkawi - One of the best experts on this subject based on the ideXlab platform.
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laboratory implementation of a neural network trajectory Controller for a dc motor
IEEE Transactions on Energy Conversion, 1993Co-Authors: S Weerasooriya, M A ElsharkawiAbstract:The laboratory implementation of a neural network Controller for high performance DC drives is described. The objective is to Control the rotor speed and/or position to follow an arbitrarily selected trajectory at all times. The Control strategy is based on indirect model reference adaptive Control (MRAC). The motor characteristics are explicitly identified through a multilayer perceptron type neural network. The output of the trained neural network is used to drive the motor in order to achieve a desired time trajectory of the Controlled variable. The neural network Controller is assembled in a commercially available PC-based Real-Time Control System shell, using software subroutines. An H-bridge, DC/DC voltage converter is interfaced with the computer to generate the specified terminal voltage sequences for driving the motor. All software and hardware components are off the shelf. The versatility of the motor/Controller arrangement is displayed through Real-Time plots of the Controlled states. >
