Transcutaneous electrical stimulation (TES) has been applied to restore or maintain the muscle activity of paralyzed patients who suffer from spinal cord injuries and related neural impairments for several decades. In...
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In this study, a new fabric defect detection algorithm base on undecimated wavelet transform is proposed. The selection scheme of wavelet decomposition scales is investigated to set the decomposition scales adaptively...
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Recognizing the user motion intention plays an important role in the study of power-assist robots. An intention-guided control strategy is proposed for the upper-limb power-assist exoskeleton. A force sensor system co...
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Recognizing the user motion intention plays an important role in the study of power-assist robots. An intention-guided control strategy is proposed for the upper-limb power-assist exoskeleton. A force sensor system comprised of force sensing resistors (FSRs) is designed to online estimate the motion intention of user upper limb. A new concept called “intentional reaching direction (IRD)” is proposed to quantitatively describe this intention. Both the state model and the observation model of IRD are obtained by enumerating the upper limb behavior modes and analyzing the relationship between the measured force signals and the motion intention. Based on these two models, the IRD can be online inferred by applying filtering technology. Guided by the estimated IRD, an admittance control strategy is assumed to control the motions of three DC motors in the joints of the robotic arm. The effectiveness of the proposed approaches is finally confirmed by the experiments on a 3-DOF robotic exoskeleton.
With the wide use of power conversion devices, harmonic currents are being injected into the power grid. Shunt Active Power Filters (SAPF) is a power electronic device to compensate the harmonic currents caused by non...
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With the wide use of power conversion devices, harmonic currents are being injected into the power grid. Shunt Active Power Filters (SAPF) is a power electronic device to compensate the harmonic currents caused by nonlinear loads. As the foundation of the harmonics recognition and compensation, harmonic extraction techniques are becoming more and more important. This paper proposes a new harmonic extraction method based on the Echo State Networks (ESN). ESN is a new type of Recurrent Neural Networks (RNN), which has much faster training speed than other types of RNN. To evaluate the dynamic system modeling capability of the ESN, the ESN with different dynamic reservoir size are discussed. The performance of the ESN based harmonic extraction method is compared with traditional methods and method based on multilayer perceptron networks (MLP). The ESN algorithm is trained and tested in MATLAB.
With more and more attention on the grid current harmonic in recent years, many control schemes of the Pulse Width Modulation Voltage Source Converter (PWM-VSC) have been investigated. Conventional PI controller has s...
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With more and more attention on the grid current harmonic in recent years, many control schemes of the Pulse Width Modulation Voltage Source Converter (PWM-VSC) have been investigated. Conventional PI controller has shown limitations such as sensitivity to load and system parameter variation. Even the stability of the system can be threatened under a large and sudden load change. In this paper, the practical situation of a VSC for industrial Micro Grid (MG) is considered and an Artificial neural network (ANN) based control method is employed to solve the problem. Meanwhile, an on-line parameter tuning algorithm is introduced for its advantage of self-tuning and system character identification. The proposed control scheme is verified through simulation based on SABER software. The simulation results have shown the advantage of the proposed method and the performance of the parameter tuning session.
In this paper, a modified method for landslide prediction is presented. This method is based on the back propagation neural network(BPNN), and we use the combination of genetic algorithm and simulated annealing algori...
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In this paper, a modified method for landslide prediction is presented. This method is based on the back propagation neural network(BPNN), and we use the combination of genetic algorithm and simulated annealing algorithm to optimize the weights and biases of the network. The improved BPNN modeling can work out the complex nonlinear relation by learning model and using the present data. This paper demonstrates that the revised BPNN modeling can be used to predict and calculate landslide deformation, quicken the learning speed of network and improve the predicting precision. Applying this thinking and method into research of some landslide in the Three Gorges reservoir, the validity and practical value of this model can be demonstrated. And it also shows that the dynamic prediction of landslide deformation is very crucial.
Recently, Gutierrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bioinspired computing devices -- spiking neural P systems (for short, SN P systems). However, the binary enc...
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Recently, Gutierrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bioinspired computing devices -- spiking neural P systems (for short, SN P systems). However, the binary encoding mechanism used in their research looks like the encoding approach in electronic circuits, instead of the style of spiking neurons (in usual SN P systems, information are encoded as the time interval between spikes). In this work, three SN P systems are constructed as adder, subtracter and multiplier, respectively. In these devices, a number is inputted to the system as the interval of time elapsed between two spikes received by input neuron, the result of a computation is the time between the moments when the output neuron spikes.
The Non-Local Means (NLM) filter uses the redundancy of information in the image to remove noise, this scheme gives some of the best results among other powerful methods such as wavelet based approaches or diffusion t...
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So far, all of the approximate Jacobian controllers for robot manipulators proposed in the literatures have assumed that the exact joint velocity measurements are available. In this paper, we propose alternative contr...
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So far, all of the approximate Jacobian controllers for robot manipulators proposed in the literatures have assumed that the exact joint velocity measurements are available. In this paper, we propose alternative controller designs without the use of joint velocity measurements. To provide the joint velocities used by the controllers, we introduce the well-known sliding mode observers to estimate the robot manipulator states. In addition, Lyapunov analysis is presented to show that the combined controller-observer designs can achieve asymptotical stability in the sliding patch. Simulation results are also presented to show the performance of the proposed methods.
Feature extraction in brain-computer interface (BCI) work is an important task that significantly affects the success of brain signal classification. In this paper, a feature extraction method of electroencephalograph...
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Feature extraction in brain-computer interface (BCI) work is an important task that significantly affects the success of brain signal classification. In this paper, a feature extraction method of electroencephalographic (EEG) signals based on wavelet packet decomposition (WPD) is used. The coefficients mean of wavelet packet decomposition and wavelet packet energy of special sub-bands are employed as the original features. The Fisher discriminant analysis (FDA) is used to measure the separabilities of those features. The features which had a higher separability will be considered as effective ones and then the final feature vector are formed. A feature vector is obtained by combining the selected features from six channels. Then, the features are classified by using the k-nearest neighbor (k-NN) algorithm. We obtained significant improvement for the speed and accuracy of the classification for data set Ia, which is a typical representative of one kind of BCI competition 2003 data. The classification results have proved the effectiveness of the proposed method.
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