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IEEE/Institute of Complex Medical Engineering International Conference on Complex Medical Engineering

作者： Xu, Qi Huang, Tao He, Jiping Wang, Yizhao Zhou, Houlun Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China Harrington Department of Bioengineering Arizona State University Tempe AZ United States Tongji Medical College Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781424493241

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 this paper, a portable and low-cost versatile stimulator for multichannel TES applications is described. The microcontroller-based programmable stimulator with four bipolar output channels send arbitrarily specified electrical pulses to the element in the electrode array for enabling the complex schemes of muscles. The amplitude and phase of each pulse in the stimulus sequence are independently variable, and the four channels are independently programmable, allowing a wide variety of stimulus patterns. We also designed an output circuit that can provide bi-phasic, voltage-regulated, charge-balanced outputs while avoiding the electrochemical erosion of electrode due to charge accumulation. The proposed stimulator has provided an opportunity for further study of a potential novel electrical stimulator with closed-loop stimulation paradigm suitable for a variety of FES applications, both for experimental and clinical studies. © 2011 IEEE.

关键词： Functional electric stimulation

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Fabric defect detection using undecimated wavelet transform

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information Technology Journal 2011年第9期10卷 1701-1708页

作者： Bi, Mingde Sun, Zhigang Department of Control Science and Engineering Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology 1037 Luoyu Road 430074 Hubei Wuhan China

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 to the fabric texture. The objective of the scheme is to enhance the energy of defective region and attenuate the energy of non-defective region. And the performance of detection results with different number of decomposition scales is also discussed. A simple and computationally effective data fusion scheme combined with amplitudes division of wavelet coefficients is used to fuse data from multiple scales together. And several features based on defective energy estimation are extracted from fused image. By examining the extracted features the proposed algorithm can provide not only the location of defects but also some detailed information about them which can be used for defect recognition and classification. Experimental results of real fabric defects are provided to validate the effectiveness and robustness of the defect detection algorithm. © 2011 Asian Network for Scientific information.

关键词： Wavelet decomposition

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control of Upper-Limb Power-Assist Exoskeleton Based on Motion Intention Recognition

Control of Upper-Limb Power-Assist Exoskeleton Based on Moti...

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2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

作者： Yongji Wang Jian Huang Weiguang Huo Jun Wu Lei Cheng Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Technol Engineering Research Center of Metallurgical Automation and Measurement Technology Ministry of Educa

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.

关键词： Joints Exoskeletons Force Humans Robot sensing systems Jacobian matrices

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Echo State Networks Based Method for Harmonic Extraction in Shunt Active Power Filters

Echo State Networks Based Method for Harmonic Extraction in ...

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Jinbang Xu Jun Yang Feng Liu Zhixiong Zhang Anwen Shen Department of Control Science & Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science & Technology Wuhan China

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.

关键词： Harmonic analysis Power harmonic filters Training Training data Reservoirs Neurons

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ANN-based control Method Implemented in a Voltage Source Converter for Industrial Micro-grid

ANN-based Control Method Implemented in a Voltage Source Con...

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Jinbang Xu Zhizhuo Wu Xuan Yang Jie Ye Anwen Shen Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Department of Control Science & Technology Huazhong University of Science & Technology Wuhan China

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.

关键词： Voltage control Artificial neural networks Power conversion Regulators Voltage measurement Frequency control

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Deformation prediction of landslide based on genetic-simulated annealing algorithm and BP neural network

Deformation prediction of landslide based on genetic-simulat...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Huangqiong Chen Zhigang Zeng Department of Control Science and Engineering Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China

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.

关键词： Terrain factors Prediction algorithms Biological neural networks Artificial neural networks Annealing Predictive models Simulated annealing

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Spiking Neural P Systems for Arithmetic Operations

Spiking Neural P Systems for Arithmetic Operations

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International Conference on Bio-Inspired Computing: Theories and Applications, BICTA

作者： Xiangxiang Zeng Tao Song Linqiang Pan Xingyi Zhang Key Laboratory on Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China Key Laboratory of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Hefei Anhui China

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.

关键词： Neurons Tin Encoding Adders Educational institutions Generators Computational modeling

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Ultrasound image denoising with multi-shape patches aggregation based non-local means

Ultrasound image denoising with multi-shape patches aggregat...

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2011 IEEE International conference on intelligent Computation and Bio-Medical Instrumentation, ICBMI 2011

作者： Chen, Wanjun Ding, Mingyue Miao, Yalin Luo, Lei Department of Information Science Xi'an University of Technology Xi'an Shannxi 710048 China College of Life Science and Technology Lmage Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan Hubei 430074 China

ISBN: (纸本)9780769546230

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 techniques. Though simple to implement and efficient in practice, the classical NLM suffers from ringing artifacts around edges when using square patches, due to an abrupt lack of redundancy of the image. This paper presents an extended NLM based on Multi-Shape Patches Aggregation (NLM-MSPA) to overcome this problem, and uses it to remove medical ultrasound images corrupted by multiplicative speckle noise. We have incorporated a preprocessing step to make the speckle noise much closer to the real additive white Gaussian noise, hence more amenable to a denoising algorithm such as NLM-MSPA. Results on real images and artificially speckled images show that the proposed scheme outperforms several classical methods chosen for comparison in its ability to reduce speckle and preserve edge details. © 2011 IEEE.

关键词： Speckle

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Approximate Jacobian control of robot manipulators without joint velocity measurements

Approximate Jacobian control of robot manipulators without j...

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IEEE International Conference on Cyber Technology in Automation, control, and intelligent Systems

作者： Xinwu Liang Xinhan Huang Min Wang Key Laboratory of Image Processing and Intelligent Control of Ministry of Education of China Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan Hubei China

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.

关键词： Joints Jacobian matrices Velocity measurement Observers Manipulators Robot kinematics

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Feature extraction of motor imagery EEG signals based on wavelet packet decomposition

Feature extraction of motor imagery EEG signals based on wav...

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IEEE/ICME International Conference on Complex Medical Engineering, CME

作者： Dingyin Hu Wei Li Xi Chen Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Department of Control Science and Engineering Huazhong University of Science & Technology Wuhan China 430074

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.

关键词： Feature extraction Classification algorithms Electroencephalography

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