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Brain Activity during Walking in Patient with Spinal Cord Injury

Brain Activity during Walking in Patient with Spinal Cord In...

2011 International Symposium on Bioelectronics and Bioinformatics(第二届国际生物医学电子学与生物信息学学术会议 ISBB 2011)

作者： Jiping He Xu Jiang Li Wei Huang Yue member of Image Processing and Intelligent Control Key Laboratory of Education Ministry of China De student of Department of Control Science and Engineering Huazhong University of Science and Technol

The capacity for walking is an important assessment to reflect the ability about how the patients who have movement disorders to control their lower limbs. Electroencephalography (EEC), which can describe brain activities, has been widely used in the field of neural engineering. The present study investigates 2D scalp topography mapping while the subjects perform gait-like movements with the assistance of a dynamic tilt table. Based on the result, we concluded that the motor function and the most nerve circuit of lower limbs in spinal cord injury (SCI) patients in level D are as normal as healthy human.

关键词： Scalp Electroencephalography Legged locomotion Surfaces Humans Spinal cord Electrodes

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A novel estimation strategy for the eye-in-hand depth-independent visual servoing

A novel estimation strategy for the eye-in-hand depth-indepe...

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International Conference on Consumer Electronics, Communications and Networks

作者： Xinwu Liang Xinhan Huang Min Wang 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 Hubei China

ISBN: (纸本)9781612844589

As we know, a novel adaptive visual servoing strategy has been proposed for the control of robot manipulators with an eye-in-hand configuration, where an adaptive law is used to estimate the unknown parameters determined by the products of the unknown 3D coordinates and the unknown camera parameters. From a practical point of view, in this paper, we propose a novel strategy for the estimation of these two types of unknown parameters. The new strategy is very useful in that it can reduce the number of the unknown parameters to be estimated. To demonstrate the feasibility of the proposed method, preliminary simulation results based on a two-link planar robot manipulator are presented in this paper.

关键词： Cameras Visual servoing Robot kinematics Manipulators Three dimensional displays Estimation

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Assignment-Driven Multi-consensus in Second-order Multi-agent Systems via Impulsive control with Heterogeneous delays

Assignment-Driven Multi-consensus in Second-order Multi-agen...

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IEEE International Conference on control and Automation

作者： Yao-Wen Wei Jing-Wen Yi Yan-Wu Wang Jiang-Wen Xiao School of Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education

ISBN: (纸本)9781509017393

An assignment-driven multi-consensus problem of multi-agent systems with heterogeneous delays is investigated in this paper. By introducing a concept of intelligent adjustment factor, a distributed impulsive protocol is designed, in which only the delayed sampled relative positions to neighbors and the relative position to the last sampling state are utilized. Combined with the characteristic of impulsive control, three steps of model transformation are performed, and the assignment-driven multi-consensus problem of the original continuous-time system is converted to the stability problem of a discrete-time expanded analogous error system. A necessary and sufficient criterion is derived to guarantee the assignment-driven multi-consensus of the multi-agent system. An interesting simulation is proposed to demonstrate the effectiveness of the theoretical result and reveal the effect of intelligent adjustment factors on the assignment-driven multi-consensus.

关键词： Protocols Delays Multi-agent systems Network topology Automation Stability criteria

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Denosing 3D Ultrasound images by Non-local Means Accelerated by GPU

Denosing 3D Ultrasound Images by Non-local Means Accelerated...

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International Conference on intelligent Computation and Bio-Medical Instrumentation (ICBMI)

作者： Song Hu WenGuang Hou Department of Biomedical Engineering Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China HuaZhong University of Science and technology Wuhan China

In Ultrasound imaging, speckle noise is the most serious problem which affects the performance of images. Non-local mean filter is a nice method to remove the speckle noise, but the algorithm' computational complexity makes it's a highly time-consuming method. In many applications like image-guided surgical intervention, real-time de-noising is required. This paper implements a NLM method accelerated by GPU for real-time denoising of 3D ultrasound images. The experimental results show the proposed accelerated de-noising method is efficient in terms of denoising quality and real-time.

关键词： Graphics processing unit Three dimensional displays Ultrasonic imaging Noise reduction Biomedical imaging Filtering algorithms Speckle

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A pushing-grasping collaborative method based on deep q-network algorithm in dual perspectives

arXiv

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arXiv 2021年

作者： Gang, Peng Jinhu, Liao Shangbin, Guan Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Aiming at the traditional grasping method for manipulators based on 2D camera, when faced with the scene of gathering or covering, it can hardly perform well in unstructured scenes that appear as gathering and covering, for the reason that can’t recognize objects accurately in cluster scenes from a single perspective and the manipulators can’t make the environment better for grasping. In this case, a novel method of pushing-grasping collaborative based on the deep Q-network in dual perspectives is proposed in this paper. This method adopts an improved deep Q-network algorithm, with an RGB-D camera to obtain the information of objects’ RGB images and point cloud from two perspectives, and combines the pushing and grasping actions, so that the trained manipulator can make the scenes better for grasping, so that it can perform well in more complicated grasping scenes. What’s more, we improved the reward function of the deep Q-network and propose the piecewise reward function to speed up the convergence of the deep Q-network. We trained different models and tried different methods in the V-REP simulation environment, and it drew a conclusion that the method proposed in this paper converges quickly and the success rate of grasping objects in unstructured scenes raises up to 83.5%. Besides, it shows the generalization ability and well performance when novel objects appear in the scenes that the manipulator has never grasped before. Copyright © 2021, The Authors. All rights reserved.

关键词： Manipulators

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TMMM: Transformer in Multimodal Sentiment Analysis under Missing Modalities

TMMM: Transformer in Multimodal Sentiment Analysis under Mis...

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International Joint Conference on Neural Networks (IJCNN)

作者： Huanyu Wu Siyang Li Dongrui Wu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The cross-task gap presents a significant challenge for multimodal models because of the differences in input-output workflows. For instance, multimodal pre-trained transformers may encounter uni-modal data during testing. To mitigate the gap, this paper introduces a Transformer in Multimodal Sentiment Analysis under Missing Modalities (TMMM) aims to perform well using missing-modal data during testing. TMMM uses a missing multimodal training approach to prevent accuracy degradation in testing. At the same time, a new network architecture allows the model to reconstruct missing modalities during testing. Classification token fusion and Mixture-of-Experts structures further enhance the model’s performance. A pre-training method utilizing contrastive learning, which can construct negative samples with positive samples, is proposed to overcome insufficient labeled data. Our experiments demonstrated the effectiveness of TMMM on two datasets with no modalities missing, i.e., it consistently achieved the highest classification accuracy and Macro-F1, which outperformed the best state-of-the-art baseline on each dataset by about 2% and 2.5%. Additionally, TMMM usually performs better than other baselines on datasets with missing modalities during testing.

关键词： Training Sentiment analysis Accuracy Contrastive learning Network architecture Transformers Brain modeling

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Distributed second order sliding mode secondary frequency control for HVDC system

Distributed second order sliding mode secondary frequency co...

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Chinese Automation Congress (CAC)

作者： Yang-Ming Dou Ming Chi Zhi-Wei Liu Key Laboratory of Image Processing and Intelligent Control Ministry of Education College of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This paper studies the secondary frequency control among non-synchronous AC areas interconnected by High Voltage Direct Current (HVDC). A distributed second order sliding mode control scheme is adopted to secondary frequency control of HVDC transmission systems to adjust the frequency of power grid to rated value, which solves the frequency disturbance caused by load power change of the DC power grid. And on this basis, the power generation in each region is reasonably distributed, so as to minimize the cost of power generation. Finally, the stability of the system is proved on an appropriate sliding manifold.

关键词： Frequency control HVDC transmission Power system stability Economics Sliding mode control Power system dynamics Manifolds

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Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression

arXiv

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arXiv 2020年

作者： Liu, Ziang Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

—In many real-world machine learning applications, unlabeled samples are easy to obtain, but it is expensive and/or time-consuming to label them. Active learning is a common approach for reducing this data labeling effort. It optimally selects the best few samples to label, so that a better machine learning model can be trained from the same number of labeled samples. This paper considers active learning for regression (ALR) problems. Three essential criteria – informativeness, representativeness, and diversity – have been proposed for ALR. However, very few approaches in the literature have considered all three of them simultaneously. We propose three new ALR approaches, with different strategies for integrating the three criteria. Extensive experiments on 12 datasets in various domains demonstrated their effectiveness. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

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Center Difference Set Membership Filter by Zonotopes for Nonlinear System

Center Difference Set Membership Filter by Zonotopes for Non...

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International Conference on Modelling, Identification and control (ICMIC)

作者： Rong Cheng Jian Huang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei P.R. China

The paper proposes an improved approach to state estimation for nonlinear discrete-time systems based on zonotopes. To overcome the inherent defect of Taylors formula, a lower-order multi-dimensional extension of Stirling's interpolation formula is used to realize the linearization of nonlinear models. A nonlinear programming method is used to optimize the guaranteed margin of linearization error to obtain a more compact bound estimation, thereby reducing the conservativeness of the algorithm. Simulation results have shown the effectiveness and improved performance of the proposed algorithm.

关键词： state estimation Taylor series Kalman filters Strips Nonlinear systems Interpolation Noise measurement

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Automatic Reading System for Analog Instruments Based on Computer Vision and Inspection Robot for Power Plant

Automatic Reading System for Analog Instruments Based on Com...

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International Conference on Modelling, Identification and control (ICMIC)

作者： Junzhe Wang Jian Huang Rong Cheng Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei P.R. China

In this paper, an automatic reading system for analog instruments has been designed for monitoring in power plants. In a general automatic reading system, there are many limitations to the reading correctness and system robustness such as the high cost for the reason that there is only one instrument could be monitored by a single system, the strict restrict of the camera angle, and being not suitable for instruments with dense scales and so on. We present some solutions to overcome these limitations. Firstly, we combine a mobile inspection robot with an image capture device and the image processing method to cut the cost of the monitoring of analog instruments in power plants. Then, we use Hough transform and perspective transform to correct the geometric distortion of images caused by camera angle. Eventually, we get the result of dense scaled instruments based on polar transform. Experiments show that our system performs quite well, and the reading error is less than the results which obtained from the general automatic reading system.

关键词： Instruments Cameras Inspection Robot kinematics Transforms Monitoring

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