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IEEE International Conference on intelligent Computing and intelligent Systems (ICIS)

作者： Fuqiang Xie Yongji Wang Chuanfeng Li Dangjun Zhao Department of Control Science and Engineering Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781424447374;9781424447541

This paper proposes a rapid trajectory optimization approach using a novel PSO algorithm, the Migrant particle swarm optimization (Migrant PSO). Imitating the behavior of a flock of migrant birds, the Migrant PSO algorithm employs stochastic search method and adaptive linear search method respectively for PSO search spaces including both continuous space and discrete space. In the example of the minimum control energy reentry trajectory optimization for X-33 vehicle model with free terminal time, some key problems such as parameterized method are argued in detail. The simulation results indicate that the Migrant PSO algorithm proves to be able to generate a complete and optimal 3DOF reentry trajectory rapidly.

关键词： Birds Stochastic processes Search methods Optimization methods Space technology Particle swarm optimization Vehicles Design optimization Programmable control Adaptive control

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Active Disturbance Rejection Guidance for Ascent Using Quadratic Transition

Active Disturbance Rejection Guidance for Ascent Using Quadr...

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International Symposium on Computer, Consumer and control (IS3C)

作者： Xuefang Lu Yongji Wang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China

This paper presents an active disturbance rejection guidance method using quadratic transition for the atmospheric ascent guidance problem. The quadratic transition is designed from the current flight states with a reference trajectory planned off-board. For any states error, the solution of the designed quadratic transition converges to the reference trajectory. The guidance command is obtained from the derivative of the designed quadratic transition. For the unknown modeling error, the guidance command is compensated using the estimated disturbances from the extended state observer. Computer simulation for GHV (Generic Hypersonic Air Vehicle) model, which includes initial states error and modeling error, shows great accuracy and effectiveness of this guidance method.

关键词： Trajectory Atmospheric modeling Computational modeling Aerodynamics Vehicles Observers Accuracy

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Adaptive event-triggered consensus for multi-agent systems with stochastic switching topologies and delays

Adaptive event-triggered consensus for multi-agent systems w...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Dong Yue Xiuxia Yin Department of Control Science and Engineering and the Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781467351942

This paper is concerned with the consensus problem for a class of Lipschitz nonlinear agents with Markov switching topologies and time-varying delays. The distributed event-triggered consensus control with an adaptive law in adjusting the coupling weights between neighboring agents is designed, which can not only guarantee the consensus performance in the mean square sense but also reduce the communication burden since the introduction of the event-triggered communication scheme. Different from the traditional event-triggers in the existing references, the parameter of the event-trigger in this paper is adaptively adjusted by using an adaptive law. A convincing simulation example is given to illustrate the theoretical results.

关键词： multi-agent system (MAS) adaptive event-triggered control random switching topology LMI Event trigger Self tuning Interim Local Management Interface Multi-agent systems Stochastic topology Markov

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Unsupervised Ensemble Learning for Class Imbalance Problems

Unsupervised Ensemble Learning for Class Imbalance Problems

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Chinese Automation Congress

作者： Zihan Liu Dongrui Wu Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China

Ensemble learning, which aggregates multiple base (weak) learners to obtain a strong learner, is an effective approach for improving the generalization performance of a machine learning model. Several completely unsupervised ensemble learning approaches have been proposed in the literature for binary classification. However, most of them only considered the case that the two classes are balanced, and hence their performances deteriorate when there is significant class imbalance, which often happens in practice. This paper proposes a spectral meta-learner for class imbalance (SMLCI) approach to explicitly consider the class imbalance. Experiments on 12 UCI datasets from various domains verified that SMLCI significantly outperformed the individual base classifiers, and also five existing unsupervised ensemble learning approaches, when the balanced classification accuracy is used as the performance measure.

关键词： Ensemble learning Unsupervised learning Class imbalance Binary classification

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An application of circumscribed circle filter in the multi-stencils fast marching method 12

An application of circumscribed circle filter in the multi-s...

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27th Annual ACM Symposium on Applied Computing, SAC 2012

作者： Liu, Hong Hung, Chih-Cheng Hu, Huaifei Yu, Mali Song, Enmin School of Computer Science Huazhong University of Science and Technology Wuhan China Key Laboratory of Education Ministry for Image Processing and Intelligent Control Wuhan Hubei 430074 China School of Computing and Software Engineering Southern Polytechnic State University Marietta GA 30060 United States

ISBN: (纸本)9781450308571

We develop an effective method for improving the segmentation result based on the Multi-Stencils Fast Marching method (MSFM). In MSFM, the gradient information of the image plays a vital role for calculating edges. It is straightforward to obtain the edge of good quality images;however, MSFM may not have robust edge maps available for images with spurious edges. Thus, a special multi-direction circumscribed circle filter is proposed to calculate the image gradient information which is then used in the MSFM. Using the new gradient information, better image contours can be obtained with MSFM. The size of the radius used in our circle filter is constant even the standard deviation of zero-mean Gaussian noise changes while the parameters of mean filter and Canny filter for gradient computation have to be correctly selected according to different noisy images. Our proposed method shows that it is effective through the experiments of image segmentation. © 2012 ACM.

关键词： image segmentation

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM image processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China

ISBN: (纸本)9781728162157

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools. © 2020 IEEE.

关键词： image reconstruction

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AI WALKUP: A Computer-Vision Approach to Quantifying MDS-UPDRS in Parkinson's Disease

arXiv

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arXiv 2024年

作者： Xiang, Xiang Zhang, Zihan Ma, Jing Deng, Yao Key Lab of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Parkinson's Disease (PD) is the second most common neurodegenerative disorder. The existing assessment method for PD is usually the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to assess the severity of various types of motor symptoms and disease progression. However, manual assessment suffers from high subjectivity, lack of consistency, and high cost and low efficiency of manual communication. We want to use a computer vision based solution to capture human pose images based on a camera, reconstruct and perform motion analysis using algorithms, and extract the features of the amount of motion through feature engineering. The proposed approach can be deployed on different smartphones, and the video recording and artificial intelligence analysis can be done quickly and easily through our APP. Copyright © 2024, The Authors. All rights reserved.

关键词： Computer vision

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Cell resistance slope combined with LVQ neural network for prediction of anode effect 6

Cell resistance slope combined with LVQ neural network for p...

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6th International Conference on intelligent control and Information processing, ICICIP 2015

作者： Zhou, Kaibo Lin, Zhikai Yu, Dengzhi Cao, Bin Wang, Ziqian Guo, Sihai School of Automation Huazhong University of Science and Technology Key Laboratory of Ministry of Education Image Processing and Intelligent Control Wuhan430074 China Guiyang Aluminum Magnesium Design and Research Institute Guiyang550004 China School of Automation Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9781479917174

It is an important part in aluminum electrolysis production to control the anode effect (AE). Since there are some shortcomings in traditional methods of anode effect prediction in aluminum electrolysis, this paper combined two methods, the slope of cell resistance and learning vector quantization (LVQ) neural network, to predict anode effect. First of all, the first prediction of anode effect will be conducted based on the slope of cell resistance. Afterwards, the inaccurate data are supposed to be re-predicted. The second prediction consists of two steps, one is to estimate the power spectrum from the signal of cell resistance by means of periodogram, the other is to re-predict the anode effect with the LVQ neural network, since the energy of frequency bands are served as the input feature variables of neural network, so as to raise the accuracy of prediction. It turned out that the success rate of ten-minute in advance prediction for anode effect can be above 85%, though just cell resistance signal is studied. © 2015 IEEE.

关键词： Forecasting

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Tiny noise, big mistakes: adversarial perturbations induce errors in brain–computer interface spellers

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National Science Review 2021年第4期8卷 78-90页

作者： Xiao Zhang Dongrui Wu Lieyun Ding Hanbin Luo Chin-Teng Lin Tzyy-Ping Jung Ricardo Chavarriaga Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology School of Civil Engineering and Mechanics Huazhong University of Science and Technology Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Swartz Center for Computational Neuroscience Institute for Neural ComputationUniversity of California San Diego Center for Advanced Neurological Engineering Institute of Engineering in Medicine University of California San Diego ZHAW Data Lab Zürich University of Applied Sciences

An electroencephalogram(EEG)-based brain–computer interface(BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g. amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a *** studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e. they can be severely attacked by adversarial perturbations,which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.

关键词： electroencephalogram brain-computer interfaces BCI spellers adversarial examples

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Robust ICA neural network and application on Synthetic Aperture Radar (SAR) image analysis

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13th International Conference on Neural Information processing, ICONIP 2006

作者： Jian, Ji Zheng, Tian Department of Computer Science and Technology Northwestern Polytechnical University Xi'an 710072 China Department of Applied Mathematics Northwestern Polytechnical University Xi'an 710072 China Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)3540464816

Independent component analysis (ICA) has shown success in the separation of sources in lots of applications. However, in synthenic aperture radar (SAR) images the noise is multiplicative, so the applicability of ICA is seriously reduced. This paper proposes a new robust independent component analysis neural network (RICANN) that improves the robustness of ICA by adding outlier rejection rule. Its application in synthetic aperture radar (SAR) is discussed. The results show the potential usage in SAR image processing problems. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Synthetic aperture radar

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