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arXiv 2017年

作者： Huang, Xiaolin Yan, Ming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing Shanghai200240 China Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University East LansingMI48824 United States

One-bit measurements widely exist in the real world and can be used to recover sparse signals. This task is known as one-bit compressive sensing (1bit-CS). In this paper, we propose novel algorithms based on both convex and nonconvex sparsity-inducing penalties for robust 1bit-CS. We consider the dual problem, which has only one variable and provides a sufficient condition to verify whether a solution is globally optimal or not. For positive homogeneous penalties, a globally optimal solution can be obtained in two steps: a proximal operator and a normalization step. For other penalties, we solve the dual problem, and it needs to evaluate the proximal operators for many times. Then we provide fast algorithms for finding analytical solutions for three penalties: minimax concave penalty (MCP), 0norm, and sorted 1penalty. Specifically, our algorithm is more than 200 times faster than the existing algorithm for MCP. Its efficiency is comparable to the algorithm for the 1penalty in time, while its performance is much better than 1. Among these penalties, sorted 1is most robust to noise in different settings. Copyright © 2017, The Authors. All rights reserved.

关键词： Compressed sensing

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Down-Level control Based on Level Prediction of Landslide Evolutionary state

Down-Level Control Based on Level Prediction of Landslide Ev...

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International Conference on Information Science and Technology (ICIST)

作者： Shu Sun Cheng Lian Xiaoping Wang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Automation Wuhan University of Technology Wuhan China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

Accurate prediction and effective control are the key to reducing the impact of landslide disasters. In practice, most of recent studies focus on landslide displacement prediction and control. In this paper, we propose a new control method based on the level prediction of landslide evolution state, namely down-level control. The core components are level predictor and interval predictor in this method. Specially, we first transform the traditional displacement regression prediction problem into a level classification prediction problem by labeling discrete category information for landslide sample points. Then the level predictor based on Multi-task learning-Stacked long-short time memory network (MTL-SLSTM) is established to predict the state of the landslide and judge whether the system needs to be controlled. Finally, we design a safe rainfall interval predictor based on bootstrap method to obtain the safe value of control variation. The effectiveness of the proposed control method is verified on Baishuihe and Shiliushubao landslides. The results show the proposed down-level control method is valid and more intuitive.

关键词： Information science Geology Transforms Predictive models Terrain factors Complexity theory Labeling

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Sensing environment with mobile robot by applying fuzzy-extended DSmT

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Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) 2008年第SUPPL. 1期36卷 113-115页

作者： Li, Xinde Huang, Xinhan Dai, Xianzhong Meng, Zhengda Key Laboratory of Image Processing and Intelligent Control Southeast University Nanjing 210096 China Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

Dezert-smarandache theory (DSmT) was extended with fuzzy theory by considering the different Fuzzy T-norm operators, in order to develop a more general and flexible combinational rule for more extensive application. At the same time, fuzzy-extended DSmT was applied to mobile robot's sensing environment with the help of new self-localization method based on δ neighboring field appearance matching and also the perception effect was compared with different T-norm operators. Finally, an effective approach to solv sensing fusion of uncertainty environment was found.

关键词： Mobile robots

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Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfaces

arXiv

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arXiv 2024年

作者： Meng, Lubin Jiang, Xue Chen, Xiaoqing Liu, Wenzhong Luo, Hanbin Wu, Dongrui Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is a common input signal for BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals, while ignoring their security. Recent studies have shown that machine learning models in BCIs are vulnerable to adversarial attacks. This paper proposes adversarial filtering based evasion and backdoor attacks to EEG-based BCIs, which are very easy to implement. Experiments on three datasets from different BCI paradigms demonstrated the effectiveness of our proposed attack approaches. To our knowledge, this is the first study on adversarial filtering for EEG-based BCIs, raising a new security concern and calling for more attention on the security of BCIs. © 2024, CC BY.

关键词： Adversarial machine learning

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Instance Consistency Regularization for Semi-Supervised 3D Instance Segmentation

arXiv

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arXiv 2024年

作者： Wu, Yizheng Pan, Zhiyu Wang, Kewei Li, Xingyi Cui, Jiahao Xiao, Liwen Lin, Guosheng Cao, Zhiguo The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China S-Lab Nanyang Technological University Singapore639798 Singapore Nanyang Technological University Singapore639798 Singapore

Large-scale datasets with point-wise semantic and instance labels are crucial to 3D instance segmentation but also expensive. To leverage unlabeled data, previous semi-supervised 3D instance segmentation approaches have explored self-training frameworks, which rely on high-quality pseudo labels for consistency regularization. They intuitively utilize both instance and semantic pseudo labels in a joint learning manner. However, semantic pseudo labels contain numerous noise derived from the imbalanced category distribution and natural confusion of similar but distinct categories, which leads to severe collapses in self-training. Motivated by the observation that 3D instances are non-overlapping and spatially separable, we ask whether we can solely rely on instance consistency regularization for improved semi-supervised segmentation. To this end, we propose a novel self-training network InsTeacher3D to explore and exploit pure instance knowledge from unlabeled data. We first build a parallel base 3D instance segmentation model DKNet, which distinguishes each instance from the others via discriminative instance kernels without reliance on semantic segmentation. Based on DKNet, we further design a novel instance consistency regularization framework to generate and leverage high-quality instance pseudo labels. Experimental results on multiple large-scale datasets show that the InsTeacher3D significantly outperforms prior state-of-the-art semi-supervised approaches. Code is available: https://***/W1zheng/InsTeacher3D. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Observer-Based Robust Containment control of Multi-agent Systems With Input Saturation

Observer-Based Robust Containment Control of Multi-agent Sys...

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Chinese control Conference (CCC)

作者： Juan Qian Xiaoling Wang Guo-Ping Jiang Housheng Su College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications and Jiangsu Engineering Lab for IOT Intelligent Robots(IOTRobot) Nanjing PR China School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry ofChina Huazhong University of Science and Technology Wuhan PR China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In this paper, the robust containment control problem of the leader-following multi-agent systems with input saturation and input additive disturbance is addressed, where the followers can be informed by multiple leaders. With the help of the lowand-high gain feedback technique and the high-gain observer approach, a distributed control algorithm for each agent is firstly designed by using the observed output information, then sufficient conditions are provided to guarantee the semi-global robust containment of the system. Finally, some numerical simulations are given to verify the correctness of the theoretical results.

关键词： Multi-agent systems Robustness Observers Electronic mail state feedback Additives

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Region stability analysis and tracking control of memristive recurrent neural network

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Neural networks : the official journal of the International Neural Network Society 2018年 98卷 51-58页

作者： Gang Bao Zhigang Zeng Yanjun Shen Hubei Key Laboratory of Cascaded Hydropower Stations Operation & Control Electrical Engineering & New Energy China Three Gorges University Yichang 443002 China. Electronic address: ctgugangbao@***. China School of Automation Huazhong University of Science and Technology Wuhan Hubei 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: zgzeng@***. Hubei Key Laboratory of Cascaded Hydropower Stations Operation & Control Electrical Engineering & New Energy China Three Gorges University Yichang 443002 China. Electronic address: shenyj@***.

Memristor is firstly postulated by Leon Chua and realized by Hewlett-Packard (HP) laboratory. Research results show that memristor can be used to simulate the synapses of neurons. This paper presents a class of recurrent neural network with HP memristors. Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations. Then it derives that n dimensional memristive recurrent neural network is composed of [Formula: see text] sub neural networks which do not have a common equilibrium point. By designing the tracking controller, it can make memristive neural network being convergent to the desired sub neural network. At last, two numerical examples are given to verify the validity of our result.

关键词： Memristive neural network Memristor Stability

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A Generalized Subspace Distribution Adaptation Framework for Cross-Corpus Speech Emotion Recognition

A Generalized Subspace Distribution Adaptation Framework for...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shaokai Li Peng Song Liang Ji Yun Jin Wenming Zheng School of Computer and Control Engineering Yantai University China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province School of Physics and Electronic Engineering Jiangsu Normal University China Key Laboratory of Child Development and Learning Science (Southeast University) Ministry of Education Research Center for Learning Science Southeast University China

In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a common low-dimensional feature subspace by utilizing a generalized subspace learning method. Second, we develop a novel distance metric to reduce the divergence between the source and target corpora, which can efficiently explore the similarity and dissimilarity information in the process of knowledge transfer. Third, to demonstrate the effectiveness of our framework, we apply GSDA to the traditional subspace learning algorithms. Finally, we conduct extensive experiments by using the low-level features and deep features on three popular emotional databases, i.e., Berlin, IEMOCAP, and CVE. The results demonstrate that the proposed framework can achieve better performance than several state-of-the-art transfer learning approaches.

关键词： Measurement Learning systems Emotion recognition Databases Transfer learning Signal processing algorithms Speech recognition

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A wearable device for repetitive hand therapy

A wearable device for repetitive hand therapy

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Kexin Xing Qi Xu Jiping He Yongji Wang Zhongwei Liu Xiaolin Huang Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Technology Huazhong University of Science and Technology Wuhan China Harrington Department of Bioengineering and Center for Neural Interface Design at the Biodesign Institute Arizona State University Tempe AZ USA Department of Rehabilitation Medicine Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China

Intensive task-oriented repetitive physical therapies provided by individualized interaction between the patient and a rehabilitation specialist can improve hand motor performance in patients survived from stroke and traumatic brain injury. However, the therapy process is long and expensive and difficult to evaluate quantitatively and objectively. The goal of this research is to develop a novel wearable device for robotic assisted hand repetitive therapy. We designed a pneumatic muscle (PM) driven therapeutic device that is wearable and provides assistive forces required for grasping and release movements. The robot has two distinct degrees of freedom at the thumb and the fingers. The embedded sensors feedback position and force information for robot control and quantitative evaluation of task performance. It has the potential of providing supplemental at-home therapy in addition to in the clinic treatment.

关键词： Medical treatment Robot sensing systems Force feedback Brain injuries Rehabilitation robotics Muscles Grasping Thumb Fingers Wearable sensors

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Molecular model for information addressing and transporting based on DNA nanotechnology

Communications in Computer and Information Science

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Communications in Computer and Information Science 2014年 472卷 29-34页

作者： Chen, Zhihua Qiang, Xiaoli Zhang, Kai Key Laboratory of Image Information Processing and Intelligent Control School of Automation Huazhong University of Science and Technology WuhanHubei430074 China College of Computer Science South-Central University for Nationalities WuhanHubei430074 China School of Computer Science Wuhan University of Science and Technology WuhanHubei430081 China

ISBN: (纸本)9783662450482

DNA-based information processing includes storage, addressing, transporting and error-correction, etc. Here we describe a feasible model that can extract and transport the information from specified sources to targeted destinations. This model is established based on the DNA nanotechnologies of hybridization, DNA strand displacement and nanoparticle aggregation reactions. By adding the extracting strands, the data strands of specified sources can be extracted, and then transported to the targets by the transporting strands. The result could be detected by observing the change of color through the naked eyes. Theoretical analysis indicates that thismodel could achieve the parallel information extracting and transporting from different specified sources to different targeted destination, which offers a realistic technology for the flexibility of managing information. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Error correction

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