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Nonlinear H ∞ observer design for one-sided Lipschitz systems

Neurocomputing 2014年 145卷 505-511页

作者： Wei Zhang Housheng Su Shengchao Su Dazhong Wang Laboratory of Intelligent Control and Robotics Shanghai University of Engineering Science Shanghai 201620 China School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China College of Information Science and Technology Donghua University Shanghai 201620 China

This paper is considered with the H ∞ observer design problem for a class of nonlinear systems with the one-sided Lipschitz condition. The systems under consideration include the well-studied Lipschitz system as a special case and possess inherent advantages with respect to conservativeness. For such systems in the presence of noises, we develop a Linear Matrix Inequality (LMI) based approach to design a nonlinear H ∞ observer by carefully dealing with the one-sided Lipschitz condition together with the quadratic inner-bounded condition. The resulting nonlinear H ∞ observer guarantees asymptotic stability of the estimation error dynamics with a prescribed H ∞ performance. Moreover, for the design purpose, the existence condition of the proposed nonlinear H ∞ observer is formulated in terms of LMIs by using a matrix generalized inverse technique. Finally, a simulation example is given to illustrate the effectiveness of the proposed design.

关键词： One-sided Lipschitz nonlinear systems Nonlinear H ∞ observers Observer design state estimation

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Transmission-Constrained Consensus of Multiagent Networks

arXiv

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arXiv 2022年

作者： Wang, Xiaotian Su, Housheng School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China

This paper studies the consensus problem for multiagent systems with transmission constraints. A novel model of multiagent systems is proposed where the information transmissions between agents are disturbed by irregular distortions or interferences (named transmission constraint functions), and this model is universal which can be applied in many cases, such as interval consensus and discarded consensus. In the transmission-constrained consensus problem, we obtain the necessary and sufficient condition that agents can converge to state consensus. Furthermore, a more general case is studied in which the system reaches an equilibrium. Based on some techniques of algebraic topology and stability theory, the existence, uniqueness and stability of the system equilibrium point can be proven, which means the system can reach an asymptotically stable equilibrium. Moreover, the state values of the equilibrium are only decided by the network structure and transmission constraint functions, but not the agents’ initial states. Finally, numerical simulations are presented to illustrate the proposed theorems and corollaries. © 2022, CC BY-NC-SA.

关键词： Directed graphs

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A Less Conservative Criterion for Exponential Mean-square Stability of Networked control Systems with Stochastic Packet Dropouts

A Less Conservative Criterion for Exponential Mean-square St...

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第三十二届中国控制会议

作者： Wen-Min Zhou Yan Wu Yan-Wu Wang Jiang-Wen Xiao School of Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan Institute of Marine Electric Propulsion CSIC

ISBN: (纸本)9781479900305

This paper is concerned with the modeling and the control of networked control systems(NCSs) with stochastic packet *** order to facilitate the technical description,network-induced time delay is assumed to be *** closed-loop NCS with stochastic packet dropout is modeled as a continuous-time system with time-varying delays and stochastic parameters. By utilizing the Lyapunov-Krasovskii functional method,the integral inequality lemma and the Gronwall-Bellman lemma,a less conservative criterion for exponential mean-square stability of NCS with stochastic packet dropout is presented in the form of nonlinear matrix ***,the state feedback gain matrix is provided based on solving a linear matrix inequality (LMI).Moreover,the state feedback gain matrix is obtained by solving a linear matrix inequality(LMI).Based on the solvability of this LMI,the allowable upper bound of continuous packet dropouts is determined,from which the maximum allowable time interval(MATI) can also be ***,a numerical example is given to demonstrate the effectiveness of our results.

关键词： exponential mean-square stability networked control system stochastic packet dropout maximum allowable time interval

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Saliency Regularization for Self-Training with Partial Annotations

Saliency Regularization for Self-Training with Partial Annot...

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International Conference on Computer Vision (ICCV)

作者： Shouwen Wang Qian Wan Xiang Xiang Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan Research Institute of Posts and Telecommunications

Partially annotated images are easy to obtain in multi-label classification. However, unknown labels in partially annotated images exacerbate the positive-negative imbalance inherent in multi-label classification, which affects supervised learning of known labels. Most current methods require sufficient image annotations, and do not focus on the imbalance of the labels in the supervised training phase. In this paper, we propose saliency regularization (SR) for a novel self-training framework. In particular, we model saliency on the class-specific maps, and strengthen the saliency of object regions corresponding to the present labels. Besides, we introduce consistency regularization to mine unlabeled information to complement unknown labels with the help of SR. It is verified to alleviate the negative dominance caused by the imbalance, and achieve state-of-the-art performance on Pascal VOC 2007, MS-COCO, VG-200, and Openimages V3.

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Quantum statistical edge detection using path integral monte carlo simulation

Communications in Computer and Information Science

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Communications in Computer and Information Science 2014年 472卷 430-434页

作者： Sun, Yangguang State Key Laboratory of Software Engineering Wuhan University WuhanHubei430072 China School of Computer Science South-Central University for Nationalities Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology WuhanHubei430074 China

ISBN: (纸本)9783662450482

A novel statistical method using path integral Monte Carlo simulation based on quantum mechanics to detect edges of interested objects was proposed in this paper. Our method was inspired by essential characteristics of quantum, and based on the quantum particle movement evolved towards the edge position with high probability density in the gradient-based image potential field. The discussion about computational complexity and parameter settings demonstrated the feasibility and robustness of our method. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Quantum theory

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Modality-aware triplet hard mining for zero-shot sketch-based image retrieval

arXiv

引用

arXiv 2021年

作者： Huang, Zongheng Sun, Yifan Han, Chuchu Gao, Changxin Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Baidu Research United States

This paper tackles the Zero-Shot Sketch-Based image Retrieval (ZS-SBIR) problem from the viewpoint of cross-modality metric learning. This task has two characteristics: 1) the zero-shot setting requires a metric space with good within-class compactness and the between-class discrepancy for recognizing the novel classes and 2) the sketch query and the photo gallery are in different modalities. The metric learning viewpoint benefits ZS-SBIR from two aspects. First, it facilitates improvement through recent good practices in deep metric learning (DML). By combining two fundamental learning approaches in DML, e.g., classification training and pairwise training, we set up a strong baseline for ZS-SBIR. Without bells and whistles, this baseline achieves competitive retrieval accuracy. Second, it provides an insight that properly suppressing the modality gap is critical. To this end, we design a novel method named Modality-Aware Triplet Hard Mining (MATHM). MATHM enhances the baseline with three types of pairwise learning, e.g., a cross-modality sample pair, a within-modality sample pair, and their combination. We also design an adaptive weighting method to balance these three components during training dynamically. Experimental results confirm that MATHM brings another round of significant improvement based on the strong baseline and sets up new state-of-the-art performance. For example, on the TU-Berlin dataset, we achieve 47.88+2.94% mAP@all and 58.28+2.34% Prec@100. Code will be publicly available. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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A Dual-branch Model for Early Detection of Alzheimer’s Disease Using Resting-state fMRI

A Dual-branch Model for Early Detection of Alzheimer’s Dise...

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IEEE Advanced Information Technology, Electronic and Automation control Conference (IAEAC)

作者： Yixuan Wang Wei Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

Alzheimer’s disease (AD) is the most prevalent form of dementia, and early diagnosis is crucial for delaying and treating AD. Resting-state functional magnetic resonance imaging (rs-fMRI), a widely used medical imaging technique, offers rich temporal and spatial data, which has led researchers to explore various feature extraction methods based on rs-fMRI images for AD identification. However, the related work still suffers from insufficient utilization of temporal and spatial information which leads to unsatisfactory early diagnosis. In this study, we propose a dual-branch fusion model to extract spatial-temporal features from rs-fMRI images. Our proposed model can extract temporal features at different levels. We developed a Class Activation Sequence (CAS) branch, which is a structure that emphasizes the function of each temporal node throughout the whole time series. Additionally, we created a time-domain local branch for local feature extraction. Further, we designed a fusion module for the model to describe temporal contextual relationships and fuse features at various levels. We tested the performance of the model on the ADNI dataset, and the experimental results show that compared with other algorithms, the dual-branch fusion model achieves higher classification accuracy on several classification tasks including early diagnosis, which proves the advantage of the dual-branch fusion model in temporal and spatial feature extraction for rs-fMRI images, and our work also provides a foundation for the temporal domain characterization of rs-fMRI images.

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Research on Public Opinion Guidance Strategy based on Interaction Constraints

Research on Public Opinion Guidance Strategy based on Intera...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Yuanyuan Xu Zhan Wu Xi Chen School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (数字)9781728143231

ISBN: (纸本)9781728143248

The dissemination of public opinion has a considerable impact on people's social lives, and the question of how both the government and the media can correctly guide public opinion and restore social stability is an issue that requires urgent attention. Combining the characteristics of public opinion dissemination in the network society leads to individual interaction. Considering the interaction between individual views and network topology, this paper combines the collaborative network model with the distance-constrained Hegselmann-Krause (HK) model to propose a consensus-guided strategy based on interaction constraints. It does this by changing the network topology and limiting the scope of individual interactions, thereby affecting the spread of public opinion. Based on the small-world network, the collaborative network model introduces the two parameters of Connection edge probability p and broken edge probability q, while also proposing a collaborative network model for Connection edge's breaking of the network based on the difference of individual opinion values. This paper designs simulation experiments by changing p, q, and the interaction distance, while also analyzing the results of public opinion's evolution from the perspectives of timeliness and the diversity of viewpoints. The experimental results show that when individuals are restricted from interacting with the surrounding environment and the scope of interaction between individuals is restricted, this will slow down the evolution of public opinion and increase the number of opinion clusters. By contrast, when it is encouraged, the interaction between individuals and the surrounding environment will make the entire system communicate more effectively within a short period of time, promoting the evolutionary benefits of public opinion considerably, shortening the convergence time of public opinion evolution to reach a steady state, and reducing the number of opinion clusters. The strategy is consist

关键词： Network topology Collaborative work Social network services Economics Government Bridges Media

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H∞ consensus control of fractional-order multi-agent systems over the directed graph

H∞ consensus control of fractional-order multi-agent system...

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第37届中国控制会议

作者： Lin Chen Wen-Sen Wang Hao Wu Yan Lei 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 State grid shanxi electric power research institute

In this paper, the H∞ consensus of fractional-order multi-agent systems with directed communication graph is investigated. It's the first time to introduce the H∞ control to investigate the consensus problem of the fractional-order multi-agent systems. In view of Mittag-Leffler stability theory and fractional Lyapunov directed method, a sufficient condition is presented to guarantee all the agents reach consensus with the desired H∞ performance. Finally, the results are verified by several numerical simulations.

关键词： Consensus H∞ control Multi-agent systems Fractional-order

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RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yichong Li Xiaojian Liu Fan Cheng Xiaoyong Pan Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions.

关键词： Proteins Protein engineering Accuracy RNA Machine learning Predictive models Data models Gene expression Bioinformatics Diseases

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