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controllability of giant connected components in a directed network

Physical Review E 2017年第4期95卷 042318-042318页

作者： Xueming Liu Linqiang Pan H. Eugene Stanley Jianxi Gao Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan 430074 Hubei China Center for Polymer Studies and Department of Physics Boston University Boston Massachusetts 02215 USA School of Electric and Information Engineering Zhengzhou University of Light Industry Zhengzhou 450002 Henan China Center for Complex Network Research and Department of Physics Northeastern University Boston Massachusetts 02115 USA

When controlling a complex networked system it is not feasible to control the full network because many networks, including biological, technological, and social systems, are massive in size and complexity. But neither is it necessary to control the full network. In complex networks, the giant connected components provide the essential information about the entire system. How to control these giant connected components of a network remains an open question. We derive the mathematical expression of the degree distributions for four types of giant connected components and develop an analytic tool for studying the controllability of these giant connected components. We find that for both Erdős-Rényi (ER) networks and scale-free (SF) networks with p fraction of remaining nodes, the minimum driver node density to control the giant component first increases and then decreases as p increases from zero to one, showing a peak at a critical point p=pm. We find that, for ER networks, the peak value of the driver node density remains the same regardless of its average degree 〈k〉 and that it is determined by pm〈k〉. In addition, we find that for SF networks the minimum driver node densities needed to control the giant components of networks decrease as the degree distribution exponents increase. Comparing the controllability of the giant components of ER networks and SF networks, we find that when the fraction of remaining nodes p is low, the giant in-connected, out-connected, and strong-connected components in ER networks have lower controllability than those in SF networks.

关键词： Network structure

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Hanjing Wang, Meineng Zhang, Luping Huang, Yu-An Li, Yangyuan Huang, Yabing Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Hubei Wuhan430074 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Yichun University School of Mathematics and Computer Science Yichun336000 China East China University of Technology School of Information Engineering Nanchang330013 China Northwestern Polytechnical University School of Computer Science Xi'an710129 China Renmin Hospital Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350386226

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Fuzzy control for Uncertain Vehicle Active Suspension Systems via Dynamic Sliding-Mode Approach

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2017年第1期47卷 24-32页

作者： Wen, Shiping Chen, Michael Z. Q. Zeng, Zhigang Yu, Xinghuo Huang, Tingwen School of Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Department of Mechanical Engineering University of Hong Kong Hong Kong Hong Kong Platform Technologies Research Institute RMIT University MelbourneVIC3001 Australia Texas A and M University at Qatar Doha23874 Qatar

This paper investigates the fuzzy control issue for uncertain active suspension systems via dynamic sliding-mode method. The Takagi-Sugeno fuzzy approach is adopted on the background of the varying masses to describe the prescribed nonlinear system in order to achieve the design targets via the method of sector nonlinearity. This paper employs the dynamic sliding-mode scheme to control nonlinear active suspension systems. In the proposed sliding-mode control scheme, the sliding surface function is formed linearly with the system states and control inputs. Then, a fuzzy dynamic term is utilized to construct the sliding-mode feedback controller. In existing results, the sliding mode is achieved and maintained with no consideration of the system perturbations. Thus, sufficient conditions are proposed to make the sliding surface reachable with the existence of the system perturbations to make the augmented system stable. Finally, simulation results are presented to verify the effectiveness of the proposed schemes. © 2013 IEEE.

关键词： Robust control

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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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CrossGLG: LLM Guides One-shot Skeleton-based 3D Action Recognition in a Cross-level Manner

arXiv

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arXiv 2024年

作者： Yan, Tingbing Zeng, Wenzheng Xiao, Yang Tong, Xingyu Tan, Bo Fang, Zhiwen Cao, Zhiguo Zhou, Joey Tianyi 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 School of Biomedical Engineering Southern Medical University Guangzhou510515 China Department of Rehabilitation Medicine Zhujiang Hospital Southern Medical University Guangzhou510280 China Singapore Singapore Singapore Singapore

Most existing one-shot skeleton-based action recognition focuses on raw low-level information (e.g., joint location), and may suffer from local information loss and low generalization ability. To alleviate these, we propose to leverage text description generated from large language models (LLM) that contain high-level human knowledge, to guide feature learning, in a global-local-global way. Particularly, during training, we design 2 prompts to gain global and local text descriptions of each action from an LLM. We first utilize the global text description to guide the skeleton encoder focus on informative joints (i.e.,global-tolocal). Then we build non-local interaction between local text and joint features, to form the final global representation (i.e., local-to-global). To mitigate the asymmetry issue between the training and inference phases, we further design a dual-branch architecture that allows the model to perform novel class inference without any text input, also making the additional inference cost neglectable compared with the base skeleton encoder. Extensive experiments on three different benchmarks show that CrossGLG consistently outperforms the existing SOTA methods with large margins, and the inference cost (model size) is only 2.8% than the previous SOTA. CrossGLG can also serve as a plug-and-play module that can substantially enhance the performance of different SOTA skeleton encoders with a neglectable cost during inference. The source code will be released soon. © 2024, CC BY.

关键词： Signal encoding

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Global Bounded Consensus of General Nonidentical Networks With Distributed Time-Delays

Global Bounded Consensus of General Nonidentical Networks Wi...

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IEEE Annual Conference on Decision and control

作者： Xiuxian Li Housheng Su Chanying Li Zheng Wang Michael Z. Q. Chen Department of Mechanical Engineering The University of Hong Kong Pokfulam Hong Kong School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 PR China National Center for Mathematics and Interdisciplinary Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781479978878

In this paper, global bounded consensus problem of general nonidentical networks with nonlinear dynamics and distributed time-delays is investigated, in which the distributed time-delays are distinct from each other. The global consensus exists in the sense of boundedness since complete consensus does not often exist in the nonidentical case. With the aid of constructing a Lyapunov-Krasovskii functional and utilizing the technique of integral partitioning, some sufficient delay-dependent conditions are derived to ensure that global bounded consensus is achieved ultimately. Finally, effectiveness of the theoretical result is illustrated by a numerical example.

关键词： Bounded Nonlinear Dynamics numerical example Distribute Automatic incident detection Distinct

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A polynomial chaos approach to robust H∞ static output-feedback control with bounded truncation error

arXiv

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arXiv 2021年

作者： Wan, Yiming Shen, Dongying E. Lucia, Sergio Findeisen, Rolf Braatz, Richard D. Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States TU Dortmund University Dortmund44227 Germany Otto-von-Guericke University Magdeburg Magdeburg39106 Germany

This article considers the H∞ static output-feedback control for linear time-invariant uncertain systems with polynomial dependence on probabilistic time-invariant parametric uncertainties. By applying polynomial chaos theory, the control synthesis problem is solved using a high-dimensional expanded system which characterizes stochastic state uncertainty propagation. A closed-loop polynomial chaos transformation is proposed to derive the closed-loop expanded system. The approach explicitly accounts for the closed-loop dynamics and preserves the L2-induced gain, which results in smaller transformation errors compared to existing polynomial chaos transformations. The effect of using finite-degree polynomial chaos expansions is first captured by a norm-bounded linear differential inclusion, and then addressed by formulating a robust polynomial chaos based control synthesis problem. This proposed approach avoids the use of high-degree polynomial chaos expansions to alleviate the destabilizing effect of truncation errors, which significantly reduces computational complexity. In addition, some analysis is given for the condition under which the robustly stabilized expanded system implies the robust stability of the original system. A numerical example illustrates the effectiveness of the proposed approach. © 2021, CC BY.

关键词： Feedback control

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Locating and Sizing Charging Station in Multi-Period to Promote Electric Vehicles Adoption in Urban Areas

SSRN

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SSRN 2023年

作者： Hu, Dandan Huang, Liu Liu, Chen Liu, Zhi-Wei The School of Management South-central Minzu University Wuhan430074 China The School of Engineering RMIT University MelbourneVIC3001 Australia The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan430074 China

The layout of public fast charging stations often fails to meet the charging demand of electric vehicle (EV) users, resulting in charging anxiety. To address this issue and promote the widespread use of EVs in urban areas, this paper presents a multiperiod locating and sizing optimization model under dynamic demand. The model incorporates GPS trajectory extraction data to identify potential charging demand points and considers the mobile load characteristics of EVs for fusion modeling. A multiperiod change formula is proposed to reflect the interaction between public fast charging stations and EV market penetration, incorporating charging opportunity and waiting time satisfaction as measures of charging service quality. To solve the model, a heuristic algorithm combining genetic algorithm (GA) and k-medoids clustering algorithm is designed. We apply the model to an actual case of Shenzhen and find that increasing investment in charging infrastructure can boost EV market penetration in the later planning period. Moreover, the gap between charging opportunity and waiting time satisfaction tends to narrow as the planning progresses. Our findings highlight the importance of optimizing the location and size of public fast charging stations to improve the charging service quality and promote the adoption of EVs. © 2023, The Authors. All rights reserved.

关键词： Location

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Extended State Observer Based Fault-Tolerant Predictive control for a Four-Mecanum Wheels Mobile Robot

Extended State Observer Based Fault-Tolerant Predictive Cont...

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

作者： Binghao Yang Dongliang Wang Ziling Wen Wu Wei Wenji Li Zhun Fan Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Guangdong China School of Department of Electronic and Information Engineering Shantou University Guangdong China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangzhou Guangdong China School of Automation Science and Engineering South China University of Technology Guangzhou Guangdong China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such tasks, whether in single-vehicle transportation or multi-vehicle collaborative transportation, the occurrence of wheel failures can potentially lead to serious safety accidents. Therefore, this paper proposes a fault-tolerant control scheme, establishing a kinematic model considering disturbances and failures, and adopts model predictive control for trajectory tracking. Additionally, an extended state observer is introduced to observe system states and counteract disturbances. To address potential failure issues, a controller reconstruction strategy is proposed. Finally, the effectiveness and superiority of the controller is validated through numerical simulation and comparative experiment.

关键词： Fault tolerance Trajectory tracking Fault tolerant systems Wheels Transportation Kinematics Predictive models Safety State estimation Predictive control

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Edge-Aware Graph Attention Network for Ratio of Edge-User Estimation in Mobile Networks

Edge-Aware Graph Attention Network for Ratio of Edge-User Es...

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International Conference on Pattern Recognition

作者： Jiehui Deng Sheng Wan Xiang Wang Enmei Tu Xiaolin Huang Jie Yang Chen Gong PCA Lab the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Hong Kong Polytechnic University Hong Kong SAR China

Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and labor-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.

关键词： Deep learning Fuses Estimation Switches Pattern recognition

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