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The Strength of Structural Diversity in Online Social Networks

Research 2021年第1期2021卷 1203-1212页

作者： Yafei Zhang Lin Wang Jonathan JHZhu Xiaofan Wang Alex‘Sandy’Pentland Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai 200240China Department of Media and Communication and School of Data Science City University of Hong KongHong Kong S.A.R.China Department of Automation Shanghai UniversityShanghai 200444China Media Lab Massachusetts Institute of TechnologyCambridgeMA 02139USA

Understanding the way individuals are interconnected in social networks is of prime significance to predict their collective *** a large-scale dataset from a knowledge-sharing website,this paper presents an exploratory investigation of the way to depict structural diversity in directed networks and how it can be utilized to predict one’s online social *** capture the structural diversity of an individual,we first consider the number of weakly and strongly connected components in one’s contact neighborhood and further take the coexposure network of social neighbors into *** show empirical evidence that the structural diversity of an individual is able to provide valuable insights to predict personal online social reputation,and the inclusion of a coexposure network provides an additional ingredient to achieve that *** synthetically controlling several possible confounding factors through matching experiments,structural diversity still plays a nonnegligible role in the prediction of personal online social *** work constitutes one of the first attempts to empirically study structural diversity in directed networks and has practical implications for a range of domains,such as social influence and collective intelligence studies.

关键词： collective networks founding

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Recognition of Degradation Scenarios for LiDAR SLAM Applications

Recognition of Degradation Scenarios for LiDAR SLAM Applicat...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Yang, Chenglin Chai, Zihao Yang, Xiaoxiao Zhuang, Hanyang Yang, Ming Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China Guangxi University of Science and Technology Key Laboratory of Automobile Components and Vehicle Technology Liuzhou545006 China

ISBN: (纸本)9781665481090

The SLAM system, which uses 3D LiDAR as the only sensor, is prone to degradation when facing a scenario with sparse structure and fewer constraints. It cannot solve the robot pose based on limited LiDAR constraint information, which leads to the localization failure and mapping failure of the SLAM system. Due to the limitations of LiDAR, it is difficult to only rely on the point cloud data provided by LiDAR to solve the problem of localization and mapping of degraded scenarios. Currently, the mainstream is to provide additional information through multi-sensor fusion and other schemes to restrict and correct the system's attitude. In the multi-source fusion system, it is still essential to determine the information reliability of each sensor source in different directions. Hence, the recognition of the degradation scenario has significant research value. In this paper, three schemes, geometric information, constraint distur-bance, and residual disturbance, are designed to quantitatively identify the degradation state of the system and estimate the degradation direction. Through experimental verification, the proposed schemes have a favorable recognition effect in the degradation scenario of the simulation environment and real environment. © 2022 IEEE.

关键词： Mapping

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Rebalancing Multi-Label Class-Incremental Learning 39

Rebalancing Multi-Label Class-Incremental Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Du, Kaile Zhou, Yifan Lyu, Fan Li, Yuyang Xie, Junzhou Shen, Yixi Hu, Fuyuan Liu, Guangcan School of Automation Southeast University China Key Laboratory of Measurement and Control of CSE Ministry of Education China NLPR MAIS CASIA China School of Electronic and Information Engineering Suzhou University of Science and Technology China Suzhou Key Laboratory of Intelligent Low Carbon Technology Application China Jiangsu Industrial Intelligent Low Carbon Technology Engineering Center China

ISBN: (纸本)157735897X

Multi-label class-incremental learning (MLCIL) is essential for real-world multi-label applications, allowing models to learn new labels while retaining previously learned knowledge continuously. However, recent MLCIL approaches can only achieve suboptimal performance due to the oversight of the positive-negative imbalance problem, which manifests at both the label and loss levels because of the task-level partial label issue. The imbalance at the label level arises from the substantial absence of negative labels, while the imbalance at the loss level stems from the asymmetric contributions of the positive and negative loss parts to the optimization. To address the issue above, we propose a Rebalance framework for both the Loss and Label levels (RebLL), which integrates two key modules: asymmetric knowledge distillation (AKD) and online relabeling (OR). AKD is proposed to rebalance at the loss level by emphasizing the negative label learning in classification loss and down-weighting the contribution of overconfident predictions in distillation loss. OR is designed for label rebalance, which restores the original class distribution in memory by online relabeling the missing classes. Our comprehensive experiments on the PASCAL VOC and MS-COCO datasets demonstrate that this rebalancing strategy significantly improves performance, achieving new state-of-the-art results even with a vanilla CNN backbone. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multi-task learning

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Learning Adaptability Analysis for Remote Sensing Image Matching via Multi-Scale Saliency and Uniqueness Feature Fusion

Learning Adaptability Analysis for Remote Sensing Image Matc...

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Advanced Algorithms and control Engineering (ICAACE), International Conference on

作者： Shichao Chen Xu Zou Kaifeng Hu Mengzhen Qi Jing Li Sheng Zhong Kaibo Zhou National Key Laboratory of Intelligent Processing Technology for Multispectral Information School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan People's Republic of China Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan People's Republic of China Key Laboratory of Smart Earth Beijing People's Republic of China Beijing Institute of Tracking and Telecommunications Technology Beijing People's Republic of China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Image matching navigation technology, known for its exceptional precision and anti-interference capabilities, has been widely applied in aircraft navigation. Adaptability analysis, a core component of this technology, is critical for enhancing matching performance. Existing deep learning-based adaptability analysis methods lack feature extraction networks specifically designed for adaptability analysis, which hinders their ability to fully capture the saliency and uniqueness features within adaptability regions. This limitation consequently restricts their performance in the adaptability analysis of remote sensing images. A novel remote sensing image adaptability network architecture named US-SwinT is proposed. The network consists of two core modules: saliency analysis and uniqueness analysis, designed to effectively utilize regional scene perception to accurately predict matchability of reference images. The multi-scale saliency analysis module adaptively fuses global contextual information with local detail features through a dynamic weighting mechanism, enhancing the model's ability to discern key regions. The uniqueness module employs cosine similarity to analyze feature similarity between regions, effectively assessing redundancy of regional features. By suppressing highly similar regional features, it enhances capture of regional differences. Experimental results demonstrate that US-SwinT outperforms traditional methods and other deep learning models across multiple key metrics, significantly improving accuracy and robustness of remote sensing image adaptability analysis.

关键词： Analytical models Adaptation models Accuracy Image matching Feature extraction Transformers Aircraft navigation Stability analysis Robustness Remote sensing

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Chinese automation Congress (CAC)

作者： Yumin Lang Jiande Wu Xun Lang Wanke Yu Chuanyan Yang Hongye Su Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China School of Automation China University of Geosciences Wuhan China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Oscillation is a significant indication of the deterioration of control system performance and needs to be carefully monitored to ensure safe and stable process operations. However, industrial processes are often prone to noisy artifacts and nonstationary trends, resulting in low signal-to-noise ratios. In order to tackle this problem, this study introduces a novel framework for enhanced trend removal and noise reduction, which is featured by the following steps:(i) The circulant singular spectrum analysis (CiSSA) is used to decompose oscillation data into several reconstructed components (RCs). (ii) The estimated power spectral density (PSD) of these RCs is then smoothed using the Savitzky-Golay filter (SGF) to eliminate noise and identify the RCs corresponding to oscillatory components. (iii) These identified RCs are subsequently reconstructed to produce purified oscillatory data. The effectiveness of the suggested approach is validated using simulations and real-world industrial case studies. The findings indicate that the method notably enhances the quality of oscillatory data.

关键词： Power filters Noise reduction Process control Market research information filters Noise measurement Oscillators Spectral analysis Monitoring Signal to noise ratio

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Quantification of Valve Stiction in control Loops Using Adaptive Differential Evolution Algorithm

Quantification of Valve Stiction in Control Loops Using Adap...

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Chinese automation Congress (CAC)

作者： Dong Yang Xun Lang Jiande Wu Wanke Yu Songhua Liu Lei Xie Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China School of Automation China University of Geosciences Wuhan China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurately and promptly quantifying stiction is crucial for valve maintenance in industrial settings and ensuring the stability of control loop performance. However, current Hammerstein-based stiction quantification methods overlook valve dynamics, potentially leading to inaccurate quantification. Therefore, this paper proposes a novel stiction quantification method based on an adaptive differential evolution algorithm. In addition to considering the impact of valve dynamics on stiction, the proposed method demonstrates a certain level of robustness against the interference of unknown noise. Finally, the effectiveness of the proposed method is demonstrated through a series of simulations and industrial case studies.

关键词： Adaptation models Heuristic algorithms Noise Interference Valves Stability analysis Robustness Data models Maintenance Classification algorithms

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Dynamic Event-Triggered Consensus on Matrix-weighted Networks:Leadless Case

Dynamic Event-Triggered Consensus on Matrix-weighted Network...

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第42届中国控制会议

作者： Lintao Dou Haoyu Wei Jian Huang Lulu Pan Haibin Shao Dewei Li the College of Intelligent Science National University of Defense Technology the Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of Chinaand Shanghai Engineering Research Center of Intelligent Control and Management

This paper examines the event-triggered consensus of the multi-agent system on matrix-weighted networks,where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the ***,a novel distributed dynamic event-triggered coordination strategy is proposed for this category of generalized networks,in which an auxiliary system is employed for each agent to dynamically adjust the triggering threshold,which plays an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno *** event-triggered control protocols are proposed to guarantee leaderless consensus for multi-agent systems on matrix-weighted networks,***,the spectrum of matrix-valued weights is crucial in event-triggered mechanism design for matrix-weighted networks,generalizing those results only applicable for scalar-weighted *** proposed approach allows each agent to broadcast and receive information only at its triggering ***,simulation examples are provided to demonstrate the theoretical results.

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Extended NNs-Based Adaptive Event-Triggered Asymptotic control for Nonlinear Systems Against Actuator Faults

Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

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International Symposium on Autonomous Systems (ISAS)

作者： Shanlin Liu Yingwei Zhang Xudong Zhao Hesheng Wang College of Information Science and Engineering Northeastern University Shenyang China State Laboratory of Synthesis Automation of Process Industry Northeastern University Shenyang China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of IntelligentControl and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent.

关键词： Actuators Backstepping Fault tolerant systems Process control Artificial neural networks control systems Approximation error

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PI2-CMA Based Trajectory Planning Method for the Tractor-Trailer Vehicle

PI2-CMA Based Trajectory Planning Method for the Tractor-Tra...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： You Wang Zehao Wang Jingchuan Wang Rui Guo Peng Xiao Institute of Medical Robotics and Department of Automation Ministry of Education Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai China State Grid Intelligence Technology CO. LTD

The motion primitive-based state lattice trajectory planning method has been widely used in the field of trajectory planning. However, due to the limitations of the search space and resolution of motion primitives, and the lack of consideration of scenario information during offline generation of motion primitives, the tractor-trailer vehicles can easily fall into "jackknife phenomenon" when making large pose adjustments in narrow scenarios, which brings dilemma for trajectory planning. In this paper, a trajectory planning algorithm is proposed based on a general model of tractor-trailer vehicle, which combines global planning and local optimization. The method first generates a series of motion primitives, including forward and backward driving conditions, and then an A ∗ based global state lattice planner is used to obtain a planning result, which is imported as the initial solution into the local trajectory optimizer based on PI 2 − CMA (Path Integral Policy Improvement with Covariance Matrix Adaptation) algorithm and dynamic potential method. Experiments show that this method significantly improves the performance in the obstacle avoidance, "jackknife phenomenon" avoidance and curvature compared to traditional motion primitive-based state lattice planning methods.

关键词：

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Robust sliding model predictive control with disturbance compensation in application to AUV pitch-axis motion

Robust sliding model predictive control with disturbance com...

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第34届中国控制与决策会议

作者： Jiuwu Hui Jingqi Yuan State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment Department of Automation and the Key Laboratory of System Control and Information Processing/Ministry of EducationShanghai Jiao Tong University

Constraints on the control input and system states as well as uncertain disturbances are the practical challenges in many motion control *** this paper,a disturbance observer(DO) with two-step disturbance compensation algorithm is proposed to deal with disturbances and enhance the robustness of the controller,while a discrete sliding mode predictive control(DSMPC) scheme is designed to handle saturation constraints on the control input and system states with the application to an autonomous underwater vehicle(AUV).The DSMPC combines the model predictive control(MPC) with the discrete sliding mode control(DSMC) using linear matrix inequalities *** stability of the proposed DSMPC are proved via Lyapunov stability ***,simulation results are presented to illustrate the effectiveness of the whole control scheme.

关键词： Disturbance observer Two-step compensation algorithm Discrete sliding mode predictive control Inequality constraints and AUV pitch-axis motion control

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