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On strong structural controllability and observability of linear time-varying systems: a constructive method

Science China(information Sciences) 2019年第1期62卷 134-147页

作者： Shaoyuan LI Jianbin MU Yishi WANG Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

In this paper, we consider the controllability and observability of generalized linear time-varying(LTV) systems whose coefficients are not exactly known. All that is known about these systems is the placement of non-zero entries in their coefficient matrices(A, B). We provide the characterizations in order to judge whether the placements can guarantee the controllability/observability of such LTV systems, regardless of the exact value of each non-zero coefficient. We also present a direct and efficient algorithm with an associated time cost of O (n+m+ν) to verify the conditions of our characterizations, where n and m denote the number of columns of A and B, respectively, and ν is number of non-zero entries in(A, B).

关键词： linear time-varying(LTV) systems strong structural properties controllability observability duality

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Air Pollution Monitoring By Integrating Local and Global information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： information leakage

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SOCIAL LEARNING WITH TIME-VARYING WEIGHTS

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Journal of systems Science & Complexity 2014年第3期27卷 581-593页

作者： LIU Qipeng FANG Aili WANG Lin WANG Xiaofan Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

This paper investigates a non-Bayesian social learning model, in which each individual updates her beliefs based on private signals as well as her neighbors＇ beliefs. The private signM is involved in the updating process through Bayes＇ rule, and the neighbors＇ beliefs are embodied in through a weighted average form, where the weights are time-varying. The authors prove that agents eventually have correct forecasts for upcoming signals, and all the beliefs of agents reach a consensus. In addition, if there exists no state that is observationally equivalent to the true state from the point of view of all agents, the authors show that the consensus belief of the whole group eventually reflects the true state.

关键词： Consensus social learning social networks time-varying weights.

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Consensus protocol for heterogeneous multi-agent systems: A Markov chain approach

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Chinese Physics B 2013年第1期22卷 569-573页

作者：朱善迎陈彩莲关新平 Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education

This paper deals with the consensus problem for heterogeneous multi-agent systems. Different from most existing consensus protocols, we consider the consensus seeking of two types of agents, namely, active agents and passive agents. The objective is to directly control the active agents such that the states of all the agents would achieve consensus. In order to obtain a computational approach, we subtly introduce an appropriate Markov chain to cast the heterogeneous systems into a unified framework. Such a framework is helpful for tackling the constraints from passive agents. Furthermore, a sufficient and necessary condition is established to guarantee the consensus in heterogeneous multi-agent systems. Finally, simulation results are provided to verify the theoretical analysis and the effectiveness of the proposed protocol.

关键词： heterogeneous multi-agent system consensus protocol Markov chain

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Symplectic Wigner Distribution in the Linear Canonical Transform Domain: Theory and Application

arXiv

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arXiv 2025年

作者： He, Yangfan Zhang, Zhichao School of Communication and Artificial Intelligence School of Integrated Circuits Nanjing Institute of Technology Nanjing211167 China School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Computational Science Application of Hainan Province Hainan Normal University Haikou571158 China Key Laboratory of Numerical Simulation of Sichuan Provincial Universities School of Mathematics and Information Sciences Neijiang Normal University Neijiang641000 China

This paper devotes to combine the chirp basis function transformation and symplectic coordinates transformation to yield a novel Wigner distribution (WD) associated with the linear canonical transform (LCT), named as the symplectic WD in the LCT domain (SWDL). It incorporates the merits of the symplectic WD (SWD) and the WD in the LCT domain (WDL), achieving stronger capability in the linear frequency-modulated (LFM) signal frequency rate feature extraction while maintaining the same level of computational complexity. Some essential properties of the SWDL are derived, including marginal distributions, energy conservations, unique reconstruction, Moyal formula, complex conjugate symmetry, time reversal symmetry, scaling property, time translation property, frequency modulation property, and time translation and frequency modulation property. Heisenberg’s uncertainty principles of the SWDL are formulated, giving rise to three kinds of lower bounds attainable respectively by Gaussian enveloped complex exponential signal, Gaussian signal and Gaussian enveloped chirp signal. The optimal symplectic matrices corresponding to the highest time-frequency resolution are generated by solving the lower bound optimization (minimization) problem. The time-frequency resolution of the SWDL is compared with those of the SWD and WDL to demonstrate its superiority in LFM signals time-frequency energy concentration. A synthesis example is also carried out to verify the feasibility and reliability of the theoretical analysis. Copyright © 2025, The Authors. All rights reserved.

关键词： Wigner-Ville distribution

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TRNet: Two-Tier Recursion Network for Co-Salient Object Detection

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Cong, Runmin Yang, Ning Liu, Hongyu Zhang, Dingwen Huang, Qingming Kwong, Sam Zhang, Wei Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi'an710129 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Lingnan University Hong Kong

Co-salient object detection (CoSOD) is to find the salient and recurring objects from a series of relevant images, where modeling inter-image relationships plays a crucial role. Different from the commonly used direct learning structure that inputs all the intra-image features into some well-designed modules to represent the inter-image relationship, we resort to adopting a recursive structure for inter-image modeling, and propose a two-tier recursion network (TRNet) to achieve CoSOD in this paper. The two-tier recursive structure of the proposed TRNet is embodied in two stages of inter-image extraction and distribution. On the one hand, considering the task adaptability and inter-image correlation, we design an inter-image exploration with recursive reinforcement module to learn the local and global inter-image correspondences, guaranteeing the validity and discriminativeness of the information in the step-by-step propagation. On the other hand, we design a dynamic recursion distribution module to fully exploit the role of inter-image correspondences in a recursive structure, adaptively assigning common attributes to each individual image through an improved semi-dynamic convolution. Experimental results on five prevailing CoSOD benchmarks demonstrate that our TRNet outperforms other competitors in terms of various evaluation metrics. The code and results of our method are available at https://***/rmcong/TRNetTCSVT2025. © 1991-2012 IEEE.

关键词： HTTP

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Spartina alterniflora invasion enhances sediment nitrification processes in coastal wetland

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Plant and Soil 2025年 1-20页

作者： Lin, Xianbiao Sun, Dongyao Wang, Zihao Gao, Dengzhou Hu, Weifang Du, Wei Key Laboratory of Geographic Information Science (Ministry of Education) East China Normal University Shanghai 200241 China Frontiers Science Center for Deep Ocean Multispheres and Earth System Key Laboratory of Marine Chemistry Theory and Technology Ministry of Education and College of Chemistry and Chemical Engineering Ocean University of China Qingdao 266100 China School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou China Key Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education School of Geographical Sciences Fujian Normal University Fuzhou 350117 China Institute of Agricultural Resources and Environment Guangdong Academy of Agricultural Sciences Guangzhou 510640 China Yunnan Provincial Key Laboratory of Soil Carbon Sequestration and Pollution Control Faculty of Environmental Science & Engineering Kunming University of Science & Technology Kunming 650500 China

Aims: Spartina alterniflora (S. alterniflora) invasion notably impacts sediment nitrogen (N) biogeochemical processes in coastal wetlands. However, its effects on different nitrification processes and their mechanisms in both surface (0–10 cm) and subsurface (10–20 cm) sediments remain unclear. Methods: Potential nitrification rates mediated by ammonia-oxidizing archaea (AOA), bacteria (AOB), and comammox Nitrospira (PARAOA, PARAOB, and comammox rates), their relative contributions, functional genes (AOA, AOB, and Ntsp-aomA), and controlling factors were investigated in both surface and subsurface sediments of native Cyperus malacensis and adjacent S. alterniflora-invaded habitats with varying years (6, 10, and 14 years) in the Minjiang River Estuary, China. Results: These three nitrification rates varied both spatially and seasonally. Summer nitrification rates (5.57 ± 2.28 μg N g–1 d–1) in both surface (0–10 cm) and subsurface (10–20 cm) sediments of these ecosystems were substantially higher than winter levels (3.78 ± 1.76 μg N g–1 d–1). S. alterniflora invasion increased nitrification rates (39.54–68.13%) and associated functional gene abundances (28.08–52.92%) in both seasons, with surface sediments (118.40–153.85%) markedly showing higher values than subsurface layers (32.80–73.17%). Additionally, the relative contribution of PARAOA in nitrification exhibited a marked enhancement with the S. alterniflora invasion. PLS-PM results further indicated that the spatiotemporal fluctuations in sediment nitrification rates were predominantly driven by organic matter accumulation and ambient temperature. Conclusions: These findings highlight the substantial influence of S. alterniflora invasion on sediment nitrification dynamics, providing fundamental data for estimating N budgets and turnover in the estuarine wetlands. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Ammonia oxidizers Comammox Estuarine wetland Nitrification Sediment Spartina alterniflora invasion

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CMRM: Collaborative Multi-agent Reinforcement Learning for Multi-objective Traffic Signal control

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IEEE Transactions on Consumer Electronics 2025年

作者： Nie, Lei Qi, Dandan Liu, Bingyi Li, Peng Bao, Haizhou He, Heng Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430065 China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China

Efficient traffic signal control is a cost-effective way to ease urban traffic congestion. Multi-agent reinforcement learning (MARL) has become a widely adopted method for optimizing traffic signal control (TSC). However, existing MARL-based methods often focus on a single optimization objective, lacking a comprehensive consideration of traffic efficiency, environmental pollution, and traffic safety. Simultaneously, these methods often fail to effectively capture the dynamic and complex interactions among agents in multi-intersection scenarios, which negatively impacts traffic efficiency. In this article, we propose a collaborative MARL-based method for multi-objective TSC, called CMRM. First, we introduce a multi-objective reward mechanism that integrates traffic efficiency, environmental impact, and safety to guide agents toward more comprehensive optimization. Second, we design a cooperation enhancement module (CEM) based on the graph attention mechanism to dynamically capture neighboring agents’ state information. This mitigates the partial observability problem in independent proximal policy optimization (IPPO) and enhances the model’s ability to capture dynamic and complex interactions among agents. Finally, we assess the performance of the proposed CMRM method using SUMO on two real traffic networks. Experimental results demonstrate that our method significantly improves traffic efficiency while reducing environmental pollution and enhancing traffic safety, compared to the best performing baseline, our method reduces CO2 emission by approximately 17.53% and 9.57%, and lowers vehicle collision risks by 44.39% and 42.85% in two different traffic networks. © 1975-2011 IEEE.

关键词： Traffic congestion

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Distributed Model Predictive control for Networked Plant-wide systems With Neighborhood Cooperation

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 108-117页

作者： Ting Bai Shaoyuan Li Yi Zheng the Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China IEEE

For large-scale networked plant-wide systems composed by physically(or geographically) divided subsystems, only limited information is available for local controllers on account of region and communication restrictions. Concerning the optimal control problem of such subsystems, a neighbor-based distributed model predictive control(NDMPC) strategy is presented to improve the global system performance. In this scheme, the performance index of local subsystems and that of its neighbors are minimized together in the determination of the optimal control input, which makes the local control decision also beneficial to its neighboring subsystems and further contributes to improving the convergence and control performance of overall *** stability of the closed-loop system is proved. Moreover, the parameter designing method for distributed synthesis is ***, the simulation results illustrate the main characteristics and effectiveness of the proposed control scheme.

关键词： Distributed control model predictive control (MPC) neighborhood cooperation plant-wide systems

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A fast network partition method for large-scale urban traffic networks

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控制理论与应用（英文版） 2013年第3期11卷 359-366页

作者： Zhao ZHOU Shu LIN Yugeng XI Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education

In order to control the large-scale urban traffic network through hierarchical or decentralized methods, it is necessary to exploit a network partition method, which should be both effective in extracting subnetworks and fast to compute. In this paper, a new approach to calculate the correlation degree, which determines the desire for interconnection between two adjacent intersections, is first proposed. It is used as a weight of a link in an urban traffic network, which considers both the physical characteristics and the dynamic traffic information of the link. Then, a fast network division approach by optimizing the modularity, which is a criterion to distinguish the quality of the partition results, is applied to identify the subnetworks for large-scale urban traffic networks. Finally, an application to a specified urban traffic network is investigated using the proposed algorithm. The results show that it is an effective and efficient method for partitioning urban traffic networks automatically in real world.

关键词： Traffic signal control Large-scale urban traffic networks Network partition

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