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Parallel Seeds:From Foundation Models to Foundation Intelligence for Agricultural Sustainability

IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 481-484页

作者： Laiyi Fu Shunkang Ling Danyang Wu Mengzhen Kang Fei-Yue Wang Hequan Sun the Shool of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong University the College of Information Engineering Northwest A&F University IEEE the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences

THE development of agriculture faces significant challenges due to population growth, climate change, land depletion, and environmental pollution, threatening global food security [1]. This necessitates the development of sustainable agriculture, where a fundamental step is crop breeding to improve agronomic or economic traits, e.g., increasing yields of crops while decreasing resource usage and minimizing pollution to the environment [2].

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Knowledge As Not Only Justified True Beliefs in Vision

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 297-299页

作者： Wenbo Zheng Fei-Yue Wang IEEE School of Computer Science and Artificial Intelligence Wuhan University of Technology Sanya Science and Education Innovation Park of Wuhan University of Technology the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences

COMPUTATIONAL knowledge vision [1] is emphasized as a novel perspective or field in this paper. It first proposes the visual hierarchy and its connection to knowledge, stating that knowledge is a justified true belief. To further the previous research, we concisely summarize our recent works and suggest a new direction that knowledge is also a thought framework in vision.

关键词： Computational modeling Visualization Artificial intelligence Data models Visual perception Visual databases Training data

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A Stochastic Traffic Flow Model-Based Reinforcement Learning Framework For Advanced Traffic Signal control

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2025年第1期26卷 714-723页

作者： Zhu, Yifan Lv, Yisheng Lin, Shu Xu, Jungang Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 101408 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In addressing the complex challenge of Traffic Signal control (TSC), Deep Reinforcement Learning (DRL) has emerged as a popular solution. In traditional DRL methods applied to TSC problems, deep neural networks are sensitive to minor input changes, which complicates accurate predictions. This ambiguity hampers algorithm convergence, speed, and overall performance. Additionally, existing DRL methods for TSC employ high-dimensional state spaces, escalating computational complexity. This study addresses these challenges by introducing an innovative approach, SLFMLight, that integrates a stochastic traffic flow model with DRL algorithm for TSC. Our method employs an innovative network update algorithm that integrates traffic flow prediction in Q-value learning process to enhance interpretability and accelerate algorithm convergence. Utilizing mode-based multi-actor networks to handle diverse traffic conditions, SLFMLight excels in decision-making towards complex traffic scenarios, especially in congested ones. Concise state definition improves computational efficiency. SLFMLight contributes to the advancement of intelligent traffic management by providing an effective DRL solution that improves interpretability, efficiency, and adaptability in TSC.

关键词： Stochastic processes Prediction algorithms Green products Turning Convergence Vectors Computational modeling Artificial neural networks Signal to noise ratio Markov decision processes Traffic signal control traffic networks deep reinforcement learning

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Data-driven finite-time adaptive attitude tracking control for spacecraft

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Advances in Space Research 2025年第12期75卷 8780-8791页

作者： Wang, Jianhui He, Guangping Bian, Guibin Geng, Shixiong College of Mechanical & Energy Engineering Beijing University Of Technology Beijing100124 China Department of Mechanical and Electrical Engineering North China University of Technology Beijing100144 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

This paper proposes a novel data-driven finite-time adaptive control method for the spacecraft attitude tracking control problem with inertial uncertainty. Based on the dynamic regression extension technique, the disturbance unit matrix is introduced, and the parameter estimation law in the form of algebraic equations is presented, which realizes the arbitrarily small convergence of parameter estimation error. The convergence of parameter estimation errors is verified through stability analysis. Globally non-singular fast terminal sliding mode control is designed to achieve finite-time arrival of the sliding surface and convergence of the system state error. The stability analysis of the closed-loop system demonstrates the effectiveness of the proposed method. Furthermore, the effectiveness and superiority of the proposed method are verified through numerical simulations of spacecraft attitude tracking control. © 2025 COSPAR

关键词： Attitude control

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Improvement of coronary microcirculation in acute myocardial ischemia rats using a nanoscale carrier SiO2@PEG loaded with Nicorandil

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DRUG DELIVERY AND TRANSLATIONAL RESEARCH 2025年 1-14页

作者： Wang, Rui Mo, Yujing Liang, Yingcong Liu, Yuanhui Sun, Zhongchan Shang, Wenting Xue, Ling Southern Med Univ Guangdong Prov Peoples Hosp Guangdong Cardiovasc Inst Guangdong Acad Med SciDept Cardiol Guangzhou 510080 Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Coronary microcirculatory dysfunction, affecting over half of acute myocardial infarction (AMI) patients, correlates significantly with AMI prognosis. Nicorandil is an effective drug that markedly improves coronary microcirculation, but current clinical formulations of Nicorandil exhibit a relatively short half-life and lack cardiac selectivity. We formulated and synthesized a variety of mesoporous silica nanoparticles (MSNs) as a drug carrier for loading and delivering Nicorandil. We performed PEG modification on MSNs to enhance their biocompatibility. The SiO2@PEG showed good serum stability, maintained a uniform spherical structure with a particle size distribution centered within 200 nm and exhibits good dispersibility. SiO2@PEG-Nicorandil showed no significant impact on AC 16 cells' viability at concentrations up to 50 mu g/mL. SiO2@PEG-Nicorandil significantly enhanced the viability of AC16 cells under oxidative stress conditions, while concurrently reducing intracellular levels of reactive oxygen species (ROS) and Ca2+. For the rat coronary microvascular dysfunction model, the SiO2@PEG-Nicorandil group demonstrated a greater decrease in thrombus formation and the expression of inflammatory cytokines, outperforming the Nicorandil group. In vivo imaging revealed that within one hour post-injection of SiO2@PEG-Nicorandil-CY7, a notable increase in CY7 fluorescence intensity was observed in the cardiac region compared to surrounding tissues. Drug concentration measurements demonstrated that Nicorandil maintained a stable concentration in cardiac blood at 48 h in the SiO2@PEG-Nicorandil group. Taken together, SiO2@PEG-Nicorandil had exhibited superior cardiac-targeting capabilities and sustained-release properties. Within a specific concentration range, it demonstrated enhanced therapeutic effects in the treatment of coronary microcirculation disorders in rats when compared to conventional Nicorandil formulations.

关键词： Acute myocardial ischemia Coronary microcirculation Nanoscale carrier Nicorandil

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Macau's New ITSS Chapter: An Experiment With New Artificial Intelligence and Decentralized Autonomous Science for Future Mobility

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IEEE INTELLIGENT TRANSPORTATION systems MAGAZINE 2025年第2期17卷 138-140页

作者： Wang, Fei Yue Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Taipa Macau Peoples R China

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

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Event-/Self-Triggered Adaptive Optimal Consensus control for Nonlinear Multiagent System With Unknown Dynamics and Disturbances

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第3期55卷 1476-1485页

作者： Wei, Qinglai Jiang, Hao Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

In this article, the optimal consensus tracking control for nonlinear multiagent systems (MASs) with unknown dynamics and disturbances is investigated via adaptive dynamic programming (ADP) technology. Taking into account the disturbance as control inputs, the optimal control problem for the nonlinear MASs is reformulated as a multiplayer zero-sum differential game. In addition, a single network ADP structure is constructed to approach the optimal consensus control policies. Subsequently, an event triggering mechanism is implemented to reduce the workload of the controller and conserve computing and communication resources. Since then, in order to further streamline the intricacies of controller design, this work is extended to self-triggered cases to alleviate the need for hardware devices to continuously monitor signals. By using the Lyapunov method, the stability of the nonlinear MASs and the uniform ultimate boundedness (UUB) of the weight estimation error of the critic neural network (NN) is proved. Finally, the simulation results for an MAS consisting of a single-link robot validate the effectiveness of the proposed control method.

关键词： Consensus control Artificial neural networks Vectors Optimal control Event detection Nonlinear dynamical systems Multi-agent systems Monitoring Hardware Directed graphs Adaptive dynamic programming (ADP) event-triggered control (ETC) nonlinear multiagent systems optimal consensus control self-triggered control (STC)

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Exploring Latent Transferability of feature components

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PATTERN RECOGNITION 2025年 160卷

作者： Wang, Zhengshan Chen, Long He, Juan Yang, Linyao Wang, Fei-Yue Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau 999078 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Feature disentanglement techniques have been widely employed to extract transferable (domain-invariant) features from non-transferable (domain-specific) features in Unsupervised Domain Adaptation (UDA). However, due to the complex interplay among high-dimensional features, the separated "non-transferable" features may still be partially informative. Suppressing or disregarding them, as commonly employed in previous methods, can overlook the inherent transferability. In this work, we introduce two concepts: Partially Transferable Class Features and Partially Transferable Domain Features (PTCF and PTDF), and propose a succinct feature disentanglement technique. Different with prior works, we do not seek to thoroughly peel off the nontransferable features, as it is challenging practically. Instead, we take the two-stage strategy consisting of rough feature disentanglement and dynamic adjustment. We name our model as ELT because it can systematically Explore Latent Transferability of feature components. ELT can automatically evaluate the transferability of internal feature components, dynamically giving more attention to features with high transferability and less to features with low transferability, effectively solving the problem of negative transfer. Extensive experimental results have proved its efficiency. The code and supplementary file will be available at https://***/ njtjmc/ELT.

关键词： Unsupervised domain adaptation Feature disentanglement Adversarial learning Dynamic learning

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Parallel control for Nonzero-Sum Games With Completely Unknown Nonlinear Dynamics via Reinforcement Learning

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2025年第4期55卷 2884-2896页

作者： Lu, Jingwei Wei, Qinglai Wang, Fei-Yue Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China

This article utilizes parallel control to investigate the problem of continuous-time (CT) nonzero-sum games (NZSGs) for completely unknown nonlinear systems via reinforcement learning (RL), and a parallel control-based NZSG (PNZSG) method is developed without reconstructing unknown dynamics or employing off-policy integral RL (IRL). First, novel dynamic control policies (DCPs) are developed for NZSGs by introducing controls into feedback, and an augmented system with augmented performance indices is constructed to derive the DCPs. Then, we theoretically analyze the effect of the DCPs on the control stability and performance indices, and the optimality of PNZSG is proven to be equivalent to the optimality of the original NZSGs. Subsequently, an IRL technique is employed to achieve the developed PNZSG method, and we show that no prior knowledge of the dynamics of NZSGs is needed to deploy the developed PNZSG method because of the augmented system and performance indices. Finally, numerical examples, including cooperative adaptive cruise control (CACC) of a vehicular platoon, demonstrate the correctness of the developed PNZSG method. The associated code is available at: https://***/lujingweihh/Adaptive-dynamic-programming-algorithms/tree/main/model_free_nonzero_sum_games.

关键词： Adaptive dynamic programming cooperative adaptive cruise control (CACC) cooperative adaptive cruise control (CACC) nonzero-sum games (NZSGs) nonzero-sum games (NZSGs) parallel control parallel control reinforcement learning (RL) reinforcement learning (RL) unknown nonlinear dynamics unknown nonlinear dynamics unknown nonlinear dynamics unknown nonlinear dynamics unknown nonlinear dynamics

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Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1885-1889页

作者： Zhang, Dengke Tang, Quan Liu, Fagui Mei, Haiqing Chen, C. L. Philip South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pengcheng Lab Dept New Network Shenzhen 518000 Peoples R China China Tobacco Guangdong Ind Corp Guangzhou 510385 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks, and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenSwap, a data augmentation technique designed explicitly for semi-supervised semantic segmentation with Vision Transformers. TokenSwap aligns well with the global attention mechanism by mixing images at the token level, enhancing the learning capability for contextual information among image patches and the utilization of unlabeled data. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies image and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under limited fine annotations.

关键词： Training Decoding Computational modeling Data models Semantic segmentation Computer vision Transformers Predictive models Optimization Image augmentation Dual-branch framework semi-supervised segmentation token swap vision transformer

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