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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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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 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 University of Technology School of Computer Science and Engineering Guangzhou510006 China Pengcheng Laboratory Department of New Network Shenzhen518000 China China Tobacco Guangdong Industrial Corporation Guangzhou510385 China Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100080 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. © 1994-2012 IEEE.

关键词： Semantic Segmentation

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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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Benefit from public unlabeled data: A Frangi filter-based pretraining network for 3D cerebrovascular segmentation

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MEDICAL IMAGE ANALYSIS 2025年 101卷 103442页

作者： Shi, Gen Lu, Hao Hui, Hui Tian, Jie Beihang Univ Sch Engn Med Beijing 100191 Peoples R China Beihang Univ Sch Biol Sci & Med Engn Beijing 100191 Peoples R China Beihang Univ Key Lab Big DataBased Precis Med Minist Ind & Informat Technol China Beijing 100191 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 10086 Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging Beijing 100190 Peoples R China Natl Key Lab Kidney Dis Beijing 100853 Peoples R China

Precise cerebrovascular segmentation in Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) data is crucial for computer-aided clinical diagnosis. The sparse distribution of cerebrovascular structures within TOF-MRA images often results in high costs for manual data labeling. Leveraging unlabeled TOF-MRA data can significantly enhance model performance. In this study, we have constructed the largest preprocessed unlabeled TOF-MRA dataset to date, comprising 1510 subjects. Additionally, we provide manually annotated segmentation masks for 113 subjects based on existing external image datasets to facilitate evaluation. We propose a simple yet effective pretraining strategy utilizing the Frangi filter, known for its capability to enhance vessel-like structures, to optimize the use of the unlabeled data for 3D cerebrovascular segmentation. This involves a Frangi filter-based preprocessing workflow tailored for large-scale unlabeled datasets and a multi-task pretraining strategy to efficiently utilize the preprocessed data. This approach ensures maximal extraction of useful knowledge from the unlabeled data. The efficacy of the pretrained model is assessed across four cerebrovascular segmentation datasets, where it demonstrates superior performance, improving the clDice metric by approximately 2%-3% compared to the latest semi- and self-supervised methods. Additionally, ablation studies validate the generalizability and effectiveness of our pretraining method across various backbone structures. The code and data have been open source at: https://***/shigen-StoneRoot/FFPN.

关键词： Cerebrovascular segmentation Self-supervised learning Frangi filter

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Parallel Medical Devices and Instruments: Integrating Edge and Cloud Intelligence for Smart Treatment and Health systems

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IEEE-CAA JOURNAL OF AUTOMATICA SINICA 2025年第4期12卷 651-654页

作者： Lin, Fei Gao, Tommy Sun, Dali Ni, Qinghua Ding, Xianting Wang, Jing Gao, David Wenzhong Wang, Fei-Yue Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau 999078 Peoples R China Univ Denver Knoebel Inst Hlth Aging Fac Dept Elect & Comp Engn Denver CO 80210 USA Shanghai Jiao Tong Univ Inst Personalized Med Sch Biomed Engn Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai 200030 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Denver Dept Elect & Comp Sci Denver CO 80210 USA Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

With the rapid development of technologies such as Artificial Intelligence (AI), edge computing, and cloud intelligence, the medical field is undergoing a fundamental transformation [1]. These technologies significantly enhance the medical system's capability to process complex data and also improve the real-time response rate to patient needs. In this wave of technological innovation, parallel intelligence, along with Artificial systems, Computational experiments, and Parallel execution (ACP) approach [2] will play a crucial role. Through parallel interactions between virtual and real systems, this approach optimizes the functionality of medical devices and instruments, enhancing the accuracy of diagnoses and treatments while enabling the autonomous evolution and adaptive adjustment of medical systems.

关键词： Electronic health record

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