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Distributed Minimum-Energy Containment Control of Continuous-Time Multi-Agent Systems by Inverse Optimal Control

IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1533-1535页

作者： Fei Yan Xiangbiao Liu Tao Feng School of Information Science and Technology Southwest Jiaotong UniversityChengdu 611756China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu 611756China

Dear Editor,This letter focuses on the distributed optimal containment control of continuous-time multi-agent systems(CTMASs)with respect to the minimum-energy performance index over fixed *** achieve this,we firstly investigate the optimal containment control problem using the inverse optimal control method,where all states of followers asymptotically converge to the convex hull spanned by the leaders while some quadratic performance indexes get minimized.A sufficient condition for existence of the distributed optimal containment control protocol is *** introducing the parametric algebraic Riccati equation(PARE),it is strictly proved that the global performance index can be used to approximate the standard minimumenergy performance index as the parameters tends to *** consequence,the standard minimum-energy cooperative containment control can be solved by local steady state feedback protocols.

关键词： optimal infinity algebraic

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Record High Temperatures in the Ocean in 2024

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Advances in Atmospheric Sciences 2025年第6期 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of Sciences University of St. Thomas School of Engineering NSF National Center for Atmospheric Research University of Auckland National Oceanic and Atmospheric Administration National Centers for Environmental Information National Research Council (CNR) Institute of Marine Sciences (ISMAR) Mercator Ocean International Department of Earth and Environmental Science University of Pennsylvania Institute of Oceanology Chinese Academy of Sciences National Marine Environmental Forecasting Center Ministry of Natural Resources of China North Carolina Institute for Climate Studies (NCICS) North Carolina State University Oceanographic Data Center Chinese Academy of Sciences College of Oceanography Hohai University National Marine Data and Information Service Istituto Nazionale di Geofisica eVulcanologia Sede di Bologna South China Sea Institute of Oceanology Chinese Academy of Sciences ESSIC/CISESS-MD University of Maryland Computer Network Information Center Chinese Academy of Sciences Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment Administration Ministry of Ecology and Environment National Meteorological Information Center China Meteorological Administration International Research Center of Big Data for Sustainable Development Goals Xiamen University Research Center for Climate and Atmosphere National Research and Innovation Agency (BRIN) Research Department European Centre for Medium-Range Weather Forecasts Department of Meteorology and Geophysics University of Vienna College of Meteorology and Oceanography National University of Defense Technology State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural Resources Stripe Inc. South San Francisco Ocean Scope CELAD

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

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Road rage patterns in Nigeria: A multi-level latent class analysis

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Case Studies on Transport Policy 2025年 20卷

作者： Labbo, Muwaffaq Safiyanu Jiang, Xinguo Wada, Surajo Abubakar de Dieu, Gatesi Jean Bala, Nuhu D. Management Science and Engineering Post-Doctoral Research Station China Three Gorges University Yichang 443002 China Department of Civil Engineering Aliko Dangote University of Science and Technology Kano Wudil Nigeria School of Transportation and Logistics Southwest Jiaotong University West Park High-Tech District Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology West Park High-Tech District Chengdu 611756 China Department of Civil Engineering Ahmadu Bello University Kaduna State Zaria 810107 Nigeria

Road crashes remain a serious concern in Nigeria, and road user behavior is a significant contributing factor. This study explores the relationship between risky driving behaviors and emotional experiences among Nigerian drivers, with a specific focus on anger expression patterns and their association with anger-induced situations. Employing a nuanced approach through multilevel latent class analysis (MLCA), the research identifies three high-level groups and four low-level classes of drivers. Among the low-level classes, Easily Angered Drivers, Travel Impatience Drivers, Prudent Drivers, and Calm Drivers exhibit distinct characteristics, while the high-level groups encompass the Physical and Verbal anger expression group, the Anger Control group, and the Use of Vehicle to express anger group. Multinomial regression results reveal that male drivers are more likely to belong to the Physical and Verbal anger expression group, aligning with prior research. Travel Impatience Drivers demonstrate a tendency to express anger physically or verbally, especially amid travel-related frustrations. Easily Angered Drivers, particularly in younger age groups, are more likely to exhibit physical and verbal anger expression and vehicular aggression. This tendency may stem from deficiencies in driver training systems, which often fail to equip novice drivers with adequate emotional regulation strategies and conflict management skills on the road. Proposed interventions include improving driver training, rigorous traffic law enforcement, and public awareness campaigns to foster responsible driving. © 2025 World Conference on Transport Research Society

关键词： Anger Expression Anger Induced Situations Multi Level Latent Class analysis Risky Driving Behavior Road Rage Road Safety

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FedLGMatch: Federated semi-supervised learning via joint local and global pseudo labeling

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Knowledge-Based Systems 2025年 320卷

作者： Zhao, Qing Chu, Jielei Li, Zhaoyu Huang, Wei Luo, Zhipeng Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China China Railway Engineering Group Limited Beijing100039 China China Railway Eryuan Engineering Group Co. Ltd. Sichuan Chengdu610031 China College of Computer and Data Science Fuzhou University China

The bulk of existing Federated Learning (FL) algorithms pay attention to supervised setting and assume that clients have fully labeled data. However, it may be impractical for all clients to obtain plenty of labels due to high annotation costs. Hence, the Federated Semi-Supervised Learning (FSSL) as a promising paradigm has better prospect in many realistic applications (e.g. medical scenario). Under Labels-at-Server scenario, most pseudo labeling based FSSL approaches use only the global model to generate pseudo-labels for unlabeled data, while the local models are ignored. When the local data distribution is much more different from the central server (e.g., Non-IID setting), the generated pseudo-labels may contain much noise, thus, resulting in more serious confirmation bias. To tackle the crucial issue, a novel Federated Semi-Supervised Learning via Joint Local and Global Pseudo Labeling (FedLGMatch) framework is proposed in this paper. The prominent advantage of the proposed FedLGMatch is that it allows local models trained in the last communication round to assist global model in generating pseudo-labels, which successfully emphasizes more clean pseudo-label learning at each client. Experimental results also show that FedLGMatch achieves significant performance improvements than other state-of-the-art models on the standard benchmark datasets. © 2025 Elsevier B.V.

关键词： Semi-supervised learning

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Multi-Attention Fusion Modeling for Sentiment analysis of Educational big data

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big data Mining and Analytics 2020年第4期3卷 311-319页

作者： Guanlin Zhai Yan Yang Heng Wang Shengdong Du the School of Information Science and Technology National Engineering Laboratory of Integrated Transportation Big Data Application TechnologySouthwest Jiaotong UniversityChengdu 611756China

As an important branch of natural language processing,sentiment analysis has received increasing *** teaching evaluation,sentiment analysis can help educators discover the true feelings of students about the course in a timely manner and adjust the teaching plan accurately and timely to improve the quality of education and *** at the inefficiency and heavy workload of college curriculum evaluation methods,a Multi-Attention Fusion Modeling(Multi-AFM)is proposed,which integrates global attention and local attention through gating unit control to generate a reasonable contextual representation and achieve improved classification *** results show that the Multi-AFM model performs better than the existing methods in the application of education and other fields.

关键词： educational big data sentiment analysis aspect-level attention

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applications of molten salt and progress of molten salt electrolysis in secondary metal resource recovery

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International Journal of Minerals,Metallurgy and Materials 2020年第12期27卷 1599-1617页

作者： Xiao-li Ming Feng Li-wen Zhang Zuo-ren Nie College of Materials Science and Engineering Key Laboratory of Advanced Functional MaterialsEducation Ministry of ChinaBeijing University of TechnologyBeijing 100124China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of TechnologyBeijing 100124China

Molten salt is an excellent medium for chemical reaction,energy transfer,and *** salt innovative technologies should be developed to recover metals from secondary resources and reserve metals from primary natural *** these technologies,molten salt electrolysis is an economic and environment-friendly method to extract metals from waste *** the perspective of molten salt characteristics,the application of molten salts in chemistry,electrochemistry,energy,and thermal storage should be comprehensively *** review discusses further directions for the research and development of molten salt electrolysis and their use for metal recovery from various metal wastes,such as magnet scrap,nuclear waste,and cemented carbide *** is placed on the development of various electrolysis methods for different metal containing wastes,overcoming some problems in electrolytes,electrodes,and electrolytic *** focus is given to future development directions for current associated processing obstacles.

关键词： molten salt application molten salt electrochemistry resource recycling cemented carbide scrap

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Regulation of morphology and visible light-driven photocatalysis of WO_(3)nanostructures by changing pH

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Rare Metals 2021年第7期40卷 1738-1745页

作者： You-Shu Fan Xiao-Li Xi Yang-Si Liu Zuo-Ren Nie Lin-Yan Zhao Qing-Hua Zhang College of Materials Science and Engineering Key Laboratory of Advanced Functional MaterialsEducation Ministry of ChinaBeijing University of TechnologyBeijing 100124China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of TechnologyBeijing 100124China

The controlled preparation of hexagonal tungsten trioxide(h-WO_(3))nanostructures was achieved by adjusting the pH of the precursor *** effect of the pH on the morphology,elemental composition,and photocatalytic performance of the samples was characterized via X-ray diffraction(XRD),scanning electron microscopy,energy dispersive X-ray spectroscopy,and Raman ***-visible(UV-Vis)spectra were used to evaluate the absorbance and the photocatalytic performance of methylene ***(PL),electrochemical impedance spectroscopy,photocurrent response and Brunauer-Emmett-Teller(BET)were used to study the optical properties,electrical performance,and specific surface area of the WO_(3)-nanostructures,*** results indicate that the WO_(3) nanorods prepared at pH=1.0 exhibit the highest photocatalytic performance(87.4%in 1 h),whereas the WO_(3) nanoblocks prepared at p H=3.0 show the *** photocatalytic performance of the one dimensional(1 D)-nanorods can be attributed to their high specific surface area and charge transfer *** h-WO_(3) nanostructures were synthesized via a simple method and without a capping *** show an excellent photocatalytic performance,which is promising for their application in environment purification.

关键词： h-WO_(3) Morphology pH Photocatalysis Charge transfer Optoelectrical properties

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Intelligent Preamble Allocation for Coexistence of mMTC/URLLC Devices:A Hierarchical Q-Learning Based Approach

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China Communications 2023年第8期20卷 44-53页

作者： Jiadai Wang Chaochao Xing Jiajia Liu National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology the School of CybersecurityNorthwestern Polytechnical UniversityXi’anShaanxi710072China

The emergence of various commercial and industrial Internet of Things(IoT)devices has brought great convenience to people’s life and *** low-power,massively connected mMTC devices(MDs)and highly reliable,low-latency URLLC devices(UDs)play an important role in different application ***,when dense MDs and UDs periodically initiate random access(RA)to connect the base station and send data,due to the limited preamble resources,preamble collisions are likely to occur,resulting in device access failure and data transmission *** the same time,due to the highreliability demands of UDs,which require smooth access and fast data transmission,it is necessary to reduce the failure rate of their RA *** this end,we propose an intelligent preamble allocation scheme,which uses hierarchical reinforcement learning to partition the UD exclusive preamble resource pool at the base station side and perform preamble selection within each RA slot at the device *** particular,considering the limited processing capacity and energy of IoT devices,we adopt the lightweight Qlearning algorithm on the device side and design simple states and actions for *** results show that the proposed intelligent scheme can significantly reduce the transmission failure rate of UDs and improve the overall access success rate of devices.

关键词： preamble allocation random access mMTC URLLC reinforcement learning

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Heterogeneous-attributes enhancement deep framework for network embedding

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Frontiers of Computer Science 2021年第6期15卷 121-131页

作者： Lisheng QIAO Fan ZHANG Xiaohui HUANG Kai LI Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230022China National Key Laboratory of Blind Signal Processing Chengdu 610041China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230022China

Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network ***,considering the complex structures and heterogeneous attributes in real-world networks,existing methods may fail to handle the inconsistencies between the structure topology and attribute ***,more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more *** that end,in this paper,we propose a heterogeneous-attributes enhancement deep framework(HEDF),which could better capture the non-linear structure and associated information in a deep learningway,and effectively combine the structure information of multi-views by the combining *** this line,the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised *** extensive validations on several real-world datasets show that our model could outperform the baselines,especially for the sparse and inconsistent situation with less training data.

关键词： network embedding heterogeneous-attributes deep framework inconsistent

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A novel deep learning–based framework for five-day regional weather forecasting

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Atmospheric and Oceanic Science Letters 2025年

作者： Congqi Cao Ze Sun Lanshu Hu Liujie Pan Yanning Zhang National Engineering Laboratory for Integrated Aero–Space–Ground–Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an China Shaanxi Provincial Meteorological Observatory Xi’an China

Deep learning–based methods have become alternatives to traditional numerical weather prediction systems, offering faster computation and the ability to utilize large historical datasets. However, the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge. In this work, we propose three key solutions: (1) motivated by the need to improve model performance in data-scarce regional forecasting scenarios, we innovatively apply semantic segmentation models, to better capture spatiotemporal features and improve prediction accuracy; (2) recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness, we introduce a novel learnable Gaussian noise mechanism that allows the model to adaptively optimize perturbations for different locations, ensuring more effective learning; and (3) to address the issue of error accumulation in autoregressive prediction, as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction, we propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance. Our method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition . Ablation experiments further validate the effectiveness of each component, highlighting their contributions to enhancing prediction performance. 深度学习逐渐替代传统数值天气预报 (NWP) 系统, 但在数据有限的中期天气预报中仍面临挑战.为此, 本文提出三项创新:首先, 引入语义分割模型增强时空特征捕捉能力, 提高预测精度;其次, 设计可学习的高斯噪声机制, 解决过拟合问题并突破传统噪声增强的局限性;最后, 提出级联预测方法, 平衡预测精度与误差控制, 缓解自回归预测的误差累积问题.该方法在华东区域AI中期气象预报竞赛中表现优异, 实验验证了各模块的有效性, 其中语义分割降低温度预测误差9.3%, 噪声机制提升降水预测F1-score 6.8%, 级联策略减少风速预测均方误差12.5%.此研究为数据受限的区域气象预报提供了新路径.

关键词： Weather forecasting Deep learning Semantic segmentation models Learnable Gaussian noise Cascade prediction 天气预报深度学习语义分割模型可学习高斯噪声级联预测

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