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第41届中国控制会议

作者： Guangxuan Song Dongmei Fu Dawei Zhang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing Shunde Graduate School of University of Science and Technology Beijing Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology Beijing National Materials Corrosion and Protection Data Center University of Science and Technology Beijing

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

knowledge is essential for the management,scheduling,and operation of all industrial *** automation refers to the automation of knowledge-based work,which is a disruptive technology of the *** in the era of big data,KG inherits the good interpretability of traditional symbolism system approaches and becomes one of the feasible and important directions to realize industrial knowledge *** paper begins with the origin of KGs,introduces the characteristics of KGs,and reviews the state of the art of KG through the cases of KG constructed and applied in general and vertical *** paper also focuses on the research progress of KG applications in industry,by identifying the current challenges of KG in industrial applications,and highlighting the substantial potential of KG to serve knowledge automation.

关键词： knowledge knowledge Graph(KG) knowledge Automation Symbolism Industry

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Towards Unified Token Learning for Vision-Language Tracking

arXiv

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arXiv 2023年

作者： Zheng, Yaozong Zhong, Bineng Liang, Qihua Li, Guorong Ji, Rongrong Li, Xianxian The Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China The Guangxi Key Laboratory of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China The School of Computer Science and Technology Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing100049 China Media Analytics and Computing Lab Department of Artificial Intelligence School of Informatics Xiamen University 361005 China

In this paper, we present a simple, flexible and effective vision-language (VL) tracking pipeline, termed MMTrack, which casts VL tracking as a token generation task. Traditional paradigms address VL tracking task indirectly with sophisticated prior designs, making them over-specialize on the features of specific architectures or mechanisms. In contrast, our proposed framework serializes language description and bounding box into a sequence of discrete tokens. In this new design paradigm, all token queries are required to perceive the desired target and directly predict spatial coordinates of the target in an auto-regressive manner. The design without other prior modules avoids multiple sub-tasks learning and hand-designed loss functions, significantly reducing the complexity of VL tracking modeling and allowing our tracker to use a simple cross-entropy loss as unified optimization objective for VL tracking task. Extensive experiments on TNL2K, LaSOT, LaSOText and OTB99-Lang benchmarks show that our approach achieves promising results, compared to other state-of-the-arts. Copyright © 2023, The Authors. All rights reserved.

关键词： Modeling languages

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Time-series surface water reconstruction method(TSWR)based on spatial distance relationship of multi-stage water boundaries

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International Journal of Digital Earth 2022年第1期15卷 2335-2354页

作者： Mingyang Li Shanlong Lu Cong Du Yong Wang Chun Fang Xinru Li Hailong Tang Muhammad Hasan Ali Baig Harrison Odion Ikhumhen International Research Center of Big Data for Sustainable Development Goals Key Laboratory of Digital Earth ScienceAerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China College of Resources and Environment University of Chinese Academy of SciencesBeijingPeople’s Republic of China School of Earth Sciences and Spatial Information Engineering Hunan University of Science and TechnologyXiangtanPeople’s Republic of China School of Geospatial Engineering and Science Sun Yat-Sen UniversityZhuhaiPeople’s Republic of China Institute of Geo-Information & Earth-Observation (IGEO) PMAS Arid Agriculture University RawalpindiRawalpindiPakistan Key Laboratory of Ministry of Education for Coastal Wetland Ecosystems College of the Environment and EcologyXiamen UniversityXiamenPeople’s Republic of China

Spatiotemporal continuity of surface water datasets widely known for its significance in the surface water dynamic monitoring and assessments,are faced with drawbacks like cloud influence,which hinders the direct extraction of data from time-series remote sensing *** study proposes a Time-series Surface Water Reconstruction method(TSWR).The initial stage of this method involves the effective use of remote sensing images to automatically construct multi-stage surface water boundaries based on Google Earth Engine(GEE).Then,we reconstructed regions the reconstruction of regions with missing water pixels using the distance relationship between the multi-stage water boundaries in previous and later *** applied to 10 large rivers around the world,this method yielded an overall accuracy of 98%for water extraction,an RMSE of 0.41 ***,time-series reconstruction tests conducted in 2020 on the Lancang and Danube rivers revealed a significant improvement in the image *** findings demonstrated that this method could not only be used to accurately reconstruct the surface water distribution missing water images,but also to depict a more pronounced time variation *** successful application of this method on GEE demonstrates its importance for use on large scales or in global studies.

关键词： Google earth engine sentinel-2 surface water reconstruction time-series surface water data

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Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas. However, extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks. This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle (UAV) to ensure data security at the physical layer. Since such a system requires trade-off policies that balance the secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor critic (TransSAC) algorithm, which is a generative artificial intelligence-enable deep reinforcement learning approach to solve the reformulated problem, so that capturing global dependencies and diversely exploring weights. Simulation results demonstrate that the TransSAC outperforms various baselines, and achieves an optimal secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results find more suitable constraint values for the system. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Mapping global lake aquatic vegetation dynamics using 10-m resolution satellite observations

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Science Bulletin 2024年第19期69卷 3115-3126页

作者： Xuejiao Hou Jinying Liu Huabing Huang Yunlin Zhang Chong Liu Peng Gong International Research Center of Big Data for Sustainable Development Goals Beijing 100094China School of Geospatial Engineering and Science Sun Yat-sen UniversityGuangzhou 510275China Key Laboratory of Comprehensive Observation of Polar Environment(Sun Yat-sen University) Ministry of EducationZhuhai 519082China Taihu Laboratory for Lake Ecosystem Research State Key Laboratory of Lake Science and EnvironmentNanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjing 210008China University of Chinese Academy of Sciences Beijing 100049China University of Chinese Academy of Sciences NanjingNanjing 211135China Department of Geography and Department of Earth SciencesThe University of Hong KongHong Kong 999077China

Aquatic vegetation is crucial for improving water quality,supporting fisheries and preserving biodiversity in *** the spatiotemporal dynamics of aquatic vegetation is indispensable for the assessment and protection of lake ***,a comprehensive global assessment of lacustrine aquatic vegetation is *** study introduces an automatic identification algorithm(with a total accuracy of 94.4%)for Sentinel-2 MSI,enabling the first-ever global mapping of aquatic vegetation distribution in 1.4 million lakes using 14.8 million images from 2019 to *** show that aquatic vegetation occurred in 81,116 lakes across six continents over the past four years,covering a cumulative maximum aquatic vegetation area(MVA)of 16,111.8 km^(2).The global median aquatic vegetation occurrence(VO,in%)is 3.0%,with notable higher values observed in South America(7.4%)and Africa(4.1%)compared with Asia(2.7%)and North America(2.4%).High VO is also observed in lakes near major rivers such as the Yangtze,Ob,and Paraná*** historical data with our calculated MVA,the aquatic vegetation changes in 170 lakes worldwide were *** shows that 72.4%(123/170)of lakes experienced a decline in aquatic vegetation from the early 1980s to 2022,encompassing both submerged and overall aquatic *** most substantial decrease is observed in Asia and *** findings suggest that,beyond lake algal blooms and temperature,the physical characteristics of the lakes and their surrounding environments could also influence aquatic vegetation *** research provides valuable information for the conservation and restoration of lacustrine aquatic vegetation.

关键词： Lakes Aquatic vegetation identification Sentinel-2 MSI Vegetation occurrence Remote sensing

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Controlled-source electromagnetic noise attenuation via a deep convolutional neural network and high-quality sounding curve screening mechanism

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Geophysics 2025年第3期90卷 WA125-WA140页

作者： Liu, Yecheng Li, Diquan Li, Jin Zhang, Xian Central South University Monitoring Ministry of Education Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Changsha China Hunan Provincial Key Laboratory of Non-ferrous Resources and Geological Hazard Detection Changsha China Central South University School of Geoscience and Info-physics Changsha China Hunan Normal University College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha China Hunan University of Finance and Economics School of Information Technology and Management Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology Changsha China

Strong noise is one of the biggest challenges in controlled-source electromagnetic (CSEM) exploration, which severely affects the quality of the recorded signal. We develop a novel and effective CSEM noise attenuation method that integrates a deep convolutional neural network (DCNN) with a high-quality sounding curve screening mechanism. This method first uses a DCNN to learn the large-scale noise characteristics of CSEM measured data and build a DCNN denoising model for attenuating the noise. Because some impulse noise remains after DCNN denoising, the high-quality sounding curve screening mechanism is innovatively applied to optimize the DCNN denoised data. The core of this method is the smoothness value (SV), which quantitatively evaluates the smoothness of the sounding curve (apparent resistivity curve). In effect, the mechanism segments the overall DCNN denoised data and calculates the corresponding sounding curve and its SV for each segment. The highest-quality sounding curve can be screened out by comparing the SVs of all curves as well as obtaining the highest-quality data segment present in the overall DCNN denoised data. Finally, our method is validated with synthetic and field data, demonstrating its feasibility and effectiveness in attenuating large-scale CSEM noise. The quality of CSEM data, which is heavily affected by noise, is significantly improved after processing by our method. © 2025 Society of Exploration Geophysicists. All rights reserved.

关键词： Deep neural networks

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EC²Detect: Real-Time Online Video Object Detection in Edge-Cloud Collaborative IoT

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IEEE Internet of Things Journal 2022年第20期9卷 20382-20392页

作者： Siyan Guo Cong Zhao Guiqin Wang Jiaqing Yang Shusen Yang National Engineering Laboratory for Big Data Analytics and the School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China Department of Computing Imperial College London London U.K. National Engineering Laboratory for Big Data Analytics and the School of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China Industrial Artificial Intelligence Center Pazhou Laboratory Guangzhou China

Video object detection is a fundamental technology of intelligent video analytics for Internet of Things (IoT) applications. However, even with extraordinary detection accuracy, predominating solutions based on deep convolutional neural networks (DCNNs) cannot achieve real-time online object detection on video streams with a low end-to-end (E2E) response latency and therefore cannot be applied to proliferating latency-sensitive IoT applications like autonomous driving requiring large-scale intelligent video analytics. To address this issue, we present EC2Detect, an edge-cloud collaborative real-time online video object detection method. Specifically, we propose a tracking-assisted object detection architecture based on edge-cloud collaboration with keyframe selection, where the accurate but heavy object detection is conducted by the Cloud on sparse keyframes adaptively selected according to their semantic variation, and the lightweight object tracking is used to localize and identify objects in other frames at edge devices. Extensive experiments of our real-world prototype demonstrate that, EC2Detect significantly outperforms state-of-the-art methods in terms of processing speed (up to $4.77\times $ faster), E2E latency (up to $8.12 \times $ lower), and edge-cloud bandwidth occupation ( $17 \times $ lower) with an acceptable mAP, which can effectively support large-scale intelligent video analytics in practice. Source code of EC2Detect is available at https://***/ECCDetect/ECCDetect .

关键词： Object detection Image edge detection Streaming media Internet of Things Collaboration Visual analytics Feature extraction

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China's current forest age structure will lead to weakened carbon sinks in the near future

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The Innovation 2023年第6期4卷 38-47页

作者： Rong Shang Jing M.Chen Mingzhu Xu Xudong Lin Peng Li Guirui Yu Nianpeng He Li Xu Peng Gong Liangyun Liu Han Liu Wenzhe Jiao Key Laboratory for Humid Subtropical Eco-Geogr aphical Processes of the Ministy of Education School of Geographical SciencesFujian Normal UniversityFuzhou 350117China Department of Geography and Planning University of TorontoTorontoOntario M5S 3G3Canada Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBejing 100101China Institute for Climate and Carbon Neutrality Department of Geography and Department of Earth SciencesUniversity of Hong KongHong Kong 999077China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Land Consolidation and Rehabilitation Center Ministry of Natural ResourcesBeijing 100035China Department of Civil and Environmental Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA

Forests are chiefly responsible for the terrestrial carbon sink that greatly re duces the buildup of CO_(2)concentrations in the atmosphere and alleviates climate *** predictions of terrestrial carbon sinks in the future have so far ignored the variation of forest carbon uptake with forest ***,we predict the role of China's current forest age in future carbon sink capacity by generating a high-resolution(30 m)forest age map in 2019 over China's landmass using satellite and forest inventory data and deriving forest growth curves using measurements of forest biomass and age in 3,121 *** China's forests currently have large proportions of young and middle-age stands,we project that China's forests will maintain high growth rates for about 15 ***,as the forests grow older,their net primary productivity will decline by 5.0%±1.4%in 2050,8.4%±1.6%in 2060,and 16.6%±2.8%in 2100,indicating weakened carbon sinks in the near *** weakening of forest carbon sinks can be potentially mitigated by optimizing forest age structure through selective logging and implementing new or improved *** finding is important not only for the global carbon cycle and climate projections but also for developing forest management strategies to enhance land sinks by alleviating the age effect.

关键词： forest carbon structure

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Dual UAV Cluster-Assisted Maritime Physical Layer Secure Communications via Collaborative Beamforming

arXiv

引用

arXiv 2024年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Hong Kong University 999077 Hong Kong

Unmanned aerial vehicles (UAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of UAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the UAV relay for remote maritime communications. However, due to the open nature of UAV channels, security issue requires special consideration. This paper proposes a dual UAV cluster-assisted system via CB to achieve physical layer security in maritime wireless communications. Specifically, one UAV cluster forms a maritime UAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other UAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of UAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Online Public Transit Ridership Flow Estimation through Passive WiFi Sensing

Online Public Transit Ridership Flow Estimation through Pass...

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第34届中国控制与决策会议

作者： Wenbo Chang Baoqi Huang Bing Jia Wuyungerile Li Gang Xu Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia A.R.Key Laboratory of Wireless Networking and Mobile Computing College of Computer Science Inner Mongolia University

Online public transit ridership flow information is helpful to improve the service quality of urban public transportation and travel experience of *** WiFi sensing collects WiFi probe requests sent by mobile devices in a non-intrusive manner,and can thus be used to monitor ridership *** with the existing methods based on manual configurations or video surveillance,passive WiFi sensing based methods demonstrate the advantages of small interference,large coverage,low cost and simple *** recent years,researchers proposed some offline methods based on passive WiFi sensing,but offering online information is still *** paper develops a public transit ridership flow monitoring system using customized WiFi sniffers,and proposes an algorithm to implement online public transit ridership flow ***,the proposed algorithm uses the bidirectional long short-term memory neural network(BiLSTM) to capture the bidirectional time series features contained in WiFi sensing data,and further integrates the attention mechanism to focus on the critical features in relation to *** experimental results,carried out on four bus routes in Hohhot,China,show that the RMSE and MAE errors of the proposed algorithm are 3.96 and 3.1,respectively,which are better than those of the other 7 commonly used online ***,the accuracy of the proposed algorithm is 5% lower than that of the corresponding offline algorithm,but it is evident that our online algorithm will stimulate more practical applications.

关键词： Passive WiFi sensing Online Public transit ridership flow Attention mechanism BiLSTM

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