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Intelligent Robotics and Automatic control (IRAC), International Conference on

作者： Tingwei Zhang Yurong Liu Xin Peng Weimin Zhong Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Engineering Research Center of Process System Engineering Ministry of Education East China University of Science and Technology Shanghai China State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

This study proposes a low-carbon operational optimization framework for fluid catalytic cracking (FCC) units and conducts a comprehensive evaluation of their economic performance, energy utilization, and environmental impacts. First, a low-carbon energy-saving operational optimization framework was developed to enhance the economic benefits of FCC units while exploring their carbon reduction potential. Subsequently, a surrogate-assisted model based on an automated machine learning (AutoML) framework was constructed to reduce computational costs. A carbon tax scenario was then introduced, converting CO 2 emissions into carbon tax values. This approach can improve the computational efficiency of the multi-objective optimization problem. The experimental results demonstrate that optimizing operational conditions increased unit-time profits by 0.5%, reduced CO 2 emissions by 1088.92 kgCO 2 eq/h, and decreased wind energy and steam energy consumption by 627.67 m 3 /h and 324.28 m 3 /h, respectively. The scientific optimization of operational parameters enhances production efficiency while mitigating environmental impacts, providing valuable guidance for energy conservation and emissions reduction in industrial processes and supporting sustainable development.

关键词： Economics Wind energy Energy conservation Production FCC Automated machine learning Computational efficiency Optimization Carbon emissions Carbon tax

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中国碳收支:1980~2021年

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Science Bulletin 2024年第1期69卷 114-124页

作者：夏晓圣任培阳王旭辉刘丹陈修治丹利何斌何洪林居为民梁敏琪陆星劼彭静覃章才夏江周郑博魏静乐旭于贵瑞朴世龙袁文平 School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon CycleSun Yat-sen UniversityZhuhai 510245China Sino-French Institute for Earth System Science College of Urban and Environmental SciencesPeking UniversityBeijing 100091China State Key Laboratory of Tibetan Plateau Earth System Resources and EnvironmentInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China State Key Laboratory of Earth Surface Processes and Resource Ecology College of Global Change and Earth System ScienceBeijing Normal UniversityBeijing 100875China Synthesis Research Center of China’s Ecosystem Research Network Key Laboratory of Ecosystem Network Observation and ModelingInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China International Institute for Earth System Science Nanjing UniversityNanjing 210023China Tianjin Key Laboratory of Water Resources and Environment Tianjin Normal UniversityTianjin 300387China Institute of Environment and Ecology Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science&TechnologyNanjing 210044China

As one of the world's largest emitters of greenhouse gases,China has set itself the ambitious goal of achieving carbon peaking and carbon ***,it is crucial to quantify the magnitude and trend of sources and sinks of atmospheric carbon dioxide(CO_(2)),and to monitor China's progress toward these *** state-of-the-art datasets and models,this study comprehensively estimated the anthropogenic CO_(2)emissions from energy,industrial processes and product use,and waste along with natural sources and sinks of CO_(2)for all of China during *** recognize the differences among various methods of estimating greenhouse emissions,the estimates are compared with China's National Greenhouse Gas Inventories(NGHGIs)for 1994,2005,2010,2012,and *** CO_(2)emissions in China have increased by 7.39 times from 1980 to 12.77 Gt CO_(2)a^(-1)in *** benefiting from ecological projects(e.g.,Three Norths Shelter Forest system Project),the land carbon sink in China has reached 1.65 Gt CO_(2)a^(-1)averaged through 2010-2021,which is almost 15.81 times that of the carbon sink in the *** average,China's terrestrial ecosystems offset 14.69%±2.49%of anthropogenic CO_(2)emissions through *** provincial-level administrative regions of China,Xizang and Qinghai,have achieved carbon neutrality according to our estimates,but nearly half of the administrative regions of China have terrestrial carbon sink offsets of less than 10%of anthropogenic CO_(2)*** study indicated a high level of consistency between NGHGIs and various datasets used for estimating fossil CO_(2)emissions,but found notable differences for land carbon *** estimates of the terrestrial carbon sinks of NGHGIs urgently need to be verified with process-based models which integrate the comprehensive carbon cycle processes.

关键词： CO_(2)emission Land carbon sink China Carbon neutrality Carbon budget

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Frequency Planning Strategies of Reducing Inter-Cell Interference for MmWave V2I Communication in Urban Scenario

Frequency Planning Strategies of Reducing Inter-Cell Interfe...

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2021 Computing, Communications and IoT Applications, ComComAp 2021

作者： Song, Yuqiao He, Danping Yan, Dong Guan, Ke Zhong, Zhangdui Kim, Junhyeong Chung, Heesang Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing China Frontiers Science Center for Smart High-speed Railway System Beijing China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing China Moving Wireless Network Research Section Electronics and Telecommunications Research Institute Korea Republic of

ISBN: (纸本)9781665427975

Millimeter wave (mmWave), as the primary frequency band for sixth-generation wireless systems, is a key technology to support lower latency and higher data rates in vehicle-to-infrastructure (V2I) communications. The ray-tracing (RT) based simulation method can be combined with channel measurements to overcome the measurement limitation in mmWave band. Because of a lack of research on using RT simulation data to analyze inter-cell interference (ICI) in specific scenarios, we conduct comprehensive research based on the RT simulation platform at Beijing Jiaotong University to study the V2I link channel in the mmWave band. In this paper, RT simulations for the V2I communication in the mmWave band (22.1-23.1 GHz) are conducted for a typical urban scenario considering high traffic flows. Three frequency planning (FP) strategies are adopted to find the best FP strategy to mitigate ICI and enhance system performance. The signal-to-interference-plus-noise ratio and the corresponding theoretical capacity are calculated. The methodology and analysis in this work will be useful in supporting researchers to design mmWave V2I communication systems in similar scenarios. © 2021 IEEE.

关键词： Millimeter waves

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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42卷 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 SciencesBeijing 100029China University of St.Thomas School of EngineeringMinnesota 55105USA NSF National Center for Atmospheric Research BoulderColorado 80307USA University of Auckland Auckland 0630New Zealand National Oceanic and Atmospheric Administration National Centers for Environmental InformationSilver SpringMaryland 20910USA National Oceanic and Atmospheric Administration National Centers for Environmental InformationAshevilleNC 28801USA National Research Council(CNR)Institute of Marine Sciences(ISMAR) Rome 00133Italy Mercator Ocean International Toulouse 31400France Department of Earth and Environmental Science University of PennsylvaniaPhiladelphiaPennsylvania 19104USA Institute of Oceanology Chinese Academy of SciencesQingdao 266071China National Marine Environmental Forecasting Center Ministry of Natural Resources of ChinaBeijing 100081China North Carolina Institute for Climate Studies(NCICS) North Carolina State UniversityAshevilleNC 28804USA Oceanographic Data Center Chinese Academy of SciencesQingdao 266071China College of Oceanography Hohai UniversityNanjing 210098China National Marine Data and Information Service Tianjin 300171China Istituto Nazionale di Geofisica e Vulcanologia Sede di BolognaBologna 40128Italy South China Sea Institute of Oceanology Chinese Academy of SciencesGuangzhou 510301China ESSIC/CISESS-MD University of MarylandCollege ParkMD 20740USA Computer Network Information Center Chinese Academy of SciencesBeijing 100083China Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment AdministrationMinistry of Ecology and EnvironmentGuangzhou 510611China National Meteorological Information Center China Meteorological AdministrationBeijing 100081China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Xiamen Universit

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.

关键词： ocean heat content sea surface temperature ocean temperature global warming climate

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Train Safety control Based on Deep Deterministic Policy Gradient with control Barrier Function

Train Safety Control Based on Deep Deterministic Policy Grad...

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IEEE International Conference on Networking, Sensing and control

作者： Guoteng Wang Jidong Lv Hongjie Liu Ming Chai Tao Tang Wentao Zhang School of Automation and Intelligence Beijing Jiaotong University Beijing China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China Beijing Zhengshi Environmental Engneering Company Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Train Relative Distance Braking Mode (RDBM) has great potential to increase line capacity by enabling shorter following distances based on train-to-train communication in-formation. However, it is difficult for traditional train control methods to ensure avoidance of potential collision risks in RDBM while dealing with the nonlinearity and uncertainty of train dynamics. In this paper, a novel train safety control method combining Deep Deterministic Policy Gradient (DDPG) and control Barrier Function(Cbf)is proposed. Firstly, the safety constraint set for adjacent trains, considering their braking capabilities, is analyzed, and the uncertainty of train dynamics is modeled using a Gaussian process. Secondly, DDPG is used to explore the optimal control strategy under the action of uncertain train dynamics, and CBF provides compensation control input to ensure safety during the learning process. Then, a DDPG-CBF algorithm is presented to achieve efficient safety controller design and implementation. Finally, sufficient numerical simulations demonstrate that, compared with DDPG algorithm alone, the DDPG-CBF algorithm can not only ensure the safe and stable operation of trains, but also learn train control strategies expe-ditiously and accurately under different operating environments, with an average efficiency increase of 38.94 %.

关键词： Rails Uncertainty Heuristic algorithms Process control Optimal control numerical simulation Stability analysis Safety Sensors numerical stability

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Channel Characterization for the mmWave Vehicle-to-Vehicle Communication in the See-through situation 13

Channel Characterization for the mmWave Vehicle-to-Vehicle C...

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13th International Symposium on Antennas, Propagation and EM Theory, ISAPE 2021

作者： Yan, Dong Guan, Ke He, Danping Kim, Junhyeong Chung, Heesang Ai, Bo Zhong, Zhangdui State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Frontiers Science Center for Smart High-speed Railway System Beijing China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing China Moving Wireless Network Research Section Electronics and Telecommunications Research Institute Korea Republic of

ISBN: (纸本)9781665418126

Millimeter-wave (mmWave) is crucial autonomous driving for its wide bandwidth and low I complex vehicular communication scenarios, the shad of large vehicles has a significant influence on th to-vehicle (V2V) channel. Thus, it is necessary to channel characteristics in this case. This paper uses a ray-tracing simulator to research the influence of the and the beam switching technology on the V2V c the 'See-Through situation'. Finally, through the ext channel parameters, the characteristics of the V2V (the mmWave band are explored. Conclusions can project ideas for the design of the vehicular communication li © 2021 IEEE.

关键词： Millimeter waves

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Synergetic roadmap of carbon neutrality and clean air for China

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Environmental Science and Ecotechnology 2023年第4期16卷 1-25页

作者： Qiang Zhang Zhicong Yin Xi Lu Jicheng Gong Yu Lei Bofeng Cai Cilan Cai Qimin Chai Huopo Chen Hancheng Dai Zhanfeng Dong Guannan Geng Dabo Guan Jianlin Hu Cunrui Huang Jianing Kang Tiantian Li Wei Li Yongsheng Lin Jun Liu Xin Liu Zhu Liu Jinghui Ma Guofeng Shen Dan Tong Xuhui Wang Xuying Wang Zhili Wang Yang Xie Honglei Xu Tao Xue Bing Zhang Da Zhang Shaohui Zhang Shaojun Zhang Xian Zhang Bo Zheng Yixuan Zheng Tong Zhu Jinnan Wang Kebin He Ministry of Education Key Laboratory for Earth System Modelling Department of Earth System ScienceTsinghua UniversityBeijing100084China Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing210044China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Institute for Carbon Neutrality Tsinghua UniversityBeijing100084China State Key Joint Laboratory for Environment Simulation and Pollution Control College of Environmental Sciences and Engineering and Center for Environment and HealthPeking UniversityBeijing100871China Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental PlanningBeijing100012China Center for Carbon Neutrality Chinese Academy of Environmental PlanningBeijing100012China National Center for Climate Change Strategy and International CooperationBeijing100035China Nansen-Zhu International Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing100029China College of Environmental Sciences and Engineering Peking UniversityBeijing100871China Institute of Environmental Policy Management Chinese Academy of Environmental PlanningBeijing100012China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control School of Environmental Science and EngineeringCollaborative Innovation Center of Atmospheric Environment and Equipment TechnologyNanjing University of Information Science and TechnologyNanjing210044China Vanke School of Public Health Tsinghua UniversityBeijing100084China Center for Energy and Environmental Policy Research Beijing Institute of TechnologyBeijing100081China China CDC Key Laboratory of Environment and Population Health National Institute of En

It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on greenhouse gas reduction,air quality improvement,and improved *** the context of carbon peak,carbon neutrality,and clean air policies,this perspective tracks and analyzes the process of the synergetic governance of air pollution and climate change in China by developing and monitoring 18 *** 18 indicators cover the following five aspects:air pollution and associated weather-climate conditions,progress in structural transition,sources,inks,and mitigation pathway of atmospheric composition,health impacts and benefits of coordinated control,and synergetic governance system and *** tracking the progress in each indicator,this perspective presents the major accomplishment of coordinated control,identifies the emerging challenges toward the synergetic governance,and provides policy recommendations for designing a synergetic roadmap of Carbon Neutrality and Clean Air for China.

关键词： Carbon neutrality Clean air Synergetic roadmap

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A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

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Plant Diseases and Pests 2024年第2期15卷 9-16页

作者： Mengmeng ZHANG Xiujuan WANG Mengzhen KANG Jing HUA Haoyu WANG Feiyue WANG State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex System Institute of AutomationChinese Academy of SciencesBeijing 100190China

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning *** demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model ***,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial *** issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data *** address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

关键词： Rice disease and pest identification Convolutional neural networks Distributed training Federated learning(FL) Open-source data sharing platform

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Dynamic Passenger Demands Oriented Optimization of Virtually Coupled Train Formation Scheduling

Dynamic Passenger Demands Oriented Optimization of Virtually...

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Chinese Automation Congress (CAC)

作者： Haobei Zhang Tao Tang Hongjie Liu Yinghui Lang School of Electronics and Information Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China

The research on virtual coupling technology in metros is a hot topic recently. Intelligent automatic train units can flexibly form virtually coupled train formations (VCTFs) when needed, so that the train capacity would be able to adapt to passenger demands, which are changing dynamically with time and space. However, matching the capacity of trains with passenger demands is a difficult decision-making optimization problem. In this work, we propose a collaborative control problem to decide when and how to create a VCTF to meet the dynamic passenger demands in a metro system. A mathematical model is formulated which can simultaneously optimize the number of related train units in a VCTF, the train dwelling time at a station and the running time in a section. Performance indicators such as train load factor and number of stranded passengers are adopted to measure the satisfaction of the operators and passengers. Then, a multi-objective genetic algorithm is adopted to obtain the Pareto optimum solutions. Finally, simulation experiments are conducted, and the relationship between line efficiency and passenger demand in different discrete time periods is analyzed to verify the correctness and feasibility of the model. Compared with the traditional timetabling method, adopting the virtual coupling technology can better adapt the train capacity to meet the time changing passenger demands, which is welcomed by both metro operators and passengers in reality.

关键词： Couplings Road transportation Rails Adaptation models Analytical models Dynamic scheduling Vehicle dynamics

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Efficient Computer Vision-Based Excavator Bucket Fill Estimation Using Depth Map and Faster R-Cnn

SSRN

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SSRN 2023年

作者： Helian, Bobo Huang, Xiaoqian Yang, Meng Bian, Yongming Geimer, Marcus Institute of Mobile Machines Karlsruhe Institute of Technology Karlsruhe76131 Germany State Key Laboratory of Fluid Power and Mechatronic System Zhejiang University Hangzhou310027 China School of Mechanical Engineering Tongji University Shanghai201804 China Shanghai Engineering Research Center for Safety Intelligent Control of Building Machinery Shanghai200032 China

Excavators play an essential role in the construction industry, and the development of autonomous excavator systems is important for increasing productivity and improving worker safety. Accurate estimation of the volume of material in the excavator bucket is a key metric for monitoring and evaluating system automation performance. In this study, a novel solution is proposed that utilizes 2D depth maps as input to a faster region convolutional neural network (Faster R-CNN) model for bucket volume fill estimation. This approach achieves a high estimation accuracy while maintaining a fast real-time processing speed. In addition, a custom loss function is proposed specifically for the classification-regression task of this study to improve the network's generalization despite the data out of distribution. Comparative experiments show that the proposed approach achieves an impressive 96.91% accuracy in fill factor estimation, demonstrating its advantages and promising potential in the practical estimation of automated excavator operations. © 2023, The Authors. All rights reserved.

关键词： Automation

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