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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Chen, Huimin Liu, Chenguang He, Zhibo Zhang, Guoqing Han, Bing State Key Laboratory of Maritime Technology and Safety Wuhan University of Technology Wuhan430063 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430063 China School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan430063 China Navigation College Dalian Maritime University Dalian116026 China State Key Laboratory of Maritime Technology and Safety Dalian116026 China National Engineering Research Center of Ship & Shipping Control System Shanghai200134 China

ISBN: (纸本)9789819639601

The paper presents a ship motion planning method that adjusts the attractive and repulsive gain parameters of the artificial potential field (APF) algorithm using the deep deterministic policy gradient (DDPG) algorithm, to address the challenges of parameter design and limited adaptability of traditional APF algorithms. This approach leverages the speed advantage of the APF planning algorithm and the exploration capability of the DDPG reinforcement learning algorithm. By optimizing the parameters of APF with DDPG, the local optima problem can be alleviated in complex obstacle environments. Simulation results demonstrate that the proposed motion planning algorithm can generate reasonable trajectories and exhibit strong adaptability in complex obstacle scenarios. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Motion planning

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A Quasi-Parallel Resonant Switched Capacitor DC-DC Converter with Wide Input Voltage Range Regulation and Enhanced Dynamic Performances

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Qi, Jingjing Wu, Xuezhi Sun, Kai Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China Beijing Jiaotong University National Active Distribution Network Technology Research Center Beijing100044 China

Wide-range and high-efficiency DC-DC converters are in high demand for various applications, such as distributed systems, electric vehicles, and DC buildings. Resonant switched capacitor converters (ReSCC) can achieve soft switching, have small passive device capacity, and exhibit low voltage stress, but the voltage ratio is difficult to be regulated continuously. In this paper, a quasi-parallel resonant switched capacitor DC-DC converter is proposed to satisfy the design requirements, which incorporates a ReSCC with soft-switching and variable voltage ratio, as well as a Buck converter. In addition, a simplified optimal trajectory controller for ReSCC is investigated, which can be combined with PI controller of Buck converter to enhance the load transient of the proposed converter. Moreover, the input voltage feedforward control is implemented with the optimized feedforward coefficient to suppress the input voltage disturbance. The proposed converter can achieve high efficiency and rapid dynamic response over a wide conversion range. The correctness of the theoretical analyses is verified by a 36V∼60V-5V/20A prototype. © 2013 IEEE.

关键词： Capacitor bank

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CPS Modeling of CNC Machine Tool Work Processes Using an Instruction-Domain Based Approach

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engineering 2015年第2期1卷 247-260页

作者： Jihong Chen Jianzhong Yang Huicheng Zhou Hua Xiang Zhihong Zhu Yesong Li Chen-Han Lee Guangda Xu National Numerical Control System Engineering Research Center Huazhong University of Science and Technology

Building cyber-physical system(CPS) models of machine tools is a key technology for intelligent manufacturing. The massive electronic data from a computer numerical control(CNC) system during the work processes of a CNC machine tool is the main source of the big data on which a CPS model is established. In this work-process model, a method based on instruction domain is applied to analyze the electronic big data, and a quantitative description of the numerical control(NC) processes is built according to the G code of the processes. Utilizing the instruction domain, a work-process CPS model is established on the basis of the accurate, real-time mapping of the manufacturing tasks, resources, and status of the CNC machine tool. Using such models, case studies are conducted on intelligent-machining applications, such as the optimization of NC processing parameters and the health assurance of CNC machine tools.

关键词： cyber-physical system （CPS） big data computer numerical control （CNC） machine tool electronic data of work processes instruction domain intelligent machining

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Low-Earth Orbital Lifetime Prediction Based on Parameter Sensitivity Analysis and Deep Learning

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Chen, Shun-Yi Yang, Zhen Wang, Hua Lu, Lin State Key Laboratory of Space System Operation and Control Changsha410073 China National University of Defense Technology College of Aerospace Science and Engineering Changsha410073 China China Astronaut Research and Training Center Beijing100094 China

This article proposes an intelligent prediction method for the orbital lifetime of resident space objects (RSOs) in low Earth orbit. This method is intended to satisfy the computational efficiency and accuracy requirements when predicting the large-scale RSOs. It helps to analyze the distribution and uncertainty of RSOs' lifetime. First, based on the semi-analytical orbit prediction model, a parameter sensitivity analysis method for the orbital lifetime is proposed, and the parameters that affect the orbital lifetime are investigated. Second, using a fully connected neural network, an orbital lifetime prediction model is established based on deep learning. The hyperparameters of the deep learning model are optimized using the black-winged kite algorithm. Finally, by combining the sensitivity analysis with the deep learning model, an intelligent prediction method is proposed for the rapid prediction of orbital lifetime. Simulation results show that the proposed intelligent prediction method accurately determines the orbital lifetime of RSOs and has higher prediction efficiency than the traditional prediction method. The research results provide an effective calculation and analysis tool for predicting the orbital lifetime of large-scale RSOs in the future. © 2025 IEEE.

关键词： Prediction models

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A MPC-based Planner Applied on a Parallel Wheel-legged Robot for Obstacle Avoidance 25

A MPC-based Planner Applied on a Parallel Wheel-legged Robot...

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Proceedings of the 4th International Conference on Computer, Artificial Intelligence and control engineering

作者： Fei Guo Huan Yu Wanhong Lin Shoukun Wang National Key Laboratory of Human Factors Engineering China Astronaut Research and Training Center Beijing China Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing China

ISBN: (纸本)9798400712647

A wheel-legged robot is equipped with Stewart parallel mechanism, constituting a reconfigurable robot which can change its wheelbase, robot body height, and achieve omnidirectional steering. The legged character effectively improves the terrain adaptability, which concerns our planning concentration. We introduced an optimization-based whole-body trajectory planning algorithm to navigate robot in rugged terrain. The planner combines terrain data and stability, allowing lower-level motion generator and controller to operate more efficiently. The Model Predictive control(MPC)-based method updates the footholds and CoG trajectories, which builds upon the support polygon constraints on optimization. The simulations of methodology working in several structure-obstacle scene demonstrated and compared the availability of approach.

关键词： Reconfigurable robot

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Modified Hough Transform Based Registration Method for Heavy-haul Railway Rail Profile with Intensive Environmental Noise

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： An, Yuntong Cao, Yuan Sun, Yongkui Su, Shuai Wang, Feng Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation Control System School of Automation and Intelligent Beijing100044 China Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing100044 China Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China

In heavy-haul railway inspections, accurate rail profile registration is essential for assessing wear and deformation of tracks. In this process, identifying the rail waist arc as key segments and determining the center of the arc as feature points are crucial steps for rough rail profile registration, often affected by noise from sensors, surface contaminants, and environmental factors. To address these challenges, a profile registration method is proposed, with the Modified Hough Transform (MHT) as a core component. The method first segments key profile sections using curvature thresholds to identify arc segments. For each point in these segments, MHT constructs circles with fixed radius and calculates their intersection points. A distance matrix based on Euclidean distances between intersection points is generated. By sorting and normalizing these distances, the method identifies local density variations and calculates density coefficients to find the circle center, without manual thresholding. Comparative experiments on simulated and field data show that the proposed method achieves high accuracy and robustness in noisy environments. The dual validation confirms its reliability, making this method effective for rail profile registration in heavy-haul railway inspections. © 1963-2012 IEEE.

关键词： Hough transforms

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Record High Temperatures in the Ocean in 2024

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Advances in Atmospheric Sciences 2025年第06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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Toward Intelligent Machine Tool

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engineering 2019年第4期5卷 679-690页

作者： Jihong Chen Pengcheng Hu Huicheng Zhou Jianzhong Yang Jiejun Xie Yakun Jiang Zhiqiang Gao Chenglei Zhang National Numerical Control System Engineering Research Center Huazhong University of Science and TechnologyWuhan 430074China

With the development of modern information technology-and particularly of the new generation of artificial intelligence(AI)technology-new opportunities are available for the development of the intelligent machine tool(IMT).Based on the three classical paradigms of intelligent manufacturing as defined by the Chinese Academy of engineering,the concept,characteristics,and systemic structure of the IMT are presented in this *** stages of machine tool evolution-from the manually operated machine tool(MOMT)to the IMT-are discussed,including the numerical control machine tool(NCMT),the smart machine tool(SMT),and the ***,the four intelligent control principles of the IMT-namely,autonomous sensing and connection,autonomous learning and modeling,autonomous optimization and decision-making,and autonomous control and execution-are presented in *** paper then points out that the essential characteristic of the IMT is to acquire and accumulate knowledge through learning,and presents original key enabling technologies,including the instruction-domain-based analytical approach,theoretical and big-data-based hybrid modeling technology,and the double-code control *** on this research,an intelligent numerical control(INC)system and industrial prototypes of IMTs are *** intelligent practices are conducted,demonstrating that the integration of the new generation of AI technology with advanced manufacturing technology is a feasible and convenient way to advance machine tools toward the IMT.

关键词： Intelligent manufacturing Intelligent machine tool Intelligent numerical controller New-generation artificial intelligence

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Abatement of CH4 and NOx emissions in marine LNG engine using a plasma-catalyst reactor: An integrated experimental and computational study

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Separation and Purification Technology 2025年 373卷

作者： Xiang, Chuang Lyu, Lin Liang, Junjie Yang, He Zhu, Neng School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Wuhan430063 China National Engineering Research Center of Ship & Shipping Control System Shanghai200135 China School of Automotive and Transportation Engineering Wuhan University of Science and Technology Wuhan430081 China

To investigate the CH4 and NOx abatement performance of a plasma-catalyst hybrid system in liquefied natural gas (LNG) engine exhaust treatment, we conducted bench-scale experiments to analyze the coupled effects of flow dynamics, thermal distribution, and concentration gradients on pollutant removal. The study systematically evaluated CH4, CO, and NOx removal efficiencies across varying engine operating conditions and specific energy inputs (SEIs), with particular emphasis on distinguishing the individual contributions of plasma and catalytic processes. A validated 2D plasma-catalyst reactor model was developed through a 0D-2D coupling methodology. This computational framework enabled detailed analysis of velocity fields, temperature profiles, and species distributions under different operating scenarios, providing mechanistic insights into CH4 and NOx removal pathways. Simulated results revealed uniform spatial distributions of velocity and thermal characteristics across multiple plasma discharge channels. Increasing SEI induced a progressive temperature elevation along the flow axis, accompanied by concomitant decreases in CH4 concentration (36% maximum reduction) and increases in CO and NOx concentrations through plasma-driven chemical conversion. Within the catalytic module, velocity profiles maintained uniformity while temperature distributions exhibited centerline maxima due to boundary heat dissipation. Elevated SEI enhanced catalytic reduction efficiencies, demonstrating progressive decreases in NO, CH4, and CO concentrations along the flow direction with contraction of high-consumption zones. Notably, NO2 concentrations followed a non-monotonic trend, initially increasing through plasma-generated NO oxidation before subsequent catalytic reduction. The integrated system achieved maximum removal efficiencies of 90.1% for CH4, 89.7% for CO, and 61.6% for NOx through synergistic plasma-catalyst interactions. Critical performance analysis quantified the plasma'

关键词： Velocity distribution

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The Enhancing Effect of the East Asian Subtropical Westerly Jet on Summer Extreme High-Temperature Events over Central-Eastern China Since the Late 1990s

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Advances in Atmospheric Sciences 2025年第3期42卷 453-466页

作者： Chujie GAO Yuyu NIU Gen LI Shanlei SUN Bo LU Chaofan LI Bei XU Jinglong HUANG Xiubao SUN College of Oceanography&Key Laboratory of Marine Hazards Forecasting Ministry of Natural ResourcesHohai UniversityNanjing 210098China Key Laboratory of Meteorological Disaster Ministry of Education(KLME)&Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Tropical Oceanography South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China Laboratory for Climate Studies and CMA-NJU Joint Laboratory for Climate Prediction Studies National Climate CenterChina Meteorological AdministrationBeijing 100081China Xiong’an Institute of Meteorological Artificial Intellegence Xiong'an 071708China Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China College of Earth and Planetary Sciences University of the Chinese Academy of SciencesBeijing 100049China College of Intelligent Science and Control Engineering Jinling Institute of TechnologyNanjing 211169China

The increase in frequency and intensity of Extreme High-temperature Events(EHEs)over Central-Eastern China(CEC)in recent decades has severely impacted social development and *** observation and reanalysis datasets,this study explores the effect of the East Asian subtropical westerly jet stream(EAJ)on the CEC EHEs for the summers spanning 1979–*** its general relative location to the right side of the upper-level jet stream exit region,CEC would theoretically suffer more EHEs with a stronger and northwardly-shifted EAJ in summer due to the likelihood of abnormal subsidence induced by the ***,such an EAJ–EHE connection has been unstable over the past four decades but has displayed an evident interdecadal *** the late 1990s,the interannual variation of the EAJ was manifested mainly by its meridional displacement in the northeastern part of East Asia;thus,the atmospheric responses were essentially located to the east of CEC,exerting less of an influence on the CEC ***,since the late 1990s,the EAJ variation has featured an intensity change in its center over the northwest portion of the CEC,which has resulted in a westward shift in atmospheric responses to cover the CEC ***,the EAJ could potentially affect the summer CEC EHEs during 2000–*** findings offer support for an in-depth understanding of the formation mechanisms of extreme weather/climate events of this nature and thus provide a scientific reference for seasonal climate predictions.

关键词： summer extreme high-temperature events East Asian subtropical jet atmospheric circulation central-eastern China interdecadal change

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