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Physics-Based Predictions of the Month-by-Month Summer Western North Pacific Anomalous Anticyclone

Journal of Climate 2025年第10期38卷 2187-2203页

作者： Zhu, Zhiwei Yang, Ying Liu, Boqi State Key Laboratory of Climate System Prediction and Risk Management Key Laboratory of Meteorological Disaster Ministry of Education Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing China

This study identified the distinct predictors of the western North Pacific anomalous anticyclone (WNPAC) in June, July, and August and revealed their independent influencing mechanisms on the formation of the summer WNPAC in each corresponding month. For the June WNPAC (AC_Jun), the North Atlantic warming (NAW) in April–May induces subsequently enhanced convection over the Indian Ocean and suppressed the convection over the tropical central Pacific, leading to AC_Jun via the Gill-type response. Meanwhile, the warming tendency over the western North Pacific from February to April, which reflects the slow decay of El Niño, results in AC_Jun through Indian Ocean warming. For the July WNPAC (AC_Jul), the cooling tendency from April to June over the central and eastern tropical Pacific is the sole predictor, which results in AC_Jul through the wind–evaporation–SST feedback. For the August WNPAC (AC_Aug), the tropical Atlantic warming (TAW) in June and July and the Bering Strait sea ice slow melting (BSSM) from April to May are the two predictors. While TAW has a similar influence on AC_Aug to that of NAW on AC_Jun, BSSM makes for AC_Aug by strengthening the westerly jet and subsequently atmosphere–ocean coupling over the North Pacific. Using these predictors, three physics-based empirical models (PEMs) for the seasonal predictions of AC_Jun, AC_Jul, and AC_Aug are conducted, which all outperform the National Centers for Environmental Prediction Climate Forecast system (CFS), shedding light on improving the seasonal prediction of the East Asian summer climate. © 2025 American Meteorological Society.

关键词： Heat flux

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Experimental Study on Geometry Characteristics of Turbulent Premixed Flames for Natural Gas/Air Mixtures

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Journal of Thermal Science 2025年第1期34卷 268-282页

作者： ZHENG Weilin WANG Qijiao XIAO Huahua CHEN Xiaoxiao XIE Fan ZENG Wen Liaoning Key Lab of Advanced Test Technology for Aerospace Propulsion System Shenyang Aerospace UniversityShenyang 110136China State Key Laboratory of Fire Science University of science and Technology of ChinaHefei 230027China AVIC Shenyang Aircraft Design and Research Institute Shenyang 110035China

In this study,focusing on the geometry characteristics of spherical expanding flame,the turbulent premixed flames of natural gas/air mixtures were investigated in a fan-stirred turbulent *** effects of initial temperature(T=300-400 K),initial pressure(P=0.1-0.3 MPa),turbulence intensity(u'=1.0-2.7 m/s),oxygen volumetric percentage(φ(O_(2))=15%-21%)and carbon dioxide volumetric percentage(φ(CO_(2))=0-20%)were delved *** flame profile under the Cartesian coordinate system was derived from the schlieren images taken by the high-speed ***,from both macroscopic and microscopic perspectives,the influence of experimental conditions on the flame geometry characteristics was explored through flame front extraction,wavelet decomposition and network *** results demonstrate that for significant flame wrinkling,changes in species concentrations and turbulence intensity have more pronounced effects on the flame wrinkling *** wrinkling of the flame front maintains a certain degree of similarity,as evidenced by the locally concentrated distribution of the angles of the maximum fluctuation *** disturbance energy under large-scale(D6-D8)disturbances exhibits relatively high values with a similar trend,exerting a significant impact on the geometry characteristics of the flame *** peaks of correlation degree are scattered either with the decomposition scale or the development of flame radius,indicating no linear correlation between different detail ***,the probability distribution of node degrees in key wrinkled regions exhibits different trends with that of large-scale wrinkling and disturbance energy,especially with changes in initial *** occurs because the number of key wrinkles varies based on the perturbation's strength or the region's ***,an increase in the fluctuation frequency of the flame's local radius may not necessarily lead to an increase in the number of key folded regions.

关键词： geometry characteristics large-scale wrinkling wavelet decomposition network topology fan-stirred turbulent combustor

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Combining Local Electromagnetic Scattering and Global Structure Features for SAR Open Set Recognition

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12572-12587页

作者： Lin, Qinjie Sun, Hao Xu, Yanjie Wang, Junyu Ji, Kefeng Kuang, Gangyao National University of Defense Technology State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology Changsha410073 China

Automatic target recognition (ATR) is crucial for synthetic aperture radar (SAR) image interpretation. However, existing SAR ATR primarily rely on algorithms from the field of computer vision, many methods have not adequately considered the imaging sensitivity of SAR images. Moreover, SAR ATR typically follows the closed set assumption, but in dynamic and open real-world environments, SAR systems may encounter target classes that have not been seen during training. To address these issues, this paper proposes a SAR open set recognition (OSR) method that combines local electromagnetic scattering and global structure features. The global structural features focus on the overall relationship between target and background, while the local electromagnetic scattering features can effectively capture the scattering characteristics of target. Hence, we construct a global and local feature guidance (GLGE) network, fuses these complementary features as prior knowledge to guide deep features. Then, a class-anchor-based loss function is constructed by Anchor loss and Tuple loss, which cluster the features of known classes targets around predefined class anchors. Finally, the distance ratio is calculated to identify known classes targets and reject unknown classes targets. The experimental results on the MSTAR and SAR-ACD datasets show that our method outperforms other OSR methods in recall, precision, F1, and accuracy, demonstrating a superior performance in OSR. © 2008-2012 IEEE.

关键词： Automatic target recognition

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Evetac Meets Sparse Probabilistic Spiking Neural Network: Enhancing Snap-Fit Recognition Efficiency and Performance

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IEEE robotics and Automation Letters 2025年第6期10卷 5353-5360页

作者： Fang, Senlin Ding, Haoran Liu, Yangjun Liu, Jiashu Zhang, Yupo Li, Yilin Kong, Hoiio Yi, Zhengkun Faculty of Data Science City University of Macau 999078 China Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen518055 China City University of Hong Kong Hong Kong University of Macau 999078 China Shenzhen Institute of Advanced Technology Shenzhen518055 China City University of Macau 999078 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China City University of Macau Faculty of Data Science 999078 China Chinese Academy of Sciences Key Laboratory of Biomedical Imaging Science and System Shenzhen518055 China

Snap-fit peg-in-hole assembly is common in industrial robotics, particularly for 3 C electronics, where fast and accurate tactile recognition is crucial for protecting fragile components. Event-based optical sensors, such as Evetac, are well-suited for this task due to their high sparsity and sensitivity in detecting small, rapid force changes. However, existing research often converts event data into dense images and processing them with dense methods, leading to higher computational complexity. In this letter, we propose a Sparse Probabilistic Spiking Neural Network (SPSNN) that utilizes sparse convolutions to extract features from the event data, avoiding computations on non-zero cells. We introduce the Forward and Backward Propagation Through Probability (FBPTP) method, which enables simultaneous gradient computation across all time steps, eliminating the need for the step-by-step traversal required by traditional Forward and Backward Propagation Through Time (FBPTT). Additionally, the Temporal Weight Prediction (TWP) method dynamically allocates weights for different time outputs, enhancing recognition performance with minimal impact on model efficiency. We integrate the Evetac sensor compactly into our robotic system and collected two datasets, named Tactile Event Ethernet (TacEve-Eth) and Tactile Event Type-C (TacEve-TC), corresponding to cantilever and annular snap-fit structures. Experiments show that the SPSNN achieves the superior trade-off between recognition performance and efficiency compared to other widely used methods, achieving the highest average recognition performance while reducing inference time by over 90% compared to FBPTT-based dense SNN baselines. © 2016 IEEE.

关键词： Robotic assembly

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Hybrid Models for Forecasting Allocative Localization Error in Wireless Sensor Networks

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International Journal of Cognitive Computing in Engineering 2025年 6卷 333-350页

作者： Li, Guo Sheng, Hongyu State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu610054 China College of Robotics Beijing Union University Beijing 100101 China

This study presents a machine learning-based approach to forecast Allocative Localization Error (ALE) in Wireless Sensor Networks (WSNs), addressing challenges such as dynamic network topologies and resource constraints. The approach utilizes Radial Basis Function (RBF) models enhanced with advanced optimization algorithms, including Coot Optimization Algorithm (COA), Smell Agent Optimization (SAO), and Northern Goshawk Optimization (NGO) to improve ALE prediction accuracy. Hybrid models (RBCO, RBSO, and RFNG) are developed by integrating these optimization techniques, which refine critical RBF parameters, such as spread and center selection, through iterative optimization. Furthermore, an ensemble framework (RSNC) combines all three optimizers with RBF to achieve superior performance. The proposed methods are validated using R2 and RMSE metrics, demonstrating their ability to minimize ALE, optimize resource allocation, and extend network lifespans. The study highlights the practical applicability of these models in real-world scenarios, such as environmental monitoring and industrial automation, offering enhanced efficiency and economic benefits. The RFNG model, in particular, achieved the lowest Mean Absolute Relative Error (MARE) of 0.049, demonstrating superior performance compared to other approaches in the test section. Moreover, RBNG obtained 0.069 and 0.978 values for the RMSE and R2, respectively, which were the most suitable values compared to other models, namely RBGO, RBSO, RSNC, and RBF. The results indicate that the proposed hybrid models significantly improve the prediction of ALE, leading to more efficient node deployment and better network management. This research provides valuable insights into leveraging machine learning for WSN optimization, benefiting researchers, network engineers, and industries relying on sensor networks for applications such as environmental monitoring, smart cities, and asset tracking. © 2025

关键词： Radial basis function networks

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Self-adjusting voxelated electrochemical three-dimensional printing of metallic microstructures

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International Journal of Extreme Manufacturing 2025年第1期7卷 420-433页

作者： Xianghe Meng Xiaomo Wu Xingjian Shen Yan Xu Hao Zhang Mingjun Chen Hui Xie State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150080People’s Republic of China

Microscale metallic structures enhanced by additive manufacturing technology have attracted extensive attention especially in microelectronics and electromechanical ***-confined electrodeposition(MCED)advances microscale 3D metal printing,enabling simpler fabrication of superior metallic microstructures in air without complex equipment or ***,accurately predicting growth rates with current MCED techniques remain challenging,which is essential for precise structure fabrication and preventing nozzle *** this work,we present a novel approach to electrochemical 3D printing that utilizes a self-adjusting,voxelated method for fabricating metallic *** from conventional voxelated printing which focuses on monitoring voxel thickness for structure control,this technique adopts a holistic *** ensures each voxel’s position is in alignment with the final structure by synchronizing the micropipette’s trajectory during deposition with the intended design,thus facilitating self-regulation of voxel position and reducing errors associated with environmental fluctuations in deposition *** method’s ability to print micropillars with various tilt angles,high density,and helical arrays demonstrates its refined control over the deposition *** electron microscopy analysis reveals that the deposited structures,which are fabricated through layer-by-layer(voxel)printing,contain nanotwins that are widely known to enhance the material’s mechanical and electrical ***,in situ scanning electron microscopy(SEM)microcompression tests confirm this enhancement,showing these structures exhibit a compressive yield strength exceeding 1 *** indentation tests provided an average hardness of 3.71 GPa,which is the highest value reported in previous work using *** resistivity measured by the four-point probe method was(1.95±0.01)×10^(−7)Ω·m,nearly 11 times that of bulk ***

关键词： additive manufacturing self-adjusting voxelated electrodeposition metallic microstructures 3D printing nanotwinned copper

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A Lower Limb Exosuit for Assisting Knee Joint in Rehabilitation During Walking

A Lower Limb Exosuit for Assisting Knee Joint in Rehabilitat...

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Mechatronics and robotics Engineering (ICMRE), International Conference on

作者： Yao Huang Haotian Ju Songhao Guo Tianjiao Zheng Jie Zhao Yanhe Zhu State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

For knee rehabilitation patients, it is difficult to maintain balance while walking, and completing a normal gait cycle is challenging. Therefore, we propose a lower limb exosuit tailored for the knee rehabilitation patients to assist in normal walking, and experimentally evaluated the assistance effect of the lower limb exosuit. We mimic the mechanism of the skeletal muscles near the knee joint, by contracting and relaxing the length of the flexible Bowden cable between the thigh and calf, generating assistive torque on the knee joint periodically. We use Inertial Measurement Unit (IMU) to measure lower limb motion parameters, identify gait cycle states, and calculate corresponding plantar pressure and knee joint torque for each phase. We compared surface electromyography (sEMG) signals of three muscle groups (biceps femoris (BF), quadriceps femoris (QF), and gastrocnemius (G)) under conditions of wearing and not wearing the exosuit. The experimental results show that compared to not wearing the exosuit, the mean normalized sEMG signals of the three muscle groups decreased by 4.61 %, 2.41 %, and 3.97% respectively while wearing it, the peak values decreased by 11.60%, 25.69%, and 14.99% respectively, with the quadriceps femoris receiving the highest degree of assistance.

关键词： Knee Legged locomotion Limbs Torque Measurement units Thigh Muscles Pressure measurement Torque measurement Robots

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Real-time Trajectory Planning for Redundant 7-dof Space Manipulators with Unknown Link Lengths

Real-time Trajectory Planning for Redundant 7-dof Space Mani...

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robotics, Control and Automation (ICRCA), International Conference on

作者： Jian Liu Zhiqi Li Shicai Shi Minghe Jin State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

Trajectory planning for redundant space manipulators in complex spatial environments is critical for ensuring successful on-orbit task execution, especially when the manipulator’s link lengths are unknown. Traditional algorithms struggle to establish the transformation between task space and joint space under such conditions, posing significant challenges to autonomous trajectory planning. This study proposes a novel trajectory planning method for a seven-degree-of-freedom redundant space manipulator with a shoulder-wrist offset. The method utilizes a trained pose control decision system to generate continuous, smooth, and singularity-free trajectories in real time, satisfying positional and orientational accuracy requirements for target capture. A neural network with strong nonlinear fitting capabilities is employed as the position control decision module for joints 1-4, trained using a "cautious exploration, greedy exploitation" strategy for efficient and safe trajectory optimization. For joints 5-7, an analytical orientation control decision module adjusts the end-effector’s attitude. Together, these modules form a pose control decision system that adheres to constraints on joint angular velocity, acceleration, and singularity avoidance. The proposed approach is validated through simulations in both static and dynamic scenarios, with simulation results confirming its effectiveness.

关键词： Accuracy Trajectory planning Simulation Position control Aerospace electronics Manipulators Control systems Real-time systems Angular velocity Manipulator dynamics

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Radiation belt electron wisp inside South Atlantic Anomaly due to terrestrial VLF transmitter observed by MSS-1

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Science China Earth Sciences 2025年第2期68卷 538-548页

作者： Yixin SUN Ying LIU Qiugang ZONG Yixin HAO Hong ZOU Yuguang YE Institute of Space Physics and Applied Technology Peking UniversityBeijing 100871China State Key Laboratory of Lunar and Planetary Science Macao University of Science and TechnologyMacao 999078China Max Planck Institute for Solar System Research Goettingen 37077Germany

The South Atlantic Anomaly represents a region within near-Earth space characterized by a significantly weaker geomagnetic field and a higher flux of energetic particles compared to other areas. It is a space weather hazards to Low-EarthOrbit satellites. There has been evidence that the Very Low Frequency(VLF) waves from the powerful ground VLF radio transmitter in Australia, known as NWC, have the capacity to scatter energetic electrons' pitch angle in the inner radiation *** scattering directs electrons into the drift loss cone, forms a “wisp”, characterized by its peak intensity outside the South Atlantic Anomaly(SAA), and a “rift” exhibiting minimal intensity within SAA. Our findings mark the initial observation of a“wisp” precipitation, an unusual occurrence with peak intensity detected inside the SAA, observed via the Macao Science Satellite-1. Enabled by the Medium-energy Electron Spectrometer onboard Macao Science Satellite-1, we were able to comprehensively measure the full pitch angle distribution at Low-Earth-Orbit. This allowed us to attribute the “wisp” within the anomaly to a specific pitch angle range just outside the drift loss cone, a measurement unattainable by previous satellites. This“wisp” occurrence aligns with previous model predictions, despite being overlooked. Moreover, we distinguished between the trapped and precipitating electron populations. Directly derived from the ratio of these populations, our analysis revealed that approximately 2%-5% of trapped electrons will be lost in this specific wisp due to the influence of the ground VLF *** results not only complement existing evidence of energetic electron pitch angle scattering facilitated by the ground VLF transmitter but also offer a quantitative estimation of its impact.

关键词： South Atlantic Anomaly Radiation belts Electrons precipitation VLF transmitter Wave-particle interaction

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A Deep Reinforcement Learning-Assisted Multimodal Multi-Objective Bi-Level Optimization Method for Multi-Robot Task Allocation

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IEEE Transactions on Evolutionary Computation 2025年第3期29卷 574-588页

作者： Yu, Yuanyuan Tang, Qirong Jiang, Qingchao Fan, Qingqing Shanghai Maritime University Logistics Research Center Shanghai201306 China Tongji University Laboratory of Robotics and Multibody System Shanghai China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China

Multi-robot task allocation (MRTA) is a challenging bi-level problem in the multi-robot cooperative systems (MRCSs) and offers an effective method for addressing complex tasks. However, dynamic /uncertain environments can easily invalidate original schemes in practical MRTA decision-makings. Further, a nested structure in MRTA problems makes computational expensive. Therefore, the two main tasks are (1) finding a sufficient number of equivalent schemes for MRTA problems to adapt to task environments;and (2) improving algorithm search efficiency in bi-level optimization problems. In this study, a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning (DRL) and large neighborhood search (LNS), called MMOEA-DL, is proposed to solve MRTA problems. In the MMOEA-DL, the task allocation problem, which is considered as the upper-level optimization problem, is solved using an improved multimodal multi-objective evolutionary algorithm (MMOEA). The travelling salesman problem (TSP) regarded as the lower-level optimization problem is addressed via end-to-end method (i.e., DRL) and LNS. By leveraging the end-to-end method to obtain the results of the lower-level optimization, the bi-level optimization problem is effectively transformed into a single-level optimization problem. To demonstrate the performance of the proposed algorithm, 16 MRTA simulation scenarios and two actual MRTA scenarios with evenly and unevenly distributed task points are introduced in the present study. The simulation results verify that the MMOEA-DL not only provides decision-makers with expanded equivalent optimal schemes to address dynamic environments or unforeseen circumstances, but also offers a novel approach to solve the multimodal multi-objective bi-level optimization problem while saving computational costs. © 1997-2012 IEEE.

关键词： Deep reinforcement learning

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