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A Vehicle Trajectory Prediction Model for Map-Free Scenes Using the Spatiotemporal Attentional Mechanism

IEEE Internet of Things Journal 2025年第9期12卷 11372-11382页

作者： Hou, Yingjun Zhang, Xizheng Zhang, Hui Cao, Xu Lu, Zhangyu Yuan, Xiaofang Hunan Institute of Engineering School of Information Science and Engineering Xiangtan411104 China Hunan University National Engineering Research Center for Robotic Visual Perception and Control Changsha410082 China

The vehicle trajectory prediction is crucial for autonomous driving. The vast majority of existing trajectory prediction schemes depend on high definition (HD) maps. However, the HD knowledge is not invariably valid under many actual traffic scenarios. When map information is unreliable, vehicle trajectory prediction is a fundamental challenge that must be overcome in autonomous driving areas. Over the vehicle–road cooperation, this work has developed a spatiotemporal-attentional-mechanism-based prediction model (STAM-P) for vehicle trajectories under map-free scenarios to precisely forecast future trajectories in the case of unreliable map information. First, a temporal transformer encoder was used to capture and encode the state information of the vehicle at different time steps for extracting the vehicle temporal features of the trajectories. Second, a spatial encoder layer consisting of the static map convolutional layer and the dynamic spatial transformer in the spatial feature extraction layer was designed to capture and encode the interactions between vehicles to obtain the vehicle spatial features of the trajectories. Finally, the obtained temporal–spatial features were inputted into a multimodal decoding layer to decode and complete the trajectory prediction. Experimental results of the Argoverse dataset revealed that the proposed model outperformed other existing map-free prediction schemes and reached the level of other map-based trajectory prediction models in certain performance metrics. © 2014 IEEE.

关键词： Prediction models

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Superhydrophobic and corrosion-resistant siloxane-modified MgAl-LDHs coatings on magnesium alloy prepared under mild conditions

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 450-463页

作者： Wenxi Zhang Zhangzelong Zhuo Dan Xu Liang Wu Zhihui Xie Chemical Synthesis and Pollution Control Key Laboratory of Sichuan Province College of Chemistry and Chemical EngineeringChina West Normal UniversityNanchong 637002China College of Materials Science and Engineering/National Engineering Research Center for Magnesium Alloys Chongqing UniversityChongqing 400044China

We have developed a superhydrophobic and corrosion-resistant LDH-W/PFDTMS composite coating on the surface of Mg *** composite comprised a tungstate-intercalated(LDH-W)underlayer that was grown at low temperature(relative to hydrothermal reaction conditions)under atmospheric pressure and an outer polysiloxane layer created from a solution containing perfluorodecyltrimethoxysilane(PFDTMS)using a simple immersion *** successful intercalation of tungstate into the LDH phase and the following formation of the polysiloxane layer were confirmed through X-ray diffraction(XRD),Fourier transform infrared(FTIR)spectroscopy,and X-ray photoelectron spectroscopy(XPS).The corrosion resistance of the LDH-W film,both before and after the PFDTMS modification,was evaluated using electrochemical impedance spectroscopy(EIS),Tafel curves,and immersion *** results showed that Mg coated with LDH-W/PFDTMS exhibited significantly enhanced corrosion protection compared to the unmodified LDHW film,with no apparent signs of corrosion after exposure to 3.5wt%NaCl solution for 15 ***,the LDH-W/PFDTMS coating demonstrated superior superhydrophobicity and self-cleaning properties against water and several common beverages,as confirmed by static contact angle and water-repellency *** results offer valuable insights into preparing superhydrophobic and corrosion-resistant LDH-based composite coatings on Mg alloy surfaces under relatively mild reaction conditions.

关键词： Mg alloy corrosion coating layered double hydroxide

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Dynamic Layered Clustering Routing Protocol Based on Hybrid-Optimized Neural Networks for UWSNs

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IEEE Internet of Things Journal 2025年第11期12卷 16155-16170页

作者： Lu, Xinmiao Yang, Longyue Wu, Qiong Zhu, Yuna Zou, Yixin Shi, Yunbo Harbin University of Science and Technology School of Measurement-Control Technology and Communications Engineering Harbin150000 China Heilongjiang Network Space Research Center School of Computer Science and Technology Harbin150000 China

To address the limitations of traditional flat routing in large-scale Underwater Wireless Sensor Networks (UWSNs), and to tackle challenges such as long delays, low bandwidth, and high error rates encountered by sensor nodes in underwater environments, this paper proposes a dynamic layered clustering routing protocol based on a hybrid optimized backpropagation neural network (PSB-NN), referred to as PSBDR. The protocol introduces non-uniform vertical layering of the network based on water depth and assigns different communication and sensing radii to nodes according to their initial layer. A trained hierarchical prediction model dynamically adjusts the virtual layer assignments of nodes throughout the network's operation. Additionally, a method is implemented for isolated nodes to join clusters via relay nodes, and a priority mechanism, along with a dynamic weight update strategy, is employed to select the optimal next hop during routing. This protocol effectively balances the workload across nodes at varying depths and maximizes the utilization of residual energy in the nodes, contributing to an extending network lifetime. Simulation results demonstrate that PSBDR enhances network performance by improving load balancing, prolonging network longevity, reducing end-to-end delay, and increasing data collection reliability. © 2014 IEEE.

关键词： Network layers

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Data-driven output regulation control for constrained linear systems

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science China(Information sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Identification of an Endogenous Production Function for the U.S. Economy 15th

Identification of an Endogenous Production Function for t...

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15th International Conference on Optimization and Applications, OPTIMA 2024

作者： Olenev, Nicholas Federal Research Center "Computer Science and Control" of the Russian Academy of Science Moscow Russia

ISBN: (纸本)9783031791185

The paper identifies an endogenous production function for the U.S. economy, represented by the distribution of production capacities across technologies with finite lifetimes. Technology characteristics (initial labor and capital intensity) are determined during the creation of the production unit. The microeconomic description of the model is based on the hypothesis that production capacity decreases with age, while the number of jobs remains the same until the production unit is liquidated when its lifetime limit is exceeded. Initial labor and capital intensity may decrease under the influence of scientific and technological progress. The parameters of the production function built on the basis of microeconomic description are determined by parallel calculations indirectly by comparing the model-calculated and statistical time series of macro-indicators of the U.S. economy in 1970–2022. A preliminary economic interpretation of the obtained results is presented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Endogenous production function Parameter identification Production capacity U.S. economy Vintage capacity model

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A Fusion Modeling Method for Ball Screw Feed System of Machine Tool 4th

A Fusion Modeling Method for Ball Screw Feed System of Machi...

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4th International Conference on Mechanical Design and Simulation, MDS 2024

作者： Huang, Dehai Duan, Jihang Yang, Jianzhong Chen, Jihong Xu, Guangda National Numerical Control System Engineering Research Center Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819778867

The feed system is an important part of the CNC machine tool, and its performance directly affects the machining quality. Establishing an accurate model is the key to achieving feed system servo performance optimization and error compensation. In this paper, a fusion modeling method is proposed for the problems of low accuracy and difficulty in identifying stiffness and damping in the traditional lumped mass modeling method. The proposed method first establishes a rigid model without stiffness and damping, then uses a data-driven model to model the unmodeled dynamics, and finally connects the rigid model and the data-driven model in series. The method simplifies the lumped mass model, avoids the problem that the stiffness and damping are difficult to identify, and the fusion of the data-driven model significantly improves the prediction accuracy of the model. The experimental results show that the fused model has higher prediction accuracy for the frequency response characteristics and displacement of the feed system. © The Author(s) 2025.

关键词： Ball screws

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Green innovation unleashed:Harnessing tungsten-based nanomaterials for catalyzing solar-driven carbon dioxide conversion

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Chinese Chemical Letters 2025年第2期36卷 108-117页

作者： Weidan Meng Yanbo Zhou Yi Zhou State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process East China University of Science and TechnologyShanghai 200237China National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery East China University of Science and TechnologyShanghai 200237China Shanghai Engineering Research Center for Multi-media Environmental Catalysis and Resource Utilization East China University of Science and TechnologyShanghai 200237China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China

Carbon dioxide photocatalytic reduction (CO_(2)-PR) is an efficient method for controlling CO_(2)emissions and generating cleaner energy while mitigating global *** oxides (WxOy) have attracted considerable attention for CO_(2)-PR due to their excellent spectral ***,comprehensive reviews are lacking on the use of WxOyfor CO_(2)-***,this review provides a detailed summary of t research progress made with WxOy-based catalysts in CO_(2)-*** also explains the fundamental principles of CO_(2)-PR and evaluates key performance indicators that affect the activity of WxOy-based photocatalysts,including yield,selectivity,stability,and apparent quantum ***,this review explores opportunities for synthesizing high-performance WxOy-based photocatalysts and highlights their potential for the green preparation of C1/C2 products through CO_(2)-*** innovative strategies aim to address the challenges and pressures associated with energy and environmental issues,particularly by enhancing artificial photosynthesis efficiency.

关键词： Carbon dioxide photoreduction Tungsten-based nanocatalyst Mechanism Performance evaluation Selectivity

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Star point positioning for large dynamic star sensors in near space based on capsule network

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Chinese Journal of Aeronautics 2025年第2期38卷 418-431页

作者： Zhen LIAO Hongyuan WANG Xunjiang ZHENG Yunzhao ZANG Yinxi LU Shuai YAO Research Center for Space Optical Engineering HIT Aerospace Laboratory Harbin Institute of TechnologyHarbin 150001China Shanghai Aerospace Control Technology Institute Shanghai 201109China

In order to solve the problem that the star point positioning accuracy of the star sensor in near space is decreased due to atmospheric background stray light and rapid maneuvering of platform, this paper proposes a star point positioning algorithm based on the capsule network whose input and output are both vectors. First, a PCTL (Probability-Coordinate Transformation Layer) is designed to represent the mapping relationship between the probability output of the capsule network and the star point sub-pixel coordinates. Then, Coordconv Layer is introduced to implement explicit encoding of space information and the probability is used as the centroid weight to achieve the conversion between probability and star point sub-pixel coordinates, which improves the network’s ability to perceive star point positions. Finally, based on the dynamic imaging principle of star sensors and the characteristics of near-space environment, a star map dataset for algorithm training and testing is constructed. The simulation results show that the proposed algorithm reduces the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) of the star point positioning by 36.1% and 41.7% respectively compared with the traditional algorithm. The research results can provide important theory and technical support for the scheme design, index demonstration, test and evaluation of large dynamic star sensors in near space.

关键词： Star point positioning Star trackers Capsule network Deep learning Dynamic imaging Near space application

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Synthesis of Reliable Design Solutions Using Statistical and Expert Information in Conceptual Aircraft Design

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Journal of Computer and Systems sciences International 2025年第1期64卷 72-83页

作者： Veresnikov, G.S. Goncharenko, V.I. Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow119333 Russia

Abstract: The conceptual phase of aircraft design is characterized by a significant degree of uncertainty in the initial data. This is largely due to the presence of random processes and incomplete information, requiring the use of nondeterministic parameters that cannot be defined by a precise number. These nondeterministic parameters are linked to parametric uncertainty, which is a key factor contributing to the increased risk of design errors. To address this challenge, this paper presents optimization models that formalize the tasks of parametric synthesis in aircraft conceptual design, with a focus on ensuring the reliability of the design solutions. Probability theory and uncertainty theory are employed to represent nondeterministic parameters. The theory of uncertainty provides decision-makers (DMs) with a powerful tool for constructing optimization models that encapsulate the formalized requirements of the designed system. © Pleiades Publishing, Ltd. 2025.

关键词： Conceptual design

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Energy Management of Intelligent FCHEVs Under Adaptive Cruise control Based on Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7339-7350页

作者： Wang, Haocong Wang, Xiaomin Southwest Jiaotong University Chengdu School of Information Science and Technology Sichuan610031 China Sichuan Provincial Engineering Research Center of Train Operation Control Technology Chengdu611756 China

The integration of advanced adaptive cruise control (ACC) with energy management strategy (EMS) is crucial for improving the fuel efficiency of fuel cell hybrid electric vehicles (FCHEVs). The nonlinearity inherent in ACC and EMS arises from complex interactions between vehicle dynamics system and composite energy sources, necessitating intricate real-time adjustments and resulting in high-dimensional dynamic optimization problems. This paper proposes a data-driven, model-free deep reinforcement learning (DRL) integrated optimization framework that emphasizes balancing multiple objectives - safety, comfort, energy saving, and energy source durability while addressing the challenges of policy convergence exacerbated by potentially sparse rewards in high-dimensional action-state spaces. Specifically, an ACC based on course learning DRL is proposed, with a featuring a staged reward mechanism for driving safety, comfort, and speed consistency to model human-like progressive learning. Then, adaptive equivalent consumption minimization is combined to design the DRL-based EMS that balances fuel economy and power source durability. Finally, the decision sets of the integration optimization framework are generated and verified. The simulation results show that the proposed method can ensure driving safety and traffic throughput, the ego vehicle improves driving comfort by 73.4% and energy saving by 11.5% compared to the preceding vehicle. © 1967-2012 IEEE.

关键词： Adaptive cruise control

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