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design and performance analysis of the vacuum system for ion beam sputtering coating machine

Vacuum 2025年 239卷

作者： Chen, Gengbiao Mo, Can College of Mechanical and Vehicle Engineering Changsha University of Science and Technology Changsha410114 China Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle Hunan Province Changsha410114 China

A high-performance vacuum system is a critical component of an ion beam sputtering (IBS) coating machine, as a clean, high-vacuum environment is essential for ensuring the quality and uniformity of sputtered coatings. This paper presents the design and performance analysis of a vacuum system tailored for IBS processes. A dual-chamber structure optimized for sputtering operations is proposed, and simulation results confirm stable gas flow and the absence of dead zones during main pump operation. Appropriate pump types are selected, and a vacuum system layout is developed to meet the stringent vacuum requirements of the coating process. To achieve accurate prediction of the system's evacuation time, a segmented pumping speed correction factor is introduced, enabling the construction of a sectional vacuum pumping model. Furthermore, a set of synchronized equipment operation commands is devised to realize efficient and automated vacuum pumping. The experimental results of the automated process closely match the model predictions. Compared to existing machines of the same type, the proposed system achieves ultimate vacuum nearly 2 h faster. © 2025 Elsevier Ltd

关键词： Vacuum pumps

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A Review of Intelligent vehicle Trajectory Tracking Control Methods Based on Overtaking Lane Change Scenarios 8th

A Review of Intelligent Vehicle Trajectory Tracking Control ...

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8th International Conference on Advances in Construction Machinery and vehicle engineering, ICACMVE 2023

作者： Yang, Dongzhao Hu, Lin Yi, Xiaojian Zhang, Xin School of Automotive and Mechanical Engineering Changsha University of Science and Technology Changsha410114 China School of Traffic and Transportation Engineering Changsha University of Science and Technology Changsha410114 China Hunan Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9789819718757

In order to achieve safe, comfortable, and stable control of lane-changing trajectories for intelligent vehicles, this paper reviews various trajectory tracking control methods for intelligent vehicles. Focusing on overtaking lane-changing trajectories, the paper provides a comprehensive review of research progress in PID, LQR, MPC, and SMC control methods both domestically and internationally. Striking a balance between addressing the challenges posed by non-integrity and multiple constraints of the vehicle, coping with the time-variability of the vehicle dynamics model, and ensuring the accuracy and computational efficiency of the vehicle model is essential for fostering the continuous development and enhancement of intelligent vehicle technology. © The Author(s) 2024.

关键词： Trajectories

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Manganese-based oxides cathodes for potassium-ion batteries: a review

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Journal of Energy Chemistry 2025年 108卷 1-18页

作者： Yue, Qinggang Xia, Maoting Zhou, Jiang Cheng, Juanjuan Lu, Bingan School of Physics and Electronics Hunan University Hunan Changsha410082 China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Hunan Changsha410082 China School of Materials Science and Engineering and Key Laboratory of Nonferrous Metal Materials Science and Engineering Ministry of Education Central South University Hunan Changsha410083 China School of Materials Science and Engineering Hunan Provincial Key Lab of Advanced Materials for New Energy Storage and Conversion Hunan University of Science and Technology Hunan Xiangtan411201 China

Potassium-ion batteries (PIBs) were recognized for their natural abundance, high theoretical output voltage, and the availability of commercialized graphite anodes. However, the development of high-performance manganese-based layered oxide cathodes—a leading candidate for PIB systems—has been fundamentally constrained by irreversible phase transitions (PT) during the cycling process, manifesting as severe structural degradation and capacity fading. This review presents a transformative paradigm integrating machine learning (ML) with multiscale characterization to analyse the complex phase transition mechanisms in Mn-based cathodes. Through systematic ML-driven interrogation of structure–property relationships, we establish quantitative descriptors for phase stability and develop predictive models for transition dynamics. Furthermore, we highlight recent breakthroughs in cross-disciplinary approaches, enabling the rational design of PT-mitigated cathode architectures. By consolidating these insights into a unified knowledge framework, this work provides strategic guidelines for developing structurally robust Mn-based cathodes and outlines future research directions for next-generation PIB systems. © 2025 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences.

关键词： Setting

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MC-SORT: A Motion Correction-Based Framework for Long-Term Multiple Object Tracking 27

MC-SORT: A Motion Correction-Based Framework for Long-Term M...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Li, Xiangyu Qin, Yunchuan Li, Ruihui Tan, Guanghua Tang, Zhuo Li, Kenli College of Computer Science and Electronic Engineering Hunan University China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University China

ISBN: (纸本)9781643685489

Long-term occlusion is one of the most formidable challenges in Multi-Object Tracking (MOT). The motion models of existing SORT-based trackers are unreliable in estimating the motion states of long-term occluded targets. This is mainly because as the occlusion period increases, the increases speed of estimation errors in the motion model increases faster. In practical applications, we believe that the estimation error of the tracker during long-term occlusion is mainly concentrated in the estimation error of the motion model on the velocity of the occluded target. In this work, we have demonstrated that in the long-term occlusion period, appropriately correcting the estimated values of the motion model on the target motion velocity and fully utilizing the temporal and attribute information of the target's historical trajectory as calculation indicators of correlation are beneficial for improving the robustness of the tracker in long-term occlusion. We refer to our proposed motion correction-based framework as MC-SORT, which mainly consists of a Momentum Compensation Module (MCM) and a Backtracking Re-association (BRA) module. The former can correct the estimated value of the target's motion state during long-term occlusion, the latter uses the temporal and attribute information of the target's historical trajectory during long-term occlusion as correlation indicators to measure the degree of correlation between the target and trajectory. Our proposed MC-SORT has the characteristics of simplicity, online, real-time, and plug-and-play, particularly improving the robustness of the tracker in long-term occlusion. The extensive experimental results on the MOT17 and MOT20 datasets demonstrate the robustness and superiority of our framework. © 2024 The Authors.

关键词： Trajectories

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Injury severity analysis of electric bike crashes in Changsha, hunan Province: taking different lighting conditions into consideration

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Transportation safety and Environment 2022年第3期4卷 43-54页

作者： Lin Hu Xiaotong Wu Xinting Hu Fang Wang Ning Wu School of Automotive and Mechanical Engineering Changsha University of Science and TechnologyChangsha 410114HunanChina Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle Changsha University of Science and TechnologyChangsha 410114HunanChina Institute for Traffic Engineering and Management Ruhr-University BochumD-44780 BochumGermany.

With the increasing use of electric bikes, electric bike crashes occur frequently. Analysing the influencing factors of electric bikecrashes is an effective way to reduce mortality and improve road safety. In this paper, spatial analysis is performed by geographicinformation system (GIS) to present the hot spots of electric bike crashes during daylight and darkness in Changsha, hunan Province,China. Based on the Ordered Probit (OP) model, we studied the risk factors that led to different severities of electric bike *** results show that the main influencing variables include age, illegal behaviour, collision type and road factors. During daylightand darkness, elderly electric bike riders over the age of 65 years have a higher probability of fatal crashes. Not following trafficsignals and reverse driving are significantly related to the severity of riders’ injuries. In darkness, frontal collisions are significantfactors causing rider injury. In daylight, more serious crashes will be caused in bend and slope road segments. In darkness, roadswith no physically separated bicycle lanes increases the risk of riders being injured. Electric bike crashes are mainly concentratedin the commercial, public service and residential areas in the main urban area. In suburbs at darkness, electric bike riders are morelikely to be seriously injured. Adding protectionmeasures, such as improved lighting, non-motorized lane facilities and interventionstargeting illegal behaviour in the hot spot areas can effectively reduce the number of electric bike crashes in complex traffic.

关键词： Electric bike crash GIS Ordered Probit model daylight darkness

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In-vehicle Network Injection Attacks Detection Based on Feature Selection and Classification

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Automotive Innovation 2024年第1期7卷 138-149页

作者： Haojie Ji Liyong Wang Hongmao Qin Yinghui Wang Junjie Zhang Biao Chen Key Laboratory of Modern Measurement and Control Technology Ministry of EducationBeijing Information Science and Technology UniversityBeijing 100192China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China School of Transportation Science and Engineering Beihang UniversityBeijing 100191China Hefei Innovation Research Institute Beihang UniversityHefei 230012China

Detecting abnormal data generated from cyberattacks has emerged as a crucial approach for identifying security threats within in-vehicle *** transmission of information through in-vehicle networks needs to follow specific data for-mats and communication protocols ***,statistical algorithms are employed to learn these variation rules and facilitate the identification of abnormal ***,the effectiveness of anomaly detection outcomes often falls short when confronted with highly deceptive in-vehicle network *** this study,seven representative classification algorithms are selected to detect common in-vehicle network attacks,and a comparative analysis is employed to identify the most suitable and favorable detection *** consideration of the communication protocol characteristics of in-vehicle networks,an optimal convolutional neural network(CNN)detection algorithm is proposed that uses data field characteristics and classifier selection,and its comprehensive performance is *** addition,the concept of Hamming distance between two adjacent packets within the in-vehicle network is introduced,enabling the proposal of an enhanced CNN algorithm that achieves robust detection of challenging-to-identify abnormal *** paper also presents the proposed CNN classifica-tion algorithm that effectively addresses the issue of high false negative rate(FNR)in abnormal data detection based on the timestamp feature of data *** experimental results validate the efficacy of the proposed abnormal data detection algorithm,highlighting its strong detection performance and its potential to provide an effective solution for safeguarding the security of in-vehicle network information.

关键词： Classification algorithm Anomaly detection In-vehicle network Feature extraction Injecting attack

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Methods for fusing uncertain results obtained from different models in accident reconstruction

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Forensic Sciences Research 2022年第2期7卷 272-278页

作者： Tiefang Zou Fenglin He School of Automobile and Mechanical Engineering Changsha University of Science and TechnologyChangshaChina Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle Changsha University of Science and TechnologyChangshaChina

Considering that almost all existing solutions of fusing different reconstructed results require experts’opinions and the issue of how to fuse probabilistic results and mixed results has not been *** solutions are *** first is based on the Monte Carlo Method(FMCM),while the second is based on the Sub-Interval Technique(FSIT).The method based on FMCM generates sample points according to the distribution of each uncertain result firstly,and then gives out the cumulative distribution function of the final fused result by statistical *** method based on FSIT gets the result fusion interval set according to lower and upper bounds of all interval results and a given length d of each sub-interval firstly,and then calculate the weighted matrix of the result fusion *** a result,the cumulative distribution function of the final fused result can also be given out by statistical ***,three real accidents are given to demonstrate the methods of FMCM and FSIT,the results of which show that both work well in practice.

关键词： Forensic sciences accident reconstruction different uncertain results Monte Carlo Method Sub-Interval Technique fusing

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Economically Optimized Distributed Model Predictive Control for Connected vehicles Using Buffer Functions

Economically Optimized Distributed Model Predictive Control ...

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2024 China Automation Congress, CAC 2024

作者： Zhu, Haotian Qin, Hongmao Bian, Yougang He, Ning State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China National Engineering Research Centre for High Efficiency Grinding College of Mechanical and Vehicle Engineering Hunan University Hunan Changsha410082 China

ISBN: (纸本)9798350368604

The cooperation among multiple connected vehicles (CVs) enhances the swarm intelligence of the transportation systems, significantly improving their overall performance. To optimize fuel consumption in vehicle fleet movements, this study proposes a fuel economy optimization approach for a fleeting vehicle swarm using improved distributed model predictive control (DMPC). In the DMPC framework, each CV shares its predicted trajectory within the predictive horizon with its neighboring CVs at each control loop. Using both the own and the neighbors' predicted trajectories, each CV solves an open-loop control optimization problem for fleet formation. In this process, a buffer function is incorporated into the cost function in DMPC to prevent frequent spacing adjustments caused by minor errors, thereby reducing fuel consumption. Simulation results demonstrate enhanced energy efficiency of CV fleets. © 2024 IEEE.

关键词： Predictive control systems

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A Surrogate-Assisted Evolutionary Framework for Expensive Multitask Optimization Problems

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Tan, Shenglian Wang, Yong Sun, Guangyong Pang, Tong Tang, Ke School of Automation Central South University Changsha China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

This paper proposes a surrogate-assisted evolutionary framework (called SELF) to solve expensive multitask optimization problems (ExMTOPs). SELF consists of two main phases: global knowledge transfer phase and local knowledge transfer phase. In the former, a multitask Gaussian process model (MTGP) is established by fusing previously evaluated solutions of multiple optimization tasks. MTGP can capture task-relevant information and the knowledge of landscapes. Then, differential evolution assisted with MTGP is proposed to preselect high-quality candidates. During the preselection, the knowledge of landscapes is transferred among multiple optimization tasks for locating promising regions quickly. In the latter, for each optimization task, Bayesian optimization is adopted to improve the quality of the best individual in the population. Moreover, the improved best individuals in the populations of multiple optimization tasks are adaptively transferred based on a transfer probability, which is computed through the task-relevant information provided by MTGP. By combining these two phases, SELF not only achieves the tradeoff between exploration and exploitation, but also utilizes the global and local knowledge transfer to improve the efficiency for solving ExMTOPs. We test SELF on seven benchmark test problems in the IEEE CEC2017 evolutionary multitask optimization competition. The results demonstrate that the performance of SELF is better than that of other seven advanced methods. In addition, we also apply SELF to deal with two real-world ExMTOPs. The designs provided by SELF exhibit the best performance among all the compared methods, verifying the potential of SELF in practical engineering applications. IEEE

关键词： Linear programming

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Dynamic mesophase transition induces anomalous suppressed and anisotropic phonon thermal transport

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npj Computational Materials 2024年第1期10卷 239-248页

作者： Linfeng Yu Kexin Dong Qi Yang Yi Zhang Zheyong Fan Xiong Zheng Huimin Wang Zhenzhen Qin Guangzhao Qin State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle EngineeringHunan UniversityChangsha410082People’s Republic of China College of Physical Science and Technology Bohai UniversityJinzhou121013People’s Republic of China Hunan Key Laboratory for Micro-Nano Energy Materials&Device and School of Physics and Optoelectronics Xiangtan UniversityXiangtan411105 HunanChina School of Physics and Microelectronics Zhengzhou UniversityZhengzhou450001China Research Institute of Hunan University in Chongqing Chongqing401133China Greater Bay Area Institute for Innovation Hunan UniversityGuangzhou511300 Guangdong ProvinceChina

The physical/chemical properties undergo significant transformations in the different states arising from phase ***,due to the lack of a dynamic perspective,transitional mesophases are largely underexamined,constrained by the high resource burden of first ***,usingmolecular dynamics(MD)simulations empowered by the machine-learning potential,we proffer an innovative paradigm for phase transition:regulating the thermal transport properties via the transitional mesophase triggered by a uniaxial force *** investigate the mechanical,electrical,and thermal transport properties of the two-dimensional carbon allotrope of Janus-graphene with strain-engineered phase ***,we found that the transitional mesophase significantly suppresses the thermal conductivity and induces strong anisotropy near the phase transition *** machine-learning-driven MD simulations,we achieved highprecision atomic-level simulations of *** results show that thermal vibrationinduced intermediate amorphous or interfacial phases induce strong and anisotropic interfacial thermal *** investigation not only endows uswith a novel perspective onmesophases during phase transitions but also enhances our holistic comprehension of the evolution of material properties.

关键词： thermal transition transport

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