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Design method of end worm gear drive,including tooth curve geometry and improved lubrication angle formula

Frontiers of Mechanical engineering 2024年第6期19卷 15-32页

作者： Jian CUI Yaping ZHAO Qingxiang MENG Zhiqiang HAO School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China School of Mechanical Engineering Yanshan UniversityQinhuangdao 066004China Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education Wuhan University of Science and TechnologyWuhan 430081China

End worm gear drives are characterized by their multi-tooth contact,compact contour,and theoretical potential to overcome some inherent flaws of cylindrical worm ***,quantitative basic research on end worm gear drives is currently inadequate,which hinders the development of this *** work focuses on the computational design of end worm gear drives and proposes a new Niemann-type *** models of the proposed drive are established,and its engagement theory is deduced *** on the derived tooth surface equations,an innovative research methodology for the tooth curve geometry of the end worm gear is created,and the tooth curve in the worm gear reference plane is proved to be a *** improved formula for the lubrication angle is developed,which can provide more rational numerical results for the ***,the modified formula is universally applicable for line contact drives and can be used to quantitatively investigate the lubrication level between the teeth for the proposed *** outcomes demonstrate the favorable characteristics of the transmission,including broad conjugate areas,even contact lines,and fine global lubrication state.

关键词： end worm gear tooth curve geometry lubrication angle meshing characteristic nonlinear equation

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The Kirkendall effect on erosion resistance of chrome-coated 25Cr3Mo2WNiV and 30SiMn2MoV gun barrel steels

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Journal of Materials science & Technology 2024年第10期177卷 246-255页

作者： C.H.Dou P.F.Jin C.Z.Wang J.J.Li C.Zhang J.F.Huang C.Zhang Key Laboratory for Advanced Materials Processing State State Key Laboratory for Advanced Metals and MaterialsUniversity of Science and Technology BeijingBeijing 100083China National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials School of Materials Science and EngineeringHenan University of Science and TechnologyLuoyang 471003China

This study aimed to investigate the erosion behavior and mechanism of a newly developed 25Cr3Mo2WNiV steel with a chrome coating using promoted ignition combustion *** erosion threshold pressure and temperature of the chrome-coated 25Cr3Mo2WNiV steel were determined to be 0.2 MPa and 254.3 K higher than those of traditional chrome-coated 30SiMn2MoV ***,Kirkendall voids and inter-diffusion between the Cr coating and matrix were first observed before *** improved erosion resistance of the chrome-coated 25Cr3Mo2WNiV steel was attributed to the suppression of the Kirkendall effect,which minimized heat generation at the Cr/matrix interface by pre-venting oxygen diffusion and reducing oxygen affinity.

关键词： Erosion resistance Kirkendall effect Cr coating Gun steel

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FedAWS: A Federated Tuning Approach with Adaptive Weight Shrinking for Pre-trained Foundation Models 13

FedAWS: A Federated Tuning Approach with Adaptive Weight Shr...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Cheng, Yubin Song, Zhihuan Jiang, Xiaoyu Yao, Le Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Hangzhou310027 China School of Mathematics Hangzhou Normal University Hangzhou China

ISBN: (纸本)9798350361674

In recent years, pre-trained foundation models have developed rapidly and have been applied to various downstream tasks through fine-tuning methods. However, due to the existence of data silos, the lack of data for fine-tuning has become a vital issue preventing the application of the foundation model to industry. In addition, the high complexity of the network makes the foundation model prone to overfitting when used for data-constrained industrial tasks. Therefore, a federated tuning approach with adaptive weight shrinking, named FedAWS, is proposed for collaborative fine-tuning between factories. FedAWS adaptively selects the weight-shrinking factor to reduce overfitting and improve the generalization of the global model without relying on additional data sets or human experience. The case study on an industrial dataset demonstrates the effectiveness of FedAWS. This successful validation provides a new way for inter-factory collaboration to apply the foundation model to industry, thus facilitating the efficient utilization of industrial data. © 2024 IEEE.

关键词： Federated Learning Fine-tuning Foundation Model Weight Shrinking

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Reinforcement learning-based unknown reference tracking control of HMASs with nonidentical communication delays

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science China(Information sciences) 2023年第7期66卷 46-57页

作者： Yong XU Zheng-Guang WU Wei-Wei CHE Deyuan MENG School of Automation Beijing Institute of Technology Institute of Cyber-Systems and Control Zhejiang University College of Mathematics and Computer Science Zhejiang Normal University Department of Automation Qingdao University School of Automation Science and Electrical Engineering Beihang University(BUAA)

This paper focuses on the optimal output synchronization control problem of heterogeneous multiagent systems(HMASs) subject to nonidentical communication delays by a reinforcement learning *** with existing studies assuming that the precise model of the leader is globally or distributively accessible to all or some of the followers, the leader's precise dynamical model is entirely inaccessible to all the followers in this paper. A data-based learning algorithm is first proposed to reconstruct the leader's unknown system matrix online. A distributed predictor subject to communication delays is further devised to estimate the leader's state, where interaction delays are allowed to be nonidentical. Then, a learning-based local controller, together with a discounted performance function, is projected to reach the optimal output synchronization. Bellman equations and game algebraic Riccati equations are constructed to learn the optimal solution by developing a model-based reinforcement learning(RL) algorithm online without solving regulator equations, which is followed by a model-free off-policy RL algorithm to relax the requirement of all agents' dynamics faced by the model-based RL algorithm. The optimal tracking control of HMASs subject to unknown leader dynamics and communication delays is shown to be solvable under the proposed RL algorithms. Finally, the effectiveness of theoretical analysis is verified by numerical simulations.

关键词： heterogeneous multiagent systems HMAS reinforcement learning RL optimal output synchronization communication delays

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A logistic-Lasso-regression-based seismic fragility analysis method for electrical equipment considering structural and seismic parameter uncertainty

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Earthquake engineering and engineering Vibration 2025年第1期24卷 169-186页

作者： Cui Jiawei Che Ailan Li Sheng Cheng Yongfeng Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education School of Civil EngineeringHarbin Institute of TechnologyHarbin 150094China School of Ocean and Civil Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Civil and Resource Engineering University of Science and Technology BeijingBeijing 100083China China Electric Power Research Institute Beijing 100055China

Damage to electrical equipment in an earthquake can lead to power outage of power *** fragility analysis is a common method to assess the seismic reliability of electrical *** further guarantee the efficiency of analysis,multi-source uncertainties including the structure itself and seismic excitation need to be considered.A method for seismic fragility analysis that reflects structural and seismic parameter uncertainty was developed in this *** proposed method used a random sampling method based on Latin hypercube sampling(LHS)to account for the structure parameter uncertainty and the group structure characteristics of electrical ***,logistic Lasso regression(LLR)was used to find the seismic fragility surface based on double ground motion intensity measures(IM).The seismic fragility based on the finite element model of an±1000 kV main transformer(UHVMT)was analyzed using the proposed *** results show that the seismic fragility function obtained by this method can be used to construct the relationship between the uncertainty parameters and the failure *** seismic fragility surface did not only provide the probabilities of seismic damage states under different IMs,but also had better stability than the fragility ***,the sensitivity analysis of the structural parameters revealed that the elastic module of the bushing and the height of the high-voltage bushing may have a greater influence.

关键词： seismic fragility uncertainty logistic lasso regression ±1000 kV main transformer sensitivity analysis

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Mechanisms of isomerization and hydration reactions of typicalβ-diketone at the air-droplet interface

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Journal of Environmental sciences 2024年第7期141卷 225-234页

作者： Yuemeng Ji Weiyong Luo Qiuju Shi Xiaohui Ma Ziqi Wu Weina Zhang Yanpeng Gao Taicheng An Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health Guangdong Key Laboratory of Environmental Catalysis and Health Risk ControlInstitute Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of EducationSchool of Environmental Science and EngineeringGuangdong University of TechnologyGuangzhou 510006China

Acetylacetone(AcAc)is a typical class ofβ-diketones with broad industrial applications due to the property of the keto-enol isomers,but its isomerization and chemical reactions at the air-droplet interface are still ***,using combined molecular dynamics and quantum chemistry methods,the heterogeneous chemistry of AcAc at the air-droplet interface was investigated,including the attraction of AcAc isomers by the droplets,the distribution of isomers at the air-droplet interface,and the hydration reactions of isomers at the air-droplet *** results reveal that the preferential orientation of two AcAc isomers(keto-and enol-AcAc)to accumulate and accommodate at the acidic air-droplet *** isomerization of two AcAc isomers at the acidic air-droplet interface is more favorable than that at the neutral air-droplet interface because the“water bridge”structure is destroyed by H_(3)O^(+),especially for the isomerization from keto-Ac Ac to *** the acidic air-droplet interface,the carbonyl or hydroxyl O-atoms of two AcAc isomers display an energetical preference to ***-diol is the dominant products to accumulate at the air-droplet interface,and excessive keto-diol can enter the droplet interior to engage in the *** photooxidation reaction of AcAc will increase the acidity of the air-droplet interface,which indirectly facilitate the uptake and formation of more *** results provide an insight into the heterogeneous chemistry ofβ-diketones and their influence on the environment.

关键词： Acetylacetone Air-droplet interface Theoretical simulation Isomerization Hydration reaction mechanism

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Deep attention convolutional neural network-based adaptive multi-source information fusion for accurate short-term photovoltaic power forecast

Deep attention convolutional neural network-based adaptive m...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Bai, Mingliang Chen, Yunxiao Zhou, Zhihao Long, Zhenhua Liu, Jinfu Yu, Daren Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Energy Science and Engineering Harbin Institute of Technology Harbin China Department of Control Science and Engineering School of Energy Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350396782

Solar energy is one of the most important renewable energy sources. Photovoltaic (PV) power generation is currently one of the most important ways to utilize solar energy. PV power has strong uncertainties and is adverse to the stability of the electricity grid. PV power forecast is an important method to tackle this issue. Conventional methods only use historical PV power data. This paper proposes a new method that uses deep Convolutional Neural Networks (CNN)-based multivariable information fusions for PV forecast. Multiple variables including global horizontal irradiation (GHI), relative humidity, air temperature, cloud thickness, wind speed, etc. are used for PV forecast. Meanwhile, clear-sky GHI estimated by McClear clear-sky models are used as physical prior knowledge. Through the attention mechanism, these variables are adaptively fused with the historical PV power data to realize an accurate PV power forecast. Forecast experiments in three-year (2017-2019) actual data of Brussels PV power stations verify the significant superiority of the proposed method over conventional forecast methods. © 2023 IEEE.

关键词： attention mechanism convolutional neural network photovoltaic power forecast solar energy

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mmHRR: Monitoring Heart Rate Recovery with Millimeter Wave Radar 30

mmHRR: Monitoring Heart Rate Recovery with Millimeter Wave R...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Mao, Ziheng He, Yuan Zhang, Jia Sun, Yimiao Xie, Yadong Guo, Xiuzhen Tsinghua University School of Software & BNRist China Zhejiang University College of Control Science and Engineering China

ISBN: (纸本)9798331515966

Heart rate recovery (HRR) within the initial minute following exercise is a widely utilized metric for assessing cardiac autonomic function in individuals and predicting mortality risk in patients with cardiovascular disease. However, prevailing solutions for HRR monitoring typically involve the use of specialized medical equipment or contact wearable sensors, resulting in high costs and poor user experience. In this paper, we propose a contactless HRR monitoring technique, mmHRR, which achieves accurate heart rate (HR) estimation with a commercial mmWave radar. Unlike HR estimation at rest, the HR varies quickly after exercise and the heartbeat signal entangles with the respiration harmonics. To overcome these hurdles and effectively estimate the HR from the weak and non-stationary heartbeat signal, we propose a novel signal processing pipeline, including dynamic target tracking, adaptive heartbeat signal extraction, and accurate HR estimation with composite sliding windows. Real-world experiments demonstrate that mmHRR exhibits exceptional robustness across diverse environmental conditions, and achieves an average HR estimation error of 3.31 bpm (beats per minute), 71% lower than that of the state-of-the-art method. © 2024 IEEE.

关键词： Heart

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Quantum walk search algorithm for multi-objective searching with iteration auto-controlling on hypercube

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Chinese Physics B 2022年第4期31卷 157-162页

作者： Yao-Yao Jiang Peng-Cheng Chu Wen-Bin Zhang Hong-Yang Ma School of Science Qingdao University of TechnologyQingdao 266033China School of Information and Control Engineering Qingdao University of TechnologyQingdao 266033China

Shenvi et *** proposed a quantum algorithm based on quantum walking called Shenvi-Kempe-Whaley(SKW)algorithm,but this search algorithm can only search one target state and use a specific search target state ***,when there are more than two target nodes in the search space,the algorithm has certain *** though a multiobjective SKW search algorithm was proposed later,when the number of target nodes is more than two,the SKW search algorithm cannot be mapped to the same quotient *** addition,the calculation of the optimal target state depends on the number of target states *** previous studies,quantum computing and testing algorithms were used to solve this *** these solutions require more Oracle calls and cannot get a high accuracy ***,to solve the above problems,we improve the multi-target quantum walk search algorithm,and construct a controllable quantum walk search algorithm under the condition of unknown number of target *** dividing the Hilbert space into multiple subspaces,the accuracy of the search algorithm is improved from p_(c)=(1/2)-O(1/n)to p_(c)=1-O(1/n).And by adding detection gate phase,the algorithm can stop when the amplitude of the target state becomes the maximum for the first time,and the algorithm can always maintain the optimal number of iterations,so as to reduce the number of unnecessary iterations in the algorithm process and make the number of iterations reach t_(f)=(π/2)(?).

关键词： multi-objective quantum walk search algorithm accurate probability

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Position control of Mobile Robot Based on Deep Reinforcement Learning 2

Position Control of Mobile Robot Based on Deep Reinforcement...

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2nd International Conference on Informatics, Networking, and Computing, ICINC 2023

作者： Hu, Guobin Chai, Hui Pu, Xi Guo, Min College of Mechanical Electronical and Engineering Wuhan University of Technology Wuhan China School of Control Science and Engineering Shandong University Ji’nan China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University of Technology Beijing China

ISBN: (纸本)9781510674745

In the field of mobile robot control, the utilization of reinforcement learning methods often faces the challenge of sparse rewards, resulting in suboptimal control performance. This paper proposes an approach that leverages the Diversity is All You Need (DIAYN) framework to dynamically generate reward functions and enhance the policy network weights of reinforcement learning algorithms. A comparative analysis is conducted with traditional reinforcement learning algorithms, namely Deep Deterministic Policy Gradient (DDPG), Deep Q-Network (DQN), and behavior-based robotic and proportional-integral-derivative (BBR_PID) planning control algorithm. The results obtained from the CoppeliaSim simulation experiment demonstrate that, under identical training conditions, the DIAYN-based DDPG algorithm exhibits superior learning capability and achieves faster convergence to optimal actions. Consequently, the mobile robot can reach the target point more efficiently and with greater stability. © 2024 SPIE.

关键词： Reinforcement learning

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