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Frequency unlocking-based MEMS bifurcation sensors

Microsystems & Nanoengineering 2023年第3期9卷 171-181页

作者： Yan Qiao Zhan Shi Yutao Xu Xueyong Wei Alaaeldin Elhady Eihab Abdel-Rahman Ronghua Huan Wenming Zhang State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina Department of Mechanics Key Laboratory of Soft Machines and Smart Devices of Zhejiang ProvinceZhejiang UniversityHangzhouChina State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’anChina Department of Systems Design Engineering University of WaterlooWaterlooONCanada

MEMS resonators exhibit rich dynamic behaviors under the internal resonance regime. In this work, we present a novel MEMS bifurcation sensor that exploits frequency unlocking due to a 1:3 internal resonance between two electrostatically coupled micro-resonators. The proposed detection mechanism allows the sensor to operate in binary (digital) and analog modes, depending on whether the sensor merely detects a significant jump event in the peak frequency upon unlocking or measures the shift in the peak frequency after unlocking and uses it in conjunction with a calibration curve to estimate the corresponding change in stimulus. We validate the success of this sensor paradigm by experimentally demonstrating charge detection. High charge resolutions are achieved in binary mode, up to 0.137 fC, and in analog mode, up to 0.01 fC. The proposed binary sensor enables extraordinarily high detection resolutions due to the excellent frequency stability under internal resonance and the high signal-to-noise ratio of the shift in peak frequency. Our findings offer new opportunities for high-performance ultrasensitive sensors.

关键词： locking bifurcation binary

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Safe Efficient Policy Optimization Algorithm for Unsignalized Intersection Navigation

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 2011-2026页

作者： Xiaolong Chen Biao Xu Manjiang Hu Yougang Bian Yang Li Xin Xu State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body the College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body the College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082 Wuxi Intelligent Control Research Institute(WICRI)of Hunan University Wuxi 214115China College of Intelligence Science and Technology Institute of Unmanned SystemsNational University of Defense TechnologyChangsha 410073China

Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and *** paper proposes a reinforcement learning(RL)method for autonomous vehicles to navigate unsignalized intersections safely and *** method uses a semantic scene representation to handle variable numbers of vehicles and a universal reward function to facilitate stable learning.A collision risk function is designed to penalize unsafe actions and guide the agent to avoid them.A scalable policy optimization algorithm is introduced to improve data efficiency and safety for vehicle learning at *** algorithm employs experience replay to overcome the on-policy limitation of proximal policy optimization and incorporates the collision risk constraint into the policy optimization *** proposed safe RL algorithm can balance the trade-off between vehicle traffic safety and policy learning *** intersection scenarios with different traffic situations are used to test the algorithm and demonstrate its high success rates and low collision rates under different traffic *** algorithm shows the potential of RL for enhancing the safety and reliability of autonomous driving systems at unsignalized intersections.

关键词： Autonomous driving decision-making reinforcement learning(RL) unsignalized intersection

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Build orientation determination of multi-feature mechanical parts in selective laser melting via multi-objective decision making

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Frontiers of Mechanical engineering 2023年第2期18卷 227-251页

作者： Hongsheng SHENG Jinghua XU Shuyou ZHANG Jianrong TAN Kang WANG Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province Zhejiang UniversityHangzhou 310027China State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou 310027China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province Hangzhou 310027China

Selective laser melting(SLM)is a unique additive manufacturing(AM)category that can be used to manufacture mechanical *** has been widely used in aerospace and automotive using metal or alloy *** build orientation is crucial in AM because it affects the as-built part,including its part accuracy,surface roughness,support structure,and build time and cost.A mechanical part is usually composed of multiple surface *** surface features carry the production and design knowledge,which can be utilized in SLM *** study proposes a method to determine the build orientation of multi-feature mechanical parts(MFMPs)in ***,the surface features of an MFMP are recognized and grouped for formulating the particular optimization ***,the estimation models of involved optimization objectives are established,and a set of alternative build orientations(ABOs)is further obtained by many-objective ***,a multi-objective decision making method integrated by the technique for order of preference by similarity to the ideal solution and cosine similarity measure is presented to select an optimal build orientation from those *** weights of the feature groups and considered objectives are achieved by a fuzzy analytical hierarchy *** case studies are reported to validate the proposed method with numerical results,and the effectiveness comparison is *** manufacturing is conducted to prove the performance of the proposed *** measured average sampling surface roughness of the most crucial feature of the bracket in the original orientation and the orientations obtained by the weighted sum model and the proposed method are 15.82,10.84,and 10.62μm,*** numerical and physical validation results demonstrate that the proposed method is desirable to determine the build orientations of MFMPs with competitive results in SLM.

关键词： selective laser melting(SLM) build orientation determination multi-feature mechanical part(MFMP) fuzzy analytical hierarchy process multi-objective decision making(MODM)

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Deformation Evolution and Perceptual Prediction for Additive Manufacturing of Lightweight Composite Driven by Hybrid Digital Twins

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Chinese Journal of Mechanical engineering 2024年第5期37卷 94-112页

作者： Jinghua Xu Linxuan Wang Mingyu Gao Chen Jia Qianyong Chen Kang Wang Shuyou Zhang Jianrong Tan Shaomei Fei State Key Lab of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310058China Zhejiang Key Lab of Advanced Manufacturing Technology School of Mechanical EngineeringZhejiang UniversityHangzhou 310058China State Key Laboratory of Materials Processing and Die and Mould Technology Huazhong University of Science and TechnologyWuhan 430074China Institute of Design Engineering Zhejiang UniversityHangzhou 310058China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore City 639798Singapore

This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins(HDT).In order to improve manufacturing quality of irregular lightweight composite through boosting conceptual design in aeronautic and aerospace engineering,the HDT meaning hybridization of physical and digital domains,including deformation and energy efficiency can be built,where the essential parameters can be perceptually predicted in advance,by virtue of the fusion of physical sensors and digital *** long short term memory(LSTM)can be employed to void vanishing gradient problem and improve predicting precision via Recurrent Neural Networks,thereby laying a foundation for the *** diverse manufacturing requirements of different regions are integrated into the parameters designing phase by attaching region weights confirmed via empiricism and in-service *** effects of slicing strategy and external support structures on manufacturing quality are considered from the perspective of improving dimensional *** manufacturing efficiency and comprehensive costs are accounted as consideration factors,which are perceptually predicted via *** designed manufacturing parameters through HDT were virtually examined by evaluating the deformation and equivalent stress distributions of fabricated lightweight component with composite material through AM process *** physical experiments were conducted to verify the HDT-based pre-designing and optimization method of manufacturing parameters via fused deposition modeling(FDM).The energy consumption of actual manufacturing process was measured via digital power meter and applied to evaluate accuracy of perceptual prediction *** dimensional accuracy and distortion distribution of the manufactured lightweight prototype made with composite material were measured through the coordinate measuring machine(CMM)and 3D optica

关键词： Deformation evolution Perceptual prediction Lightweight composite Hybrid digital twins(HDT) Long short term memory(LSTM)

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Simultaneously enhancing strength and ductility of LPBF Ti alloy via trace Y_(2)O_(3)nanoparticle addition

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Journal of Materials Science & Technology 2024年第24期191卷 146-156页

作者： Yinghang Liu Zhe Song Yi Guo Gaoming Zhu Yunhao Fan Huamiao Wang Wentao Yan Xiaoqin Zeng Leyun Wang National Engineering Research Center of Light Alloy Net Forming Shanghai Jiao Tong UniversityShanghai 200240China Deutsches Elektronen-Synchrotron(DESY) Hamburg 22607Germany State Key Laboratory of Mechanical Systems and Vibration Shanghai Jiao Tong UniversityShanghai 200240China College of Design and Engineering National University of Singapore117575Singapore State Key Laboratory of Metal Matrix Composites Shanghai Jiao Tong UniversityShanghai 200240China Zhangjiang Institute for Advanced Study Shanghai Jiao Tong UniversityShanghai 201203China

Laser powder bed fusion(LPBF)is a popular additive manufacturing(AM)technique to fabricate metal *** Ti alloys often exhibit high strength but poor *** this study,we report that trace Y_(2)O_(3) nanoparticles added to a pre-alloyed Ti-4Al-4V(Ti44)powder provides an excellent feedstock for ***-built Ti44-Y_(2)O_(3) materials exhibited a strength-ductility combination that is slightly better than heat-treated LPBF *** Y_(2)O_(3) particles may have melted or decomposed during *** electron microscopy,the addition of Y_(2)O_(3) refined α’martensite laths and weakened variant preference during β→α’*** on in situ synchrotron X-ray diffraction and elastic-viscoplastic self-consistent(EVPSC)modeling,(c+a)slip was more active in as-built Ti44-Y_(2)O_(3) than in as-built Ti64 or *** work demonstrates that LPBF can be an excellent method to fabricate metal-nanoparticle composite materials.

关键词： Titanium alloys Additive manufacturing Nanoparticles Ductility Synchrotron diffraction

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Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xuefei Chen, Mingzhe Liu, Danpu Zhang, Zhilong Quek, Tony Q. S. Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Information Systems Technology and Design Pillar Singapore University of Technology and Design 487372 Singapore

ISBN: (纸本)9798350351255

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs). © 2024 IEEE.

关键词： Reinforcement learning

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Hierarchical microstructure enables high strength and good ductility in as-cast Fe_(27)Ni_(35)Cr_(18.25)Al_(13.75)Co_(2)Ti_(2)Mo_(2) high-entropy alloy

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Journal of Materials Science & Technology 2024年第12期179卷 9-21页

作者： Jiacheng Niu Zhiqiang Fu Weiping Chen Tiwen Lu Liangyan Hao Wei Xiong Haiming Wen Guangdong Key Laboratory for Advanced Metallic Materials Processing South China University of TechnologyGuangzhou 510640China National Engineering Research Center of Near-net-shape Forming for Metallic Materials South China University of TechnologyGuangzhou 510640China Key Laboratory of Pressure Systems and Safety Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Physical Metallurgy and Materials Design Laboratory Department of Mechanical Engineering and Materials ScienceUniversity of PittsburghPittsburghPA15261United States of America Department of Materials Science and Engineering Missouri University of Science and TechnologyRollaMissouri 65409United States of America

As-cast alloys often require complex thermomechanical processing to obtain a hierarchical structure to achieve a good combination of strength and *** in this work,a novel hierarchical Fe_(27)Ni_(35)Cr_(18.25)Al_(13.75)Co_(2)Ti_(2)Mo_(2) high-entropy alloy(HEA)with ultra-high tensile strength and excellent ductility was fabricated by direct *** as-cast alloy exhibits hierarchical structure with an ul-trafine lamellar microstructure(ULM),ultrafine rhombus microstructure(URM),ultrafine vermicular mi-crostructure(UVM),nanosized precipitates and spinodal decomposition(SP)that develops during casting and *** incompatibility of face-centered cubic(FCC)and body-centered cubic(BCC)phases in the deformation process leads to heterogeneous deformation-induced(HDI)hardening,which brings the alloy a tensile yield strength(YS)of~1056 MPa,an ultimate tensile strength(UTS)of~1526 MPa and a total elongation(El)of~15.6%.Additionally,the numerous interfaces generated by the hierarchical structure absorb the energy during deformation,effectively retarding the dislocation motion and causing strong work-hardening.

关键词： Direct casting High-entropy alloy Hierarchical microstructure Mechanical properties

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Latent-Variable-Modeling-Based Fault Diagnosis for Unknown Nonlinear Distributed Parameter systems with Application to Snap Curing Oven 2

Latent-Variable-Modeling-Based Fault Diagnosis for Unknown N...

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2nd International Conference on Robotics, Automation and Intelligent Control, ICRAIC 2022

作者： Luo, Zhao-Dong Li, Han-Xiong State Key Laboratory of High-Performance Complex Manufacturing Central South University Changsha China Department of Advanced Design and Systems Engineering City University of Hong Kong Hong Kong

A novel fault detection method based on time/space separation and latent variable model is proposed for unknown nonlinear distributed parameter systems in sensor-constrained environments. By performing time/space separation, the augmented matrix formed by the spatio-temporal distribution data of the DPSs can be split into a spatial basis function (SBFs) and a time series model, and the dimensionality reduction capability of the SBF is further utilized to obtain a low-order temporal model. Then, the temporal model is further extracted by using a dynamic latent variable modeling method to obtain the dominant time components and establish the corresponding monitoring statistics. Utilizing the appropriate kernel density function, confidence bounds are selected for the monitoring statistics when the system is normal. As a data-based fault diagnosis method, it requires only the past data records of the system and no reliance on complex mathematical models. Two sets of experiments performed on a snap curing oven verified the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Fault detection

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Pedestrian trajectory prediction method based on the Social-LSTM model for vehicle collision

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Transportation Safety and Environment 2024年第3期6卷 140-151页

作者： Yong Han Xujie Lin Di Pan Yanting Li Liang Su Robert Thomson Koji Mizuno School of Mechanical and Automotive Engineering Xiamen University of TechnologyXiamen 361024China Fujian Province Key Laboratory of Advanced Design and Manufacturing of Buses Xiamen 361024China Engineering Research Institute of Xiamen Kinglong United Automobile Industry Co. Ltd.Xiamen 361000China Chalmers University of Technology Gothenburg 41296Sweden Department of Mechanical Systems Engineering Nagoya UniversityNagoya 464-8603Japan

Techniques for predicting the trajectory of vulnerable road users are important to the development of perception systems for autonomous vehicles to avoid *** most effective trajectory prediction methods,such as Social-LSTM,are often used to predict pedestrian trajectories in normal passage ***,they can produce unsatisfactory prediction results and data redundancy,as well as difficulties in predicting trajectories using pixel-based coordinate systems in collision avoidance *** is also a lack of validations using real vehicle-to-pedestrian *** address these issues,some insightful approaches to improve the trajectory prediction scheme of Social-LSTM were proposed,such methods included transforming pedestrian trajectory coordinates and converting image coordinates to world *** YOLOv5 detection model was introduced to reduce target loss and improve prediction *** DeepSORT algorithm was employed to reduce the number of target transformations in the tracking *** Perspective Transformation(IPT)and Direct Linear Transformation(DLT)theories were combined to transform the coordinates to world coordinates,identifying the collision location where the accident could *** performance of the proposed method was validated by training tests using MS COCO(Microsoft Common Objects in Context)and ETH/UCY *** results showed that the target detection accuracy was more than 90%and the prediction loss tends to decrease with increasing training steps,with the final loss value less than 1%.The reliability and effectiveness of the improved method were demonstrated by benchmarking system performance to two video recordings of real pedestrian accidents with different lighting conditions.

关键词： vehicle-to-pedestrian collisions pedestrian trajectory prediction YOLOvB DeepSORT Social-LSTM

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Research on workflow recognition for liver rupture repair surgery

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Mathematical Biosciences and engineering 2024年第2期21卷 1844-1856页

作者： Men, Yutao Zhao, Zixian Chen, Wei Wu, Hang Zhang, Guang Luo, Feng Yu, Ming Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Medical Support Technology Research Department Systems Engineering Institute Academy of Military Sciences People’s Liberation Army Tianjin300161 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

Liver rupture repair surgery serves as one tool to treat liver rupture, especially beneficial for cases of mild liver rupture hemorrhage. Liver rupture can catalyze critical conditions such as hemorrhage and shock. Surgical workflow recognition in liver rupture repair surgery videos presents a significant task aimed at reducing surgical mistakes and enhancing the quality of surgeries conducted by surgeons. A liver rupture repair simulation surgical dataset is proposed in this paper which consists of 45 videos collaboratively completed by nine surgeons. Furthermore, an end-to-end SA-RLNet, a self attention-based recurrent convolutional neural network, is introduced in this paper. The self-attention mechanism is used to automatically identify the importance of input features in various instances and associate the relationships between input features. The accuracy of the surgical phase classification of the SA-RLNet approach is 90.6%. The present study demonstrates that the SA-RLNet approach shows strong generalization capabilities on the dataset. SA-RLNet has proved to be advantageous in capturing subtle variations between surgical phases. The application of surgical workflow recognition has promising feasibility in liver rupture repair surgery. ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons.

关键词： Image classification

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