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A vector hybrid triboelectric sensor(HTS)for motion identification via machine learning

Nano Research 2023年第7期16卷 10120-10130页

作者： Nannan Zhou Hongrui Ao Xiaoming Chen Shan Gao Hongyuan Jiang School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China School of Control Engineering Northeastern University at QinhuangdaoQinhuangdao 066004China

Rapidly responding and cost-effective sensors played a crucial role in industrial detection.However,the lack of versatile strategies for identifying and classifying operating states on various practical behaviors has limited the rapid development of monitoring technology.This study developed a vector hybrid triboelectric sensor(HTS)with surface nanocrystalline containing triboelectric vibration and rotation units(triboelectric vibration unit(TVU),triboelectric rotation unit(TRU))capable of detecting the vibrational and rotary states of the device.The synchronous detection of two sensing signals can be achieved due to the hierarchical structure as the basic unit of the HTS,which contributed to reducing the volume and spatial distribution of the HTS.Based on the voltage/current/charge(U-I-Q)signal amplitudes and phase features generated by the TVU,the vibration frequency and orientation of the device can be identified by using a double-layer neural network(D-LNN),in which the accuracy reaches 96.5%and 95.5%respectively.Additionally,by combining logistic regression,D-LNN,and linear regression,the accuracy of the TRU for rotary classification exceeds 93.5%in practical application.In this study,the great potential application of the HTS combined with the machine learning methods was successfully explored and exhibited and it might speed up the development of industrial detection in the near future.

关键词： vector triboelectric sensor surface nanocrystalline hierarchical structure sensing properties state detection machine learning

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Design of Embedded state detection System for Portable Rotating Mechanical Equipment

Design of Embedded State Detection System for Portable Rotat...

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第三届国际自动化测试与仪器仪表学术会议

作者： Gu Yuhai Wang Shaohong Ha Liang Shi Yongchao (Beijing Key Laboratory on Measurement and Control of Mechanical and Electrical System Beijing Information Science & Technology University Beijing 100192 China)

＜正＞In the industrial production,the small-medium -sized rotating mechanical equipment requires a portable,low-cost,high-performance device to conduct the periodic state detection.The design of embedded state detection system for portable rotating mechanical equipment is introduced in the paper.The system hardware design uses the PXA270 core board and signal processing board.The design of system software is based on Windows CE embedded operating system and independent-designed signal acquisition and processing procedure.In the industrial field,this system conducts the state detection on many small-medium-sized rotating mechanical equipment at lower cost and in flexible way,and provides the guarantee for the stable and safe production in the factories.

关键词： Rotating mechanical equipment embedded state detection

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Dynamic Soft Sensor Modeling Based on state detection and Impulses Response Template

Dynamic Soft Sensor Modeling Based on State Detection and Im...

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第26届中国控制与决策会议

作者： Zhi Fan Jie Cao Yujie Wei College of Electrical and Information Engineering Lanzhou University of Technology Departments of Automation Tsinghua University

Accurate and reliable prediction of melt mass flow rate is crucial in polypropylene production. In order to establish an accurate prediction model, a process state detection method and a novel dynamic modeling method is proposed, and the model parameters are indentified by improved swarm optimization algorithm. A polypropylene product melt mass flow rate soft sensor model is established based on process state detection and impulses response template. According to the research on the data from real plant, the experiments demonstrate that even under dynamic state, the proposed approach can improve the prediction accuracy, and the soft sensor model has good tracking ability and meets the requirement of on-line optimal control.

关键词： Soft Sensor Melt Mass Flow Rate state detection Impulses Response Template Particle Swarm Optimization

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Detectable states, cycle fluxes, and motility scaling of molecular motor kinesin： An integrative kinetic graph theory analysis

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Frontiers of physics 2017年第6期12卷 21-32页

作者： Jie Ren Center for Phononics and Thermal Energy Science School of Physics Science and Engineering Tongji University 200092 Shanghai China

The process by which a kinesin motor couples its ATPase activity with concerted mechanical hand- over-hand steps is a foremost topic of molecular motor physics. Two major routes toward elucidating kinesin mechanisms are the motility performance characterization of velocity and run length, and single-molecular state detection experiments. However, these two sets of experimental approaches are largely uncoupled to date. Here, we introduce an integrative motility state analysis based on a theorized kinetic graph theory for kinesin, which, on one hand, is validated by a wealth of accumulated motility data, and, on the other hand, allows for rigorous quantification of state occurrences and chemomechanical cycling probabilities. An interesting linear scaling for kincsin motility performance across species is discussed as well. An integrative kinetic graph theory analysis provides a powerful tool to bridge motility and state characterization experiments, so as to forge a unified effort for the elucidation of the working mechanisms of molecular motors.

关键词： graph theory molecular motor state detection cycle flux motility scaling

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