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10th International Symposium on Test Automation and instrumentation, ISTAI 2024

作者： Qiao, Wan Liu, Xiuli Huang, Jinpeng Wu, Guoxin Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781837242672

Aiming at the fault diagnosis of rotating machinery in industrial production, which faces the problems of signal noise, variable speed and other interferences that lead to inconspicuous fault features, and the traditional methods rely on manual experience for feature extraction and lack of robustness, the present study introduces a novel fault diagnosis approach utilizing the TimesNet model. This method takes the temporal multi-periodicity as the starting point, and the one-dimensional original signal is divided into multiple two-dimensional signals by different sizes of the period respectively. Subsequently, Inception-Attention modeling is applied to extract intricate time and space information. The experiments use the planetary gearbox dataset to verify the influence of different parameters and structures of TimesNet on the diagnostic effect, and carry out comparative tests with five time-series classification models, which provide some reference value for the application of TimesNet in fault diagnosis. © The Institution of Engineering & technology 2024.

关键词： Epicyclic gears

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REARCH ON DOUBLE CUBIC INTERPOLATION COMBINED WITH GAUSSIAN FITTING SUB-PIXEL EDGE DETECTION METHOD 10

REARCH ON DOUBLE CUBIC INTERPOLATION COMBINED WITH GAUSSIAN ...

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10th International Symposium on Test Automation and instrumentation, ISTAI 2024

作者： Qi, Yinyan Wu, Guoxin Li, Yao Dong, Yuanqiu The Ministry of Education Key Laboratory of Modern Measurement and Control Technology Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781837242672

With the rapid advancements in intelligent manufacturing technology, the realization of fast and accurate measurements in aerospace, instrumentation, machinery and equipment, automotive electronics, and other sectors has become a research hotspot. Image positioning technology and edge detection methods, with their advantages of being fast, non-contact, and precise, have been widely employed. How to improve the accuracy of image measurement lies in solving the problem of accurate positioning of image edge detection, based on the problem of inaccurate extraction of directional information and large error in contour size of image edge detection technology proposes an improved sub-pixel edge detection algorithm with Gaussian fitting by double cubic interpolation. Based on preprocessing for pixel-level edge feature extraction, pixel-level edge points are acquired utilizing the least squares fitting algorithm. The normal section is determined by computing the normal intercept line, while the normal isometric line is identified through the Gaussian integration method. Sub-pixel fitting points are obtained via boundary fitting, and the final grey value is derived using an edge detection algorithm that combines double cubic interpolation and Gaussian fitting on the fitted points. The experimental results demonstrate that, through comparative testing of this method, it has been verified that it exhibits the highest image quality and is superior to most current algorithms. An improvement in the algorithm by 0.021%, 0.025%, and 0.031% in edge localization accuracy compared to the Zernike moment algorithm, Gaussian fitting algorithm, and bicubic interpolation algorithm has been achieved, effectively verifying that the image quality detected by this method is relatively high. In comparison with the point cloud method regarding fitting error, it was observed that the error index increased by 0.025% under strong burst light conditions, 0.026% under normal burst light conditions, an

关键词： Interpolation

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REVIEW OF RESEARCH ON OCCLUSION ISSUES IN RGB-D DYNAMIC SCENE 3D RECONSTRUCTION 10

REVIEW OF RESEARCH ON OCCLUSION ISSUES IN RGB-D DYNAMIC SCEN...

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10th International Symposium on Test Automation and instrumentation, ISTAI 2024

作者： Dong, Yuanqiu Wu, Guoxin Zhang, Guolong Zhou, Linglong Qi, Yinyan The Ministry of Education Key Laboratory of Modern Measurement and Control Technology Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781837242672

As innovation proceeds to advance,3D recreation innovation has ended up a critical portion of the field of computer vision. Among these, 3D reproduction innovation based on RGB-D information sources has ended up a well known approach in later a long time. Right now, the more develop inactive 3D recreation calculations have ended up progressively successful for reproducing stationary objects. Be that as it may, their adequacy lessens when parts of the scene are blocked. This paper briefly presents particular approaches to address impediment issues and the fundamental concepts of energetic 3D remaking innovation based on RGB-D information sources. The points of interest and current investigate status of energetic 3D reproduction focusing on impediment issues utilizing RGB-D information sources are examined. Moreover, the most recent progressions in 3D remaking of energetic scenes are summarized, and proposed future investigate bearings. © The Institution of Engineering & technology 2024.

关键词： 3D reconstruction

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THE ADAPTIVE MPC controlLER OF AUTONOMOUS DRIVING FORMULA FOR TRAJECTORY TRACKING 10

THE ADAPTIVE MPC CONTROLLER OF AUTONOMOUS DRIVING FORMULA FO...

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10th International Symposium on Test Automation and instrumentation, ISTAI 2024

作者： Chen, Bowen Su, Qinghua Song, Yue Yin, Zehao Wang, Liyong Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781837242672

Autonomous driving technology has gradually been applied in real life, and it has also been improved in the China Collegiate Electric Vehicle Racing. However, as one of the critical components of autonomous driving technology, trajectory tracking control still faces challenges. This paper designs a trajectory tracking controller by building a vehicle's three-degree-of-freedom dynamics model and adopting adaptive model predictive control. It adjusts the prediction horizon of the vehicle's trajectory tracking controller in real-time based on the curvature of the desired trajectory to improve the effect of trajectory tracking. Experimental results show that the tracking effect of the adaptive prediction time-domain trajectory tracking controller in the S-curve is improved by 4.97% and 11.36% compared to the fixed prediction time-domain trajectory tracking controller, respectively. For the formula track conditions, the tracking effect of the controller proposed in this paper is improved by 0.71% compared to the fixed prediction time-domain trajectory tracking controller, with an average tracking error of 5.65%. © The Institution of Engineering & technology 2024.

关键词： Adaptive control systems

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PERIODIC STATISTICAL ANALYSIS OF GAIT PHASE SEGMENTATION 10

PERIODIC STATISTICAL ANALYSIS OF GAIT PHASE SEGMENTATION

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10th International Symposium on Test Automation and instrumentation, ISTAI 2024

作者： Fu, Mingkai Wang, Shaohong Ma, Chao Key Laboratory of Modern Measurement and Control Technology Ministry of Edu. Beijing Information Science and Technology Beijing China Beijing Key Lab of Measurement and Control of Mechanical and Electrical System Beijing Information Science and Technology Beijing China

ISBN: (纸本)9781837242672

In order to achieve accurate segmentation of gait phases for precise real-time control of lower limb exoskeleton robots, the periodic statistical analysis of human motion posture by using the inertial measurement unit is conducted. In current research, the data of forward and turning gait types were acquired and every gait type data were devised into eight for different gait phase segmentation. During the process, the phase division values for different types of subjects were calculated and the phase division of different subjects with the same gait type was calculated. The standard deviations for different gaits and phases for the same experimenter are all below 10, primarily oscillated around 4. For gait data from different subjects, the percentages at the same phase are all below 3. The results showed that the results from the feasible method could better accord with the characteristics of each stage of actual *** those results could provide reference for real-time judgment of gait phase and technical support for intelligent control of lower limb exoskeletons. © The Institution of Engineering & technology 2024.

关键词： Exoskeleton (Robotics)

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LOWER LIMB GAIT RECOGNITION METHOD BASED ON KNOWLEDGE DISTILLATION 10

LOWER LIMB GAIT RECOGNITION METHOD BASED ON KNOWLEDGE DISTIL...

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10th International Symposium on Test Automation and instrumentation, ISTAI 2024

作者： Wang, Mengdi Wang, Shaohong Ma, Chao Key Laboratory of Modern Measurement and Control Technology Ministry of Edu. Beijing Information Science and Technology Beijing China Beijing Key Lab of Measurement and Control of Mechanical and Electrical System Beijing Information Science and Technology Beijing China

ISBN: (纸本)9781837242672

The need for assistive devices such as lower limb exoskeletons is steadily growing, making quick and precise gait recognition essential for optimal operation of these apparatuses. The knowledge distillation method was applied to gait recognition in this work. First, we took participant lower limb gait data, which we then resampled with step frequency taken into account. Next, multi-layer perceptron (MLP) with distinct layers were modeled by educators and students. The process of knowledge distillation was used to improve the accuracy of participant activity recognition by transferring knowledge from the sophisticated teacher model to the lightweight student model. On the training set, the teacher model's accuracy average was 96.4%. Following the process of knowledge distillation, the accuracy of the student model was 95.48%, representing a 1.12% improvement compared to the non-distilled model, with only 0.53M parameters and shorter training duration. The results demonstrate that knowledge distillation can improve the recognition speed and stability of gait recognition models, enabling more accurate and reliable results within the same patient population, thus providing more effective support for medical diagnosis and rehabilitation treatment. © The Institution of Engineering & technology 2024.

关键词： Exoskeleton (Robotics)

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HYBRID-DRIVEN MODEL FOR TEMPERATURE PREDICTION IN WET CLUTCHES 10

HYBRID-DRIVEN MODEL FOR TEMPERATURE PREDICTION IN WET CLUTCH...

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10th International Symposium on Test Automation and instrumentation, ISTAI 2024

作者： Huang, Yuanbin Li, Le Wang, Qian Wang, Liyong Key Laboratory of Modern Measurement and Control Technology in Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781837242672

A hybrid-driven model for predicting the temperature at the friction interface of wet clutch friction components was developed, employing a radial basis function neural network algorithm. This model uses temperature data obtained from wet clutch test bench experiments to compensate for the results from a thermo-mechanical coupled numerical simulation model of the friction components. The hybrid model inputs include load pressure, relative speed, radial position of the friction components, and numerically simulated temperatures at the friction interface, to output predicted temperatures under specific operating conditions. The results indicate that the predicted temperatures of the friction components under various conditions align well with experimental values, with a maximum mean relative error of 0.033 and all prediction intervals having a coefficient of determination higher than 0.97. Thus, the hybrid-driven model demonstrates high accuracy and stability in predictions, and possesses strong adaptability and predictive capabilities for different temperature distributions at the friction interface. © The Institution of Engineering & technology 2024.

关键词： Radial basis function networks

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Suppressing the Air-Breakdown Phenomenon of Electrostatic Generator for Efficient Energy Harvesting 22

Suppressing the Air-Breakdown Phenomenon of Electrostatic Ge...

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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Cao, Zeyuan Ding, Rong Teng, Junchi Wu, Zibo Ye, Xiongying Tsinghua University State Key Laboratory Of Precision Measurement Technology And Instruments Department Of Precision Instrument China

ISBN: (纸本)9784886864352

The air breakdown phenomenon is the critical factor that limits electrostatic generators' charge density and output performance. To solve this problem, a universal strategy with dual synchronous switches and an external capacitor is proposed in this work, which can effectively suppress air breakdown and improve the generator's output performance without changing the external environment. Based on the strategy, a 1.8×1.8 cm2 contact-separation mode electrostatic generator achieves a power density of 110 mW/m2/Hz with a small separation distance of 100 μm, which is 2.68 times that of a conventional one. This work provides a novel and simple way to suppress the air breakdown phenomenon and improve the output performance of electrostatic generators. © 2023 IEEJ.

关键词： Air-breakdown Contact-separation mode Electrostatic generator High energy density

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APPLICATION OF WAVELET KURTOSIS CONVOLUTIONAL NEURAL NETWORK MODEL IN ROLLING BEARING FAULT DIAGNOSIS 10

APPLICATION OF WAVELET KURTOSIS CONVOLUTIONAL NEURAL NETWORK...

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10th International Symposium on Test Automation and instrumentation, ISTAI 2024

作者： Men, Dachao Jiang, Zhanglei Liu, Xiuli Zheng, Wei Li, Zihan Zha, Zhendong Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781837242672

Aiming at the problem of substantial noise in the actual industrial environment, where the intelligent diagnostic model exhibits weak anti-noise performance and the interpretability of the extracted features is not strong, the wavelet kurtosis convolutional neural network model integrating signal processing technology and deep learning neural networks has been proposed. Firstly, the standard CNN convolution kernel has been replaced with a wavelet basis function to form a wavelet convolution layer, and the input signal undergoes decomposition into various frequency components via the wavelet filter in this layer;Secondly, a kurtosis loss layer has been used to filter the high kurtosis value components that contain more fault features, so as to achieve the filtration of the noise and the accurate localisation of the fault signals;Lastly, the efficacy of the proposed model has been verified in the rolling bearing dataset and diagnostic model. The results showed that the model can reach 90% diagnostic accuracy under -4db Gaussian white noise interference, which was higher than other diagnostic models, and had good generalisation and noise resistance. © The Institution of Engineering & technology 2024.

关键词： White noise

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Flexible pressure sensors with ultrahigh stress tolerance enabled by periodic microslits

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Microsystems & Nanoengineering 2024年第1期10卷 301-311页

作者： Song Wang Chenying Wang Yifan Zhao Yujing Zhang Yaxin Zhang Xiangyue Xu Qijing Lin Kai Yao Yuheng Wang Feng Han Yu Sun Zhuangde Jiang State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’anChina State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Instrument Science and TechnologyXi’an Jiaotong UniversityXi’anChina The Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingboChina Department of Mechanical and Industrial Engineering University of TorontoTorontoCanada

Stress tolerance plays a vital role in ensuring the effectiveness of piezoresistive sensing films used in flexible pressure ***,existing methods for enhancing stress tolerance employ dome-shaped,wrinkle-shaped,and pyramidal-shaped microstructures in intricate molding and demolding processes,which introduce significant fabrication challenges and limit the sensing *** address these shortcomings,this paper presents periodic microslits in a sensing film made of multiwalled carbon nanotubes and polydimethylsiloxane to realize ultrahigh stress tolerance with a theoretical maximum of 2.477 MPa and a sensitivity of 18.092 kPa−*** periodic microslits permit extensive deformation under high pressure(e.g.,400 kPa)to widen the detection ***,the periodic microslits also enhance the sensitivity based on simultaneously exhibiting multiple synapses within the sensing interface and between the periodic sensing *** proposed solution is verified by experiments using sensors based on the microslit strategy for wind direction detection,robot movement sensing,and human health *** these experiments,vehicle load detection is achieved for ultrahigh pressure sensing under an ultrahigh pressure of over 400 kPa and a ratio of the contact area to the total area of 32.74%.The results indicate that the proposed microslit strategy can achieve ultrahigh stress tolerance while simplifying the fabrication complexity of preparing microstructure sensing films.

关键词： robot shaped stress

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