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International Conference on Applied Nonlinear Dynamics, Vibration, and control, ICANDVC 2023

作者： Li, Huiyang Chen, Jianen Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9789819705535

The complexification-averaging method is utilized to study the amplitude-frequency response of a system connected to a parallel asymmetric nonlinear energy sink (NES) under harmonic excitation, and the Runge-Kutta method is used to analyze the change law of the frequency band of the higher branches of response and the strongly modulated responses with the assistance of the complexification-averaging method. Under shock loading, the effect of parallel asymmetric NES parameters on its vibration absorption efficiency is investigated. The results demonstrate that the frequency band of higher branches of response is widened by reducing the stiffness ratio, mass ratio, and damping ratio of the two NES, emphasizing the fact that the frequency band of the strongly modulated response can be widened or narrowed with the reduction of these parameters. Moreover, reducing the mass ratio and damping ratio of the two NESs decreases the vibration absorption efficiency when the system is subjected to a shock load. However, the vibration absorption efficiency increases by reducing the stiffness ratio. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Energy dissipation

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Flow and thermal modeling of liquid metal in expanded microchannel heat sink

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Frontiers in Energy 2023年第6期17卷 796-810页

作者： Mingkuan ZHANG Xudong ZHANG Luna GUO Xuan LI Wei RAO Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin 300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of TechnologyTianjin 300384China Key Laboratory for Thermal Science and Power Engineering of the Ministry of Education Department of Engineering MechanicsTsinghua UniversityBeijing 100084China School of Mechanical Engineering Tianjin University of CommerceTianjin 300134China Institute of Refrigeration and Cryogenics Shanghai Jiao Tong UniversityShanghai 200240China Beijing Key Laboratory of CryoBiomedical Engineering and Key Laboratory of Cryogenics Beijing 100190China

Liquid metal-based microchannel heat sinks (MCHSs) suffer from the low heat capacity of coolant, resulting in an excessive temperature rise of coolant and heat sink when dealing with high-power heat dissipation. In this paper, it was found that expanded space at the top of fins could distribute the heat inside microchannels, reducing the temperature rise of coolant and heat sink. The orthogonal experiments revealed that expanding the top space of channels yielded similar temperature reductions to changing the channel width. The flow and thermal modeling of expanded microchannel heat sink (E-MCHS) were analyzed by both using the 3-dimensional (3D) numerical simulation and the 1-dimensional (1D) thermal resistance model. The fin efficiency of E-MCHS was derived to improve the accuracy of the 1D thermal resistance model. The heat conduction of liquid metal in Z direction and the heat convection between the top surface of fins and the liquid metal could reduce the total thermal resistance (Rt). The above process was effective for microchannels with low channel aspect ratio, low mean velocity (Um) or long heat sink length. The maximum thermal resistance reduction in the example of this paper reached 36.0%. The expanded space endowed the heat sink with lower pressure, which might further reduce the pumping power (P). This rule was feasible both when fins were truncated (h_(2) < 0, h_(2) is the height of expanded channel for E-MCHS) and when over plate was raised (h_(2) > 0).

关键词： liquid metal cooling heat sink expanded microchannel flow and thermal modeling

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Research on intelligent Fault Diagnosis Method of Planetary Gearbox based on ITD and CNN 15

Research on Intelligent Fault Diagnosis Method of Planetary ...

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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Zhu, Songsong Chen, Jiwei Xie, Ruitong Song, Yuhao Zhang, Mian Zhao, Mengxiong Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9798350354010

Accurate fault diagnosis of planetary gearboxes (PGs) is crucial for preventing equipment failures and extending their lifespan. To enhance the efficiency and accuracy of PG fault diagnosis, this paper proposes a novel method integrating intrinsic time scale decomposition (ITD) with convolutional neural network (CNN). The proposed method leverages the demodulation capability of ITD to decompose and reconstruct the original signal, effectively removing irrelevant noise and isolating core fault features, and the learning ability of CNN to accurately identify the fault features. Compared to methods relying on CNN only, the proposed method not only improves the computational efficiency but also achieves higher fault diagnosis accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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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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Research on Task Scheduling Algorithms for Cloud-Edge Collaboration 19th

Research on Task Scheduling Algorithms for Cloud-Edge Collab...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Lu, Shuai Zhou, Haibo Zhao, Shuaishuai Xu, Wangbei Fang, Kai Tianjin300384 China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China College of Mathematics and Computer Sciences Zhejiang A&F University Hangzhou311300 China

ISBN: (纸本)9783031651229

With the advent of the 5G era, the development of IoT technology has been accelerated. Due to the continuous increase in the amount of data waiting to be processed from the edge, edge nodes may struggle to handle such a vast amount of data. Therefore, the technology of cloud-edge collaboration has emerged, and how to achieve cloud-edge collaborative task scheduling has become a current research hotspot. This article provides a detailed exposition of the relevant work on task scheduling in the cloud-edge environment, and outlines the common optimization objectives in the cloud-edge collaboration scenario. The methods used to solve task scheduling problems are classified and summarized, including heuristic, heuristic algorithm based on linear programming, and meta-heuristic algorithms. The advantages and disadvantages of each algorithm are analyzed. Finally, the development trends of large-scale task scheduling in the cloud-edge environment are discussed, providing valuable insights for achieving real-time performance, efficiency, and energy conservation in the Internet of Things. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Scheduling algorithms

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Nonlinear Deep Subspace Network for Micro-expression Recognition 3

Nonlinear Deep Subspace Network for Micro-expression Recogni...

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3rd Workshop on Facial Micro-Expression: advanced Techniques for Multi-Modal Facial Expression Analysis, FME 2023

作者： Feng, Weijia Xu, Manlu Chen, Yuanxu Wang, Xiaofeng Guo, Jia Dai, Lei Wang, Nan Zuo, Xinyu Li, Xiaoxue College of Computer and Information Engineering Tianjin Normal University Tianjin China Ping An Technology Shenzhen China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin China

ISBN: (纸本)9798400702853

Deep learning (DL) models have been widely studied in the field of micro-expression recognition (MER). However, micro-expressions (MEs) suffer from small number of samples and difficulty in extracting subtle and transient features, resulting in limited improvement in the recognition performance. In addition, DL models are prone to overfitting problems and are difficult to extract discriminative features of facial actions from ME images or sequences. To address these issues, we propose a MER method that combines a nonlinear deep subspace network and optical flow features. Firstly, facial motion features are captured using optical flow computation, and then the optical flow features are input into a kernel principal component analysis network (KPCANet) to further learn deeper spatio-temporal features. Finally, a linear support vector machine (SVM) is used for ME classification. Experiments conducted on four public spontaneous ME datasets including SMIC, CASME, CASME II and SAMM validate the effectiveness of the proposed method. The experimental results demonstrate that the proposed method achieves even better recognition performance compared to existing state-of-the-art MER methods. © 2023 ACM.

关键词： Principal component analysis

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Nonlinear Dynamic Behavior and Vibration Mechanism of a Large Range Rotating Elastic Beam Under Parallel Misalignment Constraints 10

Nonlinear Dynamic Behavior and Vibration Mechanism of a Larg...

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10th International Conference on Advances in Machinery, Material Science and Engineering Application, MMSE 2024

作者： Zhao, Lei Xu, Meiye Zhai, Ran Jiao, Likuan Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China School of Mechanic Engineering Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9781643685465

Large rotating flexible machines are prone to vibration at high speeds, which is a difficult engineering problem. In this study, we have deeply explored its nonlinear dynamic behavior and vibration mechanism, especially considering the effect of parallel misalignment. Through mathematical modeling and MATLAB simulations, we found that the rotational speed and the amount of misalignment are the key parameters affecting the stability of the system. The results show that the rotational speed and parallel misalignment are the sensitive parameters of the system. When the amount of misalignment is 0.002 mm, with the increase of rotational speed, the elastic beam exhibits nonlinear M-type vibration, doubly periodic and chaotic behaviors, which show high-frequency abrupt excitation;when the angular speed is 50 rad/s, with the increase of the amount of misalignment from 0.001 mm to 0.1 mm, the system exhibits the transformation of doubly periodic, chaotic and proposed periodic motions, and the characteristics of intermittent vibration are significant. These results provide an important theoretical basis for suppressing its nonlinear vibration. © 2024 The Authors.

关键词： Rotating machinery

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Surgical Gesture Recognition in Open Surgery Based on 3DCNN and SlowFast 7

Surgical Gesture Recognition in Open Surgery Based on 3DCNN ...

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7th IEEE Information Technology, Networking, Electronic and Automation control Conference, ITNEC 2024

作者： Men, Yutao Luo, Jian Zhao, Zixian Wu, Hang Luo, Feng Zhang, Guang Yu, Ming Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China Systems Engineering Institute Academy of Military Sciences People's Liberation Army Tianjin300161 China

ISBN: (纸本)9798350370782

In the field of computer-aided medicine, automated surgical gesture recognition is an important research direction. Its main applications include automated skills assessment, intraoperative guidance, and education and training systems. Current research has focused on robotic surgery, and there is less research on surgical gestures for open surgery. Therefore, in this study, a new simulated open surgery dataset was developed, and a series of surgical gestures for open surgery were defined for closure the abdomen surgery with the cooperation of professional doctors. In addition, this paper uses the classical algorithm 3DCNN and SlowFast model in the field of action recognition to perform offline recognition of surgical gestures for open surgery on the dataset. 3DCNN uses R3D and R(2+1)D models. The experimental results show that on the self-built open surgical dataset, the R3D model has the best surgical gesture recognition results, with an accuracy rate of 90.4%, a precision rate of 90.5%, and a recall rate of 90.0%. © 2024 IEEE.

关键词： Gesture recognition

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Theoretical and experimental study of amorphization and lattice transformation of pure magnesium by ultra-precision cutting method

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Journal of Magnesium and Alloys 2025年

作者： He, Chunlei Wang, Shuqi Xia, Siyu Ren, Chengzu Tianjin Key Laboratory of Equipment Design and Manufacturing Technology Tianjin University Tianjin300354 China Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System Tianjin University of Technology Tianjin300384 China

Pure magnesium is a very promising material in the fields of biomedical and engineering. Obtaining pure magnesium with superior mechanical properties has consistently been a significant challenge in the area of materials science. This study focuses on investigating the processing method and strengthening mechanism of pure magnesium by ultra-precision cutting. The research results show that the pure magnesium grains were significantly refined after ultra-precision cutting. The average grain size reduced from ∼24 µm to nanometers, and the average nano-hardness increased from 1.02 GPa to 2.82 GPa. Amorphous pure magnesium structure and body-centered cubic (BCC) lattice pure magnesium were reported. Molecular dynamics (MD) simulation confirmed that the high shear strain and hydrostatic pressure during ultra-precision cutting was the origin of amorphization and lattice transformation. The amorphous phase and a significant number of long-period stacking-ordered (LPSO) phases inside the pure magnesium were responsible for the high hardness after ultra-precision cutting. © 2025

关键词： Shear strain

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Anomaly Detection of Logistics Packages Based on CNN and Image Recognition Technology 4

Anomaly Detection of Logistics Packages Based on CNN and Ima...

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4th IEEE International Conference on Data Science and Computer Application, ICDSCA 2024

作者： Liang, Kun Zhang, Hang School of Management Tianjin University of Technology Tianjin China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350368239

To address abnormal situations such as packaging damage, liquid leakage, and item displacement during logistics package transportation, this paper introduces Convolutional Neural Network (CNN) and image recognition technology to achieve real-time anomaly detection and alarm, improving the response speed and accuracy of the logistics system. First, by designing multiple layers of convolution and pooling layers, adding Batch Normalization layers and Dropout layers to prevent overfitting, a CNN deep learning model suitable for logistics package anomaly detection is constructed. key features are extracted through image preprocessing methods such as normalization and data enhancement to ensure that the model has high recognition adaptability to various abnormal situations such as damage and leakage. Secondly, through data enhancement methods such as rotation, scaling, brightness adjustment, and transfer learning using pre-trained models, the model's adaptability in different lighting, angles, and occlusion conditions is improved. Finally, the model is applied to actual monitoring, and real-time alarms are issued for abnormalities such as damage and leakage, which are then fed back to the system closed-loop management. This method is significantly better than traditional methods in terms of detection accuracy and response speed. Under natural light environment, the model detection accuracy is as high as 98.4%, reducing false positives and false negatives. Combining CNN's anomaly detection technology effectively improves the accuracy and efficiency of logistics monitoring and provides technical support for intelligent logistics management. ©2024 IEEE.

关键词： Convolutional neural networks

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