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Electrical Engineering and control Science (IC2ECS), International Conference on

作者： Chunxiao Zhang Yansen Zhao Ting Zhang Qi Liu Bin Li Yue Ma Mengze Suo Changrui Li 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

Bionic flapping-wing robot has the advantages of good maneuverability, low noise and strong stealth performance. It is widely used in military intelligence collection, field rescue and investigation. In this paper, an optimal design method of bionic flapping-wing robot non-phase difference driving mechanism is proposed. By setting the constraints of flapping angle amplitude, fuselage height and slider stroke, the scale parameters of the mechanism are optimized by using the minimum transmission angle; on this basis, the effectiveness of fuzzy PD control in improving the tracking accuracy of the drive system is verified by means of ADAMS and MATLAB / Simulink electromechanical coupling simulation. This research has practical value for designing new flapping-wing robot and improving its control quality.

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Automated Creation of Topological Structure Models of Parallel Mechanisms for Kinematic Performance Evaluation

Automated Creation of Topological Structure Models of Parall...

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International Conference on advanced Robotics and Mechatronics (ICARM)

作者： Hongye Wu Ping Lin Yue Ma Bin Li Qi Liu Hongqing Li 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

Considering the diversity of topological structures of parallel mechanisms (PMs), this paper presents an approach for the automated generation of topological structure models based on CAD technology. In this method, joints and links are treated as fundamental elements in the skeleton model of a mechanical system, leading to the development of a library of fundamental components readily available to compose a variety of limbs and PMs. A connection graph and its data structure are then developed for describing, in a hierarchical manner, the assembly information among the fundamental components. Equipped with the knowledge and information mentioned above, the limbs and PMs can finally be assembled automatically by identifying and imposing the geometrical constraints that have been already embedded in the models. The merit of this method lies in that 3D schematic diagrams (termed topological structure models) of various limbs and PMs can be created in an illustrative manner by properly selecting a set of fundamental components and then setting their connections, leading to the digitalized and visible topological structure models that are readily for kinematic performance evaluation. Combined with the CAD-CAE integration technology for the mechanical structure design and static/dynamic performance evaluation, a digital design and simulation framework can be formulated for the rapid design of parallel robotic equipment.

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Research on Highly Anti-Humidity and Selective NO2 Detection Based on GaN/MoO3 N-N Heterojunction

SSRN

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SSRN 2024年

作者： Hong, Yutao Han, Dan Li, Donghui He, Xiuli Zhao, Li Wang, Weidong Li, Hongwei Sang, Shengbo Liang, Hua Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Bioengineering Research Center Medical Innovation Research Division Chinese PLA General Hospital Beijing China Shanxi Academy of Medical Sciences China

Effective and accuratedetection of NO2 gas is crucial for protecting the environment and public health. In this work, balsam pear-like GaN/MoO3 porous nanosheets (GaN/MoO3) composites were synthesized by the in situ assembling, the hydrothermal method, and the nitridation process to detect NO2. A series of characterization methods were used to manifest the successful synthesis and to analyze the morphology and elemental composition features of GaN/MoO3 composites. The results of the gas-sensing experiments show that at the operating temperature of 225 °C, the gas sensor based on the optimal ratio of GaN/MoO3 composites exhibits a low theoretical limit of detection (0.78 ppb) for NO2, which is significantly improved compared with the limit of detection of MoO3 (5 ppm). Further, the response value of the sensor based on GaN/MoO3 composites to 200 ppm NO2 (44.74) is 4.36 times higher than that of the sensor based on pure GaN, with an improvement in stability. Meanwhile, the sensor also possessed excellent repeatability, selectivity, stability, and splendid anti-humidity ability at 15-70 % RH. The improved gas-sensing performance is ascribed to the unique microstructure of both GaN and MoO3, and the heterojunction effect between them. This work provides a way to improve the efficiency and anti-humidity ability of real-time monitoring of NO2 for the sensors of MoO3-based composite. © 2024, The Authors. All rights reserved.

关键词： Gallium nitride

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Research on Crack Detection of Nonlinear Rotor Based on Energy Tracks 17

Research on Crack Detection of Nonlinear Rotor Based on Ener...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Zhang, Yu Liu, Jun Han, Zhu Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechanical System Design and Intelligent Control Tianjin300384 China

ISBN: (纸本)9781728164151

In rotating machinery, because of high-speed working characteristics and terrible working conditions with variable cycles and variable loads, it is easy for rotors to cause a crack initiation and propagation, which may cause serious harm or even the life-threatening. The super harmonic component is an important role of the crack detection. But it is difficult to reflect the super harmonic component in the high-speed region after super harmonic resonances. In this paper, the dynamic equation of a nonlinear rotor system with a crack is considered, the potential energy expression of the cracked rotor is derived, and the concept of energy tracks is proposed. In the distortion of energy tracks, the effects of different crack parameters and nonlinear parameters on the vibration characteristics of the rotor system are compared. The results show that the energy trajectory distortion can diagnose the crack fault. It provides a new research method for vibration response analyses and fault diagnoses of cracked rotor system. © 2020 IEEE.

关键词： Crack detection

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Research on Asymmetrical Dual-rotor system Based on the Energy Orbit 17

Research on Asymmetrical Dual-rotor System Based on the Ener...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Han, Zhu Liu, Jun Zhang, Yu Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechanical System Design and Intelligent Control Tianjin300384 China

ISBN: (纸本)9781728164151

Studying the vibration of asymmetrical dual-rotor is very important for aero engines. In this paper, based on the vibration characteristics of an aero-engine, a different novel coupled dynamic model is proposed based on the spring recovery characteristics of rotor. The model dynamic equations of asymmetrical dual-rotor are obtained by the Lagrange equation, and vibration resonance curves of high pressure and low pressure rotors are numerically simulated and theoretically analyzed by using the Runge-Kutta algorithm. The research show that the system occurs combined vibration, harmonic vibration and superharmonic vibration. In addition, the vibration occurring mechanism was investigated and verified in detail through numerical simulation. For the first time, the concept of energy orbit was introduced into the dual-rotor model system. The energy orbit represents the vibration characteristics and energy exchange process of the model system. Energy orbit is a new energy orbit method for analyzing vibration characteristics of rotor system. The new researches of this paper will show a different research direction for complex dual-rotor systems. © 2020 IEEE.

关键词： Equations of motion

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Adversarial Learning Network for Recovering Rppg Signals from Low-Resolution Images for Remote Heart Rate Measurement

SSRN

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SSRN 2024年

作者： Zhai, Dezhao Chen, Wei Li, Qinwei Yu, Ming Wu, Hang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Systems Engineering Institute Academy of Military Sciences People's Liberation Army Tianjin300161 China Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin300300 China School of Artificial Intelligence Nankai University Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, which has significant research value and application potential. However, in long-distance vital sign measurement tasks, the low resolution of facial images and insufficient pixels in the facial region severely degrade the quality of the rPPG signals, leading to a sharp decline in HR accuracy. To address this issue, this paper proposes an adversarial learning network for recovering rPPG signals from low-resolution images (ALRSL-HR). First, arbitrary resolution face images are fed into multi-channel network to extract rPPG signal features capturing rich rPPG information, generating rPPG feature blocks, and temporally aggregating for richer feature representation. These features are then inputted into the backbone network to generate adversarial examples of rPPG signals of varying qualities. Finally, a two-stage adversarial loss function is employed to enable the low-quality rPPG signals to learn the periodic characteristics of high-quality rPPG signals, recover the low-quality signals, and narrow the quality gap between rPPG signals from low-resolution images. Extensive experiments were conducted in this paper to validate the effectiveness of our model on the public COHFACE, UBFC-Phys and UBFC-rPPG datasets. The results of the experiments demonstrate that the proposed ALRSL-HR model can effectively recover degraded rPPG signals from low-resolution images. © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

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ESNet: evolution and succession network for high-resolution salient object detection 24

ESNet: evolution and succession network for high-resolution ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Hongyu Liu Runmin Cong Hua Li Qianqian Xu Qingming Huang Wei Zhang Institute of Information Science Beijing Jiaotong University & Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University & Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature; the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24.

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Automated Assembly by Two-Fingered Microhand for Fabrication of Soft Magnetic Microrobots

Automated Assembly by Two-Fingered Microhand for Fabrication...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yue Zhao Xiaoming Liu Ruixi Wang Dan Liu Masaru Kojima Qiang Huang Tatsuo Arai Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Department of Intelligent Manufacturing Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Materials Engineering Science Osaka University Osaka Japan Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Micro-assembly is an emerging method to fabricate microrobots with multiple modules or particles. However, there is always a lack of a flexible and efficient method to freely create the desired magnetic soft microrobots. In this paper, an automated assembly system based on a two-fingered microhand is presented for fabricating magnetic soft microrobots. Our proposed system can automatically pick and place components to assemble microrobots with a two-fingered micromanipulator, and orient these components through an external magnetic field. The automated assembly has the advantages of high accuracy, high speed, and high success rate. It can endow magnetic microrobots with flexible material selection, arbitrary geometry design, and programable magnetization profile. We can make full use of this system to fabricate multiple magnetic soft microrobots. The experiment results demonstrate that this system can efficiently fabricate microrobots with excellent mechanical properties, which have application potential in robotics, biomedical engineering, and environmental governance.

关键词： Three-dimensional displays Shape Micromanipulators Magnetization Manuals Three-dimensional printing Soft magnetic materials

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Significant Wave Height Prediction Based on VMD-SA-MLP-BP

Significant Wave Height Prediction Based on VMD-SA-MLP-BP

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2023 China Automation Congress, CAC 2023

作者： Wu, Yining Zhang, Runfeng Liu, Fang Liu, Zhenzhong Wu, Jianguo Dong, Haonan School of Mechanical Engineering Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin300384 China Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education China Yichang Research Institute of Testing Technology Yichang443003 China Tianjin Hanhailanfan Marine Technology Co. Ltd. Tianjin300457 China School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9798350303759

Waves seriously impact port construction, worldwide route planning, military activities, and wave power generation. To improve the accuracy of significant wave height prediction, we proposed a novel prediction method, a multi-layer perceptron combined with a backpropagation adjustment (MLP-BP) prediction model that combines mutation mode decomposition (VMD) and a simulated annealing optimization algorithm (SA). Firstly, we use the variable modulus method to decompose the significant wave height sequence data and transform the wave height sequence into multiple different sub-modes (IMF) to reduce the complexity and non-stationarity of the data. Secondly, the simulated annealing algorithm (SA) is used to optimize the weight and bias of the MLP-BP neural network to find the optimal parameter configuration and improve the performance and generalization ability of the prediction model. Finally, the decomposed sub-modal components of each significant wave height sequence are inserted into the MLP-BP neural network and the predicted values of each element are summed to obtain the final significant wave height prediction. The prediction results of the SSA-MLP-BP, PSO-MLP-BP, SA-MLP-BP, and VMD-SA-MLP-BP were compared and demonstrated that the VMD-SA-MLP-BP model performed best. The MAE, MAPE, MSE, RMSE, and R2 of the prediction evaluation indexes were 0.036 m, 11.7%, 0.004 m2, 0.067 m, and 0.983, respectively, which performed well in predicting significant wave height. © 2023 IEEE.

关键词： Variational mode decomposition

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

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

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Data Science and Computer Application (ICDSCA), IEEE International Conference on

作者： Kun Liang Hang Zhang 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: (数字)9798350368239

ISBN: (纸本)9798350368246

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.

关键词： Industries Adaptation models Accuracy Image recognition Data models Real-time systems Convolutional neural networks Anomaly detection Monitoring Logistics

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