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IEEE International Conference on Mechatronics and Automation

作者： Long Chen Manru Xi Yue Ma Bin Li Qi Liu 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

Focusing on the polishing operation of automobile hubs, this paper proposed a polishing robot by combining a 1T2R parallel mechanism (PM) and an X-Y worktable. The dimensional synthesis and static/dynamic performance analysis of the 1T2R PM are also presented with the key points as follows. Firstly, the inverse displacement model and velocity model of the PM are built to formulate the motion/force transmissibility indices on the basis of the generalized Jacobian matrix. Secondly, the dimensional synthesis problem is formulated to determine the optimized normalized dimensional parameters by maximizing the global force/motion transmissibility of the PM subject to geometric constraints without mechanical interference. Finally, the finite element (FE) model of the 1T2R PM is built with the aid of the SAMCEF, and the static stiffness and lower-order natural frequencies of the PM are evaluated. The research results of this paper provide dimensions and mechanical structures for developing the physical prototype of the automobile hub polishing robot.

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Intelligent Diagnosis of Planetary Gearboxes Based on DAE-CNN

Intelligent Diagnosis of Planetary Gearboxes Based on DAE-CN...

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Prognostics and system Health Management Conference (PHM-Qingdao)

作者： Tianbo Kang Mian Zhang Ruitong Xie Jiwei Chen Hongbiao Xiang Yuhang Wang 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

Planetary gearboxes (PGs) are the core transmission components of complex rotating machinery. The diverse forms of internal gear faults often complicate diagnosis and maintenance work. To improve the fault diagnosis accuracy of PGs, this paper proposes a method for diagnosing PG faults based on deep autoencoders (DAE) and convolutional neural networks (CNN). The method fully utilizes the deep learning capability of DAE and combines it with the precise recognition ability of CNN for fault features. Through precise learning in the encoding layer, it achieves deep dimensionality reduction of fault features and further learns these features after dimensionality reduction by CNN. Compared to traditional CNN, the method reduces the computation parameters and resource consumption, resulting in higher precision intelligent fault diagnosis. Experimental results demonstrate that DAE-CNN exhibits stronger fault diagnosis ability than traditional networks.

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Auxiliary Antenna Array Optimization for Mainlobe Interference Suppression systems

Auxiliary Antenna Array Optimization for Mainlobe Interferen...

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International Conference on Microwave and Millimeter Wave technology Proceedings

作者： Jiahao Zhang Jin Meng National Key Laboratory of Science and Technology on Vessel Integrated Power System Naval University of Engineering Wuhan CHINA

ISBN: (纸本)9781665482271

Modern radars are sensitive to the aggressive interference, especially the high-power mainlobe interference. By introducing the auxiliary antenna array, and implementing the spatial domain interference cancellation algorithm, the mainlobe interference can be suppressed, while the signal of interest is reserved. The interference cancellation performance is highly related with the auxiliary array design. In this paper, the location, the element number, and the antenna type of the auxiliary array are investigated. It is found that by adding the number of the auxiliary antenna elements, and optimizing the location of the elements, the mainlobe distortion can be successfully reduced in presence of mainlobe interference. In realistic situations, the omnidirectional auxiliary antenna element is more desired than the directional antenna element, considering the cost and coverage.

关键词： Microwave technology Microwave integrated circuits Interference cancellation Millimeter wave technology Radar Radar antennas Microwave antenna arrays

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Graph Sampling based Fairness-aware Recommendation over Sensitive Attribute Removal

Graph Sampling based Fairness-aware Recommendation over Sens...

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IEEE International Conference on Data Mining (ICDM)

作者： Shenghao Liu Guoyang Wu Xianjun Deng Hongwei Lu Bang Wang Laurence Yang James J. Park Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Smart Internet Technology School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea

Discrimination against different user groups has received growing attention in the recommendation field. To address this problem, existing works typically remove sensitive attributes that may cause discrimination through adversary learning to achieve fair recommendations. However, these approaches leverage all available interactions for learning user representations and overlook the fact that different interactions have varying relevance to users’ sensitive attributes. Ignoring this issue may weaken the effectiveness of adversary learning in removing sensitive attributes. To tackle this challenge, we propose a novel model called GS-FairRec, which distinguishes between user interactions to achieve better removal of sensitive attributes. The model consists of three modules: graph sampling-based representation learning, pseudo-user representation learning, and adversarial learning. Firstly, the graph sampling-based representation learning module removes some irrelevant neighbors from a user-item bipartite graph and employs a graph convolutional network (GCN) to learn user/item representations. Next, items that are relevant to a user’s sensitive information but do not match their preferences are defined as the user’s pseudo-interest items, which are leveraged to learn the pseudo-user representation. In the adversarial learning module, the user’s two kinds of representations are fused for adversarial learning to remove sensitive information. Additionally, we design a new metric to measure the model’s ability to remove sensitive attributes based on how a generated recommendation list discloses the user’s sensitive attributes. Finally, we conduct experiments on two real-world datasets, and our results demonstrate the superiority of our proposed model in fairness tasks.

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Federated Reinforcement Learning for RIS-Aided Non-Orthogonal Multiple Access MEC

Federated Reinforcement Learning for RIS-Aided Non-Orthogona...

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IEEE Conference on Vehicular technology (VTC)

作者： Zhong Yang Yaxing Li Hongbo Liu Fangmin He National Key Laboratory of Science and Technology on Vessel Integrated Power System Naval University of Engineering Wuhan China

ISBN: (纸本)9781665454698

A novel reconfigurable intelligent surface (RIS) aided non-orthogonal multiple access mobile edge computing (NOMA-MEC) framework is proposed to release the heavy transmission delay of the edge devices (EDs) in next-generation wireless communication networks. We formulate a stochastic optimization problem that jointly optimizes the phase-shifter design of the RIS, the task offloading decision and the computing resource allocation of MEC, to minimize the overall transmission delay of all EDs in a long-term manner. The mathematical solution for the formulated optimization problem is a long-term offline policy which is non-trivial for conventional optimization approaches due to high computational complexity and stringent delay constraint. Therefore, we propose a federated reinforcement learning (FRL) approach for the formulated optimization problem to obtain the optimal solution taking advantages of the computing resource of all EDs. Moreover, a reputation-enabled ED selection scheme is proposed in the FRL approach that takes the task offloading history into consideration. The proposed RIS-aided NOMA-MEC framework is capable of outperforming conventional orthogonal multiple access (OMA) enabled RIS-MEC networks. The proposed FRL scheme achieves a near-optimal performance when the computional task is image classification in the MNIST and the IRIS dataset.

关键词： Wireless communication NOMA Vehicular and wireless technologies Reinforcement learning Delays History Task analysis

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A Model-Data Compound Driven Method For Compensating Robot Tracking Error

A Model-Data Compound Driven Method For Compensating Robot T...

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IEEE International Conference on Mechatronics and Automation

作者： Pai Peng Qi Liu Bin Li Wei Peng Xinjie Wang Jinyuan Wang 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

Industrial robots are widely used in industrial production because of their high work efficiency and high flexibility, but their low tracking accuracy makes them unable to be applied on a large scale in high-precision manufacturing. Considering the modeling error of robot, the problem of low tracking accuracy should be solved by combining model-based methods and data-based methods. Thus, a model-data compound approach is proposed for compensating robot tracking error. This method comprehensively considers two error compensation methods of model-driven and data-driven, which constructed the input matrix by using the model-based information and the data-based information. The former includes the desired position, velocity, acceleration, torque and friction torque; and the latter includes the actual torque. For the purpose of predicting the correlation between the joint error and the historical joint motion state, the historical information of the joint motion is also added in the input matrix for improving the error prediction accuracy. Then, a convolutional neural network prediction model is constructed for predicting and compensating the tracking error of each actuated joint. The experimental results show that the proposed method has good prediction accuracy and compensation effect. Compared to joint control without using the proposed method, the root mean square of joint tracking errors was reduced by up to more than 69%.

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Multi-Resolution Planar Region Extraction for Uneven Terrains

arXiv

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arXiv 2023年

作者： Sun, Yinghan Zheng, Linfang Chen, Hua Zhang, Wei Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Computer Science University of Birmingham United Kingdom

This paper studies the problem of extracting planar regions in uneven terrains from unordered point cloud measurements. Such a problem is critical in various robotic applications such as robotic perceptive locomotion. While existing approaches have shown promising results in effectively extracting planar regions from the environment, they often suffer from issues such as low computational efficiency or loss of resolution. To address these issues, we propose a multi-resolution planar region extraction strategy in this paper that balances the accuracy in boundaries and computational efficiency. Our method begins with a pointwise classification preprocessing module, which categorizes all sampled points according to their local geometric properties to facilitate multi-resolution segmentation. Subsequently, we arrange the categorized points using an octree, followed by an in-depth analysis of nodes to finish multi-resolution plane segmentation. The efficiency and robustness of the proposed approach are verified via synthetic and real-world experiments, demonstrating our method’s ability to generalize effectively across various uneven terrains while maintaining real-time performance, achieving frame rates exceeding 35 FPS. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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An optimal design and fuzzy control method for a new flapping-wing robot

An optimal design and fuzzy control method for a new flappin...

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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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A Scheme of Digital Twin Air Combat Battlefield and Digital Twin Model design

A Scheme of Digital Twin Air Combat Battlefield and Digital ...

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Chinese Control and Decision Conference, CCDC

作者： Yi Hu Fangqi Zhang Yuanjing Ma Chinese Academy Sciences Shenyang Institute of Computing Technology University of Chinese Academy Sciences Shenyang China Chinese Academy Sciences Shenyang Institute of Computing Technology University of Chinese Academy Sciences Aviation Key Laboratory of Science and Technology on Air Combat System AVIC Shenyang Aircraft Design and Research Institute Shenyang China School of Science Shenyang Ligong University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper provides a solution concept using digital twin technology for the battlefield environment for the air combat training. The scheme is divided into two aspects: digital twin battlefield and digital twin aircraft. The digital twin battlefield scheme discusses the three-dimensional modeling method of the battlefield and the update mechanism of battlefield environment data. The digital twin aircraft model scheme discusses the construction ideas of the aircraft twin model, and discusses the future application scenarios. With the advancement of technology and the iterative update of application scenarios, the digital twin air combat training battlefield will gradually realize various functions and explore more innovative application modes, realize the virtual environment interaction and digital twin function application of the entire battlefield environment.

关键词： Training Solid modeling Atmospheric modeling Virtual environments Data models Digital twins Aircraft manufacture

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Carbon emission peaks in countries worldwide and their national drivers

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Carbon Research 2025年第1期4卷 1-16页

作者： Wu, Qian Chen, Yujie Huang, Chaoqing Zhang, Lu He, Chao School of Resource and Environmental Sciences Wuhan University Wuhan China Collaborative Innovation Center for Emissions Trading System Co-Constructed By the Province and Ministry Wuhan China Key Laboratory of Geographic Information System Ministry of Education Wuhan University Wuhan China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Guangdong Eco-Engineering Polytechnic Guangdong Province Guangzhou China State Key Laboratory of Freshwater Ecology and Biotechnology Institute of Hydrobiology Chinese Academy of Sciences Wuhan China National Science Library (Wuhan) Chinese Academy of Sciences Wuhan China

Estimating the characteristics of CO2 emission peaks through decoupling relationships is crucial for understanding global CO2 emissions and mitigating climate change. This study investigated the spatiotemporal patterns and primary sources of global CO2 emissions from 1990 to 2020. Using the Mann–Kendall test and decoupling index, we identified emission peak characteristics across countries worldwide. Furthermore, the geographically temporally weighted regression (GTWR) model was employed to examine the effects of various factors on CO2 emissions. The results indicate that global CO2 emissions increased steadily over the study period, with the power industry contributing the most (34.54%–38.62%). Countries were categorized into four groups: no-declined, plateau, passively emission declined, and proactively peaked, comprising 99, 48, 20, and 26 countries, respectively. Notably, 65.4% of proactively peaked countries were developed nations, while 65% of passively emission declined countries were developing nations. CO2 emissions exhibited positive correlations with carbon intensity, GDP per capita, and the secondary industry but negative correlations with population density, the primary and tertiary industries. These findings provide valuable insights into global CO2 emission dynamics, highlighting emission peaks and decoupling relationships. This study also offers robust scientific support for policymakers to effectively design tailored strategies for reducing carbon emissions.

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