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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Shaokun Zhang Xinde Li Chuanfei Hu Jianping Xu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Science and Technology on Information Systems Engineering Laboratory Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Recent transformer-based methods for estimating 3D human pose have gained widespread attention, achieving state-of-the-art results. Previous methods have primarily focused on capturing motion patterns of the human body at a single scale or cascading multiple scales, such as joints, bones, and body-parts. However, they are difficult to simultaneously capture spatial-temporal motion patterns of the human body at different scales due to the complex motion patterns. To address this issue, we propose Dual-scale Spatial and Temporal transFormer (DSTFormer), which can concurrently explore the spatial dependencies and temporal motion patterns of human joints and bones. Additionally, we introduce a Gcn-Spatial Transformer Block (GSTB), which introduces Graph Convolutional Networks (GCN) into transformer to enhance the exploitation of local relationships and global information between adjacent joints or bones. Extensive experiments are conducted on the Human3.6M benchmark dataset, and superior results are reported when comparing to other state-of-the-art methods. More remarkably, our model achieves to-date the best published performance, with P1 errors of 37.9 mm and 15.6 mm, respectively.

关键词： Solid modeling Three-dimensional displays Mechatronics Graph convolutional networks Computational modeling Pose estimation Transformers Bones Joints Robots

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Joint Radar-Communication Beamforming for CRB-Based Target Localization

Joint Radar-Communication Beamforming for CRB-Based Target L...

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IEEE Conference on Vehicular Technology (VTC)

作者： Tianhao Mao Jie Yang Le Liang Shi Jin National Mobile Communications Research Laboratory Southeast University Nanjing China Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

This paper studies the beamformer design for an integrated sensing and communication system where simultaneous communication for multiple downlink users and bistatic sensing for a point target are realized. Firstly, to avoid self-interference in monostatic settings, we establish the bistatic sensing model where the sensing performance is measured by the Cramér-Rao bound (CRB) for the target's coordinates and the communication performance by the signal-to-interference-plus-noise ratio (SINR). We aim to minimize the CRB while ensuring a predefined SINR for each user. Then, we derive a closed-form beamformer for the single-user case that proves near-optimal. For the multi-user case, we introduce a beamformer design algorithm based on semidefinite relaxation and a suboptimal design algorithm based on successive convex approximation with reduced complexity. Finally, numerical results are provided to validate the solutions.

关键词： Location awareness Vehicular and wireless technologies Interference cancellation Coordinate measuring machines Downlink Approximation algorithms Integrated sensing and communication

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GPIO Based Iterative Learning control for Manipulators with Time-Varying Disturbances

GPIO Based Iterative Learning Control for Manipulators with ...

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第42届中国控制会议

作者： Huangyuwei Lu Zuo Wang Jiankun Sun Xi Xiao Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex systems of Engineering Ministry of Education Shenzhen Research Institute Southeast University School of Artificial Intelligence and Automation Huazhong University of Science and Technology Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration

To improve the disturbance rejection ability and repetitive tracking accuracy of manipulators,a composite iterative learning control(ILC) scheme via generalized proportional integral observer(GPIO) is proposed.A high-order polynomial is first employed to model the time-varying disturbances that include unmodeled dynamics,parameter uncertainties and load fluctuations.A GPIO is then designed to estimate the disturbances and high-order time *** introducing estimations into a PD-type ILC at each iteration,a composite control scheme is obtained,such that the undesirable influence of time-varying disturbances can be effectively attenuated in the repetitive tracking process of *** this paper,the rigorous stability analysis of the closed-loop system is presented to guarantee that the tracking error exponentially converges to zero as the iteration number tends to *** effectiveness of the proposed control scheme is finally verified by simulation results.

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Finite-Time Continuous Terminal Sliding-Mode Speed control of Micro Gas Turbine System with Mismatched Disturbances

Finite-Time Continuous Terminal Sliding-Mode Speed Control o...

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2021 China Automation Congress, CAC 2021

作者： Sun, Yanjun Xu, Long Chen, Ning Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China China Electric Power Research Institute Nanjing210036 China

ISBN: (纸本)9781665426473

The micro gas turbine system acts as a key role in distributed renewable generation systems due to its flexibility, versatility, and low emissions. This paper focuses on the micro gas turbine speed control problem, which primarily determines the fuel flow for the turbine under variable load conditions. Due to nonlinear factors in MGTS, such as the coupling between systems parameters, and external disturbances such as electric load variation, it is a challenge to realize accurate speed control. In this paper, a continuous terminal sliding-mode control law based on a finite-time disturbance observer is designed for micro gas turbine control system. In this method, the integral of high-frequency switching function is introduced to control law. It guarantees the continuity of control signal and alleviates the chattering problem. Besides, the linear sliding mode surface is replaced by a full-order terminal sliding mode surface, and it obtains finite-time convergence and smaller speed tracking error. Furthermore, disturbance estimates are used in control law to eliminate the adverse effect of disturbances on control performance, which improves disturbance rejection ability. Finally, simulation comparison with conventional sliding-mode control validates the advantages of the composite controller. © 2021 IEEE

关键词： Disturbance rejection

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A new image denoising method based on steer ketnel regression

A new image denoising method based on steer ketnel regressio...

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2020 Jiangsu Annual Conference on Automation, JACA 2020

作者： Shi, Gongbiao Chai, Lin Jin, Lizuo Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Nanjing210096 China

ISBN: (纸本)9781839535635

In this paper, we propose a new image pyramid denoising model based on the iterative steer kernel regression (ISKR) method. By introducing an image pyramid to generate images at multiple scales, it can filter out large-scale noise on low-resolution images and preserve details on the high-resolution image, and then we fuse the denoised images of multiple resolutions into a high-quality image by up-sampling noise residual. This method can improve denoising performance efficiently. and reduce the cost of calculation. Experiments show that this model reduces calculation by about 58% compared with the ISKR method while ensuring a good denoising effect. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Image denoising

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Flexible Active Safety Motion control for Robotic Obstacle Avoidance: A CBF-Guided MPC Approach

arXiv

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arXiv 2024年

作者： Liu, Jinhao Yang, Jun Mao, Jianliang Zhu, Tianqi Xie, Qihang Li, Yimeng Wang, Xiangyu Li, Shihua The School of Automation The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Department of Aeronautical and Automotive Engineering Loughborough University LoughboroughLE11 3TU United Kingdom The College of Automation Engineering Shanghai University of Electric Power Shanghai200090 China

A flexible active safety motion (FASM) control approach is proposed for the avoidance of dynamic obstacles and the reference tracking in robot manipulators. The distinctive feature of the proposed method lies in its utilization of control barrier functions (CBF) to design flexible CBF-guided safety criteria (CBFSC) with dynamically optimized decay rates, thereby offering flexibility and active safety for robot manipulators in dynamic environments. First, discrete-time CBFs are employed to formulate the novel flexible CBFSC with dynamic decay rates for robot manipulators. Following that, the model predictive control (MPC) philosophy is applied, integrating flexible CBFSC as safety constraints into the receding-horizon optimization problem. Significantly, the decay rates of the designed CBFSC are incorporated as decision variables in the optimization problem, facilitating the dynamic enhancement of flexibility during the obstacle avoidance process. In particular, a novel cost function that integrates a penalty term is designed to dynamically adjust the safety margins of the CBFSC. Finally, experiments are conducted in various scenarios using a Universal Robots 5 (UR5) manipulator to validate the effectiveness of the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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Blendnet: Metric-Based Method for Few-Shot Image Classification

SSRN

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SSRN 2022年

作者： Yang, Jiming Da, Feipeng Southeast University School of Automation Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

Image classification task often requires a massive amount of data. Learning to adapt when little data is available could be difficult. In this work, a framework named BlendNet is proposed for few-shot learning. Unlike conventional metric-based methods, a classifier blending features from different levels is proposed to assist the framework to obtain better accuracy and generalization performance. To alleviate the variance of the network for classifying the same class of objects, an data augmentation method is exploited to obtain the image representation robustly. Extensive experiments are conducted to validate our method under the meta-learning paradigm using few-shot classification datasets. The experiments demonstrate that our proposed framework provides competitive results on both Omniglot and mini -ImageNet benchmarks. Our code is available at https: // *** / tyolm / BlendNet . © 2022, The Authors. All rights reserved.

关键词： Image classification

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Application of Integrated Sensing and Communication in Structural Health Monitoring

Application of Integrated Sensing and Communication in Struc...

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International Conference on Wireless and Optical Communications ( WOCC)

作者： Jie Yang Chao-Kai Wen Shi Jin Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan National Mobile Communications Research Laboratory Southeast University Nanjing China

ISBN: (数字)9798331539658

ISBN: (纸本)9798331539665

Integrated sensing and communication (ISAC) is a primary goal in future cellular systems. Numerous researchers have explored how cellular systems can incorporate sensing capabilities in various use cases, such as intruder detection, monitoring, tracking, and collision avoidance. However, the potential of ISAC in structural health monitoring (SHM), which involves detecting slow and subtle changes in structures, remains unexplored. This study introduces a pioneering problem model and a solution framework for SHM using ISAC technology, employing reconfigurable intelligent surfaces (RIS) as reference points for monitoring building structures. Our method is inspired by the dynamic phase configuration of RIS to generate varying radio signals, which differ from background interference and thus facilitate the acquisition of precise radio measurements from the reference points. We conduct a theoretical analysis of these radio measurements through the lens of Fisher information. Our findings confirm that increasing observation time, adding more receivers, and optimizing RIS phases significantly enhance the position error bound (PEB) and orientation error bound (OEB) of the reference points, meeting the stringent accuracy requirements of SHM. Furthermore, we develop a Bayesian inference model to recognize structural states based on the calculated PEB and OEB, enabling the validation of detection probabilities of cellular systems in identifying damaged structural conditions. Our comprehensive theoretical and numerical analysis reveals the capability of ISAC to effectively detect millimeter-level deformations.

关键词： Wireless communication Wireless sensor networks Deformation Numerical analysis Buildings Receivers Reconfigurable intelligent surfaces Integrated sensing and communication Numerical models Monitoring

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Deep Learning-Based LOS and NLOS Parameters Estimation, Tracking, and Identification

Deep Learning-Based LOS and NLOS Parameters Estimation, Trac...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Kai Chen Jun Wang Jie Yang Le Liang Chao-Kai Wen Shi Jin National Mobile Communications Research Laboratory Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Millimeter-wave (mmWave) communication systems have large bandwidths and, combined with large-scale antenna arrays and beamforming technology, facilitate higher resolution of angle and delay measurements. This, in turn, provides opportunities for integrated sensing and communication (ISAC). Accurate and reliable estimation of mmWave channel state information is crucial for ISAC. In this paper, we propose a deep learning-based method for estimating channel multipath parameters, tracking, and identifying line-of-sight (LOS) and non-line-of-sight (NLOS) paths. Specifically, we leverage a network named “you only look once (YOLO)”, a highly efficient object detection network in computer vision, to detect the angles of arrival (AOAs) and angles of departure (AODs) of multipaths from received radio frequency signals. Moreover, we use a tracking-by-detection algorithm, ByteTrack, to track the variation of each path's AOA and AOD under a mobile scenario. Thanks to these vision-based channel estimation approaches, we reveal that LOS and NLOS paths exhibit different trajectory properties, allowing us to precisely identify LOS and NLOS paths, a challenge originally in the context of ISAC. We validate the proposed estimation, tracking, and identification methods on real measurement data and demonstrate that the proposed estimation method shows better performance compared to existing methods.

关键词： Antenna measurements Computer vision Simultaneous localization and mapping Millimeter wave measurements Estimation Channel estimation Transforms Integrated sensing and communication Millimeter wave communication Antenna arrays

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Wheel Hub Automatic Production System Based on Industrial Robots 7

Wheel Hub Automatic Production System Based on Industrial Ro...

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2021 7th International Conference on Manufacturing Technology and Applied Materials, ICAMMT 2021

作者： Zhang, Bin Lu, Zongxue Wang, Qiang Cui, Shiyi Jia, Guangxin Yang, Jun School of Automation Southeast University 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Lianyungang Jari Automation CO. LTD. Lianyungang222006 China

In this thesis, a control system for a wheel hub machining line based on a Fanuc robot, which includes adopting Siemens 300PLC as the main control unit, performing wheel circumferential positioning and center correction based on vision technology, and controlling the machine tool to perform real-time tool compensation based on the measured data, is researched and designed to achieve fully automatic unmanned wheel processing. © Published under licence by IOP Publishing Ltd.

关键词： Wheels

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