检索结果-内蒙古大学图书馆

Real-Time Multi-View Human Pose Estimation System

学校读者我要写书评

暂无评论

Real-Time Multi-View Human Pose Estimation System

Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Yuan Yang Yangang Wang School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

We present a highly synchronized multi-view hardware acquisition system that simultaneously enables real-time multi-person pose estimation. We design a hardware synchronizer to achieve multi camera synchronous shooting, with a synchronization latency rate of only 10 −8 seconds. Based on this, we construct a multi-view hardware synchronous acquisition system. This system can be flexibly deployed in various environments to capture moving targets, such as bodies, clothing, hands, etc. Leveraging the distributed computing capabilities of the system, we further develop a multi-view multi-person pose estimation algorithm framework that fulfills real-time requirements and achieves an average frame rate of 40FPS in experimental settings while demonstrating robustness against human motion and occlusion.

关键词： Pose estimation Data acquisition Clothing Cameras Hardware Real-time systems Robustness

Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspective

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Shi, Yuguang The School of Automation Southeast University The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

Recently, iteration-based stereo matching has shown great potential. However, these models optimize the disparity map using RNN variants. The discrete optimization process poses a challenge of information loss, which restricts the level of detail that can be expressed in the generated disparity map. In order to address these issues, we propose a novel training approach that incorporates diffusion models into the iterative optimization process. We designed a Time-based Gated Recurrent Unit (T-GRU) to correlate temporal and disparity outputs. Unlike standard recurrent units, we employ Agent Attention to generate more expressive features. We also designed an attention-based context network to capture a large amount of contextual information. Experiments on several public benchmarks show that we have achieved competitive stereo matching performance. Our model ranks first in the Scene Flow dataset, achieving over a 7% improvement compared to competing methods, and requires only 8 iterations to achieve state-of-the-art results. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

Calibration of 3D Single-pixel Imaging systems with a Calibration Field

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Ma, Xinyue Wang, Chenxing The School of Automation The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

3D single-pixel imaging (SPI) is a promising imaging technique that can be flexibly applied to various wavebands. The main challenge in 3D SPI is that the calibration usually requires a large number of standard points as references, which are tricky to capture using single-pixel detectors. Conventional solutions involve sophisticated device deployment and cumbersome operations, resulting in hundreds of images needed for calibration. In our work, we construct a Calibration Field (CaliF) to efficiently generate the standard points from one single image. A high accuracy of the CaliF is guaranteed by the technique of deep learning and digital twin. We perform experiments with our new method to verify its validity and accuracy. We believe our work holds great potential in 3D SPI systems or even general imaging systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Calibration

A Data-Driven Modeling and Motion Control of Heavy-Load Hydraulic Manipulators via Reversible Transformation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Ma, Dexian Liu, Yirong Liu, Wenbo Zhou, Bo Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Ministry of Education Nanjing210096 China

This work proposes a data-driven modeling and the corresponding hybrid motion control framework for unmanned and automated operation of industrial heavy-load hydraulic manipulator. Rather than the direct use of a neural network black box, we construct a reversible nonlinear model by using multilayer perceptron to approximate dynamics in the physical integrator chain system after reversible transformations. The reversible nonlinear model is trained offline using supervised learning techniques, and the data are obtained from simulations or experiments. Entire hybrid motion control framework consists of the model inversion controller that compensates for the nonlinear dynamics and proportional-derivative controller that enhances the robustness. The stability is proved with Lyapunov theory. Co-simulation and Experiments show the effectiveness of proposed modeling and hybrid control framework. With a commercial 39-ton class hydraulic excavator for motion control tasks, the root mean square error of trajectory tracking error decreases by at least 50% compared to traditional control methods. In addition, by analyzing the system model, the proposed framework can be rapidly applied to different control plants. Copyright © 2024, The Authors. All rights reserved.

关键词： Industrial manipulators

Data-Driven Multi-step Nonlinear Model Predictive Control for Industrial Heavy Load Hydraulic Robot

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Ma, Dexian Zhou, Bo Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Ministry of Education Nanjing210096 China

Automating complex industrial robots requires precise nonlinear control and efficient energy management. This paper introduces a data-driven nonlinear model predictive control (NMPC) framework to optimize control under multiple objectives. To enhance the prediction accuracy of the dynamic model, we design a single-shot multi-step prediction (SSMP) model based on long short-term memory (LSTM) and multilayer perceptrons (MLP), which can directly obtain the predictive horizon without iterative repetition and reduce computational pressure. Moreover, we combine offline and online models to address disturbances stemming from environmental interactions, similar to the superposition of the robot’s free and forced responses. The online model learns the system’s variations from the prediction mismatches of the offline model and updates its weights in real time. The proposed hybrid predictive model simplifies the relationship between inputs and outputs into matrix multiplication, which can quickly obtain the derivative. Therefore, the solution for the control signal sequence employs a gradient descent method with an adaptive learning rate, allowing the NMPC cost function to be formulated as a convex function incorporating critical states. The learning rate is dynamically adjusted based on state errors to counteract the inherent prediction inaccuracies of neural networks. The controller outputs the average value of the control signal sequence instead of the first value. Simulations and experiments on a 22-ton hydraulic excavator have validated the effectiveness of our method, showing that the proposed NMPC approach can be widely applied to industrial systems, including nonlinear control and energy management. Copyright © 2024, The Authors. All rights reserved.

关键词： Excavators

Applications of Edge Computing in IoT-Driver Smart Manufacturing of Automobiles

学校读者我要写书评

暂无评论

Applications of Edge Computing in IoT-Driver Smart Manufactu...

2023 China Automation Congress, CAC 2023

作者： Yu, Yingjie Xu, Bo Liu, Lin Wang, Juncheng Li, Minghe Ma, Zhe Cao, Jianfu Co. Ltd General Business Department Changzhou China Xi'an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Xi'an China Co. Ltd Software Business Department Tianjin China

ISBN: (纸本)9798350303759

The automobile manufacturing industry is evolving towards increased connectivity and intelligence, with Industrial Internet of Things (IIoT) being a pivotal technology for realizing smart automobile production. Edge computing, complementing cloud computing in IIoT framework, presents a topic of significant interest in industry and academia with regard to coordinating efforts between cloud and edge computing. This paper provides an in-depth investigation of the current status of research, both domestically and globally, in areas such as smart automobiles manufacturing, edge computing, and data storage. It outlines the comprehensive structure of Internet of Things (IoT) within automobile manufacturing and introduces the hardware and software structures and functionalities of edge computing devices. Finally, an edge computing device designed specifically for automobile manufacturing is proposed and implemented. © 2023 IEEE.

关键词： Edge computing

Robust Delay-Dependent Stability for Uncertain Linear systems with Time-Varying Delay 49

学校读者我要写书评

暂无评论

Robust Delay-Dependent Stability for Uncertain Linear System...

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Chen, Dong-Shuai He, Yong Shangguan, Xing-Chen School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9798350331820

This paper investigates the problem of stability analysis for the uncertain linear systems with time-varying delay. Firstly, an uncertain linear system model considering time-varying delay is established. Then based on the Lyapunov-Krasovskii functional (LKF) method, a novel robust delay-dependent stability criterion is proposed, which is benefited by a new augmented LKF with more effective time-delay information and the use of a tighter integral inequality to estimate functional derivative. The stability criterion obtained is less conservative. At last, a numerical example shows the superiority and effectiveness that the method used in this paper. © 2023 IEEE.

关键词： Time delay

数据驱动框架下基于基因表达编程的非线性K-L湍流混合模型

学校读者我要写书评

暂无评论

Acta Mechanica Sinica 2023年第2期39卷 147-164页

作者：谢寒松赵耀民张又升 Key Laboratory of High Energy Density Physics Simulation Ministry of EducationCenterfor Applied Physics and Technology and College of EngineeringPeking UniversityBeijing 100871China State Key Laboratory for Turbulence and Complex Systems College of EngineeringPeking UniversityBeijing 100871China institute of Applied Physics and Computational Mathematics Beijing 100094China

由Rayleigh-Taylor(RT)、Richtmyer-Meshkov(RM)、Kelvin-Helmholtz(KH)等流体力学界面不稳定性诱导的湍流混合现象广泛存在于自然界和工程问题中,准确预测其演化具有十分重要的意义.考虑到实际问题的高雷诺数及复杂性,在可预见的未来,... 详细信息

由Rayleigh-Taylor(RT)、Richtmyer-Meshkov(RM)、Kelvin-Helmholtz(KH)等流体力学界面不稳定性诱导的湍流混合现象广泛存在于自然界和工程问题中,准确预测其演化具有十分重要的意义.考虑到实际问题的高雷诺数及复杂性,在可预见的未来,雷诺平均(RANS)方法仍将是工程实践中最具可实现性的选择.一般而言,传统RANS混合模型中至关重要的雷诺应力项是基于经典的Boussinesq线性涡粘假设封闭的.然而,这种线性模型无法充分描述在实际工程流动中发挥着重要作用的湍流各向异性特征.相比之下,非线性模型在这方面具有显著优势.本研究首次将基因表达编程(GEP)方法应用于湍流混合问题,发展了数据驱动的非线性K-L湍流混合模型.与常见的“黑箱”型机器学习模型不同,GEP模型显式给出模型方程,从而具有更强的物理可解释性.具体地,本研究基于二阶截断的广义Cayley-Hamilton非线性本构关系,利用GEP方法中的符号回归功能,形式化地给出了待封闭的模型系数与伽利略不变量之间的函数关系.此外,为了保证训练模型的物理性,我们将可实现性原则引入到了惩罚函数中.模型泛化性测试的结果表明:尽管新模型仅利用倾斜RT混合问题训练得到,但对于几个典型的混合问题均具有良好的鲁棒性.与标准的K-L模型相比,新模型不但具有更高的预测精度,而且能够更好地捕捉湍流的物理特性.此外,通过分析显式的封闭模型,本文进一步给出了新模型的预测效果得以提升的原因.

关键词： Turbulent mixing GEP method Nonlinear model Machine learning Interpretable model

Study on Self-Organizing Aggregation Method for Massive Flexible Resources and Its Simulation 4

学校读者我要写书评

暂无评论

Study on Self-Organizing Aggregation Method for Massive Flex...

4th International Conference on Advanced Electrical and Energy systems, AEES 2023

作者： Yin, Changwei Ji, Wenlu Ye, Ting Mao, Tingfeng Zhang, Kaifeng Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Power Supply Company of State Grid Jiangsu Electric Power Co. Ltd. Nanjing China

ISBN: (纸本)9798350342659

Distributed flexible resources are developing in the trend of sea quantization. In this situation, the existing resource aggregation methods are facing bottlenecks, and it is difficult to fully exploit the regulation potential of massive flexible resources. In order to achieve efficient and flexible aggregation in the scenario of massive resources, this paper proposes a self-organizing aggregation method for flexible resources based on the social network and simulates the aggregation process under the proposed method based on the multi-agent simulation platform NetLogo. Firstly, this paper outlines the idea of the self-organizing aggregation method for flexible resources, which projects flexible resources from the physical space to the social network space, and realizes flexible aggregation through the interactive attraction between resources in the social network. Secondly, this paper establishes a simulation model for flexible resource self-organizing aggregation based on the NetLogo platform. The model defines four types of agents involved in the self-organizing aggregation process and provides detailed specifications for their attributes and interaction rules. Finally, simulation experiments are designed to verify the rationality of the self-organizing aggregation method and simulation model proposed in this paper. The simulation results show that, following the idea of self-organizing aggregation, massive flexible resources can autonomously and flexibly aggregate through the interaction attraction in the social network due to the incentive of task gain, demonstrating the effectiveness and rationality of the self-organizing aggregation method and the simulation model proposed in this paper. © 2023 IEEE.

关键词： Multi agent systems