检索结果-内蒙古大学图书馆

Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

引用

Petroleum Science 2023年第2期20卷 1142-1154页

作者： Yun-Peng He Chuan-Zhi Zang Peng Zeng Ming-Xin Wang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyangLiaoning 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyangLiaoning 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyangLiaoning 110169China University of Chinese Academy of Sciences Beijing100049China Shenyang University of Technology ShenyangLiaoning 110870China School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyangLiaoning 110159China

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

关键词： Few-shot learning Indicator diagram Meta-learning Soft thresholding Sucker-rod pumping system Time–frequency signature Working condition recognition

来源：评论

学校读者我要写书评

暂无评论

Advances and Challenges in Large Model Compression: A Survey 24

Advances and Challenges in Large Model Compression: A Survey

引用

2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Xu, Yingying Song, Chunhe Sun, Yong Yu, Shimao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798400717147

With the development of computer technology, large-scale models have gradually evolved from early simple algorithms to today's complex deep neural networks with huge parameter scale and high computational complexity. These large-scale models have greatly advanced the artificial intelligence technology, which has realized revolutionary progress in the fields of image, speech, text and so on. However, the computational and storage requirements of large-scale models are also growing rapidly, which seriously constrains their deployment and application in computing resource-constrained environments. In order to solve this problem, model compression technology has emerged and has been greatly developed. In this paper, we systematically review the key technologies, application scenarios, challenges and future directions of large-scale model compression from the perspective of model compression technology. Our work aims to provide practitioners and researchers with detailed references on the development of the technology, and emphasizes the important role of model compression technology in promoting the development of lightweight artificial intelligence. © 2024 Owner/Author.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Neural Dynamics for Cooperative Motion control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

引用

IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1605-1620页

作者： Yufeng Lian Xingtian Xiao Jiliang Zhang Long Jin Junzhi Yu Zhongbo Sun the Department of Control Engineering Changchun University of TechnologyChangchun 130012China IEEE the College of Information Science and Engineering Northeastern UniversityShenyang 110819 the National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China the School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China the State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

关键词： Cooperative motion control noise-tolerant zeroing neural network(NTZNN) omnidirectional mobile manipulator(OMM) repetitive motion planning

来源：评论

学校读者我要写书评

暂无评论

Delayless controllers for Exact Model Matching and Disturbance Rejection of Time Delay systems with Measurable and Non-measurable Disturbances 12

Delayless Controllers for Exact Model Matching and Disturban...

引用

12th International Conference on Modern Circuits and systems Technologies, MOCAST 2023

作者： Koumboulis, Fotis N. Kouvakas, Nikolaos D. National and Kapodistrian University of Athens Euripus Campus Robotics Automatic Control and Cyber-Physical Systems Laboratory Department of Digital Industry Technologies School of Science Euboea Greece

ISBN: (纸本)9798350321074

Delayless control design for time delay systems is an attractive direction for networked control systems as well as other cyber physical applications. In the present paper, the problem of Exact Model Matching with simultaneous Disturbance Rejection (EMMDR) is studied for the case of left-invertible general neutral multi delay systems with measurable and non-measurable disturbances, via two types of delayless controllers. The first type of controllers is a dynamic delayless controller feeding back the measurement outputs, the measurable disturbances and the external commands. The number of the external commands is equal to the number of the control inputs. The second type of controllers is the static delayless version of the first type. For both types of controllers, the necessary and sufficient conditions for the solvability of the problem are established, and the general solutions of the controller matrices are derived. The results are illustrated through a numerical example. © 2023 IEEE.

关键词： controllers

来源：评论

学校读者我要写书评

暂无评论

Robustness to Disturbances of Ellipsoid-Shape Underwater Vehicle with Multi-Link Manipulator

Robustness to Disturbances of Ellipsoid-Shape Underwater Veh...

引用

International Russian Automation Conference (RusAutoCon)

作者： Ivan Lipko Kirill Dementiev Vadim Kramar Robotics and Intelligent Control Systems Laboratory Sevastopol State University Sevastopol Russian Federation

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

This article is devoted to the issue of analyzing the influence of external disturbances on the underwater vehicle in the form of a flattened ellipsoid body with a multilink manipulator when performing technological operations. The relevance of the study is related to the automation of such operations as underwater welding, transfer of objects, twisting, bending, etc., performed underwater. For this purpose, we have built a mathematical model of the underwater apparatus with an ellipsoid shape, indicated the ways of influence of moments and forces arising in the manipulator on the apparatus body. It was found out that this influence leads to nonlinear effects in transients. This influence is different, depending on the axis in which the motion of the apparatus is performed. Since the body is flattened in the horizontal axis, the most unpleasant effect occurs in the vertical motion, due to the increased hydrodynamic resistance. The model is considered without a control loop in the apparatus, and it is the effect of the housing on the accuracy of movements of a multilink manipulator with a standard control system in the form of PID controllers that is important to us.

关键词： Automation Shape Welding Manipulators control systems Robustness Transient analysis Underwater vehicles Standards Ellipsoids

来源：评论

学校读者我要写书评

暂无评论

Safety-Aware Trajectory Tracking using High-Order control Barrier Functions 63

Safety-Aware Trajectory Tracking using High-Order Control Ba...

引用

63rd IEEE Conference on Decision and control, CDC 2024

作者： Rousseas, Panagiotis Panagou, Dimitra Kyriakopoulos, Kostas National Technical University of Athens Control Systems Laboratory School of Mechanical Engineering Greece University of Michigan Department of Robotics and the Department of Aerospace Engineering MI United States Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350316339

A novel method on safety-aware trajectory tracking is presented in this work. The proposed scheme employs control Barrier Functions (CBFs) along with a latent-space representation over the barrier and its derivatives. While such representations have also been employed in the literature, the innovation of the proposed method lies in formulating a novel tracking problem on the latent space, enabling for the shape of linear class -K functions that are included in the safety condition induced by the control barrier function to be chosen constructively. Additionally, the aforementioned safety condition is modified based on the latent-space tracking through the addition of an extra term. This enables maintaining safety guarantees and is demonstrated to provide decreased tracking errors compared to the nominal control barrier function scheme. Numerical simulations validate the efficacy of the proposed scheme, as well as its ability for online implementation. © 2024 IEEE.

关键词： Safety engineering

来源：评论

学校读者我要写书评

暂无评论

Characteristics of laser-induced breakdown spectroscopy of liquid slag

引用

Plasma Science and Technology 2024年第2期26卷 86-93页

作者：董长言于洪霞孙兰香李洋刘修业周平黄少文 State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016People's Republic of China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016People's RepublicofChina Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169People's Republic of China Shenyang University of Technology Shenyang 110870People's Republic of China Shenyang Ligong University Shenyang 1i0142People's Republic of China Shandong Iron and Steel Company Ltd. Jinan 271105People's Republic of China

Rapid online analysis of liquid slag is essential for optimizing the quality and energy efficiency of steel production. To investigate the key factors that affect the online measurement of refined slag using laser-induced breakdown spectroscopy(LIBS), this study examined the effects of slag composition and temperature on the intensity and stability of the LIBS spectra. The experimental temperature was controlled at three levels: 1350℃, 1400℃, and 1450℃. The results showed that slag composition and temperature significantly affected the intensity and stability of the LIBS spectra. Increasing the Fe content and temperature in the slag reduces its viscosity, resulting in an enhanced intensity and stability of the LIBS spectra. Additionally, 42 refined slag samples were quantitatively analyzed for Fe, Si, Ca, Mg, Al, and Mn at 1350℃, 1400℃, and 1450℃.The normalized full spectrum combined with partial least squares(PLS) quantification modeling was used, using the Ca Ⅱ 317.91 nm spectral line as an internal standard. The results show that using the internal standard normalization method can significantly reduce the influence of spectral fluctuations. Meanwhile, a temperature of 1450℃ has been found to yield superior results compared to both 1350℃ and 1400℃, and it is advantageous to conduct a quantitative analysis of the slag when it is in a “water-like” state with low viscosity.

关键词： laser-induced breakdown spectroscopy(LIBS) slag temperature composition viscosity internal standard normalization partial least squares(PLS)

来源：评论

学校读者我要写书评

暂无评论

Trajectory control of Multi-Axis Robotic Arms Based on Physics-Informed Neural Networks and Nonlinear Model Predictive control

Trajectory Control of Multi-Axis Robotic Arms Based on Physi...

引用

2024 China Automation Congress, CAC 2024

作者： Wang, Yiqing Xia, Changqing Jin, Xi Xu, Chi Zhang, Yanzhu School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350368604

Multi-axis robotic arms are extensively utilized in intelligent manufacturing scenarios, with trajectory control in flexible scenarios constituting a primary challenge. Physics-Informed Neural Networks (PINNs) represent advanced methods that integrate physical laws with data-driven approaches. Despite their outstanding performance in integrating theory and data, research on their application for controlling robotic arms in complex scenarios remains limited. This paper establishes a nonlinear dynamic model based on the physical relationships between the axes of robotic arms. In the absence of prior data, Sobol sequence random sampling is employed to generate data, which are subsequently trained using PINNs. Considering system noise, the Extended Kalman Filter (EKF) is utilized to predict the next state in noisy environments, and Nonlinear Model Predictive control (NMPC) is implemented to control the robotic arm for trajectory tracking, achieving real-time control. Simulation results demonstrate that the proposed Discrete Physics-Informed Predictive control (DPIPC) method exhibits smaller position and velocity errors with less fluctuation compared to the method in the reference, indicating superior control capabilities. © 2024 IEEE.

关键词： Extended Kalman filters

来源：评论

学校读者我要写书评

暂无评论

A lightweight insulator string defect detection method based on improved YOLOv5 4

A lightweight insulator string defect detection method based...

引用

4th International Conference on Electronic Communication, Computer Science and Technology, ECCST 2024

作者： Gao, Pengpei Song, Chunhe Zhang, Yue State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

In recent years, the use of drones to assess the safety status of insulators in power transmission systems has become a trend. To facilitate the application of intelligent algorithms on drone platforms, this paper proposes a lightweight insulator string defect detection method based on an improved YOLOv5. Firstly, to decrease the parameter count in the YOLOv5 backbone module, we opted for GhostNet. Considering the limitations of GhostNet in feature extraction, a lightweight attention mechanism is designed and integrated with GhostNet to enhance the accuracy of capturing defect features in insulator strings. Additionally, to further reduce parameters in the neck part of the YOLOv5 model, Ghost Convolution is introduced. Experimental results demonstrate that the proposed model performs excellently on the insulator string dataset and meets the requirements for real-time defect detection of insulators using drones. © Published under licence by IOP Publishing Ltd.

关键词： Insulating materials

来源：评论

学校读者我要写书评

暂无评论

Improving Collision-Free Success Rate For Object Goal Visual Navigation Via Two-Stage Training With Collision Prediction

arXiv

引用

arXiv 2025年

作者： Lian, Shiwei Zhang, Feitian Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China

The object goal visual navigation is the task of navigating to a specific target object using egocentric visual observations. Recent end-to-end navigation models based on deep reinforcement learning have achieved remarkable performance in finding and reaching target objects. However, the collision problem of these models during navigation remains unresolved, since the collision is typically neglected when evaluating the success. Although incorporating a negative reward for collision during training appears straightforward, it results in a more conservative policy, thereby limiting the agent’s ability to reach targets. In addition, many of these models utilize only RGB observations, further increasing the difficulty of collision avoidance without depth information. To address these limitations, a new concept—collision-free success is introduced to evaluate the ability of navigation models to find a collision-free path towards the target object. A two-stage training method with collision prediction is proposed to improve the collision-free success rate of the existing navigation models using RGB observations. In the first training stage, the collision prediction module supervises the agent’s collision states during exploration to learn to predict the possible collision. In the second stage, leveraging the trained collision prediction, the agent learns to navigate to the target without collision. The experimental results in the AI2-THOR environment demonstrate that the proposed method greatly improves the collision-free success rate of different navigation models and outperforms other comparable collision-avoidance methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：