检索结果-内蒙古大学图书馆

Chinese control Conference (CCC)

作者： Langyu Li Hao Wang Zhixin Xie Zixuan Liu Wei Cui Yu Pan State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China School of Automation Science and Engineering South China University of Technology Guangzhou China School of Mechanical Engineering and Automation Harbin Institude of Technology Shenzhen China

A numerical technique called Simulated Ising Annealing (SIA) uses digital computers to obtain approximations of the ground states of Ising models. The quadratic unconstrained binary optimization (QUBO) problem in combinatorial optimization, which is challenging to solve through exhaustive search, has significant linkages to the Ising model. Therefore, SIA's main objective is to identify a better solution in finite iterations. In order to enhance the performance of the simulated annealing procedure, we adopt a Gaussian augmented Hamiltonian Monte Carlo (GAHMC) method in this study. We contrasted our algorithm with Gibbs sampling and local quantum annealing method. Finally, we discuss its application on QUBO problem.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The Research on Sheet Metal Part Recognition Technology Based on Improved Mask R-CNN

The Research on Sheet Metal Part Recognition Technology Base...

引用

Machine Learning and computer Application (ICMLCA), International Conference on

作者： Mingfei Liu Jun Liu Kecheng Zhou Jian Wang Key Laboratory of Industrial Intelligence Technology on Chemical Process School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang Liaoning Province China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

The visual recognition of sheet metal parts is a crucial component of industrial automation sorting, particularly under complex lighting and background interference conditions. To enhance the accuracy of sheet metal part recognition, this paper curates a dataset of sheet metal parts acquired by an intelligent light field image sensor and proposes a visual recognition model, PA-CBAM, based on an improved Mask R-CNN. By improving the original feature pyramid FPN and introducing the Convolutional Block Attention Module (CBAM), the model enhances multi-scale feature fusion and the focus on key features: (1) Bottom up paths and shortcut connections have been added between different layers of the traditional FPN feature pyramid, thereby improving multi-scale object detection capability. (2) CBAM further optimizes the model's channel and spatial attention mechanisms, effectively enhancing the capture of edge details and the suppression of background noise. Experimental results show that the proposed PA-CBAM model significantly improves detection and segmentation performance compared to traditional methods, particularly in terms of edge recognition accuracy for sheet metal parts under complex lighting conditions.

关键词： Visualization Accuracy Image edge detection Metals Lighting Object detection Feature extraction Light fields Optimization Sorting

来源：评论

学校读者我要写书评

暂无评论

Time Series Anomaly Detection Using Temporal 2D-Variation Multi-Layer Feature Fusion Times Network

Time Series Anomaly Detection Using Temporal 2D-Variation Mu...

引用

IEEE International Conference on Cyber Technology in automation, control, and Intelligent systems

作者： Tao Cui Yueming Zhu Yao Liu Jindi Liu Pipechina Southwest Pipeline Company Chengdu Sichuan China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Instrumentation Technology and Economy Institute Beijing

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

With the popularity of the Internet of Things (IoT) technology, people are beginning to monitor the safety and stability of the system by establishing a sensing network so that anomalies can be detected and disposed of at an early stage. The resulting large amount of time series data has triggered scholars' interest in multivariate time series anomaly detection. In the past, this task was usually achieved by directly using one-dimensional time series, but due to the complexity of time series, it is difficult to perform effective data mining. To solve this problem, we use timesNet to convert one-dimensional sequences into two-dimensional tensors for time series modelling. At the same time, improvements are also made in terms of both block and overall network architecture so that the model can fuse multi-layer features to obtain stronger characterisation and anomaly detection. Experiments on real-world datasets show that our improvement is 0.16%, 1.88% and 1.19% higher than the precision, recall and f1-score of baseline.

关键词： Tensors Computational modeling Time series analysis Feature extraction Data models Stability analysis Sensors Safety Internet of Things Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

IVR-SSM: Industrial Vehicular Robot Safe & Smart Move Using Wheel-Encoder and Inertial-RGB-Depth Measurements

IVR-SSM: Industrial Vehicular Robot Safe & Smart Move Using ...

引用

International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Yuanhao Liu Wei Liang Chu Wang Xin Zhang Huanbing Gao Yinlong Zhang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences School of Information and Electrical Engineering Shandong Jianzhu University Shandong Key Laboratory of Intelligent Buildings Technology Jinan China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Safe & smart move (SSM) aims to help industrial vehicular robot (IVR), such as warehousing robots, running safely and smartly by detecting the obstacles in front and maintaining a safe distance & attitude. However, the traditional SSM methods suffer the drawbacks of limited ranges, inaccurate regression of safety distance & orientation between the robot and the forward unexpected obstacles, and the inappropriate IVR control strategies. To solve these issues, this paper designs an innovative real-time safety-aware framework using the complementary Wheel-Encoder and Inertial-RGB-Depth measurements to ensure the IVR running safely, reasonably and reliably. To estimate the robot running states, we take advantages of the complementary wheel encoder, monocular RGB images and inertial measurements to obtain the robot positions, orientations, angular and linear velocities in a tightly-coupled manner. To improve the safety relative distance & attitude estimation accuracy, we exploit the RGB and depth measurements in a task-oriented fashion. The forward obstacles with the specific contours are detected using the Mask-RCNN model, followed by the Gaussian Mixture Model to obtain the forward obstacle depth. Eventually, an IVR control strategy is designed to maintain the robot safety distance and orientation, and to keep the minimum damage to the robot mechanical system. The presented method has been evaluated on the developed industrial vehicular robot safety perception platform. The experimental results prove the competitiveness against the state-of-the-art methods.

关键词： Service robots Warehousing Robot vision systems Wheels Reliability engineering Safety Mobile robots Mechanical systems Velocity measurement Gaussian mixture model

来源：评论

学校读者我要写书评

暂无评论

Distance- And velocity-based collision avoidance for time-varying formation control of second-order multi-agent systems

引用

IEEE Transactions on Circuits and systems II: Express Briefs 2021年第4期68卷 1253-1257页

作者： Pang, Zhong-Hua Zheng, Chang-Bing Sun, Jian Han, Qing-Long Liu, Guo-Ping Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology Beijing100144 China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC3122 Australia Department of Artificial Intelligence and Automation Wuhan University Wuhan430072 China

This brief addresses the time-varying formation control problem with collision avoidance for second-order multi-agent systems. By taking both distances and velocities between agents into account, a novel collision avoidance scheme is proposed on the basis of conventional distance-based artificial potential field methods. Compared with the latter, the proposed scheme can more accurately determine possible collision situations and thus significantly reduce possible collision areas, which consequently not only can reduce energy consumption, but also has as few adverse effects on formation maintenance as possible. Then a time-varying formation control protocol is designed so as to achieve zero steady-state position and velocity formation tracking errors without collision. Finally, theoretical analysis, numerical demonstration, as well as comparative simulation are provided to verify the effectiveness of the proposed method. © 2004-2012 IEEE.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

A Recurrent Attention and Interaction Model for Pedestrian Trajectory Prediction

引用

IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1361-1370页

作者： Xuesong Li Yating Liu Kunfeng Wang Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the University of Chinese Academy of SciencesBeijing 100049China College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE

The movement of pedestrians involves temporal continuity,spatial interactivity,and random *** a result,pedestrian trajectory prediction is rather *** existing trajectory prediction methods tend to focus on just one aspect of these challenges,ignoring the temporal information of the trajectory and making too many *** this paper,we propose a recurrent attention and interaction(RAI)model to predict pedestrian *** RAI model consists of a temporal attention module,spatial pooling module,and randomness modeling *** temporal attention module is proposed to assign different weights to the input sequence of a target,and reduce the speed deviation of different *** spatial pooling module is proposed to model not only the social information of neighbors in historical frames,but also the intention of neighbors in the current *** randomness modeling module is proposed to model the uncertainty and diversity of trajectories by introducing random *** conduct extensive experiments on several public *** results demonstrate that our method outperforms many that are state-ofthe-art.

关键词： Deep learning long short-term memory(LSTM) recurrent attention and interaction(RAI)model trajectory prediction

来源：评论

学校读者我要写书评

暂无评论

Adapting Human Mesh Recovery with Vision-Language Feedback

arXiv

引用

arXiv 2025年

作者： Xu, Chongyang Huang, Buzhen Zhang, Chengfang Feng, Ziliang Wang, Yangang College of Computer Science Sichuan University Chengdu610065 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education The School of Automation Southeast University Nanjing210096 China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou646000 China

Human mesh recovery can be approached using either regression-based or optimization-based methods. Regression models achieve high pose accuracy but struggle with model-to-image alignment due to the lack of explicit 2D-3D correspondences. In contrast, optimization-based methods align 3D models to 2D observations but are prone to local minima and depth ambiguity. In this work, we leverage large vision-language models (VLMs) to generate interactive body part descriptions, which serve as implicit constraints to enhance 3D perception and limit the optimization space. Specifically, we formulate monocular human mesh recovery as a distribution adaptation task by integrating both 2D observations and language descriptions. To bridge the gap between text and 3D pose signals, we first train a text encoder and a pose VQ-VAE, aligning texts to body poses in a shared latent space using contrastive learning. Subsequently, we employ a diffusion-based framework to refine the initial parameters guided by gradients derived from both 2D observations and text descriptions. Finally, the model can produce poses with accurate 3D perception and image consistency. Experimental results on multiple benchmarks validate its effectiveness. The code will be made publicly available. © 2025, CC BY.

关键词： Mesh generation

来源：评论

学校读者我要写书评

暂无评论

Method of formation of reference control signals for redundant manipulators

Method of formation of reference control signals for redunda...

引用

2021 International Conference on Automatics and Energy, ICAE 2021

作者： Filaretov, V.F. Gubankov, A.S. Gornostaev, I.V. Far Eastern Federal University Department of Automation and Robotics Vladivostok690091 Russia Institute of Automation and Control Processes FEB RAS Robotics Laboratory Vladivostok690041 Russia Institute of Marine Technology Problems FEB RAS Laboratory of Robotic Systems Vladivostok690091 Russia

The paper is devoted to preservation of dynamic control accuracy of working tools of multilink manipulators when they move along arbitrary spatial trajectories, taking into account the design limits in all degrees of freedom and special cases of position of their links. Preservation of control accuracy is proposed to be ensured by eliminating reach of all degrees of freedom of the manipulators to the limits and to indicated special positions, characterized by ambiguity in solving the inverse kinematic problems of the manipulators, as well as excluding the reach of their working tools to boundaries of working area due to use of a redundant degree of freedom when approaching indicated undesirable positions. The performed simulation has confirmed efficiency of the proposed method. © 2021 institute of Physics Publishing. All rights reserved.

关键词： Degrees of freedom (mechanics)

来源：评论

学校读者我要写书评

暂无评论

Prior Knowledge Informed Neural Network for Subsurface Flow Prediction

Prior Knowledge Informed Neural Network for Subsurface Flow ...

引用

IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng 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 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

Data-driven deep learning has been widely applied to solve subsurface flow problems. However, it is difficult to achieve accurate prediction with pure data-driven methods, especially when there is only a small amount of valid data. Prior knowledge is pre-existent and comes from an independent source. The neural network can be informed by prior knowledge to improve algorithm performance even with limited and sparse data. In this study, prior knowledge informed neural network (PKINN) is proposed to predict subsurface flow. The prior knowledge, on the one hand, is embedded in the neural networks as integrated loss function to inform the training process of deep learning methods. On the other hand, it is used as part of input term of neural network to improve calculation accuracy. Subsurface flow models are built to verify the proposed PKINN model. The results show that the PKINN method can solve subsurface flow problems effectively.

关键词： Knowledge engineering Deep learning Training Industrial electronics Solid modeling Accuracy Neural networks

来源：评论

学校读者我要写书评

暂无评论

Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflage

Shifting Spotlight for Co-supervision: A Simple yet Efficien...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yang Hu Jinxia Zhang Kaihua Zhang Yin Yuan Jiale Huang Zechao Zhan Xin Wang Key Laboratory of Measurement and Control of CSE School of Automation Southeast University China Advanced Ocean Institute of Southeast University China School of Computer Science Nanjing University of Information Science and Technology China Alibaba Group China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Camouflaged object detection (COD) remains a challenging task in computer vision. Existing methods often resort to additional branches for edge supervision, incurring substantial computational costs. To address this, we propose the Co-Supervised Spotlight Shifting Network (CS 3 Net), a compact single-branch framework inspired by how shifting light source exposes camouflage. Our spotlight shifting strategy replaces multi-branch designs by generating supervisory signals that highlight boundary cues. Within CS 3 Net, a Projection Aware Attention (PAA) module is devised to strengthen feature extraction, while the Extended Neighbor Connection Decoder (ENCD) enhances final predictions. Extensive experiments on public datasets demonstrate that CS 3 Net not only achieves superior performance, but also reduces Multiply-Accumulate operations (MACs) by 32.13% compared to state-of-the-art COD methods, striking an optimal balance between efficiency and effectiveness.

关键词： computer vision Image edge detection Object detection Signal processing Feature extraction Acoustics Decoding Computational efficiency Speech processing Light sources

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：