检索结果-内蒙古大学图书馆

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Tuan Van Vo Minh Nhat Vu Baoru Huang An Vuong Ngan Le Thieu Vo Anh Nguyen FPT Software AI Center Vietnam Automation & Control Institute TU Wien Austria Austrian Institute of Technology (AIT) GmbH Austria Imperial College London UK Department of Computer Science & Computer Engineering University of Arkansas USA National University of Singapore Singapore Department of Computer Science University of Liverpool UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Grasp detection is an essential task in robotics with various industrial applications. However, traditional methods often struggle with occlusions and do not utilize language for grasping. Incorporating natural language into grasp detection remains a challenging task and largely unexplored. To address this gap, we propose a new method for language-driven grasp detection with mask-guided attention by utilizing the transformer attention mechanism with semantic segmentation features. Our approach integrates visual data, segmentation mask features, and natural language instructions, significantly improving grasp detection accuracy. Our work introduces a new framework for language-driven grasp detection, paving the way for language-driven robotic applications. Intensive experiments show that our method outperforms other recent baselines by a clear margin, with a 10.0% success score improvement. We further validate our method in real-world robotic experiments, confirming the effectiveness of our approach.

关键词： Visualization Attention mechanisms Accuracy Service robots Source coding Semantic segmentation Natural languages Grasping Transformers Feature extraction

来源：评论

学校读者我要写书评

暂无评论

S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

Linear Encryption Techniques for Counteracting Information-based Stealthy Attacks

Linear Encryption Techniques for Counteracting Information-b...

引用

International Conference on control, automation, Robotics and Vision (ICARCV)

作者： Jun Shang Hanwen Zhang Weixiong Rao Yiguang Hong Department of Control Science and Engineering National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Sci-ence and Technology Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China School of Software Engineering Tongji University Shanghai China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This study explores linear encryption techniques to protect against information-based stealthy attacks on re-mote state estimation. Utilizing smart sensors equipped with local Kalman filters, the system transmits innovations rather than raw measurements via wireless networks. However, this transmission is susceptible to malicious data interception and manipulation by attackers. To safeguard against these stealthy threats, encryption and decryption modules are integrated into the system. This research aims to assess the effectiveness of the encryption strategy when faced with information-based stealthy attacks. A key contribution of this paper is the adoption of the most comprehensive attack models, moving away from the conventional reliance on innovation-based linear attack models. Our results demonstrate that the proposed linear encryption approach effectively mitigates stealthy attacks under certain mild conditions. The efficacy of the encryption is further validated through numerical examples, corroborating the theoretical advancements presented in this paper.

关键词： Technological innovation automation Wireless networks Encryption Numerical models Kalman filters State estimation Robots Intelligent sensors

来源：评论

学校读者我要写书评

暂无评论

A Comparison Analysis between Two and Three Levels Inverter to Grid-Connected Used the Photovoltaic System

A Comparison Analysis between Two and Three Levels Inverter ...

引用

Electronics, Energy and Measurement (IC2EM), International Conference on

作者： Fayçal Hadj Mihoub Sidi Moussa Abdelkader Morsli Abdelhalim Tlemçani Hocine Belmili Hassan Nouri Electrical Engineering and Automation Research Laboratory (LREA) University of Medea Medea Algeria Renewable Energy Development Center (CDER) Bou-Ismail Solar Equipment Development Unit (UDES) Tipaza Algeria Department of Engineering Design and Mathematics Power Systems Electronics and Control Research Group Bristol U.K.

This article presents a comparative study of two topologies of three-phase photovoltaic inverters connected to the grid, between the usual two-level inverter and three-level NPC (Neutral Point Clamped) inverter. Inverter control and the boost converter MPPT controller are based on the SRF-dq method and the INC algorithm respectively. The comparison relies on evaluating the overall efficiency and harmonic distortion of the inverter. All circuits are modeled and simulated using Matlab-Simulink. The results obtained indicate satisfaction with the quality, of the current and voltage injected into the electrical grid, and reflect that both inverters are efficient and comply with IEEE-519 standard recommendations, but the three-level inverter performs better results than the two-level inverter.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

arXiv

引用

arXiv 2024年

作者： Sun, Li Huang, Zhenhao Wu, Hua Ye, Junda Peng, Hao Yu, Zhengtao Yu, Philip S. School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Department of Computer Science University of Illinois ChicagoIL United States

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph. In the literature, most GSL solutions either primarily focus on structure refinement with task-specific supervision (i.e., node classification), or overlook the inherent weakness of GNNs themselves (e.g., over-squashing), resulting in suboptimal performance despite sophisticated designs. In light of these limitations, we propose to study self-supervised graph structure-feature co-refinement for effectively alleviating the issue of over-squashing in typical GNNs. In this paper, we take a fundamentally different perspective of the Ricci curvature in Riemannian geometry, in which we encounter the challenges of modeling, utilizing and computing Ricci curvature. To tackle these challenges, we present a self-supervised Riemannian model, DeepRicci. Specifically, we introduce a latent Riemannian space of heterogeneous curvatures to model various Ricci curvatures, and propose a gyrovector feature mapping to utilize Ricci curvature for typical GNNs. Thereafter, we refine node features by geometric contrastive learning among different geometric views, and simultaneously refine graph structure by backward Ricci flow based on a novel formulation of differentiable Ricci curvature. Finally, extensive experiments on public datasets show the superiority of DeepRicci, and the connection between backward Ricci flow and over-squashing. Codes of our work are given in https://***/RiemanGraph/. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

CS-Net: Contribution-based Sampling Network for Point Cloud Simplification

arXiv

引用

arXiv 2025年

作者： Guo, Tian Chen, Chen Yuan, Hui Mao, Xiaolong Hamzaoui, Raouf Hou, Junhui School of Control Science and Engineering Shandong University Ji’nan250061 China School of Software Shandong University Ji’nan250061 China School of Engineering and Sustainable Development De Montfort University LeicesterLE1 9BH United Kingdom Department of Computer Science City University of Hong Kong Hong Kong

Point cloud sampling plays a crucial role in reducing computation costs and storage requirements for various vision tasks. Traditional sampling methods, such as farthest point sampling, lack task-specific information and, as a result, cannot guarantee optimal performance in specific applications. Learning-based methods train a network to sample the point cloud for the targeted downstream task. However, they do not guarantee that the sampled points are the most relevant ones. Moreover, they may result in duplicate sampled points, which requires completion of the sampled point cloud through post-processing techniques. To address these limitations, we propose a contribution-based sampling network (CS-Net), where the sampling operation is formulated as a Top-k operation. To ensure that the network can be trained in an end-to-end way using gradient descent algorithms, we use a differentiable approximation to the Top-k operation via entropy regularization of an optimal transport problem. Our network consists of a feature embedding module, a cascade attention module, and a contribution scoring module. The feature embedding module includes a specifically designed spatial pooling layer to reduce parameters while preserving important features. The cascade attention module combines the outputs of three skip connected offset attention layers to emphasize the attractive features and suppress less important ones. The contribution scoring module generates a contribution score for each point and guides the sampling process to prioritize the most important ones. Experiments on the ModelNet40 and PU147 showed that CS-Net achieved state-of-the-art performance in two semantic-based downstream tasks (classification and registration) and two reconstruction-based tasks (compression and surface reconstruction). CS-Net also achieved high average precision for objection detection on the KITTI LiDAR point cloud dataset, demonstrating its effectiveness in three-dimensional object detection. Co

关键词： Importance sampling

来源：评论

学校读者我要写书评

暂无评论

Excellent high-temperature friction and wear resistance of high–Cr die steels by trace in–situ nano-ceramics

引用

Ceramics International 2025年第14期51卷 18540-18552页

作者： Bao, Ze-Ju Yang, Hong-Yu Dong, Bai-Xin Zhong, Xin-Miao Li, Yi-Lu Li, Ai-Min Wang, Cheng-Gang Li, Zhi-Gang Meng, Jia Qiao, Jian Kang, Jie Zhu, Ming Shu, Shi-Li Xu, Hui-Xia Qiu, Feng Jiang, Qi-Chuan State Key Laboratory of Automotive Simulation and Control Jilin University China Key Laboratory of Automobile Materials Ministry of Education and Department of Materials Science and Engineering Jilin University Renmin Street NO. 5988 Jilin Province Changchun130025 China ZhonghangShangda Superalloys Materials Co. Ltd No.16 Wangong Avenue Qinghe County Xingtai City Hebei Province 054800 China Technology Research and Development Casting & Forging Research Institute FAW Foundry Co. Ltd Crossing of Hexie Street & Bingwu Road Automotive Industry Development Zone Changchun Jilin Province 130013 China Department and Test Center FAW-Volkswagen Automotive Co. Ltd Jilin Province Changchun130011 China School of Mechatronic Engineering and Automation Foshan University No. 33 Guangyun Road Nanhai District Guangdong Province Foshan528231 China Jilin Liyuan Precision Manufacturing Co. Ltd. No. 5729 Xi'ning Road Economic Development Zone Jilin Province LiaoYuan136299 China Zhenjiang Xianfeng Automotive Parts Co. Ltd Dantu High Tech Industrial Park Jiangsu Province Zhenjiang212000 China School of Mechanical and Aerospace Engineering Jilin University Renmin Street NO. 5988 Changchun130025 China Jiangsu Tiangong Corporation Ltd A8 Tonggang Road Jiangsu Province Danyang212000 China

In this study, TiC + TiB2 nano-ceramics reinforced high–Cr die steels were successfully prepared using aluminum-based master alloy as a carrier for the nano-ceramics. The impact of nano-ceramics on the microstructure of steels as well as the tensile properties and friction and wear properties under high temperature conditions were explored. The incorporated nano-ceramics can significantly refine the microstructure of steels, resulting in finer and more uniformly precipitated carbides, thereby effectively improving the mechanical properties of steels. Compared with unmanipulated high–Cr die steels, the high-temperature (550 °C) yield strength of high–Cr steels reinforced with 0.02 wt% nano-ceramic was increased from 963 MPa to 1274 MPa. The volume wear rate of nano-ceramics reinforced high Cr steels reached 11.9 × 10−13 m3/m, 3.4 × 10−13 m3/m, 5.6 × 10−13 m3/m and 6.3 × 10−13 m3/m under the four conditions of 400 °C-30 N-300 r/min, 550 °C-20 N-300 r/min, 550 °C-30 N-300 r/min and 550 °C-40 N-300 r/min, which were reduced by 23.7 %, 34.6 %, 34.9 % and 35.1 % respectively. These results provide a new insight into improving the high-temperature friction and wear properties of steels. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Aluminum alloys

来源：评论

学校读者我要写书评

暂无评论

DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

DeepRicci: Self-supervised Graph Structure-Feature Co-Refine...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Li Sun Zhenhao Huang Hua Wu Junda Ye Hao Peng Zhengtao Yu Philip S. Yu School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Department of Computer Science University of Illinois at Chicago IL USA

关键词：

来源：评论

学校读者我要写书评

暂无评论

Passivity-Based Distributed Event-Triggered Flocking control of Port-Hamiltonian Systems

引用

IEEE control Systems Letters 2025年 9卷 342-347页

作者： Aranda-Escolastico, Ernesto Abdelrahim, Mahmoud Fernandez-Amoros, David Guinaldo, Maria Colombo, Leonardo Department of Software and Systems Engineering Universidad Nacional de Educación a Distancia (UNED) Juan del Rosal 16 Madrid 28040 Spain Prince Sultan University Renewable Energy Laboratory College of Engineering Riyadh 11586 Saudi Arabia Computer Science and Automatic Control Department Universidad Nacional de Educación a Distancia (UNED) Juan del Rosal 16 Madrid 28040 Spain Centre for Automation and Robotics (CSIC-UPM) Ctra. M300 Campo Real Km 0200 Madrid Arganda del Rey 28500 Spain

This paper addresses the problem of flocking motion in the context of port-Hamiltonian systems. The port-Hamiltonian framework offers advantages in modeling networks, dissipation, and heterogeneous agents. In this regard, designing flocking controllers in port-Hamiltonian form significantly increases their practical applications. Additionally, event-triggered control has proven effective in reducing bandwidth consumption. However, challenges remain in combining flocking requirements with energy-dissipating terms. Therefore, we propose a novel event-triggered flocking controller suitable for port-Hamiltonian systems. The method is validated through numerical simulations of a group of hexarotors. © 2017 IEEE.

关键词： flocking passivity. port-Hamiltonian systems event-triggered control multi-agent systems

来源：评论

学校读者我要写书评

暂无评论

Concept of Intelligent Energy Grid control Based on Upgraded Neural Network

Concept of Intelligent Energy Grid Control Based on Upgraded...

引用

2020 International Conference on Electrotechnical Complexes and Systems, ICOECS 2020

作者： Shikulskaya, Olga Urumbaeva, Olga Shalaev, Timofey Astrakhan State University of Architecture and Civil Engineering Fire Safety and Water Use Department Astrakhan Russia Astrakhan State Technical University Automation and Control Department Astrakhan Russia State Autonomous Institution "gormedtehnika" Software Development Department Moscow Russia

ISBN: (纸本)9781728191157

The development of electric power systems requires an improvement in their operation quality, which, among other things, depends to a large extent on the possibility of predicting the electric load of the power system. Electrical load forecasting provides basic baseline information for decision making in power system control. Previously used single-factor time series forecasts based on regression methods are unable to take into account the totality of all factors affecting on electricity consumption, such as the weather forecast for the coming day;duration of light day;possibility of atmospheric precipitation or clouds, season, schedule of electric loads;condition of wear of power supply networks;previously recorded irregularities in the operation of the electric grid complex, the operation mode of the largest consumers;solvency of clients, etc. The need to use heterogeneous quantitative and qualitative parameters makes artificial intelligence use, neural network and expedient. The features of the approach proposed by the authors are the consideration in the neural network of the influence of a pair of neurons on the neuron of the intermediate layer and the dynamic change in the number of input parameters. © 2020 IEEE.

关键词： Electric power utilization

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：