检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Shen, Tianqi Liu, Shaohua Feng, Jiaqi Ma, Ziye An, Ning Department of Computer Science City University of Hong Kong Hong Kong Image Processing Center Beihang University China Shen Yuan Honors College Beihang University China Research Institute of Mine Artificial Intelligence China Coal Research Institute China State Key Laboratory of Intelligent Coal Mining and Strata Control China

Gaussian Splatting (GS) has emerged as a crucial technique for representing discrete volumetric radiance fields. It leverages unique parametrization to mitigate computational demands in scene optimization. This work introduces Topology-Aware 3D Gaussian Splatting (Topology-GS), which addresses two key limitations in current approaches: compromised pixel-level structural integrity due to incomplete initial geometric coverage, and inadequate feature-level integrity from insufficient topological constraints during optimization. To overcome these limitations, Topology-GS incorporates a novel interpolation strategy, Local Persistent Voronoi Interpolation (LPVI), and a topology-focused regularization term based on persistent barcodes, named PersLoss. LPVI utilizes persistent homology to guide adaptive interpolation, enhancing point coverage in low-curvature areas while preserving topological structure. PersLoss aligns the visual perceptual similarity of rendered images with ground truth by constraining distances between their topological features. Comprehensive experiments on three novel-view synthesis benchmarks demonstrate that Topology-GS outperforms existing methods in terms of PSNR, SSIM, and LPIPS metrics, while maintaining efficient memory usage. This study pioneers the integration of topology with 3D-GS, laying the groundwork for future research in this area. Copyright © 2024, The Authors. All rights reserved.

关键词： Interpolation

来源：评论

学校读者我要写书评

暂无评论

Federated Motor imagery Classification for Privacy-Preserving Brain-Computer Interfaces

arXiv

引用

arXiv 2024年

作者： Jia, Tianwang Meng, Lubin Li, Siyang Liu, Jiajing Wu, Dongrui The Key Laboratory The Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University Science and Technology Research Institute Shenzhen518063 China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this challenge. This paper proposes Federated classification with local Batch-specific batch normalization and Sharpness-aware minimization (FedBS) for privacy protection in EEG-based motor imagery (MI) classification. FedBS utilizes local batch-specific batch normalization to reduce data discrepancies among different clients, and sharpness-aware minimization optimizer in local training to improve model generalization. Experiments on three public MI datasets using three popular deep learning models demonstrated that FedBS outperformed six state-of-the-art FL approaches. Remarkably, it also outperformed centralized training, which does not consider privacy protection at all. In summary, FedBS protects user EEG data privacy, enabling multiple BCI users to participate in large-scale machine learning model training, which in turn improves the BCI decoding accuracy. © 2024, CC BY.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

On the stability of distributed secondary control for DC microgrids with grid-forming and grid-feeding converters

引用

AUTOMATICA 2023年第1期155卷

作者： Liu, Xiao-Kang Wang, Yan-Wu Liu, Zhi-Wei Huang, Yuehua Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China

This paper investigates the stability problem of a single-bus DC microgrid with mixed grid-forming/gridfeeding converters and the constant impedance, constant current, and constant power (ZIP) loads. Considering the different output properties of grid-forming and grid-feeding converters, distributed secondary controllers with mixed V-I droop and I-V droop controllers are designed to achieve accurate bidirectional current sharing and voltage regulation. As few converters have access to the information of the common bus, an observer is designed for each local controller to estimate the bus voltage. Then, the observer state is used to generate voltage correction term in the feedback loop, which gets potential faster regulation compared with the existing approaches that use pinning gains. Based on the rank-one perturbation analysis, a stability criterion of DC microgrid is given for the case of ZI load, where global voltage regulation and current sharing are guaranteed. Then, the stability criterion is extended to the case of ZIP load, where local stability of the DC microgrid is guaranteed. Both numerical examples and experimental tests are given to verify the effectiveness of the theoretical results. & COPY;2023 Elsevier Ltd. All rights reserved.

关键词： DC microgrid Distributed secondary control Current sharing Voltage regulation

来源：评论

学校读者我要写书评

暂无评论

Three-dimensional task allocation for smart transportation in spatial crowdsourcing: An intelligent role division approach

引用

ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Feng, Zhenhui Xiao, Renbin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Spatial Crowdsourcing (SC) is an effective way to solve real world problems based on the Internet. As a core issue of SC, crowdsourcing task allocation has become the research hotspot. Existing researches usually focus on the matching of two types of objects: users and workers. However, some new SC platforms provide more flexible services which have a third type of task objects. In this paper, a kind of three-dimensional matching problem for transportation service (3DM-TS) based on real-world scenarios is investigated. In terms of problem construction, we consider three types of task objects, and prove the proposed 3DM-TS is NP-hard. To address the 3DM-TS problem, we propose an intelligent role division approach for online task allocation (IRD-OTA) based on quasi real-time solving framework, which can be divided into three stages. The first is task sensing. By introducing the batch-based mode, we handle the data of crowdsourcing tasks to extract the information of task objects. Second, we propose a roles division model based on the attraction-repulsion mechanism. The model performs matching based on the interaction forces between different objects, to obtain preliminary matching results. Finally, a threshold-based group adjustment strategy is designed to locally adjust the matching groups with lower utility. In the process of real-time task allocation, the task objects are matched according to the three stages of "task sensing-roles division-group adjustment", and the algorithm can obtain the final matching results after multiple batches of information iteration and feedback. In this paper, the IRD-OTA is examined on real dataset and synthetic dataset. Experimental results confirm the performance advantage of IRD-OTA in comparison with other approaches. In addition, we further investigate the effect of different parameter values on the performance, and verify the scalability of our proposal.

关键词： Spatial crowdsourcing Task allocation Role division Attraction-repulsion mechanism

来源：评论

学校读者我要写书评

暂无评论

Adaptive-Neural-Network-Based Shape control for a Swarm of Robots

引用

COMPLEXITY 2018年第1期2018卷

作者： Lan, Xuejing Wu, Zhenghao Xu, Wenbiao Liu, Guiyun Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Guangdong Peoples R China MOE Key Lab Image Proc & Intelligence Control Wuhan 430074 Hubei Peoples R China Guangdong Inst Metrol Guangzhou 510405 Guangdong Peoples R China Ctr Intelligent Equipment & Internet Connected Sy Guangzhou Guangdong Peoples R China

This paper considers the region-based formation control for a swarm of robots with unknown nonlinear dynamics and disturbances. An adaptive neural network is designed to approximate the unknown nonlinear dynamics, and the desired formation shape is achieved by designing appropriate potential functions. Moreover, the collision avoidance, velocity consensus, and region tracking are all considered in the controller. The stability of the multirobot system has been demonstrated based on the Lyapunov theorem. Finally, three numerical simulations show the effectiveness of the proposed formation control scheme to deal with the narrow space, loss of robots, and formation merging problems.

关键词： Dynamics

来源：评论

学校读者我要写书评

暂无评论

Landslide evolution state prediction and down-level control based on multi-task learning

引用

KNOWLEDGE-BASED SYSTEMS 2022年第0期238卷 107884-107884页

作者： Sun, Shu Wang, Xiaoping Li, Junnan Lian, Cheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat 1037 Luoyu Rd Wuhan Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control 1037 Luoyu Rd Wuhan Peoples R China Wuhan Univ Sci & Technol Sch Automat 122 Luoshi Rd Wuhan Peoples R China

In this paper, multi-task learning is introduced into the study of landslide evolution state prediction and control. Firstly, we define two landslide evolution states and propose a method of landslide evolution state level classification prediction. Specifically, we use Gaussian mixture model (GMM) to reconstruct labeled data sets and establish a landslide evolution state level prediction model based on Multi-Task Learning-Stacked Long-Short Term Memory (MTL-SLSTM), and then use task weight rules to design multi-task losses for network training. The parallel multi-step prediction of the evolution state is achieved by the above works. Secondly, considering the spatial-temporal correlation of different monitoring points on the same landslide, we analyze spatial-temporal data to construct spatial-temporal features, and design a multi-task correlation learning mechanism combined multi-task weight learning and multi-task relationship learning methods to construct spatial-temporal relation. The landslide multi-point prediction model based on Multi-Task Correlation Learning-Stacked Long Short Term Memory (MTCL-SLSTM) achieves single-step prediction of the evolution state of multiple monitoring points with high accuracy. Finally, according to the idea of neural direct inverse model control, we propose down-level control method based on the prediction of the landslide evolution state. We build an interval prediction network based on bootstrap method and model selection strategies, and then a safe rainfall interval predictor is trained offline. Moreover, we design an online landslide down-level control process combined the landslide evolution state level predictor, which realizes single-step control of single-point of the dangerous landslide. Furthermore, the effectiveness of the proposed method is verified on Baishuihe landslide.& nbsp;(c) 2021 Elsevier B.V. All rights reserved.

关键词： Landslide Evolution state prediction Spatial-temporal prediction Multi-task learning Landslide control Down-level control

来源：评论

学校读者我要写书评

暂无评论

Multi-level graph convolutional network with automatic graph learning for hyperspectral image classification

arXiv

引用

arXiv 2020年

作者： Wan, Sheng Gong, Chen Pan, Shirui Yang, Jie Yang, Jian PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Faculty of Information Technology Monash University ClaytonVIC3800 Australia

Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification. However, the current GCN-based methods treat graph construction and image classification as two separate tasks, which often results in suboptimal performance. Another defect of these methods is that they mainly focus on modeling the local pairwise importance between graph nodes while lack the capability to capture the global contextual information of HSI. In this paper, we propose a Multi-level GCN with Automatic Graph Learning method (MGCN-AGL) for HSI classification, which can automatically learn the graph information at both local and global levels. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions, which helps encode the spatial context to form the graph information at local level. Moreover, we utilize multiple pathways for local-level graph convolution, in order to leverage the merits from the diverse spatial context of HSI and to enhance the expressive power of the generated representations. To reconstruct the global contextual relations, our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level. Then inference can be performed along the reconstructed graph edges connecting faraway regions. Finally, the multi-level information is adaptively fused to generate the network output. In this means, the graph learning and image classification can be integrated into a unified framework and benefit each other. Extensive experiments have been conducted on three real-world hyperspectral datasets, which are shown to outperform the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： image classification

来源：评论

学校读者我要写书评

暂无评论

Electrified Vehicles and the Smart Grid: The ITS Perspective

引用

IEEE TRANSACTIONS ON intelligent TRANSPORTATION SYSTEMS 2014年第4期15卷 1388-1404页

作者： Cheng, Xiang Hu, Xiaoya Yang, Liuqing Husain, Iqbal Inoue, Koichi Krein, Philip Lefevre, Russell Li, Yaoyu Nishi, Hiroaki Taiber, Joachim G. Wang, Fei-Yue Zha, Yabing Gao, Wen Li, Zhengxi Peking Univ Beijing 100871 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Huazhong Univ Sci & Technol Key Lab Minist Educ Image Proc & Intelligent Control Sch Automat Wuhan 430074 Peoples R China Colorado State Univ Ft Collins CO 80523 USA N Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA Keio Univ Keio Adv Res Ctr Yokohama Kanagawa 2238522 Japan Keio Univ Dept Syst Design Engn Yokohama Kanagawa 2238522 Japan Univ Illinois Grainger Ctr Elect Machinery & Elect Dept Elect & Urbana IL 61801 USA Univ N Dakota Grand Forks ND 58202 USA Univ Texas Dallas Dept Mech Engn Richardson TX 75083 USA Clemson Univ Clemson SC 29634 USA Chinese Acad Sci Key Lab Complex Syst & Intelligence Sci Beijing 100864 Peoples R China Natl Univ Def Technol Sch Informat Syst & Management Changsha 410073 Hunan Peoples R China Peking Univ Beijing 100871 Peoples R China North China Univ Technol Beijing 100144 Peoples R China

Vehicle electrification is envisioned to be a significant component of the forthcoming smart grid. In this paper, a smart grid vision of the electric vehicles for the next 30 years and beyond is presented from six perspectives pertinent to intelligent transportation systems: 1) vehicles;2) infrastructure;3) travelers;4) systems, operations, and scenarios;5) communications;and 6) social, economic, and political.

关键词： Energy efficiency intelligent transportation systems smart grid

来源：评论

学校读者我要写书评

暂无评论

Sparse Prior Is Not All You Need: When Differential Directionality Meets Saliency Coherence for Infrared Small Target Detection

arXiv

引用

arXiv 2024年

作者： Zhou, Fei Fu, Maixia Qian, Yulei Yang, Jian Dai, Yimian Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China Nanjing Marine Radar Institute Nanjing China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Infrared small target detection is crucial for the efficacy of infrared search and tracking systems. Current tensor decomposition methods emphasize representing small targets with sparsity but struggle to separate targets from complex backgrounds due to insufficient use of intrinsic directional information and reduced target visibility during decomposition. To address these challenges, this study introduces a Sparse Differential Directionality prior (SDD) framework. SDD leverages the distinct directional characteristics of targets to differentiate them from the background, applying mixed sparse constraints on the differential directional images and continuity difference matrix of the temporal component, both derived from Tucker decomposition. We further enhance target detectability with a saliency coherence strategy that intensifies target contrast against the background during hierarchical decomposition. A Proximal Alternating Minimization-based (PAM) algorithm efficiently solves our proposed model. Experimental results on several real-world datasets validate our method’s effectiveness, outperforming ten state-of-the-art methods in target detection and clutter suppression. Our code is available at https://***/GrokCV/SDD. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

SCSQ-MDD: A Detection Approach for Multi-Style Strokes of Chinese Characters

SSRN

引用

SSRN 2023年

作者： Zhou, Tian Xie, Wu Zhang, Huimin Fan, Yong Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China School of Mechanical and Electrical Engineering Guilin University of Electronic Science and Technology Guilin China

In the Chinese character writing task of the robotic arms, the stroke category and position information should be extracted by object detection. The detection algorithms based on predefined anchor frames have difficulty in resolving the differences among many different styles of Chinese character strokes. While the deformable detection transformer (deformable DETR) algorithms without predefined anchor frames result in some invalid sampling points having no contribution to the feature update of the current reference point due to the random sampling of sampling points in the deformable attention module. These processes cause the effectiveness of correlation calculations between reference points in Chinese strokes and their surrounding sampled points is limited. So that the speed of vector learning stroke features in the detection head is reduced. In view of this problem, a new detection method of multi-style strokes of Chinese characters via SCSQ-MDD (Simple Conditional Spatial Query Mask Deformable DETR) is proposed in this paper. Firstly, a mask prediction layer is jointly determined using the shallow feature map of the Chinese character image and the query vector of the transformer encoder, which is used to filter the points with actual contribution and resample the points without contribution, so that the randomness of correlation calculation among reference points is solved. Secondly, by separating the content query and spatial query of the transformer deocder, the content embedding and spatial embedding can be separately focused on when cross-attention computations are performed. Thus the dependence of the prediction task on the content embedding is relaxed and the training process is simplified. Finally, the detection model without predefined anchor frames based on deformable DETR called SCSQ-MDD is constructed using the mask mechanism and the simple conditional spatial query mechanism, and trained and validated on a multi-style Chinese character stroke dataset

关键词： Object detection

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：