检索结果-内蒙古大学图书馆

Chinese Control Conference (CCC)

作者： Weidong Yan Zhaoliang Xu Yang Li The Department of Automation Science and Electrical Engineering Beihang University Beijing China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China

In this study, a multi-degree-of-freedom (Multi-DOF) robot (MDR) system based on a LightGBM-driven electroencephalogram (EEG) decoding model is designed and developed to assist subjects with hand motor dysfunction in their daily activities and neurorehabilitation. The system mainly consists of a motor imagery electroencephalogram (MI-EEG) evoking layer, an intention decoding layer, and an interaction executive layer. The MI-EEG evoking layer initially displays a virtual reality (VR) motion imagination scenario, instructing the subjects to imagine a real hand gripping movement, simultaneously collecting the electrical EEG signals and preprocessing the EEG signals. Secondly, in the intention decoding layer, a network combining temporal-spectral feature fusion and LightGBM (TSFF-LightGBM) for MI-BCI classification is used to more effectively boost brain decoding accuracy and decrease decoding time. Finally, in the interaction executive layer, the Multi-DOF wearable robot is developed to offer hand grasp motion kinesthetic feedback and visual feedback synced with MI. The following are the key benefits of the proposed MDR system: (1) We propose a new lightweight network structure more suitable for brain computer interface (BCI) interaction systems, achieving more accurate decoding and shorter identification time in different data sets, which helps to improve the practicability of the system and promote the practical clinical application of BCI rehabilitation technology. (2) We integrate BCI, VR, a wearable Multi-DOF robot, motion kinesthetic feedback, and visual feedback to improve human-machine interaction. Compared to the most recent investigations, the average accuracy of the MDR system on the publicly accessible datasets BCI IV 2a and HGD reached 75.89% and 93.53%, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Road Extraction Based on Deep Learning Using Sdgsat-1 Nighttime Light data

Road Extraction Based on Deep Learning Using Sdgsat-1 Nightt...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Qiyuan Xie Hui Li Linhai Jing Kongwen Zhang Zijin School of Geology and Mining Fuzhou University Fuzhou China International Research Center of Big Data for Sustainable Development Goals Beijing China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Hainan Key Laboratory of Earth Observation Hainan Aerospace Information Research Institute Wenchang China School of Computing University of the Fraser Valley Abbotsford Canada

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Previous road extraction research based on remote sensing data mainly used traditional optical remote sensing imagery acquired during the daytime, but some roads on the images were affected by problems such as building shadows and similar spectral features of road with other materials, which reduced the accuracy of road extraction. The Glimmer Imager (GI) equipped on the Sustainable Development Science Satellite 1 (SDGSAT-1) provides nighttime light (NTL) images with relatively high spatial resolution (RGB: 40m; PAN: 10m), which show detail of road networks. In order to explore the potential of the NTL data obtained from SDGSAT-1 on road extraction, a deep learning framework combining ResUnet and Dynamic Snake Convolution (DSC) module was proposed in this study. The experimental results demonstrated the effectiveness of SDGSAT-1 NTL on road extraction tasks and the extracted roads can be used for road dataset updating.

关键词： Deep learning Satellites Roads Soft sensors Feature extraction data mining Task analysis

来源：评论

学校读者我要写书评

暂无评论

D4M: dataset Distillation via Disentangled Diffusion Model

arXiv

引用

arXiv 2024年

作者： Su, Duo Hou, Junjie Gao, Weizhi Tian, Yingjie Tang, Bowen School of Computer Science and Technology UCAS United Kingdom Sino-Danish College UCAS United Kingdom Department of Computer Science NCSU United States School of Economics and Management UCAS United Kingdom Research Center on Fictitious Economy and Data Science CAS China Key Laboratory of Big Data Mining and Knowledge Management CAS China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation UCAS United Kingdom Institute of Computing Technology CAS China

dataset distillation offers a lightweight synthetic dataset for fast network training with promising test accuracy. To imitate the performance of the original dataset, most approaches employ bi-level optimization and the distillation space relies on the matching architecture. Nevertheless, these approaches either suffer significant computational costs on large-scale datasets or experience performance decline on cross-architectures. We advocate for designing an economical dataset distillation framework that is independent of the matching architectures. With empirical observations, we argue that constraining the consistency of the real and synthetic image spaces will enhance the cross-architecture generalization. Motivated by this, we introduce dataset Distillation via Disentangled Diffusion Model (D4M), an efficient framework for dataset distillation. Compared to architecture-dependent methods, D4M employs latent diffusion model to guarantee consistency and incorporates label information into category prototypes. The distilled datasets are versatile, eliminating the need for repeated generation of distinct datasets for various architectures. Through comprehensive experiments, D4M demonstrates superior performance and robust generalization, surpassing the SOTA methods across most aspects. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

An Effective Pull-based Congestion Control Protocol for Multi-Identifier Network 8

An Effective Pull-based Congestion Control Protocol for Mult...

引用

8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Yin, Bin Li, Hui Bai, He Wu, Zhengqi Lu, Ping Ye, Qiongwei Yang, Yong Wu, Jianping Shenzhen Graduate School Peking University Shenzhen China Foshan Sai Si Chan Technology Co Shenzhen China JiuJiang University Shenzhen China Foshan University Shenzhen China The Cloud Computing and It Institute of Zte Corporation Shenzhen China Yunnan University of Finance & Economics Yunnan China School of Mathematics and Big Data Foshan University Foshan China China Communications Services Construction Co.Ltd China

ISBN: (纸本)9781665450515

With the rapid development of the network, the IP network have exposed many issues, such as the exhausted IP addresses, security risks and inapplicability to the streaming media. To solve these issues, Multi-Identifier Network (MIN) is proposed as a novel network architecture with excellent features. In the MIN architecture, content can be retrieved from multiple sources and multiple paths. To fit the nature of MIN, congestion control protocols should consider the impact of in-network caching, multi-path forwarding. In this paper, we propose a practical congestion control protocol, which detects congestion based on CoDel algorithm and then notices consumers by explicitly marking data packets, so that consumers can adjust their interest sending rate. Meanwhile, it employs a caching strategy based on content popularity to reduce interest retransmission rate. Our simulation results show that the proposed scheme achieves a higher total throughput than existing work. © 2022 IEEE.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Toward Building Human-Like Sequential Memory Using Brain-Inspired Spiking Neural Models

引用

IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10143-10155页

作者： Malu Zhang Xiaoling Luo Jibin Wu Ammar Belatreche Siqi Cai Yang Yang Haizhou Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things Sichuan University of Science and Engineering Yibin China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Computer and Information Sciences Faculty of Engineering and Environment Northumbria University Newcastle upon Tyne U.K. Department of Electrical and Computer Engineering National University of Singapore Lower Kent Ridge Rd Singapore Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong (CUHK) Shenzhen China

The brain is able to acquire and store memories of everyday experiences in real-time. It can also selectively forget information to facilitate memory updating. However, our understanding of the underlying mechanisms and coordination of these processes within the brain remains limited. However, no existing artificial intelligence models have yet matched human-level capabilities in terms of memory storage and retrieval. This study introduces a brain-inspired spiking neural model that integrates the learning and forgetting processes of sequential memory. The proposed model closely mimics the distributed and sparse temporal coding observed in the biological neural system. It employs one-shot online learning for memory formation and uses biologically plausible mechanisms of neural oscillation and phase precession to retrieve memorized sequences reliably. In addition, an active forgetting mechanism is integrated into the spiking neural model, enabling memory removal, flexibility, and updating. The proposed memory model not only enhances our understanding of human memory processes but also provides a robust framework for addressing temporal modeling tasks.

关键词： Neurons Biological system modeling Computational modeling Brain modeling Dendrites (neurons) Oscillators Neural activity Membrane potentials Encoding Adaptation models

来源：评论

学校读者我要写书评

暂无评论

A Multi-Head Convolution Network with Attention Consistency for Facial Expression Recognition

A Multi-Head Convolution Network with Attention Consistency ...

引用

Chinese Control Conference (CCC)

作者： Wenkang Liu Mingyi Sun Yang Li the Department of Automation Science and Electrical Engineering Beihang University Beijing China the Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China

In recent years, the demand for facial expression recognition applications has increased rapidly, and its research has received extensive attention from researchers. However, the current recognition methods based on deep learning ignore the idea of multiple head attention and semantic consistency, resulting in the model only paying attention to the local area of the feature map. In addition, the model's inconsistent attention to the images before and after the flip, resulting the poor robustness, poor interpretation, and other shortcomings in the model. To address the above problems, we propose an Affinity Separation Loss (ASLoss), which improves the separability of samples through clustering. Moreover, a Separate Multi-head Attention block (SMA), and a Zonal Loss (ZLoss) are also designed to decentralize the model's attention. Experimental results demonstrate that our proposed MACNet method achieves competitive recognition performance on two public datasets RAF-DB and FERPlus.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Node-collaboration-informed Graph Convolutional Network for Precise Representation to Undirected Weighted Graphs

arXiv

引用

arXiv 2022年

作者： Wang, Ying Yuan, Ye Luo, Xin The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

An undirected weighted graph (UWG) is frequently adopted to describe the interactions among a solo set of nodes from real applications, such as the user contact frequency from a social network services system. A graph convolutional network (GCN) is widely adopted to perform representation learning to a UWG for subsequent pattern analysis tasks such as clustering or missing data estimation. However, existing GCNs mostly neglects the latent collaborative information hidden in its connected node pairs. To address this issue, this study proposes to model the node collaborations via a symmetric latent factor analysis model, and then regards it as a node-collaboration module for supplementing the collaboration loss in a GCN. Based on this idea, a Node-collaboration-informed Graph Convolutional Network (NGCN) is proposed with three-fold ideas: a) Learning latent collaborative information from the interaction of node pairs via a node-collaboration module;b) Building the residual connection and weighted representation propagation to obtain high representation capacity;and c) Implementing the model optimization in an end-to-end fashion to achieve precise representation to the target UWG. Empirical studies on UWGs emerging from real applications demonstrate that owing to its efficient incorporation of node-collaborations, the proposed NGCN significantly outperforms state-of-the-art GCNs in addressing the task of missing weight estimation. Meanwhile, its good scalability ensures its compatibility with more advanced GCN extensions, which will be further investigated in our future studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection 39

Look Inside for More: Internal Spatial Modality Perception f...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

A Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization Approach for Community Detection

arXiv

引用

arXiv 2023年

作者： Liu, Zhigang Luo, Xin College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

Community is a fundamental and critical characteristic of an undirected social network, making community detection be a vital yet thorny issue in network representation learning. A symmetric and non-negative matrix factorization (SNMF) model is frequently adopted to address this issue owing to its great interpretability and scalability. However, it adopts a single latent factor matrix to represent an undirected network for precisely representing its symmetry, which leads to loss of representation learning ability due to the reduced latent space. Motivated by this discovery, this paper proposes a novel Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization (CFS) model that adopts three-fold ideas: a) Representing a target undirected network with multiple latent factor matrices, thus preserving its representation learning capacity;b) Incorporating a symmetry-regularizer that preserves the symmetry of the learnt low-rank approximation to the adjacency matrix into the loss function, thus making the resultant detector well-aware of the target network's symmetry;and c) Introducing a graph-regularizer that preserves local invariance of the network's intrinsic geometry, thus making the achieved detector well-aware of community structure within the target network. Extensively empirical studies on eight real-world social networks from industrial applications demonstrate that the proposed CFS model significantly outperforms state-of-the-art models in achieving highly-accurate community detection results. Copyright © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Teaching and Learning of Representations on Graphs 23

Self-Supervised Teaching and Learning of Representations on ...

引用

2023 World Wide Web Conference, WWW 2023

作者： Wan, Liangtian Fu, Zhenqiang Sun, Lu Wang, Xianpeng Xu, Gang Yan, Xiaoran Xia, Feng Key Laboratory for Ubiquitous Network Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian China State Key Laboratory of Marine Resource Utilization in South China Sea School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China Research Center of Big Data Intelligence Research Institute of Artificial Intelligence Zhejiang Lab Hangzhou China School of Computing Technologies Rmit University Melbourne Australia

ISBN: (纸本)9781450394161

Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification. © 2023 ACM.

关键词： Classification (of information)

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：