检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Li, Xiaohuan Qin, Shaowen Tang, Xin Kang, Jiawen Ye, Jin Zhao, Zhonghua Niyato, Dusit Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Nanning530001 China School of Automation Guangdong University of Technology Guangzhou510006 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530000 China School of Computer and Electronic Information Guangxi University Nanning530000 China College of Computing and Data Science Nanyang Technological University Singapore

Industrial Metaverse leverages the Industrial Internet of Things (IIoT) to integrate data from diverse devices, employing federated learning and meta-computing to train models in a distributed manner while ensuring data privacy. Achieving an immersive experience for industrial Metaverse necessitates maintaining a balance between model quality and training latency. Consequently, a primary challenge in federated learning tasks is optimizing overall system performance by balancing model quality and training latency. This paper designs a satisfaction function that accounts for data size, Age of Information (AoI), and training latency. Additionally, the satisfaction function is incorporated into the utility functions to incentivize node participation in model training. We model the utility functions of servers and nodes as a two-stage Stackelberg game and employ a deep reinforcement learning approach to learn the Stackelberg equilibrium. This approach ensures balanced rewards and enhances the applicability of the incentive scheme for industrial Metaverse. Simulation results demonstrate that, under the same budget constraints, the proposed incentive scheme improves at least 23.7% utility compared to existing schemes without compromising model accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Edge-Enhanced Cascaded MRF for SAR Image Segmentation

引用

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Liu, Mengmeng Shang, Ronghua Liu, Kang Feng, Jie Wang, Chao Xu, Songhua Li, Yangyang Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Shaanxi Xi’an710071 China Xidian University School of Computer Science and Technology Shaanxi Xi’an710071 China Research Center for Data Hub and Security Zhejiang Lab Hangzhou311100 China The Second Affiliated Hospital of Xi’an Jiaotong University Department of Health Management Institute of Medical Artificial Intelligence Xi’an710004 China

Markov random fields (MRFs) effectively capture local contextual information by modeling the spatial dependencies between pixels, which helps highlight details and enhances segmentation smoothness. To fully exploit MRF for synthetic aperture radar (SAR) image segmentation, we propose a novel edge-enhanced cascaded MRF (ECMRF) approach. Specifically, we introduce multiple edge-constrained filters to emphasize SAR image boundaries and provide relatively clean features. Building on this, we present a cascaded MRF framework that sequentially integrates region-level and pixel-level segmentations with feature perturbation and fusion to generate the final segmentation output. The framework comprises four key components: 1) a region-level MRF (RMRF), regulated by edge features, to achieve precise region segmentation;2) a pixel-level MRF (PMRF) with selective label smoothing to refine edges and reduce noise clusters;3) equal-channel feature perturbation (EC-FP) to increase feature diversity;and 4) a random probability-based feature fusion (RP-FF) scheme to merge the input features. The experimental results demonstrate that our ECMRF outperforms six state-of-the-art comparable methods, underscoring its competitive performance. © 1980-2012 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

DRSC: Dual-Reweighted Siamese Contrastive Learning Network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced data

引用

IEEE Internet of Things Journal 2025年

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Collaborative Perception datasets for Autonomous Driving: A Review

arXiv

引用

arXiv 2025年

作者： Wang, Naibang Shang, Deyong Gong, Yan Hu, Xiaoxi Song, Ziying Yang, Lei Huang, Yuhan Wang, Xiaoyu Lu, Jianli Beijing100083 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China State Key Laboratory of Intelligent Green Vehicle and Mobility Tsinghua University Beijing100084 China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence School of Computer Science and Technology Beijing Jiaotong University China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore School of Mechatronics Engineering Harbin Institute of Technology 92 West Dazhi St. Harbin China Department of Electronic & Electrical Engineering University of Bath United Kingdom

Collaborative perception has attracted growing interest from academia and industry due to its potential to enhance perception accuracy, safety, and robustness in autonomous driving through multi-agent information fusion. With the advancement of Vehicle-to-Everything (V2X) communication, numerous collaborative perception datasets have emerged, varying in cooperation paradigms, sensor configurations, data sources, and application scenarios. However, the absence of systematic summarization and comparative analysis hinders effective resource utilization and standardization of model evaluation. As the first comprehensive review focused on collaborative perception datasets, this work reviews and compares existing resources from a multi-dimensional perspective. We categorize datasets based on cooperation paradigms, examine their data sources and scenarios, and analyze sensor modalities and supported tasks. A detailed comparative analysis is conducted across multiple dimensions. We also outline key challenges and future directions, including dataset scalability, diversity, domain adaptation, standardization, privacy, and the integration of large language models. To support ongoing research, we provide a continuously updated online repository of collaborative perception datasets and related literature: https://***/frankwnb/Collaborative-Perceptio n-datasets-for-Autonomous-Driving. © 2025, CC BY-SA.

关键词： Sensor data fusion

来源：评论

学校读者我要写书评

暂无评论

Identifying eclipsing binary stars with TESS data based on a new hybrid deep learning model

arXiv

引用

arXiv 2025年

作者： Shan, Ying Chen, Jing Zhang, Zichong Wang, Liang Zou, Zhiqiang Li, Min College of Computer Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China Nanjing Institute of Astronomical and Optics & Technology Chinese Academy of Sciences Nanjing210042 China CAS Key Laboratory of Astronomical Optics & Technology Nanjing Institute of Astronomical Optics & Technology Nanjing210042 China University of Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Jiangsu Nanjing211135 China

Eclipsing binary systems (EBs), as foundational objects in stellar astrophysics, have garnered significant attention in recent years. These systems exhibit periodic decreases in light intensity when one star obscures the other from the observer’s perspective, producing characteristic light curves (LCs). With the advent of the Transiting Exoplanet Survey Satellite (TESS), a vast repository of stellar LCs has become available, offering unprecedented opportunities for discovering new EBs. To efficiently identify such systems, we propose a novel method that combines LC data and generalized Lomb-Scargle periodograms (GLS) data to classify EBs. At the core of this method is CNN Attention LSTM Net (CALNet), a hybrid deep learning model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and an Attention Mechanism based on the Convolutional Block Attention Module (CBAM). We collected 4,225 EB samples, utilizing their 2-minute cadence LCs for model training and validation. CALNet achieved a recall rate of 99.1%, demonstrating its robustness and effectiveness. Applying it to TESS 2-minute LCs from Sectors 1 to 74, we identified 9,351 new EBs after manual visual inspection, significantly expanding the known sample size. This work highlights the potential of advanced deep-learning techniques in large-scale astronomical surveys and provides a valuable resource for further studies on EBs. © 2025, CC BY-SA.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Efficient Adaptive Label Refinement for Label Noise Learning

arXiv

引用

arXiv 2025年

作者： Zhang, Wenzhen Cheng, Debo Lu, Guangquan Zhou, Bo Li, Jiaye Zhang, Shichao School of Computer Science and Engineering Guangxi Normal University Guangxi Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guangxi Guilin541004 China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guangxi Guilin541004 China UniSA STEM University of South Australia AdelaideSA5095 Australia Guangxi Collaborative Innovation Center of Modern Sericulture and Silk Hechi University Guangxi Hechi546300 China The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Hangzhou310027 China

Deep neural networks are highly susceptible to overfitting noisy labels, which leads to degraded performance. Existing methods address this issue by employing manually defined criteria, aiming to achieve optimal partitioning in each iteration to avoid fitting noisy labels while thoroughly learning clean samples. However, this often results in overly complex and difficult-to-train models. To address this issue, we decouple the tasks of avoiding fitting incorrect labels and thoroughly learning clean samples and propose a simple yet highly applicable method called Adaptive Label Refinement (ALR). First, inspired by label refurbishment techniques, we update the original hard labels to soft labels using the model’s predictions to reduce the risk of fitting incorrect labels. Then, by introducing the entropy loss, we gradually ‘harden’ the high-confidence soft labels, guiding the model to better learn from clean samples. This approach is simple and efficient, requiring no prior knowledge of noise or auxiliary datasets, making it more accessible compared to Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Exfiltration-resistant Proxy Re-Encryption for IoT data Sharing in Unreliable Clouds

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhou, Yuyang Zhao, Liang Li, Fagen Takagi, Tsuyoshi School of Cyberspace Security Hainan University Haikou570000 China Department of Mathematical Informatics Graduate School of Information Science and Technology The university of Tokyo Tokyo113-8656 Japan School of Cyber Science and Engineering Sichuan University Chengdu610065 China Key Laboratory of Data Protection and Intelligent Management [Sichuan University Ministry of Education China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

The sharing of Internet of Things (IoT) data plays an extensive role in our everyday lives. Exploring secure and efficient methods for data sharing is a prominent area of research. Proxy re-encryption (PRE) in the cloud provides a solution. However, traditional PRE schemes are vulnerable to algorithm substitution attacks (ASA). Moreover, in most PRE schemes, the proxy can know the relevant identity information of the data owner or the data recipient through the re-encryption key, and some current PRE schemes cannot guarantee strong resistance to collision. To address the limitations of traditional PRE schemes and offer a robust solution for secure and efficient data sharing in IoT application, we propose a reverse firewall for proxy re-encryption (PRE-RF) scheme, which we implement using the JPBC library. The security analysis reveals that the PRE-RF scheme offers effective resistance against ASA, while also providing strong collision security, chosen plaintext attack security, and key-private security. Compared to similar PRE schemes with more security features, our scheme reduces the length of the re-encryption keys and ciphertext, thereby reducing the storage cost of the system;also, our scheme’s computational cost is lower, thereby improving the operational efficiency of the system. Furthermore, as security levels rise, the portion of time dedicated to reverse firewall operations within our scheme diminishes. These advantages collectively render it a secure and efficient choice for sharing IoT data in unreliable cloud environments. © 2004-2012 IEEE All rights reserved.

关键词： Ciphertext

来源：评论

学校读者我要写书评

暂无评论

FDSR-INT: A Flexible On-Demand In-Band Telemetry Approach for Aerial Computing Networks

引用

IEEE Internet of Things Journal 2025年

作者： Xu, Jinghan Xu, Xiaolong Zhao, Juan Gao, Honghao Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing The School of Computer Science Nanjing210023 China Jinling Institute of Technology School of Computer Science Nanjing211169 China Shanghai University School of Computer Engineering and Science Shanghai200444 China Gachon University College of Future Industry Gyeonggi-Do 461-701 Korea Republic of

In-band network telemetry (INT) is a new network measurement technique that provides real-time, fine-grained packet-level network measurements. However, standard INT lacks the flexibility to perform configurable on-demand network measurements. In this work, we propose a flexible on-demand network measurement mechanism (FDSR-INT) based on a dual bitmap by combining the programmability of segment routing based on IPv6 (SRv6) and the telemetry efficiency of the INT to achieve customizable network measurements. By designing the dual bitmap, the telemetry information of personalized probe nodes is supported, and the telemetry efficiency is improved. SRv6 is employed to direct measurement probe packets, ensuring coverage of a specified set of network targets. Its inherent programmability enables INT to conduct customized per-hop network measurements. The designed flexible INT field structure, which appends traceability information along with telemetry information, reduces the information traceability overhead in the control plane and improves the network compatibility of the mechanism. We solve the optimal probing path by solving a traveler problem in an auxiliary graph and design a greedy pathcutting algorithm to maximize the number of nodes for single packet probing while satisfying the packet length constraints to improve the success rate of the probing task. Finally, we implemented FDSR-INT using P4 and verified its performance experimentally in the constructed *** integrated simulation dynamic environment. FDSR-INT saves 30% of the data plane bandwidth and 54% of the data plane bandwidth on *** interfaces compared with SONM-SR-INT, etc. Furthermore, it has low control plane processing overhead and probe path transmission overhead. © 2014 IEEE.

关键词： Bandwidth

来源：评论

学校读者我要写书评

暂无评论

Spin-orbit torque effect in silicon-based sputtered Mn_(3)Sn film

引用

Chinese Physics B 2024年第10期33卷 394-399页

作者： Sha Lu Dequan Meng Adnan Khan Ziao Wang Shiwei Chen Shiheng Liang School of Physics Hubei UniversityWuhan 430062China Key Laboratory for Intelligent Sensing System and Security of Ministry of Education Wuhan 430062China

Noncollinear antiferromagnet Mn_(3)Sn has shown remarkable efficiency in charge-spin conversion,a novel magnetic spin Hall effect,and a stable topological antiferromagnetic state,which has resulted in great interest from researchers in the field of spin-orbit *** research has primarily focused on the spin-orbit torque effect of epitaxially grown noncollinear antiferromagnet Mn_(3)Sn ***,this method is not suitable for large-scale industrial *** this study,amorphous Mn_(3)Sn films and Mn_(3)Sn/Py heterostructures were prepared using magnetron sputtering on silicon *** spin-torque ferromagnetic resonance measurement demonstrated that only the conventional spin-orbit torque effect generated by in-plane polarized spin currents existed in the Mn_(3)Sn/Py heterostructure,with a spin-orbit torque efficiency of ***,we prepared the perpendicular magnetized Mn_(3)Sn/CoTb heterostructure based on amorphous Mn_(3)Sn film,where the spin-orbit torque driven perpendicular magnetization switching was achieved with a lower critical switching current density(3.9×10^(7)A/cm^(2))compared to Ta/CoTb *** research reveals the spin-orbit torque effect of amorphous Mn_(3)Sn films and establishes a foundation for further advancement in the practical application of Mn_(3)Sn materials in spintronic devices.

关键词： spintronics noncollinear antiferromagnetism spin-orbit torque

来源：评论

学校读者我要写书评

暂无评论

MMAGL: Multiobjective Multiview Attributed Graph Learning for Joint Clustering of Hyperspectral and LiDAR data

引用

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Zijia Cai, Yaoming Gong, Wenyin Liu, Xiaobo Zeng, Cheng Yu, Gan Hubei University School of Artificial Intelligence Wuhan430078 China Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education Wuhan430078 China Zhongnan University of Economics and Law School of Information Engineering Wuhan430073 China China University of Geosciences School of Computer Science Wuhan430074 China China University of Geosciences School of Automation Wuhan430074 China Fuyang Normal University College of Information Engineering Fuyang236037 China

The joint clustering of multimodal remote sensing (RS) data represents a multiobjective optimization challenge involving conflicting modality-specific objectives and diverse regularization objectives. Current approaches to multiview subspace clustering (MVSC) often oversimplify this task by transforming it into a weighted single-objective optimization problem, neglecting the intricate interactions between objectives and leading to suboptimal subspace representations. The presence of quadratic decision variables in MVSC renders direct application on large-scale RS data impracticable using multiobjective evolutionary algorithms (MOEAs). To overcome this challenge, we propose a novel MVSC method termed multiobjective multiview attributed graph learning (MMAGL). Instead of optimizing every self-representation coefficient individually, our method transforms MVSC into a link prediction task over a sparse attributed graph that fuses different modalities. We incorporate superpixel-based sample reduction and proximity-based population coding, leveraging spatial and structural priors, respectively. This results in a significantly compressed decision space, enabling optimization with MOEAs. To fully exploit node attributes and the graph structure, we redefine self-representation using contrastive learning and introduce an efficient graph filtering (GF) through a generalized spectral graph convolution, enhancing clustering discriminability. The proposed MMAGL constitutes a hybrid and versatile framework, adaptable to any MOEA. Extensive experimental evaluations demonstrate that our MMAGL method surpasses the current state-of-the-art on multimodal RS benchmarks (e.g., with nearly 2% gain on Trento and 3% on Houston) on overall accuracy. © 2025 IEEE.

关键词： Proximity sensors

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：