检索结果-内蒙古大学图书馆

Deep Graph Reinforcement Learning for UAV-Enabled Multi-User Secure Communications

IEEE Transactions on Mobile Computing 2025年

作者： Tang, Xiao Zhao, Kexin Shen, Chao Du, Qinghe Wang, Yichen Niyato, Dusit Han, Zhu Research & Development Institute Northwestern Polytechnical University Shenzhen518063 China National Key Laboratory of Wireless Communications Chengdu611731 China State Key Laboratory of Intelligent Game Taicang215400 China Northwestern Polytechinical University School of Electronics and Information Xi'an710072 China Xi'an Jiaotong University Faculty of Electronics and Information Engineering Xi'an710049 China Nanyang Technological University College of Computing and Data Science Singapore University of Houston Department of Electrical and Computer Engineering Houston77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

While unmanned aerial vehicles (UAVs) with flexible mobility are envisioned to enhance physical layer security in wireless communications, the efficient security design that adapts to such high network dynamics is rather challenging. The conventional approaches extended from optimization perspectives are usually quite involved, especially when jointly considering factors in different scales such as deployment and transmission in UAV-related scenarios. In this paper, we address the UAV-enabled multi-user secure communications by proposing a deep graph reinforcement learning framework. Specifically, we reinterpret the security beamforming as a graph neural network (GNN) learning task, where mutual interference among users is managed through the message-passing mechanism. Then, the UAV deployment is obtained through soft actor-critic reinforcement learning, where the GNN-based security beamforming is exploited to guide the deployment strategy update. Simulation results demonstrate that the proposed approach achieves near-optimal security performance and significantly enhances the efficiency of strategy determination. Moreover, the deep graph reinforcement learning framework offers a scalable solution, adaptable to various network scenarios and configurations, establishing a robust basis for information security in UAV-enabled communications. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Learning robust rule representations for abstract reasoning via internal inferences 22

Learning robust rule representations for abstract reasoning ...

引用

Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Wenbo Zhang Likai Tang Site Mo Sen Song Xianggen Liu College of Computer Science Sichuan University and Department of Biomedical Engineering Tsinghua University and Laboratory of Brain and Intelligence Tsinghua University Department of Biomedical Engineering Tsinghua University and Laboratory of Brain and Intelligence Tsinghua University College of Electrical Engineering Sichuan University College of Computer Science Sichuan University

ISBN: (纸本)9781713871088

Abstract reasoning, as one of the hallmarks of human intelligence, involves collecting information, identifying abstract rules, and applying the rules to solve new problems. Although neural networks have achieved human-level performances in several tasks, the abstract reasoning techniques still far lag behind due to the complexity of learning and applying the logic rules, especially in an unsupervised manner. In this work, we propose a novel framework, ARII, that learns rule representations for Abstract Reasoning via Internal Inferences. The key idea is to repeatedly apply a rule to different instances in hope of having a comprehensive understanding (i.e., representations) of the rule. Specifically, ARII consists of a rule encoder, a reasoner, and an internal referrer. Based on the representations produced by the rule encoder, the reasoner draws the conclusion while the referrer performs internal inferences to regularize rule representations to be robust and generalizable. We evaluate ARII on two benchmark datasets, including PGM and I-RAVEN. We observe that ARII achieves new state-of-the-art records on the majority of the reasoning tasks, including most of the generalization tests in PGM.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimizing resource allocation in flex-grid entanglement distribution networks

Optimizing resource allocation in flex-grid entanglement dis...

引用

2022 IEEE Photonics Conference, IPC 2022

作者： Alnas, Jude Alshowkan, Muneer Nageswara Rao, S.V. Peters, Nicholas A. Lukens, Joseph M. University of Alabama Department of Electrical and Computer Engineering TuscaloosaAL35401 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States Advanced Computing Methods Section Oak Ridge National Laboratory Oak RidgeTN37831 United States

We introduce a model for the distribution of frequency-polarization hyper-entangled photon pairs in a flexible-grid optical network. In order to optimize entanglement fidelity and entangled bit rate, we apply a geneti... 详细信息

ISBN: (数字)9781665434874

ISBN: (纸本)9781665434874

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Graph Prompt Learning: A Comprehensive Survey and Beyond

arXiv

引用

arXiv 2023年

作者： Sun, Xiangguo Zhang, Jiawen Wu, Xixi Cheng, Hong Xiong, Yun Li, Jia Department of Systems Engineering and Engineering Management Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong Hong Kong University of Science and Technology Guangzhou China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration with graph data, a cornerstone in our interconnected world, remains nascent. This paper presents a pioneering survey on the emerging domain of graph prompts in AGI, addressing key challenges and opportunities in harnessing graph data for AGI applications. Despite substantial advancements in AGI across natural language processing and computer vision, the application to graph data is relatively underexplored. This survey critically evaluates the current landscape of AGI in handling graph data, highlighting the distinct challenges in cross-modality, cross-domain, and cross-task applications specific to graphs. Our work is the first to propose a unified framework for understanding graph prompt learning, offering clarity on prompt tokens, token structures, and insertion patterns in the graph domain. We delve into the intrinsic properties of graph prompts, exploring their flexibility, expressiveness, and interplay with existing graph models. A comprehensive taxonomy categorizes over 100 works in this field, aligning them with pre-training tasks across node-level, edge-level, and graph-level objectives. Additionally, we present, ProG, a Python library, and an accompanying website, to support and advance research in graph prompting. The survey culminates in a discussion of current challenges and future directions, offering a roadmap for research in graph prompting within AGI. Through this comprehensive analysis, we aim to catalyze further exploration and practical applications of AGI in graph data, underlining its potential to reshape AGI fields and beyond. ProG and the website can be accessed by https: //***/WxxShirley/Awesome-Graph-Prompt, and https://***/sheldonresearch/ProG, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

Contributions of Industry 4.0 to resilience achievement in the context of COVID-19 pandemic

引用

IFAC-PapersOnLine 2022年第10期55卷 3226-3231页

作者： Saloua Said Hafida Bouloiz Maryam Gallab Systems engineering and decision support laboratory Ibn Zohr University ENSA Agadir Morocco. MIS-LISTD Laboratory Computer Science Department Mines-Rabat School (ENSMR) Rabat Morocco.

In the totally unprecedented context of the COVID-19 health crisis, the widespread adoption of Industry 4.0 technologies, and the great interest in resilience, have been stronger than ever. Within this framework, the present paper outlines the involvement of technologies emerging from the fourth industrial revolution in the fight against the epidemic expansion, and the results of this implication in terms of strengthening and achieving resilience in diverse fields. In order to gain a fuller understanding of these points, fourteen resilience domains related to the COVID-19 pandemic are defined. On the other hand, the third section of this paper digs into the literature to expose a variety of Industry 4.0 solutions developed to cope with the sanitary crisis. Afterwards, a fuzzy cognitive map is elaborated, using mental modeler, in order to emphasize the causal links between Industry 4.0 technologies and resilience domains. Subsequently, a simulation of this model is performed to evaluate the contribution of an optimized joint use of Industry 4.0 core technologies in the achievement of resilience in its different dimensions during the COVID-19 pandemic, and to discuss how the identified gaps or weaknesses can be addressed.

关键词： COVID-19 Fuzzy Cognitive Mapping Industry 4.0 Modeling Resilience Simulation

来源：评论

学校读者我要写书评

暂无评论

Piezoelectric Nanocavity Interface for Strong Coupling between a Superconducting Circuit, Phonon, and Spin

引用

Physical Review Applied 2023年第6期19卷 064051-064051页

作者： Hamza Raniwala Stefan Krastanov Lisa Hackett Matt Eichenfield Dirk R. Englund Matthew E. Trusheim Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Sandia National Laboratories Albuqurque New Mexico USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA DEVCOM Army Research Laboratory Adelphi Maryland 20783 USA

We introduce a hybrid tripartite quantum system for strong coupling between a semiconductor spin, a mechanical phonon, and a microwave excitation of a superconducting circuit. Consisting of a piezoelectric resonator with an integrated diamond strain concentrator, this system achieves microwave-acoustic and spin-acoustic coupling rates of approximately megahertz or greater, allowing simultaneous ultrahigh cooperativities (approximately 103 and approximately 102, respectively). From finite-element modeling and master-equation simulations, we estimate superconducting-circuit-to-spin quantum state transfer fidelities exceeding 0.95 on the basis of separately demonstrated device parameters. We anticipate that this device will enable hybrid quantum architectures that leverage the advantages of both superconducting circuits and solid-state spins for information processing, memory, and networking.

关键词： Optomechanics Piezoelectric devices Quantum information with hybrid systems Quantum interconnects Semiconductor quantum optics Superconducting quantum optics Transducers

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of MIMO-HARQ Assisted V2V Communications With Keyhole Effect

arXiv

引用

arXiv 2022年

作者： Zhang, Huan Liao, Zhengtao Shi, Zheng Yang, Guanghua Dou, Qingping Ma, Shaodan The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China

Vehicle-to-vehicle (V2V) communications under dense urban environments usually experience severe keyhole fading effect especially for multi-input multi-output (MIMO) channels, which degrades the capacity and outage performance due to the rank deficiency. To avoid these, the integration of MIMO and hybrid automatic repeat request (HARQ) is proposed to assist V2V communications in this paper. By using the methods of integral transforms, the outage probabilities are derived in closed-form for different HARQ-assisted schemes, including Type I-HARQ, HARQ with chase combining (HARQ-CC), and HARQ with incremental redundancy (HARQ-IR). With the results, meaningful insights are gained by conducting the asymptotic outage analysis. Specifically, it is revealed that full time diversity order can be achieved, while full spatial diversity order is unreachable as compared to MIMO-HARQ systems without keyhole effect. Moreover, we prove that the asymptotic outage probability is a monotonically increasing and convex function of the transmission rate. More importantly, although HARQ-IR performs better than HARQ-CC owing to its higher coding complexity, this advantage becomes negligible in the large-scale array regime. Finally, the numerical results are verified by Monte-Carlo simulations along with some in-depth discussions. © 2022, CC BY.

关键词： Functions

来源：评论

学校读者我要写书评

暂无评论

Unpaired Quad-Path Cycle Consistent Adversarial Networks for Single Image Defogging

SSRN

引用

SSRN 2023年

作者： Liu, Wei Chen, Cheng Jiang, Rui Lu, Tao Xiong, Zixiang School of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China Department of Electrical and Computer Engineering Texas A&M University College StationTX77843 United States

Adversarial learning-based image defogging methods have been extensively studied in computer vision due to their remarkable performance. However, most existing methods have limited defogging capabilities for real cases because they are trained on the paired clear and synthesized foggy images of the same scenes. In addition, they have limitations in preserving vivid color and rich textual details in defogging. To address these issues, we develop a novel generative adversarial network, called quad-path cycle consistent adversarial network (QPC-Net), for single image defogging. QPC-Net consists of a Fog2Fogfree block and a Fogfree2Fog block. In each block, there are three learning-based modules, namely, fog removal, color-texture recovery, and fog synthetic, which sequentially compose dual-path that constrain each other to generate high quality images. Specifically, the color-texture recovery model is designed to exploit the self-similarity of texture and structure information by learning the holistic channel-spatial feature correlations between the foggy image with its several derived images. Moreover, in the fog synthetic module, we utilize the atmospheric scattering model to guide it to improve the generative quality by focusing on an atmospheric light optimization with a novel sky segmentation network. Extensive experiments on both synthetic and real-world datasets show that QPC-Net outperforms state-of-the-art defogging methods in terms of quantitative accuracy and subjective visual quality. © 2023, The Authors. All rights reserved.

关键词： Fog

来源：评论

学校读者我要写书评

暂无评论

The Goldilocks Framework: Towards Selecting the Optimal Approach to Conducting AI Projects

The Goldilocks Framework: Towards Selecting the Optimal Appr...

引用

AI engineering - Software engineering for AI (WAIN), IEEE/ACM Workshop on

作者： Rimman Dzhusupova Jan Bosch Helena Holmströ m Olsson Dept. of Electrical &#x0026 Instrumentation Control &#x0026 Safety Systems McDermott The Hague The Netherlands Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Media Technology Malm&#x00F6 University Malm&#x00F6 Sweden

Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies’ industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hesitant bipolar fuzzy set-based decision system for electric vehicle charging station location planning

引用

Sustainable Energy, Grids and Networks 2025年 43卷

作者： Thilagasree, Chakkarapani Sumathi Jayakumar, Thippan Ramya, Lakshmanaraj Suvitha, Krishnan Pamucar, Dragan Pedrycz, Witold Kureethara, Joseph Varghese Department of Mathematics Sri Ramakrishna Mission Vidyalaya College of Arts and Science Coimbatore India Department of Mathematics Bharathiar University Coimbatore641 046 India Centre for Nonlinear Systems Chennai Institute of Technology Tamilnadu Chennai India Department of Applied Mathematical Science College of Science and Technology Korea University Sejong Korea Republic of Yuan Ze University College of Engineering Department of Industrial Engineering and Managemente Taoyuan City Taiwan Industrial Engineering Department Faculty of Engineering and Natural Sciences İstinye University İstanbul Turkey Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada Bangalore India

The selection of electric vehicle (EV) charging station locations is a critical challenge that significantly affects the growth and acceptance of the EV industry. As EVs offer a sustainable solution to fossil fuel depletion and environmental pollution, identifying optimal charging station sites involves dealing with uncertain, inconsistent, and conflicting criteria. To address these challenges, this paper presents an innovative decision-making framework based on Hesitant Bipolar-Valued Fuzzy Sets (HBVFSs), which account for both positive and negative hesitant membership values to better model uncertainty in expert judgments. A novel hybrid Multi-Criteria Decision-Making (MCDM) technique is proposed, combining the Step-wise Weight Assessment Ratio Analysis (SWARA) and Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA) methods to determine robust criteria weights within the HBVFS environment. The Preference Ranking Organization METHod for Enrichment Evaluation II (PROMETHEE-II) is employed for the final site ranking. This integrated approach enables a more comprehensive and reliable evaluation of potential locations by incorporating both qualitative and quantitative factors. The proposed methodology has practical applications in real-world infrastructure planning and supports more resilient decision-making in sustainable transportation networks. The results demonstrate the model's effectiveness and adaptability in addressing the site selection problem under uncertainty. © 2025 Elsevier Ltd

关键词： Hybrid vehicles

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：