检索结果-内蒙古大学图书馆

A hybrid boundary integral-PDE approach for the approximation of the demagnetization potential in micromagnetics

Advances in Computational Mathematics 2025年第3期51卷 1-26页

作者： Arjmand, Doghonay Calzada, Víctor Martínez Department of Information Technology Division of Scientific Computing Uppsala University Uppsala Sweden Departamento de Ingeniería Aeronáutica Universidad Politécnica Metropolitana de Hidalgo Hidalgo México

The demagnetization field in micromagnetism is given as the gradient of a potential that solves a partial differential equation (PDE) posed in $$\mathbb {R}^d$$ . In its most general form, this PDE is supplied with continuity condition on the boundary of the magnetic domain, and the equation includes a discontinuity in the gradient of the potential over the boundary. Typical numerical algorithms to solve this problem rely on the representation of the potential via the Green’s function, where a volume and a boundary integral terms need to be accurately approximated. From a computational point of view, the volume integral dominates the computational cost and can be difficult to approximate due to the singularities of the Green’s function. In this article, we propose a hybrid model, where the overall potential can be approximated by solving two uncoupled PDEs posed in bounded domains, whereby the boundary conditions of one of the PDEs are obtained by a low cost boundary integral. Moreover, we provide a convergence analysis of the method under two separate theoretical settings: periodic magnetization and high-frequency magnetization. Numerical examples are given to verify the convergence rates.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Novel Technique for Image Captioning Based on Hierarchical Clustering and Deep Learning

引用

SN Computer Science 2025年第4期6卷 1-13页

作者： Rahman, Rizwan Ur Kumar, Pavan Mohan, Aditya Aziz, Rabia Musheer Tomar, Deepak Singh School of Computing Science and Engineering VIT Bhopal University Kothrikalan Sehore 466114 India School of Advanced Science and Languages VIT Bhopal University Kothrikalan Sehore 466114 India Department of CSE and IT Jay Pee University of Information Technology Solan 173234 India State Planning Institute (New Division) Lucknow 226001 India Department of CSE and IT Maulana Azad National Institute of Technology Bhopal 462003 India

The advent of deep learning has brought about significant progress in the field of computer vision, leading to its extensive application in various domains. One of the most important tasks in computer vision is image captioning, which is essential to enable machines to understand and describe visual content in natural language. Applications ranging from improving user experiences in digital media to assistive devices for the blind or visually challenged require this capability. In this paper, we introduce a novel method for accomplishing the image-captioning task with a focus on data reduction through clustering techniques. Our approach aims to maintain or surpass the accuracy achieved by prior studies. Our unique approach uses clustering algorithms to reduce data and addresses the issues of data and computing intensity. It is expected to improve the accuracy and efficiency of picture captioning models. Additionally, we investigate the outcomes of utilizing single long short-term memory (LSTM) as opposed to stacked LSTMs for generating captions. Finally, the MS-COCO benchmark image dataset is used to analyze the performance of proposed approaches, their contributions, and relevance are highlighted, emphasizing the significance of the proposed approach and shedding light on a potential research direction for the image captioning approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks Deep learning Hierarchical clustering Image captioning Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Multi-Agent DRL for Queue-Aware Task Offloading in Hierarchical MEC-Enabled Air-Ground Networks

arXiv

引用

arXiv 2025年

作者： Hevesli, Muhammet Seid, Abegaz Mohammed Erbad, Aiman Abdallah, Mohamed Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar College of Engineering Qatar University Doha Qatar

Mobile edge computing (MEC)-enabled air-ground networks advance 6G wireless networks by utilizing aerial base stations (ABSs) such as unmanned aerial vehicles (UAVs) and high altitude platform stations (HAPS) to provide dynamic services to ground IoT devices (IoTDs). These IoTDs support real-time applications like multimedia and Metaverse services, which demand high computational resources and strict quality of service (QoS) guarantees, specifically in terms of latency and efficient task queue management. However, IoTDs often face constraints in energy and computational power, requiring efficient queue management and task scheduling to maintain QoS. To address these challenges, UAVs and HAPS are deployed to supplement the computational limitations of IoTDs by offloading tasks for distributed processing. Due to UAVs’ resource limitations, particularly in terms of power and coverage area, HAPS are used to enhance their capabilities and extend coverage. Overloaded UAVs may relay tasks to HAPS, creating a multi-tier computing system. This paper addresses the overall energy minimization problem in the MEC-enabled air-ground integrated network (MAGIN) by optimizing UAV trajectories, computing resource allocation, and queue-aware task offloading decisions. The optimization problem is highly complex due to the nonconvex and nonlinear nature of this hierarchical system, which traditional methods cannot effectively solve. To tackle this, we reformulate the problem as a multi-agent Markov decision process (MDP) with continuous action spaces and heterogeneous agents. We propose a novel variant of multi-agent proximal policy optimization (MAPPO) with Beta distribution (MAPPO-BD) to solve this problem. Extensive simulations show that MAPPO-BD significantly outperforms other baselines, achieving superior energy savings and efficient resource management in MAGIN, while adhering to constraints related to queue delays and edge computing capabilities. © 2025, CC BY.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Three-dimensional surface topography analysis of die-sinking electrical discharge machined eco-friendly beryllium-free copper alloys

引用

The International Journal of Advanced Manufacturing technology 2025年 1-43页

作者： Vijayan, Vipin Saju, Kaiprappady Kunchu Sundaresan, Jithin Division of Mechanical Engineering School of Engineering Cochin University of Science and Technology Kochi India Department of Mechanical Engineering Muthoot Institute of Technology and Science Ernakulam India Business Information Systems Program Information Systems Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

Beryllium-copper alloys, the most widely used copper alloys, are utilised extensively in diverse sectors, including the electrical, electronics, instrumentation, metallurgy, aerospace, automotive, petrochemical, machinery manufacturing, and die and mould-making industries. The main drawback of these alloys is that they produce the toxic component, beryllium oxide, which can lead to a chronic lung disease known as berylliosis. Beryllium-free copper alloys, such as copper-nickel-silicon-chromium, are eco-friendly, less costly, and possess properties similar to those of beryllium-copper alloys. Hence, they are now replacing beryllium-copper alloys in the applications mentioned earlier. Due to their high strength and hardness, these alloys are often fabricated into components using unconventional machining techniques, such as electrical discharge machining. Electrical discharge machining is particularly advantageous in industrial applications where precisely controlled random surface textures on these alloys are required. However, despite the industrial significance, research on the electrical discharge machining process of copper-nickel-silicon-chromium alloys is scarce. Therefore, the current work aims to address this research gap by conducting an experimental investigation of the random surfaces generated on copper-nickel-silicon-chromium alloy components after the die-sinking electrical discharge machining process through a comprehensive three-dimensional surface topography analysis. Three-dimensional surface topography parameters overcome the drawbacks of two-dimensional roughness parameters by considering the majority of surface points. This work investigates the effects of input factors, including electrode material, dielectric fluid material, flushing condition, and current on nearly all relevant areal texture (3D) parameters. ANOVA is performed to study the level of significance of each input parameter. The regression analyses reveal that current is the most si

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Lightweight Committee-Based Approach for Privacy-Preserving Federated Learning

A Lightweight Committee-Based Approach for Privacy-Preservin...

引用

Consumer Communications and Networking Conference, CCNC IEEE

作者： Elmahdi Bentafat Noureddine Lasla Abdullatif Albaseer Mohamed Abdallah Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar National School of Artificial Intelligence (ENSIA) Algiers Algeria

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Despite its advantages for privacy-preserving data-driven modeling, federated learning is vulnerable to privacy breaches, as demonstrated by recent attacks on its privacy properties. It has been proven that sharing the weights alone is insufficient to protect the underlying data. In this work, we provide a solution for sharing the aggregated weights with a central server while safeguarding the privacy of individual client weights. Our solution introduces a decentralized committee election mechanism, eliminating the need for a trusted party. The election phase is based on verifiable random functions (VRFs), whereas the aggregation phase is based on Elliptic curve cryptography and multi-party secret-sharing schemes. Our experimental results show that our solution outperforms the proposed solutions in terms of communication and computation costs. Overall, our approach offers a robust solution for privacy-preserving federated learning without compromising its accuracy and without relying on a third party.

关键词： Data-driven modeling Data privacy Costs Accuracy Federated learning Voting Elliptic curve cryptography Privacy breach Servers Cryptography

来源：评论

学校读者我要写书评

暂无评论

Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

引用

IEEE Transactions on Automation Science and Engineering 2025年

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

来源：评论

学校读者我要写书评

暂无评论

FIDSUS: Federated Intrusion Detection for Securing UAV Swarms in Smart Aerial computing

引用

IEEE Internet of Things Journal 2025年第9期12卷 11312-11328页

作者： Deng, Jiangtao Wang, Wei Wang, Lina Bashir, Ali Kashif Gadekallu, Thippa Reddy Feng, Hailin Lv, Meilei Fang, Kai Zhejiang A&F University College of Mathematics and Computer Science Hangzhou311300 China Quzhou University College of Electrical and Information Engineering Quzhou324000 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Shenzhen518172 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BX United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Rajpura140401 India Lovely Professional University Division of Research and Development Phagwara144411 India Chitkara University Center of Research Impact and Outcome Rajpura140401 India

The dynamic environment of UAV swarms in forest management is characterized by communication instability, heterogeneous nodes, and frequent topology changes due to challenging terrain. These systems are vulnerable to network attacks, requiring advanced intrusion detection technologies. Traditional methods struggle with rapid changes due to data privacy concerns and centralized computational limits, while existing federated learning (FL) algorithms lack robustness against client heterogeneity and dynamic data distribution, especially in complex forest environments. To address these challenges, we propose federated intrusion detection for securing UAV swarms (FIDSUS). FIDSUS improves intrusion detection systems by leveraging collaborative sensing among UAVs, enabling better monitoring and response to security threats in forestry. By quantifying the similarity between UAVs’ local feature extractors through an affinity matrix, FIDSUS guides the aggregation of feature extractors, improving detection capabilities. It also uses AI-driven aerial and distributed computing to enhance data processing efficiency and decision-making speed. The framework addresses data heterogeneity by cross-round feature fusion, improving detection in dynamic environments. Experimental results on the NSL-KDD and UNSW-NB15 datasets show that FIDSUS outperforms existing FL methods with a 4%–34% accuracy improvement. FIDSUS shows robustness and accuracy in dynamic environments, providing an effective solution for securing UAV swarms in forestry. © 2014 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Invoking an Efficient Deep Learning Approach for Real-Time Detection of False Data Injection Attacks

Invoking an Efficient Deep Learning Approach for Real-Time D...

引用

2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Eddin, Maymouna Ez Massaoudi, Mohamed Abu-Rub, Haitham Shadmand, Mohammad Abdallah, Mohamed Bcgovic, Miroslav Ghrayeb, Ali Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States Hamad Bin Khalifa University Information and Computing Technology Division Doha Qatar University of Illinois at Chicago Department of Electrical and Computer Engineering Chicago United States

ISBN: (纸本)9798331541125

With the increasing complexity of power systems and their vulnerability to cyber-attacks, robust detection mechanisms are crucial for ensuring the grid's transient and steady-state stability and resiliency. This study introduces an efficient deep learning framework to detect false data injection attacks (FDIAs) in power systems. Leveraging the MATPOWER environment, we simulate a realistic power grid scenario to generate datasets comprising normal operations and various FDIA scenarios. Utilizing advanced deep learning techniques, including convolutional neural networks enhanced with channel attention mechanisms and transformer encoders, our model processes sequential power system data to discern subtle attack signatures from normal fluctuations. The proposed methodology employs a stratified k-fold cross-validation approach on resampled datasets to address class imbalance, ensuring robust model evaluation. The model's performance is meticulously assessed using accuracy metrics and confusion matrices, demonstrating its capability to effectively identify and differentiate between normal and compromised states in power systems. Results on the IEEE 39-bus and 300-bus systems highlight that this approach significantly enhances detection capabilities, achieving high accuracy and practical detection times of less than 2 milliseconds for the IEEE 39-bus system, which addresses the real-time requirements. These promising results suggest that such models can be pivotal for preemptive security measures and real-time monitoring of power grids. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Angle-resolved magneto-chiral anisotropy in a non-centrosymmetric atomic layer superlattice

引用

Science Bulletin 2025年第9期70卷 1406-1409页

作者： Cheng, Long Bao, Mingrui Zhang, Xue Zhang, Jingxian Yang, Qun Li, Qiang Cao, Hui Qiu, Dawei Li, Hui Cheng, Guanglei Zhou, Hua Zuo, Jian-Min Zhou, Xiaodong Shen, Jian Zhu, Zhifeng Wang, Wenbo Zhai, Xiaofang School of Physical Science and Technology ShanghaiTech University Shanghai201210 China CAS Key Laboratory of Microscale Magnetic Resonance and School of Physical Sciences University of Science and Technology of China Hefei230026 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China X-ray Science Division Advanced Photon Source Argonne National Laboratory LemontIL60439 United States Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Department of Materials Science and Engineering and Materials Research Laboratory University of Illinois Urbana–Champaign UrbanaIL61801 United States ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai201210 China

来源：评论

学校读者我要写书评

暂无评论

HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images

引用

Computers in Biology and Medicine 2025年 190卷 109991-109991页

作者： Ahmad, Zahoor Al-Thelaya, Khaled Alzubaidi, Mahmood Joad, Faaiz Gilal, Nauman Ullah Mifsud, William Boughorbel, Sabri Pintore, Giovanni Gobbetti, Enrico Schneider, Jens Agus, Marco Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Kite Pharma Santa MonicaCA United States Visual and Data-intensive Computing CRS4 Italy Qatar Computing Research Institute Hamad Bin Khalifa University Qatar Foundation Doha Qatar

We introduce an end-to-end framework for the automated visual annotation of histopathology whole slide images. Our method integrates deep learning models to achieve precise localization and classification of cell nuclei with spatial data aggregation to extend classes of sparsely distributed nuclei across the entire slide. We introduce a novel and cost-effective approach to localization, leveraging a U-Net architecture and a ResNet-50 backbone. The performance is boosted through color normalization techniques, helping achieve robustness under color variations resulting from diverse scanners and staining reagents. The framework is complemented by a YOLO detection architecture, augmented with generative methods. For classification, we use context patches around each nucleus, fed to various deep architectures. Sparse nuclei-level annotations are then aggregated using kernel density estimation, followed by color-coding and isocontouring. This reduces visual clutter and provides per-pixel probabilities with respect to pathology taxonomies. Finally, we use Morse–Smale theory to generate abstract annotations, highlighting extrema in the density functions and potential spatial interactions in the form of abstract graphs. Thus, our visualization allows for exploration at scales ranging from individual nuclei to the macro-scale. We tested the effectiveness of our framework in an assessment by six pathologists using various neoplastic cases. Our results demonstrate the robustness and usefulness of the proposed framework in aiding histopathologists in their analysis and interpretation of whole slide images. © 2025 The Authors

关键词： Image annotation

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：