检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Zhou, Jiasi Zhou, Cong Sun, Yanjing Tellambura, Chintha School of Medical Information and Engineering Xuzhou Medical University Xuzhou221004 China School of Electronic and Information Engineering Harbin Institute of Technology Harbin150001 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada

Extremely large-scale antenna arrays enhance spectral efficiency and spatial resolution in integrated sensing and communication (ISAC) networks while expanding the Rayleigh distance, triggering a shift from conventional far-field plane waves to near-field (NF) spherical waves. However, full-digital beamforming is infeasible due to the need for dedicated radio frequency (RF) chains. To address this, NF-ISAC with a rate-splitting multiple access (RSMA) scheme is developed for advanced interference management, considering fully-connected and partially-connected hybrid analog and digital (HAD) beamforming architectures. Specifically, the Cramér-Rao bound (CRB) for joint distance and angle sensing is derived, and the achievable performance region between the max-min communication rate and the multi-target CRB is defined. To fully characterize the Pareto boundary of the CRB-rate region, a sensing-centric minimization problem is formulated under communication rate constraints for two HAD beamforming architectures. A penalty dual decomposition (PDD)-based double-loop algorithm is developed to optimize fully-connected HAD beamformers. To reduce computational complexity, a two-stage design algorithm for fully connected HAD beamforming is also proposed. Additionally, the PDD-based double-loop algorithm is extended to the partially-connected HAD architecture. Simulations demonstrate the proposed schemes and algorithms: 1) achieve performance comparable to a fully digital beamformer with fewer RF chains, 2) outperform space division multiple access and far-field ISAC, and 3) yield enhanced CRB-rate trade-off performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Cramer-Rao bounds

来源：评论

学校读者我要写书评

暂无评论

LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Xue, Xingsi Zhang, Fuquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University New Engineering Industry College Putian China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Minjiang University College of Computer and Control Engineering Fuzhou China Sichuan Conservatory of Music Digital Media Art Key Laboratory of Sichuan Province Chengdu China Minjiang University Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Fuzhou China Fuzhou China

Driver drowsiness electroencephalogram (EEG) signal monitoring can promptly alert drivers of their drowsiness in edge-end Consumer Electronics (CE), thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end Lightweight Dual Graph Convolutional Network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. To further enhance the application of CE, we apply Adaptive Pruning Optimization (APO) to the channels and neurons of LDGCN, which nearly halves the inference latency. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization, outperforming existing state-of-the-art algorithms. © 1975-2011 IEEE.

关键词： Neurons

来源：评论

学校读者我要写书评

暂无评论

Visualizing the Local Atomic Environment Features of Machine Learning Interatomic Potential

arXiv

引用

arXiv 2025年

作者： Shao, Xuqiang Zhang, Yuqi Zhang, Di Gao, Tianxiang Liu, Xinyuan Gan, Zhiran Meng, Fanshun Li, Hao Yang, Weijie School of Control and Computer Engineering North China Electric Power University Baoding071003 China Tohoku University Sendai980-8577 Japan Department of Power Engineering School of Energy and Power Engineering North China Electric Power University Hebei Baoding071003 China School of Science Liaoning University of Technology Jinzhou12100l China

This paper addresses the challenges of creating efficient and high-quality datasets for machine learning potential functions. We present a novel approach, termed DV-LAE (Difference Vectors based on Local Atomic Environments), which utilizes the properties of atomic local environments and employs histogram statistics to generate difference vectors. This technique facilitates dataset screening and optimization, effectively minimizing redundancy while maintaining data diversity. We have validated the optimized datasets in high-temperature and high-pressure hydrogen systems as well as the α-Fe/H binary system, demonstrating a significant reduction in computational resource usage without compromising prediction accuracy. Additionally, our method has revealed new structures that emerge during simulations but were underrepresented in the initial training datasets. The redundancy in the datasets and the distribution of these new structures can be visually analyzed through the visualization of difference vectors. This approach enhances our understanding of the characteristics of these newly formed structures and their impact on physical processes. © 2025, CC BY.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Functional Size Measurement With Conceptual Models: A Systematic Literature Review

引用

Journal of Software: Evolution and Process 2025年第5期37卷

作者： Ala Arman Emiliano Di Reto Massimo Mecella Giuseppe Santucci Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

The demand for efficient functional size measurement (FSM) methods in the competitive software market today is undeniable. However, incomplete and imprecise system specifications pose significant challenges, particularly in scenarios that require fast, flexible, and accurate software size estimation, such as public tenders. Although the integration of conceptual models within FSMs offers a promising solution to these issues, a systematic exploration of such methods remains largely unexplored. This work evaluates FSM methods that integrate conceptual models by analyzing studies from the past 20 years. It highlights key contributions and advances in proposed conceptual model-based FSM methods. In addition, the study examines their limitations and challenges, offering insights for future improvements. A systematic literature review (SLR) was conducted to guide the research process. The review was organized around three research questions, each targeting the study's key objectives: (1) to explore FSM methods utilizing conceptual models, (2) to summarize proposals for their improvement, and (3) to identify the limitations of the proposed enhancements. Primary studies span two decades (2004–2024), with peaks in 2008 and 2015, averaging one to two studies annually. Of the 1371 initial studies, 13 were selected using strict criteria. These studies are categorized into Measurement Techniques (30.77%), Automation (38.46%), and Application-Specific topics (30.77%). The contributions of the primary studies are analyzed in terms of their approaches Repeatability and Validation . Repeatability is assessed by examining whether the primary studies proposed a formal model when using real datasets. In contrast, Validation focuses on whether the studies were tested in real-world projects. A total of 46.15% of the primary studies utilize formal models, whereas 53.85% rely on nonformal models, although dataset size is often unspecified. Most studies validate their methods using 1 to 30

关键词： Common Software Measurement International Consortium (COSMIC) conceptual models function point analysis (FPA) functional size measurement (FSM) systematic literature review (SLR)

来源：评论

学校读者我要写书评

暂无评论

Point-annotation supervision for robust 3D pulmonary infection segmentation by CT-based cascading deep learning

引用

computers in Biology and Medicine 2025年 187卷 109760-109760页

作者： Chu, Yuetan Wang, Jianpeng Xiong, Yaxin Gao, Yuan Liu, Xin Luo, Gongning Gao, Xin Zhao, Mingyan Huang, Chao Qiu, Zhaowen Meng, Xianglin The Department of Critical Care Medicine First Affiliated Hospital of Harbin Medical University Harbin China Thuwal Saudi Arabia The Cancer Institute and Department of Nuclear Medicine Fudan University Shanghai Cancer Center Shanghai China College of Computer and Control Engineering Northeast Forestry University Harbin China Ningbo China The Department of Prosthodontics First Affiliated Hospital of Harbin Medical University Harbin China

Infected region segmentation is crucial for pulmonary infection diagnosis, severity assessment, and monitoring treatment progression. High-performance segmentation methods rely heavily on fully annotated, large-scale training datasets. However, manual labeling for pulmonary infections demands substantial investments of time and labor. While weakly supervised learning can greatly reduce annotation efforts, previous developments have focused mainly on natural or medical images with distinct boundaries and consistent textures. These approaches is not applicable to pulmonary infection segmentation, which should contend with high topological and intensity variations, irregular and ambiguous boundaries, and poor contrast in 3D contexts. In this study, we propose a cascading point-annotation framework to segment pulmonary infections, enabling optimization on larger datasets and superior performance on external data. Via comparing the representation of annotated points and unlabeled voxels, as well as establishing global uncertainty, we develop two regularization strategies to constrain the network to a more holistic lesion pattern understanding under sparse annotations. We further encompass an enhancement module to improve global anatomical perception and adaptability to spatial anisotropy, alongside a texture-aware variational module to determine more regionally consistent boundaries based on common textures of infection. Experiments on a large dataset of 1,072 CT volumes demonstrate our method outperforming state-of-the-art weakly-supervised approaches by approximately 3%–6% in dice score and is comparable to fully-supervised methods on external datasets. Moreover, our approach demonstrates robust performance even when applied to an unseen infection subtype, Mycoplasma pneumoniae, which was not included in the training datasets. These results collectively underscore rapid and promising applicability for emerging pulmonary infections. © 2025

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

A Profit-Oriented Attack by Virtual Power Plants for Economic Risk Transfer: Profitability Analysis and Defense Strategy

引用

IEEE Systems Journal 2025年

作者： Wang, Luyu Wu, Jiping Ding, Zhetong Zhang, Kaifeng Sikdar, Biplab Southeast University Key Laboratory of Measurement and Control of CSE School of Automation Nanjing 210096 China National University of Singapore Department of Electrical and Computer Engineering 119077 Singapore NARI Technology Limited Company Nanjing 211100 China

Virtual power plants (VPP) may face the risk of economic losses arising from the deviation between day-ahead schedule and real-time energy delivery. This article introduces a novel profit-oriented attack by some greedy VPP (G-VPP) for the first time, which aims to transfer its risk of economic losses to other normal VPPs (N-VPPs) and obtain a risk-free profit. Particularly, the G-VPP launches the load-altering attack (LAA) on N-VPPs to fail their real-time (RT) generation schedule via modifying the generation instructions of the managed resources. The optimal LAA strategy considering stealthiness is proposed to avoid priori detection and the optimal bidding strategy using CVaR is proposed to quantify bidding risk and maximize profit. To defend against such profit-oriented attacks, this article constitutes the first attack traceability research. A defense scheme with attack detection and attacker identification is proposed, which is executed by VPPs and the independent system operator (ISO). VPPs employ an attack detection algorithm based on complex features, and if any, report it to the ISO. The ISO constructs a knowledge graph of electricity market transactions and embeds a rules-based identification algorithm to identify suspected attackers. Simulation results show the effectiveness of the defense scheme and Neo4j is used to visualize. © 2007-2012 IEEE.

关键词： Cyber-physical system security electricity market profit-oriented cyberattack

来源：评论

学校读者我要写书评

暂无评论

Distributed Prescribed-Time Observer for Nonlinear Systems

arXiv

引用

arXiv 2025年

作者： de Heij, Vincent Niazi, M.B. Umar Johansson, Karl H. Ahmed, Saeed Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This paper proposes a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite limited local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that asymptotically approach infinity as the prescribed time is approached. The theoretical convergence is rigorously proven and validated through numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Nonlinear simulations

来源：评论

学校读者我要写书评

暂无评论

Constrained Optimization with Decision-Dependent Distributions

引用

IEEE Transactions on Automatic control 2025年

作者： Wang, Zifan Liu, Changxin Parisini, Thomas Zavlanos, Michael M. Johansson, Karl H. KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures StockholmSE-10044 Sweden East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy Duke University Department of Mechanical Engineering and Materials Science DurhamNC United States

In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems with decision-dependent distributions has assumed either the absence of constraints or fixed constraints. This work considers a more general setting where the constraints can also dynamically adjust in response to changes in the decision variables. Specifically, we consider linear constraints and analyze the effect of decision-dependent distributions in both the objective function and constraints. Firstly, we establish a sufficient condition for the existence of a constrained equilibrium point, at which the distributions remain invariant under retraining. Moreover, we propose and analyze two algorithms: repeated constrained optimization and repeated dual ascent. For each algorithm, we provide sufficient conditions for convergence to the constrained equilibrium point. Furthermore, we explore the relationship between the equilibrium point and the optimal point for the constrained decision-dependent optimization problem. Notably, our results encompass previous findings as special cases when the constraints remain fixed. To show the effectiveness of our theoretical analysis, we provide numerical experiments on both a market problem and a dynamic pricing problem for parking based on real-world data. © 1963-2012 IEEE.

关键词： Optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Online Joint Power Allocation and Task Scheduling for LEO Satellite Networks

Online Joint Power Allocation and Task Scheduling for LEO Sa...

引用

IEEE Conference on Wireless Communications and Networking

作者： Zheyuan Li Lijun He Juncheng Wang Ziye Jia Yanting Wang Chau Yuen School of Software Northwestern Polytechnical University China School of Information and Control Engineering China University of Mining and Technology China Department of Computer Science Hong Kong Baptist University China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics China School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The excessive proliferation of Low Earth Orbit (LEO) satellites inescapably bring the explosive growth of space data in LEO Satellite Networks (LSNs). Meanwhile, the stochastic arrivals of space data together with the time-varying satellite-ground links in LSNs pose significant challenges for offloading a large volume of space data from LSNs to ground stations. To circumvent these challenges, we systematically study the energy-constrained online data offloading problem to jointly optimize power allocation and task scheduling for LSNs. First, we leverage Lyapunov optimization to decouple our formulated long-term stochastic joint optimization problem into a set of per-time-slot subproblems. Then, each subproblem is decoupled into a task scheduling problem and a power allocation problem. Next, we derive the optimal solution to the power allocation problem and propose a multi-armed bandit based quasi-optimal solution to the task scheduling problem. Finally, extensive simulation results show that our proposed algorithm has superior performance over the state-of-the-art solutions.

关键词： Space vehicles Satellites Simulation Low earth orbit satellites Scheduling Explosives Resource management Optimization Space stations

来源：评论

学校读者我要写书评

暂无评论

Theory of phase reduction for systems with higher-order interactions: a route to Kuramoto models on hypergraphs

arXiv

引用

arXiv 2025年

作者： León, Iván Muolo, Riccardo Hata, Shigefumi Nakao, Hiroya Department of Applied Mathematics and Computer Science Universidad de Cantabria Santander39005 Spain Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Graduate School of Science and Engineering Kagoshima University Kagoshima890-0065 Japan Research Center for Autonomous Systems Materialogy Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8501 Japan

Phase reduction is a powerful technique in the study of nonlinear oscillatory systems. Under certain assumptions, it allows us to describe each multidimensional oscillator by a single phase variable, giving rise to simple phase models such as the Kuramoto model. Classically, the method has been applied in the case where the interactions are only pairwise (two-body). However, increasing evidence shows that interactions in real-world systems are not pairwise but higher-order, i.e., many-body. Although synchronization in higher-order systems has received much attention, analytical results are scarce because of the highly nonlinear nature of the framework. In this paper, we fill the gap by presenting a general theory of phase reduction for the case of higher-order interactions. We show that the higher-order topology is preserved in the phase reduced model at the first order and that only odd couplings have an effect on the dynamics when certain symmetries are present. Additionally, we show the power and ductility of the phase reduction approach by applying it to a population of Stuart-Landau oscillators with an all-to-all configuration and with a ring-like hypergraph topology;in both cases, only the analysis of the phase model can provide insights and analytical results. © 2025, CC BY-NC-ND.

关键词： Topology

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：