检索结果-内蒙古大学图书馆

Resource Allocation for Space-Air-Ground Integrated Networks:A Comprehensive Review

Journal of Communications and Information Networks 2024年第1期9卷 1-23页

作者： Hui Liang Zhiqing Yang Guobin Zhang Hanxu Hou School of Electrical Engineering and Intelligentization Dongguan University of TechnologyDongguan 523808China Peng Cheng Laboratory Shenzhen 518055China School of Electronics and Information Engineering Shenzhen UniversityShenzhen 518061China School of Electrical Engineering and Intelligentization Dongguan University of TechonologyDongguan 523808China School of Information and Communication Engineering Guangzhou Maritime UniversityGuangzhou 510725China

The space-air-ground integrated networks (SAGIN) has emerged as a critical paradigm to address the growing demands for global connectivity and enhanced communication services. This paper gives a thorough review of the strategies and methodologies for resource allocation within SAGIN, focusing on the challenges and solutions within its complex structure. With the advent of technologies such as 6G, the dynamics of resource optimization have become increasingly complex, necessitating innovative approaches for efficient management. We examine the application of mathematical optimization, game theory, artificial intelligence (AI), and dynamic optimization techniques in SAGIN,offering insights into their effectiveness in ensuring optimal resource distribution, minimizing delays, and maximizing network throughput and stability. The survey highlights the significant advances in AI-based methods,particularly deep learning and reinforcement learning, in tackling the inherent challenges of SAGIN resource allocation. Through a critical review of existing literature, this paper categorizes various resource allocation strategies, identifies current research gaps, and discusses potential future directions. Our findings highlight the crucial role of integrated and intelligent resource allocation mechanisms in realizing the full potential of SAGIN for next-generation communication networks.

关键词： space-air-ground integrated networks resource allocation 6G optimization theory game theory artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

A New hybrid Conjugate Gradient Method Based on a Convex Combination for Multiobjective optimization

引用

Operations Research Forum 2025年第2期6卷 1-26页

作者： Yahaya, Jamilu Kumam, Poom Yahaya, Mahmoud Muhammad Center of Excellence in Theoretical and Computational Science (TaCS-CoE) and KMUTT Fixed Point Research Laboratory King Mongkut’s University of Technology Thonburi (KMUTT) 126 Pracha-Uthit Road Bang Mod Thung Khru Bangkok 10140 Thailand NCAO Research Center Fixed Point Theory and Applications Research Group Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut’s University of Technology Thonburi (KMUTT) 126 Pracha-Uthit Rd Bang Mod Thung Khru Bangkok 10140 Thailand Department of Mathematics Faculty of Physical Sciences Ahmadu Bello University Zaria Nigeria

Conjugate gradient methods are a crucial class of techniques for solving unconstrained optimization problems. While the Hestenes-Stiefel and other conjugate gradient methods have recently been extended to multiobjective optimization setting-notably the Hestene-Stiefel failed to ensure descent direction. In contrast, the Conjugate Descent method consistently ensures a descent direction in this context. In this paper, we introduce a hybrid conjugate gradient method that combines a modified Hestenes-Stiefel method with the Conjugate Descent method through a convex combination. The convex coefficient parameter is derived using the Dai-Liao conjugacy condition and the condition for Newton’s direction. Crucially, this hybrid approach guarantees sufficient descent property under the Wolfe line search conditions while establishing global convergence under mild assumptions-without necessarily requiring an algorithmic restarts or assuming convexity on the objective functions. Preliminary numerical results demonstrate the clear advantages of this hybrid method over some existing conjugate gradient techniques. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Conjugate gradient method hybrid conjugate gradient Multiobjective optimization Sufficient descent condition

来源：评论

学校读者我要写书评

暂无评论

Learning to Branch in Combinatorial optimization With Graph Pointer Networks

引用

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui Wang Zhiming Zhou Kaiwen Li Tao Zhang Ling Wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex *** the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

来源：评论

学校读者我要写书评

暂无评论

Scenario Modeling-Aided AP Placement optimization Method for Indoor Localization and Network Access

引用

China Communications 2024年第3期21卷 37-50页

作者： Pan Hao Chen Yu Qi Xiaogang Liu Meili School of Mathematics and Statistics Xidian UniversityXi’an 710126China Science and Technology on Near-Surface Detection Laboratory Wuxi 214035China School of Software Nanchang Hangkong UniversityNanchang 330063China

Owing to the ubiquity of wireless networks and the popularity of WiFi infrastructures,received signal strength(RSS)-based indoor localization systems have received much *** placement of access points(APs)significantly influences localization accuracy and network ***,the indoor scenario and network access are not fully considered in previous AP placement optimization *** study proposes a practical scenario modelingaided AP placement optimization method for improving localization accuracy and network *** order to reduce the gap between simulation-based and field measurement-based AP placement optimization methods,we introduce an indoor scenario modeling and Gaussian process-based RSS prediction *** that,the localization and network access metrics are implemented in the multiple objective particle swarm optimization(MOPSO)solution,Pareto front criterion and virtual repulsion force are applied to determine the optimal AP ***,field experiments demonstrate the effectiveness of the proposed indoor scenario modeling method and RSS prediction model.A thorough comparison confirms the localization and network access improvement attributed to the proposed anchor placement method.

关键词： indoor localization MOPSO network access RSS prediction

来源：评论

学校读者我要写书评

暂无评论

Cooperative Resource Allocation for NOMA-MEC Multi-Cell Network

引用

IEEE Transactions on Vehicular Technology 2025年第6期74卷 9027-9042页

作者： Cai, Yujin Zhang, Zaichen Huang, Yongming Yu, Wenwu Nie, Xiaokai Liu, Hongzhe Southeast University School of Mathematics Jiangsu Nanjing211189 China Southeast University National Mobile Communications Research Laboratory Nanjing211189 China Purple Mountain Laboratories Nanjing211111 China Southeast University School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518057 China

Integrating non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) enables IoT nodes to offload their complicated tasks to MEC servers. Most of the existing resource allocation schemes in NOMA-MEC networks are based on the centralized optimization framework, which may increase the communicational and computational burden on the central unit. In this paper, the NOMA-MEC multi-cell network without the central unit is considered. To minimize the task completion time and offloading cost, a joint optimization problem including sub-channel allocation, transmit power, offloaded proportions, computing frequencies of IoT nodes and MEC servers, and price of MEC resources is investigated. In order to tackle the formulated mixed integer nonlinear programming problem, a two-stage low-complexity distributed algorithm based on the cooperation of all base stations (BSs) is proposed. Specifically, in Stage 1, a distributed heuristic sub-channel allocation algorithm is designed based on the sub-channel gains. In Stage 2, with the obtained sub-channel allocation result, a cooperative distributed continuous variable optimization algorithm is devised based on the distributed optimization and alternating optimization methods. Numerical results demonstrate the convergence of the proposed two-stage algorithm for three interference cases. Besides, the impacts of system parameters and schemes on the network performance are analyzed. More importantly, the resource allocation time can be significantly shortened by employing the cooperation of BSs compared with the centralized algorithm © 1967-2012 IEEE.

关键词： Nonlinear programming

来源：评论

学校读者我要写书评

暂无评论

ALGORITHM DEVELOPMENT FOR THE optimization OF CUTTING TOOL TRAJECTORIES ON CNC MACHINE

引用

UPB Scientific Bulletin, Series D: Mechanical Engineering 2025年第1期87卷 89-104页

作者： Ziani, Belkheir Rahou, Mohammed Sbaa, Fethi Dept.of Mechanical University of Tlemcen Algeria IS2M laboratory Algeria ESSA of Tlemcen Algeria

CNC machines are an essential tool in the manufacturing industry as they are used for producing complex parts. The precision and quality of these parts depend on the accuracy of the cutting trajectory and axis programming. To improve these performances, interpolation methods such as Cubic Spline and Lagrange interpolation exist. This method is capable of providing more precise trajectories and more uniform cutting speeds, resulting in superior cutting quality and precision. This paper presents a new algorithm for the optimization of cutting tool paths for machining on 5-axis CNC machines. This algorithm is based on the combination of Lagrange interpolation and salt cubic interpolation. The results of this study have been simulated and validated by a real case. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Interpolation

来源：评论

学校读者我要写书评

暂无评论

Process in laser drilling of deep microholes without taper on metal materials

引用

Science China(Technological Sciences) 2024年第1期67卷 37-59页

作者： FAN RunZe MEI XueSong CUI JianLei State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710054China

Deep microhole machining is currently a prominent research area within the aerospace field,encompassing blade film cooling and fuel injection control ***,taper defects in metal materials may lead to performance degradation or even structural damage over a component’s *** and helical drilling,facilitated by ultrashort pulse lasers,have proven more suitable for achieving high-precision,deep holes in metal ***,excessive repetition rates can also result in severe thermal *** methods are commonly employed for controlling taper,including parameter optimization,assistance,and secondary *** laser beam drilling is widely utilized and has been integrated into relevant machining systems for commercial *** deep microholes include film cooling holes and injection *** drilling is a potential machining method for new materials in the aerospace *** laser drilling processing has been studied,numerous related scientific challenges and technical difficulties must be addressed before practical implementation.

关键词： aerospace laser drilling deep microhole taper control

来源：评论

学校读者我要写书评

暂无评论

Sizing,optimization,control and energy management of hybrid renewable energy system-A review

引用

Energy and Built Environment 2022年第4期3卷 399-411页

作者： Chouaib Ammari Djamel Belatrache Batoul Touhami Salim Makhloufi LEESI Laboratory Faculty of Sciences and TechnologyUniversity of Adrar01000Algeria Laboratory of Sustainable Development and Computing(LDDI) University of Adrar01000 AdrarAlgeria Department natural sciences and life Faculty of Sciences and TechnologyUniversity of Adrar01000Algeria

To meet fast growing in energy demand,all energy sources have to be *** energies are unlim-ited and clean but the most problem with them is their intermittent *** overcome this problem a mixture of several energy sources is made to obtain what called hybrid renewable energy *** paper aims at pre-senting and analyzing a deep literature review of the recent paper published in hybrid renewable energy *** review focuses on four essential categories of hybrid renewable energy system which is sizing(using soft-ware or using traditional methods),optimization(classical,artificial and hybrid methods),control(centralized,distributed and hybrid control)and energy management(technical objective,economic objective and techno-economic objective).Furthermore,this paper compares between different methods used in each category.

关键词： Renewable energy Sizing optimization Control Energy management

来源：评论

学校读者我要写书评

暂无评论

An Overview of Sequential Approximation in Topology optimization of Continuum Structure

引用

Computer Modeling in Engineering & Sciences 2024年第4期139卷 43-67页

作者： Kai Long Ayesha Saeed Jinhua Zhang Yara Diaeldin Feiyu Lu Tao Tao Yuhua Li Pengwen Sun Jinshun Yan State Key Laboratory of Alternate Electrical Power Systemwith Renewable Energy Sources North China Electric Power UniversityBeijing102206China Faculty of Materials and Manufacturing Beijing University of TechnologyBeijing100124China New Energy and Energy Storage Division China Southern Power Grid Technology Co.Ltd.Guangzhou510080China School of Mechanical Engineering Inner Mongolia University of TechnologyHohhot010051China College of Energy and Power Engineering Inner Mongolia University of TechnologyHohhot010051China

This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum *** structures,commonly encountered in engineering applications,often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design *** a result,sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization *** the past several decades,topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical *** comparison to the large-scale equation solution,sensitivity analysis,graphics post-processing,etc.,the progress of the sequential approximation functions and their corresponding optimizersmake sluggish ***,particularly novices,pay special attention to their difficulties with a particular ***,this paper provides an overview of sequential approximation functions,related literature on topology optimization methods,and their *** from optimality criteria and sequential linear programming,the other sequential approximate optimizations are introduced by employing Taylor expansion and intervening *** addition,recent advancements have led to the emergence of approaches such as Augmented Lagrange,sequential approximate integer,and non-gradient approximation are also *** highlighting real-world applications and case studies,the paper not only demonstrates the practical relevance of these methods but also underscores the need for continued exploration in this ***,to provide a comprehensive overview,this paper offers several novel developments that aim to illuminate potential directions for future research.

关键词： Topology optimization sequential approximate optimization convex linearization method ofmoving asymptotes sequential quadratic programming

来源：评论

学校读者我要写书评

暂无评论

Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective optimization

引用

IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1801-1817页

作者： Ye Tian Haowen Chen Haiping Ma Xingyi Zhang Kay Chen Tan Yaochu Jin the Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China the Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230601China IEEE the Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China the Department of Computing The Hong Kong Polytechnic UniversityHong Kong SARChina the Faculty of Technology Bielefeld UniversityBielefeld 33619Germany

Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search *** evolutionary algorithms are good at solving small-scale multi-objective optimization problems,they are criticized for low efficiency in converging to the optimums of *** contrast,mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems,but they have difficulties in finding diverse solutions for ***,how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains *** this paper,a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient *** the one hand,conjugate gradients and differential evolution are used to update different decision variables of a set of solutions,where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto *** the other hand,objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions,and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its *** comparison with state-of-the-art evolutionary algorithms,mathematical programming methods,and hybrid algorithms,the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.

关键词： Conjugate gradient differential evolution evolutionary computation large-scale multi-objective optimization mathematical programming

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：