检索结果-内蒙古大学图书馆

Joint Optimization of Base Station clustering and Service Caching in User-Centric MEC

IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第5期23卷 6455-6469页

作者： Qin, Langtian Lu, Hancheng Lu, Yao Zhang, Chenwu Wu, Feng Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China

Edge service caching can effectively reduce the delay or bandwidth overhead for acquiring and initializing applications. To address single-base station (BS) transmission limitation and serious edge effect in traditional cellular-based edge service caching networks, in this paper, we proposed a novel user-centric edge service caching framework where each user is jointly provided with edge caching and wireless transmission services by a specific BS cluster instead of a single BS. To minimize the long-term average delay under the constraint of the caching cost, a mixed integer non-linear programming (MINLP) problem is formulated by jointly optimizing the BS clustering and service caching decisions. To tackle the problem, we propose JO-CDSD, an efficiently joint optimization algorithm based on Lyapunov optimization and generalized benders decomposition (GBD). In particular, the long-term optimization problem can be transformed into a primal problem and a master problem in each time slot that is much simpler to solve. The near-optimal clustering and caching strategy can be obtained through solving the primal and master problem alternately. Extensive simulations show that the proposed joint optimization algorithm outperforms other algorithms and can effectively reduce the long-term delay and caching cost.

关键词： Optimization Delays Wireless communication Task analysis Costs Servers clustering algorithms Generalized benders decomposition Lyapunov optimization mobile edge computing service caching user-centric network

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Deep clustering With Hard Balanced Constraint: Application in Disciplinary-Focused Student Section Formation

引用

IEEE ACCESS 2024年 12卷 98239-98253页

作者： Chantamunee, Siripinyo Thamrongrat, Pornpon Thanathamathee, Putthiporn Chaisriya, Kannattha Nizam, Dinna Nina Mohd Walailak Univ Informat Innovat Ctr Excellence Nakhon Si Thammarat 80160 Thailand Walailak Univ Sch Engn & Technol Nakhon Si Thammarat 80160 Thailand Walailak Univ Sch Informat Nakhon Si Thammarat 80160 Thailand Univ Malaysia Sabah Fac Comp & Informat User Experience Res Grp Labuan 87008 Malaysia

Effective student group formation is crucial in higher education to foster collaborative learning environments. Grouping students by academic disciplines enhances peer-to-peer interactions and facilitates in-depth discussions on specialized topics. However, due to classroom space and resource constraints, it is challenging to accommodate all students from similar disciplines in one class. This necessitates a grouping method that can ensure a balanced distribution of students across available groups. Traditional K-means clustering, commonly used for this purpose, often results in inconsistent group sizes and fails to guarantee a balanced distribution of group members. Hard balanced clustering, which strictly enforces precise size limits on each cluster, offers a promising alternative for organizing balanced student sections to optimize classroom utilization. Nonetheless, most hard balanced clustering methods are limited in feature learning capability, which can lead to the overlooking of significant data patterns and result in ineffective clustering. To address this limitation, this paper introduces a new unsupervised model, Deep Hard Balanced clustering (DHBC), which integrates hard balanced clustering with a deep learning framework to enhance feature learning. DHBC incorporates a balanced clustering mechanism within the optimization process of an Autoencoder architecture. It enhances the generated latent space representation by introducing a joint loss function that combines reconstruction and balanced clustering objectives, ensuring the embedded representation supports a balanced distribution of students. The model optimizes balanced clustering centroids during training. Comparative experiments conducted on real-world student enrollment datasets, evaluated by WCSS scores, demonstrate DHBC's superiority in creating more cohesive and balanced student groups compared to state-of-the-art methods.

关键词： clustering algorithms Linear programming Feature extraction Representation learning Decoding Deep learning Computer architecture Encoding Hard balanced clustering balanced clustering deep clustering deep autoencoder group formation

来源：评论

学校读者我要写书评

暂无评论

FCFilter: Feature selection based on clustering and genetic algorithms

FCFilter: Feature selection based on clustering and genetic ...

引用

Congress on Evolutionary Computation

作者： Charles H. P. Ferreira Debora M. R. de Medeiros Fabiana Santana Center of Mathematics Computation & Cognition Federal University of ABC (UFABC) Brazil Faculty of Education Science Technology & Mathematics University of Canberra Australia

The search for patterns in big amounts of textual data, or text mining, can be at once rewarding and challenging. The patterns can reveal tendencies, similarities and predictions, but the information is usually implicit and difficult to be validated. Classification is one of the most relevant research areas in text mining, and it usually consists of predicting the class of a textual document based on a set of documents previously organized into different classes, such as author or topic. Choosing the words to compose the feature set is crucial to a proper classification. A well selected feature set can improve the performance of the classification method and enlighten the interpretation of the classification model adjusted to the data. This paper introduces the Feature Cluster Filter (FCFilter) method for feature selection. FCFilter eliminates the need to input or optimize the number of clusters by grouping the words in a sufficiently high number of clusters. Genetic algorithms are applied to optimize the combination of groups that will provide the final feature set. The method is based on the selection of features that are good predictors for text classification by clustering features and selecting only the suitable clusters. Experiments performed to evaluate the FCFilter with the Reuters-21578, SCY-Genes and SCY-Clusters datasets showed a significant reduction in the feature-value table dimensionality with slight improvements in the classification accuracy when compared to the baselines. The results are very promising, indicating potential improvements in the research on feature selection for text mining.

关键词： Genetic algorithms Biological cells Text mining Data models clustering algorithms Support vector machines Optimization

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient clustering Routing for WSNs Based on Multiobjective Quantum Adaptive Gray Wolf Optimization

引用

IEEE SENSORS JOURNAL 2025年第8期25卷 14080-14098页

作者： Xu, Mengying Zu, Yunxiao Zhou, Jie Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Network Informat Secur Xinjiang Prod & Construct Corps Key Lab Comp Intelligence Shihezi 832000 Peoples R China Shihezi Univ Dept Informat Sci & Technol Shihezi 832000 Peoples R China

Maximizing longevity and improving the communication quality of wireless sensor networks (WSNs) are two pivotal objectives in addressing clustering routing issues. However, existing methods suffer from low quality of service (QoS), and accumulating unnecessary monitoring data results in a reduced network lifetime. In this regard, a new multiobjective quantum adaptive gray wolf optimization-based energy-efficient clustering routing (MQAGWO-EECR) algorithm is proposed, which vigorously prolongs the network lifetime and considerably reduces delay and packet loss rate. Two novel operators named quantum calculation and adaptive adjustment are devised in conjunction with gray wolf optimization (GWO), which significantly enhances solution quality and obviously improves convergence rate. Additionally, a new energy-efficient clustering routing (EECR) model involves sleep scheduling, cluster head (CH) selection, and multihop routing, which accurately captures the data transmission process. To be specific, sleep scheduling alternates nodes between active and sleep modes to reduce unnecessary energy usage. Moreover, clustering routing is designed by concurrently considering energy cost, node density, the distance between CH nodes and the base station (BS), delay, and packet loss rate, ensuring CH selection is rational and significantly guaranteeing QoS performance. The experimental results demonstrate that the proposed MQAGWO-EECR effectively optimizes energy efficiency and further extends network longevity by at least 11.09% compared to energy-efficient multihop LEACH-based clustering (EM-LEACH), KSA-DEEC, and GAPSO-H. Additionally, it decreases transmission delay by at least 5.27% and reduces packet loss rate by at least 4.84% compared to the other three algorithms.

关键词： Routing Wireless sensor networks clustering algorithms Energy efficiency Optimization Energy consumption Delays Costs Monitoring Quality of service clustering routing gray wolf optimization (GWO) sleep scheduling wireless sensor networks (WSNs)

来源：评论

学校读者我要写书评

暂无评论

On the Security of Distributed Multi-Agent K-Means clustering With Local Differential Privacy

引用

IEEE ACCESS 2024年 12卷 124751-124763页

作者： Shi, Congcong Huang, Xiuli Yu, Pengfei State Grid Smart Grid Res Inst Co ltd State Grid Lab Power Cyber Security Protect & Moni Nanjing 210003 Peoples R China

In a distributed scenario, the process of multiple agents collaborating and interacting with the server to iteratively implement k-means clustering analysis can be easily exploited by attackers, posing a huge privacy threat. Therefore, a local differential privacy k-means method (LDPKmeans) was proposed, which can effectively address the privacy protection problem in multi-agent systems. In this paper, we propose an effective attack method based on multi-agent model, which shows that the basic proposal of LDPKmeans will leak the real information of user agents if the attacker only obtains the cluster information and cluster centroid of each user. Furthermore, we enhance the attack method to crack the improved LDPKmeans method with privacy enhancement, enabling us to infer the cluster information of each user agent in the server. In other words, LDPKmeans seriously leak user agent privacy in distributed multi-agent systems if the server is untrusted. Theoretical analysis and experiments evaluate the effectiveness of our attack scheme. The results show that our method can effectively attack the distributed LDPKmeans scheme compared with the state-of-the-art attack methods. Specifically, our attack method can reduce the average relative error of inferring the true value before k-menas convergence on the 3D Road Network and Shuttle datasets by about 54% and 75% respectively when k=5.

关键词： Servers Privacy Vectors clustering algorithms Security Distributed databases Proposals Multi-agent systems Differential privacy distributed k-means local differential privacy security problem

来源：评论

学校读者我要写书评

暂无评论

Computationally Efficient Unsupervised Deep Learning for Robust Joint AP clustering and Beamforming Design in Cell-Free Systems

引用

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第5期24卷 4250-4266页

作者： Chen, Guanghui Wang, Zheng Lin, Hongxin Huang, Yongming Yang, Luxi Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Pervas Commun Ctr Nanjing 211111 Peoples R China

In this paper, we consider robust joint access point (AP) clustering and beamforming design with imperfect channel state information (CSI) in cell-free systems. Specifically, we jointly optimize AP clustering and beamforming with imperfect CSI to simultaneously maximize the worst-case sum rate and minimize the number of AP clustering under power constraint and the discrete constraint of AP clustering. Through transformations, the intractable simultaneous optimization of continuous and discrete variables is reduced to optimizing only the sparsity of the continuous variables, facilitating a computationally efficient unsupervised deep learning algorithm. In addition, to further reduce the computational complexity, a computationally effective unsupervised deep learning algorithm is proposed to implement robust joint AP clustering and beamforming design with imperfect CSI in cell-free systems. Numerical results demonstrate that the proposed unsupervised deep learning algorithm achieves a higher worst-case sum rate under a smaller number of AP clustering with computational efficiency.

关键词： Array signal processing clustering algorithms Deep learning Optimization Computational efficiency Indexes Wireless communication Computational modeling Computational complexity Backhaul networks Cell-free systems beamforming access point clustering imperfect channel state information unsupervised deep learning

来源：评论

学校读者我要写书评

暂无评论

clustering-Based Dual-Population Co-Evolutionary Algorithm for 2D Segmentation Coding in NAND Flash Memory

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第7期71卷 3603-3607页

作者： Luo, Jianjun Chen, Menghao Fan, Lingyan Liu, Hailuan Gao, Lijuan Feng, Guorui Hangzhou Dianzi Univ Hangzhou 310018 Zhejiang Peoples R China Hangzhou Hualan Microelect Co Ltd Hangzhou 310018 Zhejiang Peoples R China China Elect Technol Grp Corp Res Inst 52 Hangzhou 311121 Zhejiang Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China

In NOT-AND (NAND) flash memory-based solid-state drive (SSD), the error checking and correction (ECC) module is configured to ensure the stability of stored data. With the increase of raw bit error rate (RBER), the traditional error correction coding mode is not only difficult to meet the requirements, but also the efficiency of data repair is extremely low because the error bits cannot be located, when the error correction cannot be completed. Therefore, an innovative error correction coding mode, called two-dimensional (2D) segmentation coding mode, is proposed. To our knowledge, this is the first attempt to apply this coding mode to NAND flash memory. It improves error correction capability and data recovery efficiency by coding information bits in rows and columns separately. We propose a dual-population co-evolution algorithm based on clustering algorithm to optimize the segmentation method for better efficiency. Under the condition of satisfying all the constraints, the solutions obtained by the proposed algorithm achieve the balance of rewrite efficiency, coding efficiency and coding time. Experimental results show that the proposed algorithm performs well compared with six state-of-the-art evolutionary algorithms (EAs) in solving the proposed constrained multi-objective optimization problem.

关键词： Encoding Statistics Sociology Error correction codes Flash memories clustering algorithms Linear programming NAND flash memory 2D block error correction code Bose-Chaudhuri-Hocquenghem code constrained multiobjective optimization evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Coherency-Constrained Spectral clustering for Power Network Reduction

IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY

引用

IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY 2025年 12卷 88-99页

作者： Baquedano-Aguilar, Mario D. Meyn, Sean Bretas, Arturo Univ Florida Dept Elect & Comp Engn Gainesville FL 32603 USA Quanta Technol LLC Raleigh NC 27607 USA Sandia Natl Labs Albuquerque NM 87123 USA Univ Grenoble Alpes Grenoble INP CNRS G2Elab F-38000 Grenoble France

This paper presents a methodology for reducing the complexity of large-scale power network models using spectral clustering, aggregation of electrical components, and cost function approximation. Two approaches are explored using unconstrained and constrained spectral clustering to determine areas for effective system reduction. Once the system areas are determined, both loads and generators by type are aggregated, and their new cost function is approximated through polynomial curve-fitting or statistical methods. The performance of reduced networks is evaluated in terms of their ability to follow the true daily cost of the original system over a 24-hour period considering a set of several days. Two test systems are taken as test beds. Application of the methodology to a modified version of the IEEE 39-bus system reduces it from 17 generators to a 4-bus system and 9 generators with about 93% of accuracy. Similarly, the IEEE 118-bus system is reduced from 19 generators to a 3-bus system with three aggregated units achieving over 99% of accuracy. These findings address scalability challenges and enhance accuracy for high and mid-loading level conditions, and by aggregating thermal units with similar cost functions.

关键词： Generators Power system dynamics Laplace equations Cost function Load flow Power systems Transmission line matrix methods clustering algorithms Transmission line measurements Power measurement Aggregation of electrical components function approximation network reduction spectral graph theory UCED

来源：评论

学校读者我要写书评

暂无评论

User Security-Oriented Information-Centric IoT Nodes clustering With Graph Convolution Networks

引用

IEEE INTERNET OF THINGS JOURNAL 2024年第5期11卷 8311-8326页

作者： Gharib, Anastassia Ibnkahla, Mohamed Princess Sumaya Univ Technol Dept Comp Engn Amman 11941 Jordan Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

Information-centric Internet of Things (IoT) sensor networks allow users to access data directly from the sensing layer. This is done through cluster heads (CHs), which are selected as a result of IoT nodes' clustering. To respond to users' data requests, CHs aggregate, encrypt, and store locally sensed data in rounds. For data encryption, security resources are allocated to sensor nodes every round. To satisfy user security needs, very often, security resources are overutilized leading to higher energy consumption and shorter network lifetime. Meanwhile, sensor nodes' and users' mobility may result in link failures. Therefore, efficient clustering and security resource allocation is required to ensure users' data and security needs are satisfied while optimizing network resource utilization. Graph convolution networks (GCNs) can help to address this challenge. GCNs perform learning on graphs while considering non-Euclidean nodes' relations and features. Using GCNs, user awareness, and IoT nodes' features can be incorporated into the cluster-based management of mobile information-centric IoT sensor networks. Therefore, this article proposes user-aware clustering with security resource allocation (USRA) using GCNs. In USRA, the proposed clustering algorithm improves communication reliability by optimizing users' and nodes' coverage. Meanwhile, the proposed security resource allocation plan prevents overutilization of security resources by considering user security needs in each cluster. Compared to existing works, USRA achieves lower energy consumption on security while ensuring high user security satisfaction. This promotes a longer network lifetime. USRA further contributes to higher communication reliability and throughput with stable data delivery latency to users.

关键词： Security Resource management clustering algorithms Internet of Things Energy consumption Convolution Encryption clustering graph convolution networks (GCNs) security user satisfaction wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Joint Optimization of Power Generation and Voyage Scheduling in Ship Power System Based on Operating Scene clustering and Multitask Deep Reinforcement Learning

引用

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2024年第4期10卷 8432-8442页

作者： Shang, Chengya Fu, Lijun Xiao, Haipeng Bao, Xianqiang Xu, Xinghua Naval Univ Engn Natl Key Lab Electromagnet Energy Wuhan 430033 Peoples R China

The variability in sailing environment and load demand, along with seasonal fluctuations in port electricity prices, presents significant challenges for operating ship power systems (SPSs) in all-electric ships (AESs). However, traditional single-task deep reinforcement learning (DRL) struggles to cope with the high randomness of SPS operation scenarios. This article proposes a novel real-time joint optimization method for power generation and voyage scheduling in SPSs that considers operating scene clustering and multitask DRL (MTDRL). A unified triplet is used to describe operational uncertainty, and the SPS operation scenes are clustered. Then, the importance weighted actor-learner architecture (IMPALA) combined with random network distillation (RND) mechanism, what is called IMPALA-RND algorithm, is applied to minimize operational costs by adjusting power generation and voyage scheduling. The proposed method can achieve differentiated learning for clustered multitask operational scenes and enhance the agent's capability to explore unknown states. A case study is analyzed based on historical operational datasets of four-DG SPS. Numerical results verify the superiority and real-time performance of the proposed algorithm for joint optimization in multitask operation scenarios.

关键词： Optimization clustering algorithms Costs Multitasking Marine vehicles Power systems Generators Energy management strategy multitask deep reinforcement learning (MTDRL) random network distillation (RND) scene clustering ship power systems (SPSs)

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：