检索结果-内蒙古大学图书馆

Efficient Backbone Network Construction in Wireless Artificial Intelligent Computing Systems

Tsinghua Science and Technology 2025年第5期30卷 2300-2319页

作者： Ming Sun Xinyu Wu Yi Zhou Jin-Kao Hao Zhang-Hua Fu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China LERIA Université d'Angers Angers France Shenzhen Institute of Artificial Intelligence and Robotics for Society the Chinese University of Hong Kong Shenzhen China

In wireless artificial intelligent computing systems, the construction of backbone network, which determines the optimum network for a set of given terminal nodes like users, switches, and concentrators, can be naturally formed as the Steiner tree problem. The Steiner tree problem asks for a minimum edge-weighted tree spanning a given set of terminal vertices from a given graph. As a well-known graph problem, many algorithms have been developed for solving this computationally challenging problem in the past decades. However, existing algorithms typically encounter difficulties for solving large instances, i.e., graphs with a high number of vertices and terminals. In this paper, we present a novel partition-and-merge algorithm for effectively handle large-scale graphs. The algorithm breaks the input network into small subgraphs and then merges the subgraphs in a bottom-up manner. In the merging procedure, partial Steiner trees in the subgraphs are also created and optimized by an efficient local optimization. When the merging procedure ends, the algorithm terminates and reports the final solution for the input graph. We evaluated the algorithm on a wide range of benchmark instances, showing that the algorithm outperforms the best-known algorithms on large instances and competes favorably with them on small or middle-sized instances.

关键词： Steiner trees Wireless communication Heuristic algorithms Merging Benchmark testing Cyber-physical systems Search problems Graph theory partitioning algorithms Optimization

来源：评论

学校读者我要写书评

暂无评论

Learning From Crowds by Class-Specific Instance Weighting

引用

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Ji, Qiang Jiang, Liangxiao Zhang, Wenjun Li, Chaoqun China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Sch Math & Phys Wuhan 430074 Peoples R China

In crowdsourcing scenarios, we can acquire each instance's multiple noisy label set from crowd workers and then infer its integrated label via label integration. To further enhance the quality of integrated labels, some noise correction algorithms have been proposed in recent years. Most of them aim to partition the dataset into a clean set and a noise set, followed by training one or multiple classifiers on the clean set to rectify the instances in the noise set. However, they overlook the fact that the class distribution of the clean set is often inconsistent with that of the noise set, resulting in the subpar correction performance of trained classifiers. To mitigate this inconsistency, this paper proposes a class-specific instance weighting-based noise correction (CIWNC) algorithm. In CIWNC, each class's weight is computed based on the class distribution of the clean set firstly. Subsequently, a class-specific weight is computed for each instance using the weight of the class that its integrated label belongs to, as well as its multiple noisy label set. Finally, a classifier is trained on the instance weighted clean set to rectify the instances in the noise set. Experimental results on 34 simulated and two real-world datasets demonstrate that CIWNC outperforms existing state-of-the-art noise correction algorithms significantly.

关键词： Noise Classification algorithms Noise measurement Crowdsourcing Prediction algorithms Clustering algorithms Supervised learning Training Inference algorithms partitioning algorithms Crowdsourcing learning noise correction instance weighting class-specific instance weighting

来源：评论

学校读者我要写书评

暂无评论

Fixed-Time Event-Triggered Bipartite Consensus of Multiagent Systems Under Time-Varying Disconnected Topologies

引用

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年

作者： Liu, Xiaoyang He, Haibin Jiang, Zhuyan Fan, Lu Yu, Wenwu Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

This article focuses on the fixed-time bipartite consensus problems of multiagent systems under time-varying disconnected topologies. Different from the jointly connected topology, an enhanced fixed-time local pinning algorithm is proposed to overcome challenges posed by disconnected signed networks without introducing additional topological assumptions. Especially, the motion tendencies of isolated agents in cooperative-competitive networks are thoroughly discussed. Event-triggered control with the impulsive effect is utilized to achieve the bipartite consensus of MASs with minimal energy consumption, where the total energy function does not need to be monotonic, and the Zeno behavior can be avoided. Finally, the efficacy of the designed protocol is demonstrated through two numerical examples.

关键词： Topology Event detection Network topology Sensors partitioning algorithms Multi-agent systems Control systems Protocols Cybernetics Time-varying systems Bipartite consensus disconnected topology fixed-time event-triggered control multiagent systems

来源：评论

学校读者我要写书评

暂无评论

MADFM: Multi-Layer Adaptive Driver Fatigue Monitoring Model Based on Steering Wheel Signals

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Zhu, Jinglong Liu, Qiang Zhao, Zhangzhen Lu, Yu Yao, Zikai Li, Qing Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 518107 Peoples R China Guangdong Marshell Elect Technol Co Zhaoqing 526238 Peoples R China Sun Yat Sen Univ Guangzhou Automobile Res Inst Joint Lab Intelligent Transportat & Artificial Int Shenzhen 518107 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Sydney Sch Aerosp Mech & Mechatron Engn Sydney NSW 2006 Australia

Driver fatigue poses a serious threat to road safety. Although most vehicles are equipped with driver fatigue monitoring models, the performance of existing models is not satisfactory due to the complexity and unclear reliability of fatigue features. To address these issues, this study proposed a multi-layer adaptive driver fatigue monitoring (MADFM) model, based on the steering wheel signals obtained from 25 participants in a driving simulator. The steering wheel signals were sequentially processed through an adaptive window partitioning algorithm based on sample entropy, feature extraction, and an adaptive outlier point removal algorithm based on local outlier factor. Finally, a multi-scale adaptive feature selection (AFS) algorithm based on global-local correlation was applied to obtain the optimal feature set D-o. AFS algorithm was then validated using a random forest model and compared with existing feature selection algorithms. The results showed that after adopting the AFS algorithm, the model's fatigue monitoring accuracy reached 93.71%, which was 10.21% higher than the algorithm without using feature selection. Compared to four common feature selection algorithms, AFS algorithm demonstrated superior performance in terms of monitoring accuracy, precision, recall and interpretability. The interpretability analysis results revealed that steering wheel angle and lateral acceleration features were the most sensitive features affecting the accuracy of the fatigue monitoring model. This model could improve the monitoring performance of driver fatigue, prompting safer driving behaviors, and reducing traffic accidents risk.

关键词： Fatigue Monitoring Feature extraction Wheels Vehicles Adaptation models partitioning algorithms Hidden Markov models Accuracy Real-time systems Driving fatigue feature selection adaptive model machine learning traffic safety

来源：评论

学校读者我要写书评

暂无评论

Partition-Based Cache Replacement to Manage Shared L2 Caches

引用

Chinese Journal of Electronics 2025年第3期23卷 464-467页

作者： Juan Fang Jing Wang Chengyan Li Zhicheng Yao Weimao Ke College of Computer Science Beijing University of Technology Beijing China College of Information Engineering Capital Normal University Beijing China College of Computing and Informatics Drexel University Chapel Hill NC USA

The Least recently used (LRU) replacement has been widely employed by on-chip shared caches. With the increase of cache associativity, the performance gap between LRU and theoretical optimal re-placement algorithm is significant, motivating us to design alternative replacement algorithms to improve cache performance. We propose a Partition-based cache replacement (PCR) algorithm to manage multi-core shared L2 caches. PCR takes into consideration recency and frequency information of cache access, and cache resource interference among multiple competing applications. PCR is effective to filter out the less frequently reused blocks through a dynamic insertion/promotion policy based on the target partitions, and ensures that some fraction of the working set is retained in the cache to contribute to more hits. Our evaluation shows that PCR improves performance of an 8-core system by an average of 11.3% over LRU-based cache.

关键词： Power demand Multicore processing Heuristic algorithms Education Interference Filtering algorithms Information filters partitioning algorithms System-on-chip

来源：评论

学校读者我要写书评

暂无评论

Optimizing Pinning-Synchronization and Mining Pinned-Nodes of Directed Networks

引用

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年

作者： Liu, Hui Lu, Manqiao Zhang, Xi Li, Zengyang Chen, Guanrong Zeng, Zhigang Lu, Jinhu Huazhong Univ Sci & Technol MOE Engn Res Ctr Autonomous Intelligent Unmanned S Sch ArtificialIntelligence & Automat Key Lab Image Proc & Intelligent Control Educ Mini Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Brain Inspired Intelligent Syst Wuhan 430074 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Cent China Normal Univ Sch Comp Sci Wuhan 430079 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

Pinning control provides an effective approach to controlling large-scale networks and conserving control resources. This article presents a solution to pinning synchronization in directed networks with a precise index that measures the pinning synchronization capability of directed networks, capturing full topological information about the networks. Building upon this index, the article utilizes matrix analysis tools, such as the non-negative matrix theory and strongly connected decomposition to analyze the impact of network structures and controller parameters on the network synchronizability. Specifically, the study investigates the influence of the in-degree of unpinned nodes, the difference between in-degrees and out-degrees of nodes, strong connectivity components, and the linear feedback control gains on the network synchronizability. Moreover, the article addresses the challenge of optimally selecting pinned nodes by using a graph partitioning algorithm and a greedy node selection algorithm, which can be applied to effectively select pinned nodes in a large-scale network. Extensive simulations on a range of real-world directed networks validate the efficiency of the proposed algorithms and demonstrate their superiority over seven baseline algorithms.

关键词： Synchronization Vectors Laplace equations partitioning algorithms Eigenvalues and eigenfunctions Complex networks Symmetric matrices Indexes Couplings Automation Directed network pinning control spectral graph theory synchronization

来源：评论

学校读者我要写书评

暂无评论

Optimizing Autonomous Area Search: A Synergistic Approach Using Voronoi Graphs and the Boustrophedon Algorithm

Optimizing Autonomous Area Search: A Synergistic Approach Us...

引用

International Conference on Computer and Automation Engineering, ICCAE

作者： Shashi Shekhar Ankita Kumari Central Research Laboratory Bharat Electronics Limited Ghaziabad India

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Efficient area search is critical in autonomous systems for applications like agricultural automation and disaster response. This paper introduces a novel method combining Voronoi graph creation with the Boustrophedon algorithm to optimize path planning and coverage. Voronoi graphs segment the search area into manageable regions, while the Boustrophedon algorithm ensures thorough coverage with a sweeping pattern. Our integrated approach, evaluated through simulations and experiments, shows significant improvements in coverage efficiency and path optimization. This research advances autonomous navigation and offers a robust solution for efficient area search, with future work focusing on dynamic environments and real-time applications.

关键词： Automation Heuristic algorithms Disasters Surveillance Space missions Path planning Robustness Real-time systems partitioning algorithms Optimization

来源：评论

学校读者我要写书评

暂无评论

Leveraging LSTM Networks for Adaptive Deep Learning in Connected Autonomous Vehicles

Leveraging LSTM Networks for Adaptive Deep Learning in Conne...

引用

IEEE Conference on Wireless Communications and Networking

作者： Robert Rauch Juraj Gazda Marco Levorato Department of Computers and Informatics Technical University of Kosice Kosice Slovakia Computer Science Department University of California Irvine Irvine USA

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In response to the escalating demand for high-performance applications in Connected Autonomous Vehicles (CAVs), there has been an increased effort to accelerate Computer Vision (CV) tasks. Innovations in the field suggest that partitioning Deep Learning (D L) models, which are pivotal for CV tasks, can effectively utilize the computational strengths of both CAVs and proximal edge servers. This is achieved by splitting the DL models into two parts, where execution begins on the CAV, then the intermediate results are offloaded to complete processing on the edge server. Moreover, the precision and response time of these applications vary significantly, necessitating the adoption of early exit strategies. These strategies involve modifying DL models with auxiliary branches that allow for early termination at various stages, enabling the model to exit processing early when application requirements are met. To navigate the trade-offs between accuracy and latency, it is imperative to choose the model's optimal split and exit point that align with the current environmental conditions, such as server data throughput and computational load. To achieve this, we employ a state-of-the-art method that utilizes the Deep Deterministic Policy Gradient (DDPG) algorithm, augmented by Long Short-Term Memory networks. Additionally, to refine the training of the DDPG's actor network, Imitation Learning (IL) is integrated. IL employs a heuristic exhaustive search to retrospectively identify the most effective model partition and exit point.

关键词： Deep learning Adaptation models Computational modeling Computer architecture Mathematical models partitioning algorithms Servers Long short term memory Autonomous vehicles Load modeling

来源：评论

学校读者我要写书评

暂无评论

Development of an Image Clustering Algorithm for Nanoparticle Extraction

Development of an Image Clustering Algorithm for Nanoparticl...

引用

International Russian Smart Industry Conference (SmartIndustryCon)

作者： Mikhail Savelev Vladislav Emelyanov Alexei Zakharov Department of Software Engineering Murom Institute Vladimir State University Murom Russian Federation

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The article explores the application of the k-means method and its improved versions for clustering nanomaterial images. The purpose of the study is to develop an effective clustering method capable of identifying nanomaterial particles in images with high noise levels. The standard k-means method is compared with its modifications, including as k-means++, MiniBatch k-means, and Elkan, to optimize pixel allocation. The unique properties of these modified methods, including their approaches to assigning pixels to centroids and forming clusters, are examined in detail. Noise reduction filters are described as a preprocessing step to enhance clustering. Practical recommendations for optimizing the clustering process are provided, which can improve accuracy, reliability, and noise resilience. The proposed method is based on clustering techniques that partition images into regions corresponding to the average intensity values within those regions.

关键词： Resistance Image segmentation Accuracy Noise Noise reduction Clustering algorithms partitioning algorithms Resource management Standards Resilience

来源：评论

学校读者我要写书评

暂无评论

A Fuzzy C-Means Clustering Algorithm for Real Medical Image Segmentation

A Fuzzy C-Means Clustering Algorithm for Real Medical Image ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Feifei Zhang Fei Shi Dayong Ren Yue Li School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of medical image processing, the presence of noise can often result in the obfuscation of crucial details, which in turn can have a detrimental impact on the accuracy of clinical diagnoses. In order to effectively remove noise and improve segmentation performance, this paper proposes a refined Fuzzy C-Means (FCM) algorithm, designated as MKL-FCM. The method commences with Poisson denoising, which enables more effective handling of the noise characteristics inherent to medical images. Subsequently, multi-scale Kullback-Leibler divergence is utilised to analyse local image information across varying scales, facilitating the differentiation between tissues and pathological regions. Furthermore, tight wavelet frames are capable of capturing fine image details, while reverse optimisation of the objective function serves to correct errors in feature reconstruction, thereby enhancing the accuracy of the segmentation process. The experimental results demonstrate that MKL-FCM enhances both image clarity and segmentation accuracy, and outperforms existing methods in terms of efficiency.

关键词： Image segmentation Accuracy Noise reduction Noise Signal processing algorithms Clustering algorithms Linear programming partitioning algorithms Speech processing Medical diagnostic imaging

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：