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检索条件"主题词=Partitioning Algorithms"

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Enhancing Community Detection with Weighted Louvain

Enhancing Community Detection with Weighted Louvain

引用

International Conference on Web Research (ICWR)

作者： Akram Karimi Zarandi Ali Kamandi School of Engineering Science College of Engineering University of Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Community detection in complex networks plays a crucial role in analyzing data structures. The Louvain algorithm is a widely used method for community detection; however, it can be improved by incorporating edge weight information. In this study, we introduce a weighted version of the Louvain algorithm and compare it with the standard Louvain, Leiden, and Girvan-Newman methods. Experiments were conducted on six networks, including three small graphs (fewer than 500 nodes) and three large graphs (approximately 2500 nodes). The results show that the weighted Louvain algorithm not only achieves higher modularity than the original Louvain and other methods, but also reduces the execution time compared to the original Louvain. This improvement is attributed to leveraging edge weight information, which leads to faster convergence and more precise network partitioning.

关键词： Accuracy Image edge detection Heuristic algorithms Scalability Clustering algorithms Complex networks Approximation algorithms partitioning algorithms Standards Optimization

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Representative Community Detection algorithms for Attribute-Free Networks

Representative Community Detection Algorithms for Attribute-...

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International Conference on Computer and Automation Engineering, ICCAE

作者： Dongming Chen Xin Zou Yuxing He Mingzhao Xie Dongqi Wang Software College Northeastern University Shenyang China

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

In the field of network analysis, methods for identifying community structure often involve optimizing a specific objective function to achieve a single optimal allocation from network nodes to communities. However, in practice, we often encounter multiple division schemes with high quality scores that are close to the overall optimum. In fact, an accurate depiction of the community structure is more appropriately achieved by a series of high-quality division schemes rather than relying on a single optimal solution alone. However, such a collection of network divisions may be difficult to interpret, as its size may rapidly expand to hundreds or even thousands. To this end, this paper introduces an innovative strategy to reveal the diversity of network community structures more comprehensively and to provide a set of more globally-visioned delineation results by clustering similar network divisions and then selecting representative divisions from each cluster.

关键词： Clustering methods Network analyzers Complex networks Network coding Linear programming partitioning algorithms Resource management Object recognition Detection algorithms Periodic structures

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Sum-Rate Maximization for Partitioned-IRS-Assisted Hybrid NOMA Networks

Sum-Rate Maximization for Partitioned-IRS-Assisted Hybrid NO...

引用

IEEE Conference on Wireless Communications and Networking

作者： Songchao Chen Fang Liu Yuanan Liu School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Intelligent Reflecting Surfaces (IRS) have emerged as a revolutionary solution in next-generation (6G) wireless communication systems. Comprising numerous programmable reflecting units, IRS technology improves data transmission rates by dynamically adjusting the phase shifts of reflected signals. Non-orthogonal multiple access (NOMA) is a technique that can greatly enhance spectral efficiency and system sum-rate. In this paper, we consider a partitioned-IRS-assisted NOMA system in which a base station (BS) transmits superposed downlink signals to multiple users. Firstly, we establish the system sum-rate maximization problem of optimal joint IRS phase shifts and the partition sub-surfaces matching scheme by adapting combined channel strength(CSS)-based hierarchical pairing algorithm. However, since the formulated problem is non-convex, we use an alternating optimization (AO) technique to decompose the non-convex optimization problem into two subproblems for iterative solving. To further optimize the sum-rate, we also propose a NOMA user selection criteria based on the minimum signal-to-noise ratio (SNR) difference. The simulation results show a significant improvement in the performance of the IRS-assisted NOMA system compared to various traditional benchmarking schemes.

关键词： Wireless communication NOMA Spectral efficiency System performance Simulation Downlink partitioning algorithms Iterative methods Optimization Signal to noise ratio

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An Efficient Dense Pedestrian Detection Model Based on RT-DETR

An Efficient Dense Pedestrian Detection Model Based on RT-DE...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Chaofan Wu Tao He Kun Yang School of Computer Science University of South China Hengyang China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Pedestrian detection is a vital study field within object detection. With the development of deep learning, models perform exceptionally well in pedestrian detection for sparse scenarios. However, their performance often fails in complex and crowded dense pedestrian scenarios, particularly when there is a concentration of small objects. Challenges such as low detection accuracy, high miss rates, and high false-positive rates persist in these scenarios. To enhance dense pedestrian detection performance, we propose a model named RT-DETR-LKG, based on RT-DETR. The improved model incorporates LSKA (Large Separable Kernel Attention) and the GELAN (Generalized Efficient Layer Aggregation Network) modules (LKG). The backbone network integrates the LSKA, which generates spatial attention weights for feature maps via multiple convolutional layers. This process enables feature extraction across various receptive fields and applies weighting to input channels, resulting in more refined feature extraction and enhancement. The hybrid encoder network applies the GENLAN module. It achieves rich feature extraction with minimal computational cost by combining channel partitioning and repeated convolutions. The network's feature extraction ability is also kept. In bounding box regression, the WIoU loss function is used instead of the GIoU loss function to improve anchor selection for occluded and small object prediction boxes. Experimental results indicate that the new RT-DETR algorithm performs better in crowded pedestrian scenarios. It has a mean average precision of 86.1%, which is 1.2% higher than the original model.

关键词： Deep learning Pedestrians Attention mechanisms Accuracy Object detection Feature extraction Prediction algorithms Power electronics partitioning algorithms Kernel

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Dcha: Distributed-Centralized Heterogeneous Architecture Enables Efficient Multi-Task Processing for Smart Sensing

Dcha: Distributed-Centralized Heterogeneous Architecture Ena...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Erxiang Ren Cheng Qu Li Luo Yonghua Li Zheyu Liu Xinghua Yang Qi Wei Fei Qiao School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China MakeSens AI Beijing China College of Science Beijing Forestry University Beijing China Department of Precision Instrument Tsinghua University Beijing China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

The rapid development of artificial intelligence (AI) has accelerated the progression of IoT technology into the smart era. Integrating AI processing capabilities into IoT devices to create smart sensing systems holds significant promise. In this work, we propose a distributed-centralized heterogeneous architecture that enables efficient multitask processing for smart sensing. This architecture improves the operational efficiency of sensing systems and enhances the deployment scalability through collaborative computing across end, edge, and center nodes. Specifically, we partition the network in traditional centralized sensing systems into several parts and perform algorithm-hardware co-design for each part on its respective deployment platform. We developed a sample design to validate the proposed architecture. By implementing a lightweight image encoder, we achieved an 88x reduction in encoder parameters and up to 9873x energy gain, facilitating deployment on resource-constrained devices. Experimental results demonstrate that the proposed architecture effectively reduces overall energy consumption by 0.0573x to 0.0889x, while maintaining robust multitask inference capabilities. Moreover, energy consumption reductions of 2.88x to 3.22x on edge nodes and 6311.56x to 10037.23x on end nodes were observed.

关键词： Performance evaluation Energy consumption Soft sensors Scalability Collaboration Computer architecture Sensors partitioning algorithms Internet of Things Artificial intelligence

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Image Computation for Quantum Transition Systems

Image Computation for Quantum Transition Systems

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Xin Hong Dingchao Gao Sanjiang Li Shenggang Ying Mingsheng Ying Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Centre for Quantum Software and Information University of Technology Sydney Sydney Australia

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

With the rapid progress in quantum hardware and software, the need for verification of quantum systems becomes increasingly crucial. While model checking is a dominant and very successful technique for verifying classical systems, its application to quantum systems is still an underdeveloped research area. This paper advances the development of model checking quantum systems by providing efficient image computation algorithms for quantum transition systems, which play a fundamental role in model checking. In our approach, we represent quantum circuits as tensor networks and design algorithms by leveraging the properties of tensor networks and tensor decision diagrams. Our experiments demonstrate that our contraction partition-based algorithm can greatly improve the efficiency of image computation for quantum transition systems.

关键词： Quantum system Tensors Europe Model checking Software Hardware partitioning algorithms Computational efficiency Quantum circuit

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Exploiting Boosting in Hyperdimensional Computing for Enhanced Reliability in Healthcare

Exploiting Boosting in Hyperdimensional Computing for Enhanc...

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Design, Automation and Test in Europe Conference and Exhibition

作者： SungHeon Jeong Hamza Errahmouni Barkam Sanggeon Yun Yeseong Kim Shaahin Angizi Mohsen Imani University of California Irvine DGIST New Jersey Institute of Technology

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Hyperdimensional computing (HDC) enables efficient data encoding and processing in high-dimensional spaces, benefiting machine learning and data analysis. However, under-utilization of these spaces can lead to overfitting and reduced model reliability, especially in data-limited systems-a critical issue in sectors like healthcare that demand robustness and consistent performance. We introduce BoostHD, an approach that applies boosting algorithms to partition the hyperdimensional space into subspaces, creating an ensemble of weak learners. By integrating boosting with HDC, BoostHD enhances performance and reliability beyond existing HDC methods. Our analysis highlights the importance of efficient utilization of hyperdimensional spaces for improved model performance. Experiments on healthcare datasets show that BoostHD outperforms state-of-the-art methods. On the WESAD dataset, it achieved an accuracy of 98.37% ± 0.32%, surpassing Random Forest, XGBoost, and On-lineHD. BoostHD also demonstrated superior inference efficiency and stability, maintaining high accuracy under data imbalance and noise. In person-specific evaluations, it achieved an average accuracy of 96.19%, outperforming other models. By addressing the limitations of both boosting and HDC, BoostHD expands the applicability of HDC in critical domains where reliability and precision are paramount.

关键词： Accuracy Computational modeling Medical services Boosting Stability analysis Robustness partitioning algorithms Thermal stability Random forests Overfitting

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Optimal Topology Design in Radially Distributed Electrification Scenarios with Distributed Generation

Optimal Topology Design in Radially Distributed Electrificat...

引用

Grid Edge Technologies Conference & Exposition (Grid Edge), IEEE PES

作者： Weixi Wang Robert Flores Jack Brouwer Advanced Power and Energy Program University of California Irvine Irvine California

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

It is known that climate change related power outages could be addressed with microgrids. Forming reliable microgrids requires connecting distributed energy sources so that electricity balance can be achieved. In this paper, a straightforward topology design methodology that focuses upon improving local power balance in island operation mode while observing power quality requirements and keeping most loads in service is proposed and then applied to various real-life electrification and islanding scenarios. A revised Multilevel Graph partitioning algorithm is implemented and compared with several other methods. The quantified results present a better balance of power and are considered adding to reliability within the community, with the proposed algorithm achieving the overall best degree of local power balance.

关键词： Islanding Simulation Power quality Microgrids Voltage Electrification partitioning algorithms Topology Power system reliability Reliability

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DNN Inference Acceleration and Reliable Task Offloading in Mobile-Edge Computing

DNN Inference Acceleration and Reliable Task Offloading in M...

引用

International Conference on Computing, Networking and Communications (ICNC)

作者： Changke Wang Xiaowen Huang Wenqian Zhang Guanglin Zhang College of Information Science and Technology Donghua University Shanghai China

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Most computation-intensive tasks are offloaded to high-performance edge servers, making the acceleration of deep neural networks (DNNs) a prominent research topic in mobile-edge computing networks. Deploying computationally demanding deep learning networks on resource-constrained mobile devices poses significant challenges. We first propose the overlapped partitioning DNNs, along with mobility-aware task offloading, focusing on analyzing the trade-offs between DNN inference acceleration and offloading reliability design insights. We then establish a collaborative DNN partitioning and offloading scenario, derive an acceleration and reliability model for DNN inference, and define the collaborative partitioning and offloading problem. Finally, we propose two algorithms: self-attention deterministic Policy Gradient (SAD) and optimal allocation algorithm (OAA). SAD globally optimizes task offloading strategies for mobile-edge DNNs, while OAA fine-tunes the strategy, optimizing inference acceleration and reliability between neighboring communication nodes. The simulation evaluations demonstrate the proposed scheme’s superiority.

关键词： Collaboration Artificial neural networks Prediction algorithms Reliability engineering Inference algorithms Mobile handsets partitioning algorithms Servers Resource management Optimization

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$k$-Subspace Method

$k$-Subspace Method

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Yasuhito Fujii Yoriaki Murata Hitoshi Sakano Interdisciplinary Faculty of Science and Engineering Shimane University Matsue Japan Graduate School of Natural Science and Technology Shimane University Matsue Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

We propose the k-subspace method, a clustering method based on the subspace method, and the k-subspace classification method, a pattern recognition method that utilizes multiple subspaces for each class. Conventional clustering algorithms, such as the k-means method, operate based on centroids. However, in image recognition, subspaces provide a more essential representation. Existing techniques, such as CLAFIC, define a single subspace per class. However, real-world data often exhibit significant intra-class variations, such as diverse character styles within the same class. We hypothesize that partitioning subspaces into subsets, which account for intra-class variations, will enhance classification accuracy. In this study, we examine the feasibility of partitioning a sub-space into $k$ subclasses. We apply the proposed method to image recognition and experimentally demonstrate its effectiveness.

关键词： Handwriting recognition Image recognition Machine learning algorithms Clustering algorithms Training data Machine learning Solids Classification algorithms partitioning algorithms Convergence

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