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检索条件"主题词=partitioning algorithms"

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Anchor-Oriented Localized Voronoi partitioning for GPS-denied Multi-Robot Coverage

Anchor-Oriented Localized Voronoi Partitioning for GPS-denie...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Aiman Munir Ehsan Latif Ramviyas Parasuraman School of Computing University of Georgia Athens GA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Multi-robot coverage is crucial in numerous applications, including environmental monitoring, search and rescue operations, and precision agriculture. In modern applications, a multi-robot team must collaboratively explore unknown spatial fields in GPS-denied and extreme environments where global localization is unavailable. Coverage algorithms typically assume that the robot positions and the coverage environment are defined in a global reference frame. However, coordinating robot motion and ensuring coverage of the shared convex workspace without global localization is challenging. This paper proposes a novel anchor-oriented coverage (AOC) approach to generate dynamic localized Voronoi partitions based around a common anchor position. We further propose a consensus-based coordination algorithm that achieves agreement on the coverage workspace around the anchor in the robots’ relative frames of reference. Through extensive simulations and real-world experiments, we demonstrate that the proposed anchor-oriented approach using localized Voronoi partitioning performs as well as the state-of-the-art coverage controller using GPS.

关键词： Location awareness Robot motion Precision agriculture Target tracking Robot kinematics Heuristic algorithms partitioning algorithms Environmental monitoring Intelligent robots Global Positioning System

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DIAPASON: Differentiable Allocation, partitioning and Fusion of Neural Networks for Distributed Inference

DIAPASON: Differentiable Allocation, Partitioning and Fusion...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Federico Nicolás Peccia Alexander Viehl Oliver Bringmann FZI Research Center for Information Technology Germany University of Tübingen Germany

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Concerns in areas such as privacy, energy consumption, climate gas emissions, and costs, push the trend of migrating neural network inference from being executed on the cloud to embedded edge devices. We present our novel approach DIA-PASON to overcome restrictions brought on by the computing and application requirements that impede their execution on resource-constrained embedded devices. Our approach addresses these challenges by distributing the inference across multiple computing instances, which could be anything ranging from a multi-CPU configuration on the same SoC to geographically distributed devices. In contrast to recent efforts which tend to apply heuristics to reduce the search space of their problem definition to solve it in a timely fashion, our novel problem definition applies the concept of continuous relaxation to the categorical selection of partitioning, layer fusion, and allocation opportunities. This approach overcomes the problem of the poor exploration of the actual search space that arises when removing potential distribution opportunities during problem simplifications. We conduct numerical simulations and ablation experiments on each one of the configuration parameters of our algorithm by distributing several widely used neural networks. Finally, we compare DIAPASON against the commonly used MoDNN baseline and a state-of-the-art approach, CoopAI, achieving a 44 % and 12 % mean speed-up respectively.

关键词： Privacy Neural networks Search problems Prediction algorithms Numerical simulation partitioning algorithms Resource management

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MT-PART: Metamorphic-Testing-Based Adaptive Random Testing Through partitioning

MT-PART: Metamorphic-Testing-Based Adaptive Random Testing T...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Zhihao Ying Dave Towey Tsong Yueh Chen Zhi Quan Zhou School of Computer Science University of Nottingham Ningbo China Zhejiang People's Republic of China Department of Computer Science and Software Engineering Swinburne University of Technology Hawthorn VIC Australia School of Computing and Information Technology University of Wollongong Wollongong NSW Australia

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Metamorphic Testing (MT) has been repeatedly proven effective in detecting software faults. MT detects faults by checking the Metamorphic Relations (MRs) among Source Test Cases (STCs) and Follow-up Test Cases (FTCs) and the corresponding outputs. Metamorphic Groups (MGs) denote the associated STCs and FTCs. The performance of MT relates strongly to the MRs and MGs. However, previous studies that on MG generation mainly focused on improving the effectiveness (i.e. fault-detection capability) of MT, but to some extent overlooked the efficiency. This paper proposes a new kind of MG generation algorithms called Metamorphic-Testing-based Adaptive Random Testing through partitioning (MT-PART). These algorithms at-tempt to improve both the effectiveness and the efficiency of MT by dynamically partitioning the input domain and generating new STCs and FTCs that are uniformly distributed over their corresponding input domains. Through empirical experiments, we found that our algorithms are able to significantly outper-form other existing MG generation algorithms in terms of test efficiency, while maintaining good test effectiveness.

关键词： Heuristic algorithms Software algorithms Software partitioning algorithms Testing

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Research on the Generation Method of Power Grid partitioning Schemes Under the Background of High Proportion of New Energy Integration

Research on the Generation Method of Power Grid Partitioning...

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Energy Systems and Electrical Power (ICESEP), International Conference on

作者： Zijing LU Zishou Lil Jun Tang Hongkun Chen State Grid Hubei Electric Power Co. Ltd State Grid Corporation of China Wuhan Hubei China School of Electrical Engineering and Automation Wuhan University Wuhan Hubei China Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network Wuhan University Wuhan Hubei China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

Power grid partition is utilized widely for simplifying network structure, facilitating system monitoring and scheduling, and is an inevitable trend in development of large power grid. However, in the context of large-scale new energy integration, the uncertainty and volatility of its output have a significant impact on the operational characteristics of the power grid. Aiming at the shortcomings of traditional power grid partitioning methods that do not consider the impact of new energy integration, a generation method of power grid partitioning schemes considering the background of new energy integration is proposed. Firstly, a typical set of new energy output scenarios is obtained based on scenario generation and scenario reduction; Afterwards, the definition of electrical distance is given, the electrical distance matrix of the network is constructed, and a correction coefficient is defined based on the typical scenario set of new energy output to modify the electrical distance matrix. Then, the Dijkstra algorithm is used to expand it to the full dimensional electrical distance matrix; Finally, affinity propagation algorithm is used to complete the power grid partitioning, and a case study is conducted on an improved IEEE 39 node system to verify the effectiveness and rationality of the proposed method.

关键词： Renewable energy sources Uncertainty Affinity propagation Market research Power grids partitioning algorithms Scenario generation

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Leveraging the Cuboidal partitioning for Low Complexity CTU Structure Prediction in Versatile Video Coding

Leveraging the Cuboidal Partitioning for Low Complexity CTU ...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Md. Zahirul Islam Tanvir Ahmed Redoy Ashek Ahmmed Manoranjan Paul Manzur Murshed Department of CSE Rajshahi University of Engineering and Technology Bangladesh School of Computer Science University of Sydney Australia School of Computing and Mathematics Charles Sturt University Australia School of Info Technology Deakin University Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

As the capacity of communications systems is currently being outpaced by the increasing demand for access to audio-visual services, the versatile video coding (VVC) standard has emerged. It shows around 40% bit rate savings over the predecessor codec, the high efficiency video coding (HEVC) standard. However, this coding gain is obtained with a significant increase in the computational complexity associated with VVC. The cuboidal partitioning algorithm has shown capabilities in modeling both the global and local commonality of a video frame and discovers a set of non-overlapping rectangular regions, known as cuboids, over the current frame. Since cuboids are highly homogeneous and are computationally efficient, in this paper, it is proposed to employ their intrinsic homogeneity information in predicting the coding tree unit (CTU) structure of VVC. Experimental results show that up to 10.5% complexity reduction for intra coded 4K resolution video sequences, with an increase in bit rate by 1.4%.

关键词： Computational modeling Bit rate Video sequences Prediction algorithms Encoding partitioning algorithms Computational efficiency Standards High efficiency video coding Periodic structures

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Counting Triangles of Graphs via Matrix partitioning

Counting Triangles of Graphs via Matrix Partitioning

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IEEE Workshop on Machine Learning for Signal Processing

作者： Georgios Kollias Vassilis Kalantzis Lior Horesh Shashanka Ubaru Panagiotis A. Traganitis IBM Research Thomas J. Watson Research Center Yorktown Heights NY USA Dept. of Electrical and Computer Engineering Michigan State University East Lansing MI USA

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

Counting the number of triangles is an important task in the computation of several network-related metrics such as transitivity ratio, link recommendation, near-clique subgraph detection, and clustering coefficient. This contribution presents a new algorithm based on non-overlapping subgraph decomposition and matrix partitioning. The part of the adjacency matrix associated with each submatrix is replaced by a low-rank approximation resulting to complexity savings. The proposed algorithm is also suitable for high-latency architectures as well as graphs whose entries are updated over time. Several practical and theoretical aspects are discussed while numerical experiments on real-world graphs demonstrate the potential of the proposed algorithm.

关键词： Measurement Machine learning algorithms Message passing Signal processing algorithms Clustering algorithms Signal processing Parallel processing Approximation algorithms partitioning algorithms Matrix decomposition

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Submodel partitioning in Hierarchical Federated Learning: Algorithm Design and Convergence Analysis

Submodel Partitioning in Hierarchical Federated Learning: Al...

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IEEE International Conference on Communications (ICC)

作者： Wenzhi Fang Dong-Jun Han Christopher G. Brinton Elmore Family School of Electrical and Computer Engineering Purdue University

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Hierarchical federated learning (HFL) has demon-strated promising scalability advantages over the traditional “star-topology” architecture-based federated learning (FL). How-ever, HFL still imposes significant computation, communication, and storage burdens on the edge, especially when training a large-scale model over resource-constrained Internet of Things (IoT) devices. In this paper, we propose hierarchical independent submodel training (HIST), a new FL methodology that aims to address these issues in hierarchical settings. The key idea behind HIST is a hierarchical version of model partitioning, where we partition the global model into disjoint submodels in each round, and distribute them across different cells, so that each cell is responsible for training only one partition of the full model. This enables each client to save computation/storage costs while alleviating the communication loads throughout the hierarchy. We characterize the convergence behavior of HIST for non-convex loss functions under mild assumptions, showing the impact of several attributes (e.g., number of cells, local and global aggregation frequency) on the performance-efficiency tradeoff. Finally, through numerical experiments, we verify that HIST is able to save communication costs by a wide margin while achieving the same target testing accuracy.

关键词： Training Costs Accuracy Federated learning Computational modeling partitioning algorithms Internet of Things

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Scaling Knowledge Graph Embedding with Parallel TransE and Graph partitioning

Scaling Knowledge Graph Embedding with Parallel TransE and G...

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ACS/IEEE International Conference on Computer Systems and Applications

作者： Khouloud Ammar Wissem Inoubli Sami Zghal Engelbert Mephu Nguifo University of Tunis El Manar Faculty of Sciences of Tunis LIPAH-LR11ES14 Tunis Tunisia Univ. Artois CNRS UMR 8188 CRIL Lens France Universite de Jendouba Jendouba Tunisia University Clermont Auvergne CNRS Clermont Auvergne INP LIMOS Clermont-Ferrand France

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Knowledge graph embedding has emerged as a fundamental technique to represent entities and relationships in knowledge graphs within low-dimensional vector spaces. Among these methods, translation-based approaches stand out by treating relations as translations from head entities to tail entities, achieving state-of-the-art results. However, the training process of these methods can be prohibitively time-consuming, especially for large knowledge graphs, posing significant challenges in practical applications. As knowledge graphs grow in size and complexity, surpassing the capacities of existing systems, there is an urgent need for scalable solutions in knowledge representation learning. These graphs, comprising millions of nodes and billions of edges, serve as powerful data structures for representing and understanding complex networks of knowledge. Translation-based models, particularly TransE, have been instrumental in encoding structured information about entities and their relationships in low-dimensional embedding spaces. However, the current implementation of TransE is constrained to single-node machines, limiting its scalability and applicability. To address these limitations, this paper proposes leveraging parallel computing technique, such as parallel TransE with graph partitioning, to enhance scalability and efficiency in knowledge graph embedding.

关键词： Training Systematics Scalability Computational modeling Knowledge graphs Tail Parallel processing Prediction algorithms Vectors partitioning algorithms

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Adaptive DNN partitioning Method for Pipeline Parallel Training with Time-Varying Resources

Adaptive DNN Partitioning Method for Pipeline Parallel Train...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Shengang Deng Zhi Ling Mingrui Zhu Shuangwu Chen Xiaofeng Jiang Jian Yang University of Science and Technology of China Hefei China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Pipeline parallelism provides a flexible and scalable approach for training large deep neural networks (DNNs). To realize efficient pipeline parallel training, deploying model partitions that adapt to the resource configurations is crucial. However, due to the time-varying nature prevalent in real-world applications, model partitions can quickly become outdated, resulting in imbalanced workload distribution. Consequently, maintaining load-balanced model partitions under time-varying resource scenarios poses a challenge. In this paper, we propose an adaptive partitioning method that dynamically adjusts model partitions in response to variations in available resources, ensuring high training efficiency. First, we derive the execution duration model and redistribution cost model of pipeline training systems. Then, we perform a joint optimization of the two factors with the goal of minimizing the training time. Based on this, we implement a prototype system called ARPipe, in which a Fast partitioning Decision (FPD) algorithm is proposed to quickly determine optimized model partitions. Experimental evaluations show that under real-world circumstances with time-varying hardware resources, ARPipe can reduce training time by up to 30% in comparison to the state-of-the-art method GPipe when training the ResNet50 model on the CIFAR10 dataset.

关键词： Training Adaptation models Costs Computational modeling Pipelines Prototypes Parallel processing partitioning algorithms Optimization Load modeling

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Automatic Layout of One-Line Diagrams for Large Transmission Systems Based on Graph partitioning

Automatic Layout of One-Line Diagrams for Large Transmission...

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IEEE General Meeting Power& Energy Society

作者： Chen Yang Jingyu Wang Dongyuan Shi School of Electrical and Electronic Engineering Huazhong University of Science and Technology

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

One-line diagrams are widely used in power system applications, but their automatic layout remains a challenge. Currently, most approaches are dedicated to solving the layout of small-scale power transmission systems to produce aesthetically pleasing one-line diagrams. To extend them to large-scale systems, this paper proposes an integrated framework for the automatic layout of one-line diagrams based on graph partitioning. Graph partitioning allows a large graph to be divided into multiple subgraphs, and the layout for each subgraph is optimized using mixed-integer linear programming (MILP). These optimized subgraphs are then reassembled into a large graph using the subgraph assembly algorithm and the tie-line routing strategy. The case studies demonstrate that the proposed framework can efficiently generate one-line diagrams that satisfy aesthetic criteria. The output layout can preserve the relative position of nodes as in the original layout, which significantly improves its readability. Moreover, the time consumption of the proposed framework is acceptable for practical use.

关键词： Layout Software algorithms Power transmission Parallel processing Routing Software partitioning algorithms Mixed integer linear programming Assembly Optimization

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