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检索条件"主题词=Partitioning Algorithms"

共 15781 条记录，以下是521-530 订阅

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A Flexible and High-Precision Activation Function Unit Based on Equi-Error partitioning Algorithm

A Flexible and High-Precision Activation Function Unit Based...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Zelong Yuan Siwei Yuan Pengyu Liu Chen Yin Lei Xu Weiguang Sheng Naifeng Jing Department of Micro-Nano Electronics Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

The diversity of activation functions has gradually increased to accommodate different tasks in modern deep neural networks (DNNs). However, these novel activation functions involve more nonlinear operations relative to traditional activation functions, which increases the computational complexity. To address these issues, a piecewise linear (PWL) approximation algorithm called Equi-Error partitioning Algorithm is proposed in this paper. The algorithm aims at balancing the errors between segments and solves the problem of excessive precision that exists in other PWL approximation methods and achieves on average 30.07× better mean squared error compared to the previous works. Based on this algorithm, we propose an activation function unit (AFU) which enables the addressing scheme of non-uniform segments and provides reconfigurability for all common activation functions by reloading parameters. End-to-end evaluation with several DNNs shows the accuracy loss is all less than 0.06% with 64 segments.

关键词： Accuracy Circuits and systems Artificial neural networks Approximation algorithms partitioning algorithms Approximation methods Task analysis

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Efficient Microprocessor Design Space Exploration via Space partitioning

Efficient Microprocessor Design Space Exploration via Space ...

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Zijun Jiang Yanpdi Lyu Microelectronics Thrust The Hong Kong University of Science and Technology (Guangzhou)

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Design space exploration (DSE) has long been a very important topic in electronic design automation (EDA). As the diversity of applications and the complexity of integrated circuits have grown rapidly in recent years, conventional DSE frameworks become less effective and efficient. This is due to the time-consuming nature of design point evaluation and the challenge of exploring high-dimensional design spaces. To address these issues, this paper proposes a DSE framework for microprocessors with a novel multi-objective optimization algorithm to find optimal designs with fewer samples. The proposed algorithm utilizes space partitioning and Bayesian optimization to efficiently explore high-dimensional design spaces in microprocessors.

关键词： Design automation Costs Microprocessors Space exploration partitioning algorithms Bayes methods Complexity theory Optimization

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A Generalized partitioning Strategy for Distributed Control

A Generalized Partitioning Strategy for Distributed Control

引用

IEEE Conference on Decision and Control

作者： Alessandro Riccardi Luca Laurenti Bart De Schutter Delft Center for Systems and Control (DCSC) Delft University of Technology Delft The Netherlands

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The partitioning problem is a key problem for distributed control techniques. The problem consists in the definition of the subnetworks of a dynamical system that can be considered as individual control agents in the distributed control approach. Despite its relevance and the different approaches proposed in the literature, no generalized technique to perform the partitioning of a network of dynamical systems is present yet. In this article, we introduce a general approach to partitioning for distributed control. This approach is composed by an algorithmic part selecting elementary subnetworks, and by an integer program, which aggregates the elementary components according to a global index. We empirically evaluated our approach on a distributed predictive control problem in the context of power systems, obtaining promising performances in terms of reduction of computation speed and resource cost, while retaining a good level of performance.

关键词： Costs Heuristic algorithms Aggregates Decentralized control Prediction algorithms Power systems partitioning algorithms Indexes Dynamical systems Predictive control

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Cluster partitioning Method for Honeycomb Distribution Networks Based on the Improved Newman Algorithm

Cluster Partitioning Method for Honeycomb Distribution Netwo...

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New Energy and Power Engineering (ICNEPE), International Conference on

作者： Chao Cai Zhiyuan Zuo Jun Han Yunjiang Zhang Wenjie Pan State Grid Jiangsu Electric Power Economic and Technical Research Institute Nanjing China School of Electric Power Engineering Nanjing Institute of Technology Nanjing China

ISBN: (数字)9798331516086

ISBN: (纸本)9798331516093

With the integration of large-scale distributed power sources into honeycomb distribution network (HDN), which causes the challenges of increased uncertainty in output power, we propose a cluster partitioning strategy. It's based on the improved Newman algorithm for HDN clustering. Firstly, the topological structure characteristics of the HDN were analyzed. Secondly, to evaluate the effectiveness of the cluster partitioning, a comprehensive index which includes spatial distance indicator and power balance coefficient indicator for HDN clusters was established to assess the result of the partitioning. Then, proposing a cluster partitioning method based on the improved Newman algorithm. Finally, to validate the effectiveness of the proposed method, a HDN model was selected for simulation. This method could mitigate the randomness of their output power.

关键词： Power engineering Uncertainty Network topology Clustering algorithms Distribution networks partitioning algorithms Topology Indexes Power generation

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Collaborative Path Planning and Target Search in Multi-Robot Systems with PID-Controlled Uniform Area partitioning

Collaborative Path Planning and Target Search in Multi-Robot...

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International Conference on Computer and Information Technology (ICCIT)

作者： Sanath Kumar Das Arpan Sur Md. Farhad Hossain Department of Electrical and Electronic Engineering Bangladesh University of Engineering Engineering and Technology Dhaka Bangladesh School of Information Technology and Systems University of Canberra Australia

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Autonomous exploration with target searching using multi-robot systems is a key area of research with applications in search-and-rescue, disaster response, environmental monitoring and space exploration. These systems enable faster and more efficient coverage of unknown environments compared to singlerobot solutions. However, challenges in equal area partitioning, efficient coverage path planning, and accurate target detection remain significant. This paper introduces a novel multi-robot exploration framework for ground robots operating within unknown convex polygonal environments, coordinated by a Unmanned Aerial Vehicle (UAV) as a central coordinator, forming an effective semi-centralized system. The approach overcomes the limitations of high overlap, inefficient coverage, and the lack of explicit area allocation in previous methods through a dynamic PID-controlled bisector-based partitioning strategy. By dividing the exploration region equitably among the robots, our approach minimizes coverage time and ensures efficient exploration. Each sub-region is discretized into uniform grid points, and an optimized path is generated using a Traveling Salesman Problem (TSP) formulation. Moreover, radio-frequency identification (RFID)-based target detection scheme is proposed to detect targets in a search and rescue operation to reduce false positives, overcoming challenges commonly faced by visual-based detection methods. Simulation results demonstrate the effectiveness of the proposed method in balancing exploration workloads, reducing coverage time, and improving detection reliability.

关键词： Robot kinematics Simulation Object detection Autonomous aerial vehicles Path planning partitioning algorithms Reliability Multi-robot systems Optimization Radiofrequency identification

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The Cluster partitioning and Autonomous Optimization Control Based on the Dung Beetle Optimization Algorithm

The Cluster Partitioning and Autonomous Optimization Control...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： PengFei Yang TengShuo Li Zhe Yin Ruijin Zhu College of Electrical Engineering Tibet University of Agriculture and Animal Husbandry Lin zhi China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

To address the voltage limit issues caused by the large-scale integration of distributed photovoltaic (PV) systems into current distribution networks, this paper proposes a voltage control strategy based on cluster-based autonomous optimization. The strategy aims to minimize PV generation loss and line active power loss by optimizing the active and reactive power output of PV inverters. Using k-means clustering, the paper introduces an improved K-means clustering method based on the Dung Beetle Optimization (DBO) algorithm, considering the silhouette coefficient as the evaluation metric for network partitioning. The cluster autonomous optimization control rapidly adjusts the voltage within the cluster by updating the optimal solution and virtual balance node within the group, achieving voltage control through active power reduction. The proposed method is applied to a 10.5kV line in China to verify its effectiveness and feasibility.

关键词： Photovoltaic systems Reactive power Clustering methods Simulation Clustering algorithms Static VAr compensators System integration partitioning algorithms Voltage control Optimization

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Scalable Co-Clustering for Large-Scale Data Through Dynamic partitioning and Hierarchical Merging

Scalable Co-Clustering for Large-Scale Data Through Dynamic ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zihan Wu Zhaoke Huang Hong Yan Department of Electrical Engineering City University of Hong Kong Hong Kong

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Co-clustering simultaneously clusters rows and columns, revealing more fine-grained groups. However, existing co-clustering methods suffer from poor scalability and cannot handle large-scale data. This paper presents a novel and scalable co-clustering method designed to uncover intricate patterns in high-dimensional, large-scale datasets. Specifically, we first propose a large matrix partitioning algorithm that partitions a large matrix into smaller submatrices, enabling parallel co-clustering. This method employs a probabilistic model to optimize the configuration of submatrices, balancing the computational efficiency and depth of analysis. Additionally, we propose a hierarchical co-cluster merging algorithm that efficiently identifies and merges co-clusters from these submatrices, enhancing the robustness and reliability of the process. Extensive evaluations validate the effectiveness and efficiency of our method. Experimental results demonstrate a significant reduction in computation time, with an approximate 83% decrease for dense matrices and up to 30% for sparse matrices.

关键词： High dimensional data Scalability Heuristic algorithms Computational modeling Merging Probabilistic logic Robustness partitioning algorithms Sparse matrices Cybernetics

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Optimization Layout Method of Pressure Monitoring Points in Water Supply Network Based on Density Peak Improved partitioning Around Medoids Algorithm

Optimization Layout Method of Pressure Monitoring Points in ...

引用

International Conference on Advanced Computer Theory and Engineering, ICACTE

作者： ChenLei Xie ZeYou Feng HongYu Zhao QianSheng Fang Anhui Province Key Laboratory of intelligent Building and Building Energy Saving Anhui Jianzhu University Hefei China

ISBN: (数字)9798331539887

ISBN: (纸本)9798331539894

In recent years, with the continuous expansion of the water supply network scale, leakage problems have also occurred frequently, so the leakage monitoring of urban water supply networks has received increasing attention. How to scientifically and reasonably arrange pressure monitoring points in the water supply network is the key to real-time and effective monitoring of leakage. In response to this issue, this article proposes an optimized layout method for pressure monitoring points in water supply networks based on density peak improved partitioning Around Medoids (PAM) algorithm. Firstly, by obtaining the spatial attributes of the nodes in the water supply network, the sensitivity of the nodes is calculated, and the node feature matrix data is constructed. Then, use the density peak algorithm to determine the initial clustering center of the PAM algorithm. Finally, the algorithm is used to cluster the node feature matrix data and determine the location and number of pressure monitoring points within the urban water supply network monitoring area. The experimental results show that compared with the traditional PAM algorithm, this algorithm effectively solves the problem of poor result stability caused by the randomness of the initial clustering center, and significantly improves stability and coverage node rate. The improved PAM algorithm achieves a coverage node rate of 97.76% for the layout plan of pressure monitoring points, effectively ensuring that pressure monitoring points can comprehensively and reasonably grasp the hydraulic information of the entire urban water supply network, which has guiding significance for the leakage monitoring of urban water supply networks.

关键词： Sensitivity Layout Clustering algorithms Stability analysis Real-time systems partitioning algorithms Topology Monitoring Thermal stability Optimization

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Parallel Neighborhood Expansion Based Graph Edge partitioning Algorithm

Parallel Neighborhood Expansion Based Graph Edge Partitionin...

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IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Jijie Huang Yang Chen Department of Electronics and Information Engineering Foshan University Foshan China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

This paper proposes a novel parallel neighborhood expansion-based algorithm for graph edge partitioning aimed at addressing computational efficiency and scalability issues in large-scale graph data processing. The algorithm employs innovative parallel strategies and load balancing techniques, effectively enhancing the speed and quality of graph edge partitioning. Experimental results demonstrate outstanding performance of the algorithm when handling graphs ranging one hundred thousand to millions of edges. Compared to existing methods, it significantly improves execution speed while maintaining comparable partitioning quality. In highly parallel environments, the algorithm exhibits excellent scalability and load balancing performance. Moreover, it demonstrates impressive memory efficiency, enabling processing of even larger-scale graph data.

关键词： Social networking (online) Scalability Memory management Load management Data processing Distance measurement partitioning algorithms Computational efficiency

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System partitioning Primary Frequency Regulation Capability Evaluation Based on DBSCAN Clustering

System Partitioning Primary Frequency Regulation Capability ...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Weizheng Gong Shaoqi Yu Yin Yao Zhenfei Yao Bo Xu Department of Automation East Subsection of State Grid Corporation of China Shanghai China College of Electric Engineering Shanghai University of Electric Power Shanghai China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

The high proportion of new energy sources and large-capacity DC feeds have led to a continuous weakening of system inertia support and primary frequency regulation capabilities. This has highlighted the temporal and spatial dispersion of system frequency response. This dispersion introduces certain errors in evaluating primary frequency regulation capabilities, resulting in unnecessary economic losses for units participating in frequency response and significantly reducing their initiative. To mitigate the impact of frequency dispersion on the primary frequency regulation capability of units, this paper proposes a system partitioning method based on the DBSCAN clustering algorithm. Firstly, to effectively reduce the impact of frequency response dispersion, the DBSCAN clustering algorithm is introduced to partition the power system, with the optimal partitioning results evaluated using the DB index. Then, sensitivity analysis is used to perform disturbance analysis on nodes within each region to determine the optimal frequency measurement point. Finally, a partial regional system of the East China Power Grid is established in the PSD-BPA simulation software to verify the effectiveness of the proposed method. Simulation results show that this method can effectively reduce the impact of frequency dispersion on primary frequency regulation assessment results, making the evaluation more accurate and reliable.

关键词： Economics Accuracy Clustering algorithms Frequency response Regulation Power grids Frequency measurement partitioning algorithms Frequency control Dispersion

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