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检索条件"主题词=Partitioning Algorithms"

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An Effective Optimization Algorithm for Synthesizing Wideband Thinned Array Antenna

An Effective Optimization Algorithm for Synthesizing Wideban...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Shuyi Chen Hao Wen Pengfei Gu Zi He Dazhi Ding Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

This paper proposes an optimization algorithm for a sparse wideband planar antenna array based on the covariance matrix adaptive evolutionary strategy algorithm. By dividing the planar antenna array into multiple regions, the goal of achieving faster optimization results is achieved. When directly applying the covariance matrix adaptive evolutionary strategy algorithm to optimize a large number of antenna elements in an antenna array, the computer may run slowly or even fail to produce results. For a given antenna array aperture, before using the covariance matrix adaptive evolutionary strategy algorithm to optimize the antenna array, the array can be divided into several sub-arrays symmetrically, so that the running efficiency of the optimization algorithm will be greatly improved.

关键词： Adaptive arrays Planar arrays Aperture antennas partitioning algorithms Broadband antennas Covariance matrices Electromagnetics

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Comparison of Different Partition Clustering algorithms under the Network Flow Detection Scenario

Comparison of Different Partition Clustering Algorithms unde...

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IEEE International Conference on Communication and Information Systems (ICCIS)

作者： Zhe Bu Shuaichen Ye Security Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9798331531171

ISBN: (纸本)9798331531188

The clustering algorithm has always been considered as an effective manner to address the network flow filtering and detecting issue. Among all clustering algorithms, partition clustering is the most extensively used due to its numerous advantages such as simplicity and scalability. Many researches have made efforts on improving the performance of the partition clustering algorithm to adapt different application scenarios, thus several categories are generated. However, research regarding systematic comparison among different categories under the same scenario is still lacking, which brings uncertainty to the network flow detection. Therefore, this paper selects four typical partition clustering algorithms as examples and compares their performance under the same dataset. Some conclusions can be drawn as reference for network security professionals to carry out preliminary traffic analysis.

关键词： Uncertainty Systematics Filtering Scalability Clustering algorithms Network security Robustness partitioning algorithms Testing Information systems

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Computing Approximate Centerpoints in Polynomial Time

Computing Approximate Centerpoints in Polynomial Time

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Annual IEEE Symposium on Foundations of Computer Science

作者： Yeshwanth Cherapanamjeri Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology Cambridge USA

ISBN: (数字)9798331516741

ISBN: (纸本)9798331516758

The centerpoint is arguably the most natural generalization of the median to higher dimensions. Intuitively, a centerpoint of a point set is such that any hyperplane passing through the point results in an approximately balanced partition of the point set. Helly's theorem guarantees the existence of a point with depth $\Omega(1/d)$ which is also known to be the best possible. On the other hand, polynomial time algorithms for approximating centerpoints only guarantee a point with depth $\Omega(1/d^{2})$ , established nearly three decades ago. Unfortunately, even the simpler problem of testing whether a candidate point is a centerpoint is hard. In this paper, we present a novel notion of approximation along with a new algorithmic approach that enables efficient computation of a $\Omega(1/d)-\mathbf{depth}$ point. Our main result is a randomized algorithm that computes an $\varepsilon -\mathbf{approximate}$ centerpoint of depth $\Omega(1/d)$ ; that is, the point returned by the algorithm is at most $\varepsilon$ away from the halfspaces characterizing points of depth at least $\Omega(1/d)$ along any direction. Furthermore, the runtime of our algorithm is polynomial in $n, d, 1/\varepsilon, \log(1/\delta)$ where $\delta$ denotes the failure probability of the algorithm. Our approach is based on a reduction to the smoothed setting where each point is is given an independent Gaussian perturbation of scale $\varepsilon$ . In contrast to prior work, our algorithm is based on techniques from continuous optimization and leverages a novel connection between the problem of testing an approximate centerpoint and the Radial Isotropic Transformation, a central tool with diverse applications in mathematics and computer science. We show that the solution to this testing problem yields an approximate separation oracle for the set of large depth points, enabling its use in a gradient-descent style approach to compute centerpoints.

关键词： Computer science Runtime Perturbation methods Approximation algorithms Polynomials partitioning algorithms Computational efficiency Optimization Testing

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Distribution Systems Clustering Through Voltage Unbalance Sensitivity to Photovoltaic Presence

Distribution Systems Clustering Through Voltage Unbalance Se...

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Annual Conference of Industrial Electronics Society

作者： Rafael M Leite Mário Oleskovicz Daniel Barbosa Flavio B. Costa Department of Electrical and Computer Engineering University of São Paulo Brazil Department of Electrical and Computer Engineering Federal University of Bahia Brazil Electrical and Computer Engineering Department Michigan Technological Univesity Houghton U.S.A

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Distributed photovoltaic micro and mini-generators are gaining prominence as they become more accessible to end consumers. These systems promise to reduce expenses related to electrical energy and offer potential financial returns. One way to enhance the application and planning of the distribution network and its hosting capacity is by partitioning these systems based on the impact of distributed generation on power quality. This study aims to develop a generic network partitioning method using the Spectral Clustering algorithm, enhanced with a balancing algorithm to ensure similar cluster sizes. The input data for this method come from modeling actual standardized electrical systems and simulating the individual impact of photovoltaic generation on each low-voltage feeder connected to the primary grid. The unbalance sensitivity coefficient, which quantifies the change in the voltage unbalance factor at one node due to the variation of active power at another, is used as a metric. Considering all simulation states, the ratios of these coefficients per node form the elements of the Sensitivity matrix. By applying Balanced Spectral Clustering to this Sensitivity matrix, the method can identify continuous electrical regions that highlight the optimal partitioning of the tested distribution system. This proposed method may serve as an initial framework for defining the hosting capacity of smaller regions, taking into account the characteristics of the entire electrical system while managing its restrictive factors locally.

关键词： Photovoltaic systems Low voltage Sensitivity Limiting Power quality Clustering algorithms Voltage Planning partitioning algorithms Capacity planning

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Research on Improved Adaptive Spectral Clustering HRM-ISC Algorithm

Research on Improved Adaptive Spectral Clustering HRM-ISC Al...

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Signal Processing and Intelligent Computing (SPIC), International Conference on

作者： Hongxia Wang Weijian Tong School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

As a crucial component in the realm of data mining, In the past few years, the focus of researchers in the field of data mining has significantly shifted towards the exploration of clustering analysis, garnering widespread interest and recognition, and a considerable multitude of theories and methods have been produced. Based on the conventional clustering algorithm, spectral clustering converts the partition of the data set into the partition of the graph, and can identify the clusters composed of arbitrary shape sample data. The traditional spectral clustering algorithm NJW has problems such as difficult parameter selection in the clustering process and poor clustering effect on non-convex or non-uniform density data sets. In this paper, a hot region mining algorithm built upon improved spectral clustering (HRM-ISC) is suggested. In the first place, The enhancement of the local scale parameter in the self-tuning algorithm is achieved by incorporating the standard deviation of the sample neighborhood, which avoids the interference of the local scale parameter of the sample by noise points and the parameter selection depends on the empirical value to the maximum extent, so that the similarity matrix of the sample reflects the distribution information of the sample more accurately. Secondly, The conventional spectral clustering approach can not automatically estimate the optimal number of clusters and the eigenvector selection is difficult. According to the matrix perturbation theory, an adaptive spectral clustering algorithm utilizing eigengap is used to solve the problem. Finally, to tackle challenge of sensitivity to the initial clustering center encountered in the K-Means algorithm, the K-Medoids algorithm with variance maximization of initial clustering center was employed as a substitute.

关键词： Sensitivity Shape Perturbation methods Noise Clustering algorithms Signal processing algorithms Matrices partitioning algorithms Data mining Standards

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Resource Allocation with Service Affinity in Large-Scale Cloud Environments

Resource Allocation with Service Affinity in Large-Scale Clo...

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International Conference on Data Engineering

作者： Zuzhi Chen Fuxin Jiang Binbin Chen Yu Li Yunkai Zhang Chao Huang Rui Yang Fan Jiang Jianjun Chen Wu Xiang Guozhu Cheng Rui Shi Ning Ma Wei Zhang Tieying Zhang ByteDance Inc. University of California Berkeley Xi’ an Jiaotong University South China University of Technology

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Containerization has garnered substantial favor among cloud service providers. Nevertheless, the notable network overhead incurred between containers has prompted concerns within the community. In cloud resource scheduling, collocating service containers that frequently communicate to the same machine - termed “service affinity” - is instrumental in enhancing application performance. In response to this concern, we present a solution that harnesses service affinity and collocates containers to enhance the overall system performance and stability. To maximize the benefits of collocating containers, it is necessary to calculate a new schedule that optimally and efficiently maximizes service affinity, especially within the expansive domain of industry-scale cloud environments. In pursuit of this, we leverage the skewness property of affinity and machine learning to fuse solver-based algorithms, thereby assuring both quality and efficiency for problems at scale. Our methodology encompasses the partitioning of a given task into discrete subproblems, with a keen focus on resolving the most critical ones. Via a graph neural network classifier, we assign each subproblem to be solved independently using methods based on off-the-shelf solvers in our algorithm pool - namely, MIP-based, or column generation. This strategic approach enables the efficient computation of a schedule for a cloud cluster that fully optimizes the overall service affinity. We further propose a heuristic algorithm to compute executable container migration plans for practical use, facilitating the transition to the new placement where service affinity is well optimized. Our solution has been deployed in our large-scale production environment, covering over a million cores within ByteDance. Through the successful real-world production deployment, our approach exhibits an average improvement in end-to-end latency by 23.75% and a reduction in request error rates by 24.09% compared to the original system.

关键词： Schedules System performance Heuristic algorithms Production Containers partitioning algorithms Classification algorithms

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HeNCoG: A Heterogeneous Near-memory Computing Architecture for Energy Efficient GCN Acceleration

HeNCoG: A Heterogeneous Near-memory Computing Architecture f...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Seung-Eon Hwang Duyeong Song Jongsun Park School of Electrical Engineering Korea University Seoul Republic of Korea

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Graph convolutional network (GCN), which first applies convolutional operations to process graph data, has gained attention in various tasks involving relational data. Previous GCN accelerators have been designed with heterogeneous cores, considering two stages of inference (aggregation and combination), or with a unified core based on the inference of multi-layer as an iterative sparse-dense matrix multiplication. However, those prior works have suffered from an unnecessary large number of multiply-accumulate (MAC) operations and/or main memory accesses. In this paper, we propose HeNCoG, a GCN accelerator that utilizes a heterogeneous MAC array core for the combination stage and a near-memory computing core for the aggregation stage. In HeNCoG, considering that the number of MAC operations is significantly reduced when changing the stage execution order, the combination stage is executed first with a row-stationary dataflow. In the aggregation stage, magneto-resistive random-access memory (MRAM)-based near-memory computing is employed to reduce the number of main memory accesses needed to access the adjacency matrix in the graph dataset. Graph partitioning and double buffering techniques are also applied to further improve hardware efficiencies. Simulation results show that the HeNCoG architecture reduces execution cycles by 97% and memory accesses by 42% compared to previous works.

关键词： Circuits and systems Magnetic cores Simulation Memory architecture Energy efficiency Hardware partitioning algorithms

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Energy- and Cost-Oriented Optimization of Hybrid Coded Storage in Edge Caching Systems

Energy- and Cost-Oriented Optimization of Hybrid Coded Stora...

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IEEE International Conference on Web Services (ICWS)

作者： Haojuan Zhang Yali Wang Peiyan Yuan Junna Zhang College of Computer and Information Engineering Henan Normal University Xinxiang China College of Computing Science and Artificial Intelligence Suzhou City University Suzhou China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

As the size of data clusters grows, the issues of storage redundancy, data availability, and the cost of edge caching systems become the focus of attention. Although the multi-copy strategy improves data availability, its redundancy cost is high and does not fully consider file data characteristics. Erasure codes techniques have been widely studied as an alternative, but the selection of a suitable coding scheme remains a challenge. To this end, we propose a hybrid coding caching scheme. Firstly, we predict data types using machine learning methods to ensure that the most valuable content is stored. Secondly, for the characteristics of the multi-copy strategy, low-density parity check (LDPC), and Reed-Solomon coding (RS), we propose an adaptive data partitioning strategy (ADPRF) to select appropriate coding schemes for different data. Finally, by combining the ideas of dynamic programming and heuristic algorithms, we propose a redundancy-aware edge collaborative caching algorithm (RPCO) with joint optimization of energy and cost to determine the optimal set of collaborative nodes and the optimal cache locations for files. Compared with the traditional responsive caching scheme, this algorithm can reduce the system cost by 13.8% and access latency by 11.5%.

关键词： Costs Web services Heuristic algorithms Redundancy Collaboration Encoding Parity check codes partitioning algorithms Dynamic programming Optimization

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Faster algorithms for a Lexicographic Bi-Criteria Line Balancing Problem with Totally Ordered Precedence Constraints

Faster Algorithms for a Lexicographic Bi-Criteria Line Balan...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Shinji Imahori Yoshiyuki Karuno Yuma Ueda Faculty of Science and Engineering Chuo University Tokyo Japan Faculty of Mechanical Engineering Kyoto Institute of Technology Kyoto Japan Mechanodesign Program Graduate School of Science and Technology Kyoto Institute of Technology Kyoto Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

The line balancing has been an important production strategy which aims at redressing the balance of workloads on machines in an assembly type of production system. In this paper, a lexicographic bi-criteria optimization model of the line balancing is revisited for a mass-production of one homogeneous product. A sequence of machines and a totally ordered set of tasks are given. The machines are considered to be situated along with a production line, according to their sequence. Precedence relations between tasks are specified by the total order of tasks. The task set also consists of a given number of disjoint subsets, namely categories such that tasks in a category are considered to constitute a certain kind of family of technical skills. Each task has a positive processing time and a category index. A solution to the problem is expressed by an assignment of tasks to machines, and for the feasibility of a solution, the assignment must obey the totally ordered precedence constraint on tasks. The sum of processing times of tasks assigned to a machine in a feasible solution is the load of the machine, and the number of category indexes of tasks assigned to a machine is referred to as the degree of diverseness of the task subset. The primary objective of the problem is to minimize the maximum machine load, and the second objective is to minimize the maximum degree of diverseness of tasks assigned to a machine. The lexicographic bi-criteria line balancing problem with totally ordered precedence constraints has been known to admit a polynomial time exact algorithm, whose time complexity is linear in the number of machines and squared in the number of tasks. In this paper, a faster polynomial time exact algorithm is proposed, which improves the time complexity to be squared in the number of tasks.

关键词： Production systems Heuristic algorithms Optimization models Polynomials partitioning algorithms Complexity theory Indexes Time complexity Assembly Sorting

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Joint DNN Partition and Thread Allocation Optimization for Energy-Harvesting MEC Systems

Joint DNN Partition and Thread Allocation Optimization for E...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Yizhou Shi Liying Li Yue Zeng Peijin Cong Junlong Zhou School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Deep neural networks (DNNs) have demonstrated exceptional performance, leading to diverse applications across various mobile devices (MDs). Considering factors like portability and environmental sustainability, an increasing number of MDs are adopting energy harvesting (EH) techniques for power supply. However, the computational intensity of DNNs presents significant challenges for their deployment on these resource-constrained devices. Existing approaches often employ DNN partition or offloading to mitigate the time and energy consumption associated with running DNNs on MDs. Nonetheless, existing methods frequently fall short in accurately modeling the execution time of DNNs, and do not consider to use thread allocation for further latency and energy consumption optimization. To solve these problems, we propose a dynamic DNN partition and thread allocation method to optimize the latency and energy consumption of running DNNs on EH-enabled MDs. Specifically, we first investigate the relationship between DNN inference latency and allocated threads and establish an accurate DNN latency prediction model. Based on the prediction model, a DRL-based DNN partition (DDP) algorithm is designed to find the optimal partitions for DNNs. A thread allocation (TA) algorithm is proposed to reduce the inference latency. Experimental results from our test-bed platform demonstrate that compared to four benchmarking methods, our scheme can reduce DNN inference latency and energy consumption by up to 37.3% and 38.5%.

关键词： Energy consumption Instruction sets Heuristic algorithms Artificial neural networks Predictive models Prediction algorithms Inference algorithms partitioning algorithms Resource management Optimization

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