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检索条件"主题词=partitioning algorithms"

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Intelligent Task Offloading Method for Vehicular Edge Computing Based on Improved-SAC

Intelligent Task Offloading Method for Vehicular Edge Comput...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Tiyin Xiao Yuanyuan Qi Tao Shen Yan Feng Lin Huang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Kunming University of Science and Technology Kunming China Yunnan Provincial Academy of science and technology Kunming China

ISBN: (纸本)9781665479691

In the era of rapid development of the Internet of Vehicles (10V), how to use vehicular edge computing to process high-complexity intelligent tasks in real time is an urgent problem to be solved. To cope with the offloaded intelligent task interruption in IoV caused by mobility, this paper proposes an offloading model based on improved Soft Actor-Critic (Improved-SAC) algorithm to minimize the latency. Using the divisibility to task partition, we divide intelligent tasks into multiple layers to reduce task interruption. The problem formulation of minimizing the latency is obtained by jointly considering vehicular mobility, location, computing power and coverage of edge nodes. This paper utilizes Value-Difference Based Exploration (VDBE) and dual experience playback pool to improve the efficiency of sample data utilization and the exploration ability of SAC, and introduces Improved-SAC algorithm into knowledge plane. The numerical results show that the Improved-SAC algorithm can reduce the latency by up to 64% compared with Q-learning and DQN algorithm, and generate near-optimal offloading decisions within 70ms.

关键词： Deep learning Q-learning Computational modeling Collaboration Real-time systems partitioning algorithms Information management

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Faults detecting of high-dimension gas turbine by stacking DNN and LLM

Faults detecting of high-dimension gas turbine by stacking D...

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Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS)

作者： Seyed Mohammad Emad Oliaee Mahdi Aliyari Shoorehdeli Mohammad Teshnehlab Control Engineering Dept K. N. Toosi University of Technology Tehran Iran

Increasing the input dimension cause curse of dimensions which make the inefficiency of methods, especially in analysis and interpretation practically. In this paper, a new two-part structure for fault diagnosing and identifying (FDI) of high-dimension systems has been presented. The first part of which consists of Auto-Encoder (AE) as Deep Neural Networks (DNNs) to produce features engineering process and summarize the features, and the second part Local Models Networks (LMNs) with LOcal LInear MOdel Tree (LOLIMOT) algorithm to model outputs. Then the residual which generated by comparing output of system and obtained models in each condition is used to alarm faults. Standard laboratory Gas Turbine data is the case study for this structure. Finally, by comparing the simulated results with the several reliable works, the effectiveness of this proposed structure is well illustrated.

关键词： Turbines partitioning algorithms Training Fuzzy systems Computational modeling Feature extraction Fault detection

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Local conditions for critical and principal manifolds

Local conditions for critical and principal manifolds

引用

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

作者： Umut Ozertem Deniz Erdogmus CSEE Department Oregon Health and Sciences University Portland OR USA

Principal manifolds are essential underlying structures that manifest canonical solutions for significant problems such as data de- noising and dimensionality reduction. The traditional definition of self-consistent manifolds rely on a least-squares construction error approach that utilizes semi-global expectations across hyperplanes orthogonal to the solution. This definition creates various practical difficulties for algorithmic solutions to identify such manifolds, besides the theoretical shortcoming that self-intersecting or nonsmooth manifolds are not acceptable in this framework. We present local conditions for critical and principal manifolds by introducing the concept of subspace local maxima. The conditions generalize the two conditions that characterize stationary points of a function to stationary surfaces. The proposed framework yields a unique set of principal points which can be partitioned into principal curves and manifolds of any intrinsic dimensionality. A subspace-constrained fixed-point algorithm is proposed to determine the principal graph.

关键词： Noise reduction Principal component analysis partitioning algorithms Unsupervised learning Feature extraction Lead Eigenvalues and eigenfunctions Surface reconstruction Subspace constraints Piecewise linear techniques

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A local search operator in Quantum Evolutionary Algorithm and its application in Fractal Image Compression

A local search operator in Quantum Evolutionary Algorithm an...

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International Conference on Computer and Automation Engineering, ICCAE

作者： Hossein Mohamadi Ali Nodehi Mohamad Tayarani Islamic Azad University Iran

ISBN: (纸本)9781424455690

Fractal Image Compression is an optimization problem in the class of NP-Hard problems. Quantum Evolutionary Algorithm is a novel optimization algorithm proposed for class of combinatorial problems like Knapsack problem. While QEA is highly suitable for NP-Hard problems, QEA is not widely used in Fractal Image Compression. In order to improve the performance of QEA in Fractal Image Compression, this paper proposes a local search operator for QEA. The proposed algorithm uses Simulated Annealing algorithm in its search process. The SA is performed on observed possible solution to help the algorithm escaping from local optima. The proposed Simulated Annealing Quantum Evolutionary Algorithm (SAQEA) for fractal image compression is tested on several images like Lena, Pepper and Baboon for several times and is compared with QEA and GA. Experimental results show better performance for the proposed algorithm than QEA and GA and in comparison with full search, the proposed algorithm reaches suitable solutions with much less computation complexity.

关键词： Evolutionary computation Fractals Image coding Genetic algorithms Simulated annealing NP-hard problem Quantum computing Convergence partitioning algorithms Testing

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Structure-oriented clustering of XML documents: A transactional approach

Structure-oriented clustering of XML documents: A transactio...

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International IEEE Conference on Intelligent Systems, IS

作者： Gianni Costa Riccardo Ortale ICAR CNR Rende Cosenza Italy

Clustering XML documents by structure has been, generally, accomplished by looking at the occurrence of one pre-established type of structural component in the structures of the XML documents. It is likely that focusing only on one type of structural component may produce clusters with a certain extent of inner structural inhomogeneity, because of uncaught differences in the structures of the XML documents or for an inappropriate choice of structural component. To overcome these limitations, a new parameter-free approach to clustering XML document is proposed, that allows to consider simultaneously multiple types of structural components to isolate structurally-homogeneous clusters of XML documents. The idea behind the approach is to represent each XML document as a transaction of boolean feature, enlightening of suitable selection of its structural components. A parameter-free clustering scheme is, then, used to isolate structural homogeneous clusters. A comparative evaluation over both real and synthetic XML data provides evidence of effectiveness and efficacy of the devised approach.

关键词： XML Vegetation Clustering algorithms partitioning algorithms Electronic mail Focusing Nonhomogeneous media

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k-Means Partition of Monthly Average Insolation Period Data for Turkey

k-Means Partition of Monthly Average Insolation Period Data ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Mehmet Yesilbudak Ilhami Colak Ramazan Bayindir Faculty of Engineering and Architecture Nevsehir Haci Bektas Veli University Nevsehir Turkey Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey Department of Electrical and Electronics Engineering Faculty of Technology Ankara Turkey

ISBN: (纸本)9781509061686

Solar power penetration has made the site-specific energy ratings an essential necessity for utilities, independent systems operators and regional transmission organizations. Since, it leads to the reliable and efficient energy production with the increased levels of solar power integration. This study concentrates on the partitional clustering analysis of monthly average insolation period data for the 75 provinces in Turkey. Together with the k-means clustering algorithm, we use Pearson Correlation, Cosine, Squared Euclidean and City-Block distance measures for the high-dimensional neighborhood measurement and utilize the silhouette width for validating the achieved clustering results. In consequence of comparing the star glyph plots with the k-means clustering results, the most productive and the most unfavorable places among all provinces are mined on the basis of monthly average insolation period.

关键词： Clustering algorithms partitioning algorithms Correlation Solar radiation Algorithm design and analysis Classification algorithms Forecasting

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An efficient multiprocessor merge algorithm

An efficient multiprocessor merge algorithm

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International Conference on Databases, Parallel Architectures and Their Applications

作者： P.J. Varman B.R. Iyer D.J. Haderle Elec.& Computer Engineering Department Rice University Houston TX USA Data Base Technology Institute IBM Corporation San Jose CA USA Data Base Technology Institute IRM Corporation San Jose CA USA

An efficient algorithm for merging two memory-resident sorted lists is described. The algorithm is based on a novel low-cost partitioning algorithm that is used to split the two lists among an arbitrary number of processors in a way that ensures load balance during the merge. The algorithm has direct applications in memory-resident databases, as well as for handling record pointers in disk-resident databases. It may be used for parallel sorting, table access using multiple indexes, and parallel sort-merge joins. A feature of the partitioning algorithm is that it may itself be parallelized efficiently; the parallel implementation reduces partitioning time, which may become significant if the number of processors gets large. If p is the number of processors and N is the total number of elements in both runs combined, the serial and parallel versions of the partitioning algorithm require time O(p log (N/p)), and O(log p+log(N/p)), respectively.< >

关键词： partitioning algorithms Merging Sorting Parallel algorithms Spatial databases Read-write memory Writing Database systems Information retrieval Indexes

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Constraints generation for analog circuits layout

Constraints generation for analog circuits layout

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International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Qingsheng Hao Song Chen Xianlong Hong Yi Su Sheqin Dong Zhiyi Qu Department of Computer Science and Technology Tsinghua University Beijing China Department of Computer Science & Technology Lanzhou University of Technology Gansu China

ISBN: (纸本)0780386477

In this paper, we present constraint definitions, how to extract them, the existing extraction methods, and our methods. Because the analog signal is sensitive to noise and parasitics, various layout constraints have to be taken into account in the design of high performance analog circuits. In this paper, two methods are proposed to extract these layout constraints from analog circuits, such as grouping, symmetry and matching. Circuits are represented as bipartite graphs. Group constraints among devices are generated by means of signal flow analysis on the circuit. Symmetry constraints are extracted by solving graph isomorphism. Matching constraints can be generated by pattern recognition. The experimental results demonstrate the effectiveness and efficiency of the proposed methods.

关键词： Analog circuits Bipartite graph Circuit noise Routing Crosstalk partitioning algorithms Power generation Compaction Noise figure Noise generators

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for Data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

引用

IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy Data preprocessing Software algorithms Distributed databases Parallel processing Software partitioning algorithms

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An Efficient Fault Recovery Algorithm in Multiprocessor Mixed-Criticality Systems

An Efficient Fault Recovery Algorithm in Multiprocessor Mixe...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Guangdong Liu Ying Lu Shige Wang Department of Computer Science and Engineering University of Nebraska-Lincoln Lincoln NE USA Electrical and Controls Integration Lab General Motor Global Research and Development Warren MI USA

ISBN: (纸本)9781479909735

Recent years, there is an increasing interest of integrating mixed-criticality functionalities onto a shared computing platform in automotive, avionics and the control industry. The benefits of such an integration include reduced hardware cost and high computational performance. Also, new challenges appear as a result of the integration since interferences across tasks with different criticalities are introduced and these interferences could potentially lead to catastrophic results. Failures are likely to be more frequent due to the interferences. Hence, it is becoming increasingly important to deal with faults in mixed-criticality systems. Although several approaches have been proposed to handle failures in mixed-criticality systems, they come either with a high cost due to a hardware replication (spatial redundancy) or with a poor utilization due to re-execution (time redundancy). In this paper, we study a scheme that provides fault recovery through task reallocations in response to permanent faults in multiprocessor mixed-criticality systems. We present an algorithm to minimize the number of task reallocations while retaining the promise that the most critical applications continue to meet their deadlines. The performance evaluation of the proposed algorithm is carried out by comparing it with two baseline algorithms. In order to evaluate the performance of algorithms from the perspective of mixed-criticality systems, we choose the state of art metric called ductility to formally measure the effects of deadline misses for tasks with different criticality levels. Under this metric, a high-criticality task is considered more important than all low-criticality tasks combined. The simulation results confirm the effectiveness of our proposed algorithm in both minimizing the number of task reallocations and retaining the promised performance of high-criticality tasks.

关键词： partitioning algorithms Scheduling Noise measurement Processor scheduling Resource management Redundancy

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