The programmed load spectrum is an important foundation for reliability tests, reliability design, and life prediction. Because of the various working conditions and limited load information of machine tools, this cha...
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The programmed load spectrum is an important foundation for reliability tests, reliability design, and life prediction. Because of the various working conditions and limited load information of machine tools, this characteristic needs to be fully considered when preparing the programmed load spectrum. In this article, a framework for compiling the programmed load spectrum of machine tools is established based on time-domain extrapolation and a clustering algorithm. First, the average conditional exceedance rate (ACER)-Weibull time-extrapolation method is proposed for load extrapolation. This method uses the average conditional exceedance rate method and the Weibull distribution to extrapolate the load above the maximum threshold and below the minimum threshold, respectively. Secondly, the *** clustering algorithm is used to divide the intervals of the programmed load spectrum, and the joint probability density function of the mean and amplitude is established using the Copula function. Finally, this framework is applied to compile the programmed load spectrum of computer numerical control lathes, and the cutting force spectrum is compiled, demonstrating the superiority of the proposed method.
Identifying influential spreaders is crucial for understanding the dynamics of information diffusion within complex networks. Several centrality methods have been proposed to address this, but these studies often conc...
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Identifying influential spreaders is crucial for understanding the dynamics of information diffusion within complex networks. Several centrality methods have been proposed to address this, but these studies often concentrate on only one aspect. To solve this problem, we introduce a dual-perspective approach which considers both global and local perspectives for identifying influential nodes in complex networks. From a global perspective, if a node has the capability to efficiently transmit information to various clusters within a network, then the information originating from that node will quickly spread across a large area. From a local perspective, when a node has a greater number of neighbors-especially those that are significant within the network-the information emanating from that node is less likely to be confined to a localized region. Based on this understanding, we first design a novel clustering method to detect groups in which the connections among nodes are denser than those with the rest of the network. The most influential nodes in each group are identified as global critical nodes. Subsequently, the local influence of a node is defined by the number and significance of its neighboring nodes. Ultimately, nodes are ranked according to their local influence, their proximity to the global critical nodes using the shortest paths, and the importance of these global critical nodes. To evaluate the performance of the proposed method, the susceptible-infected-removed (SIR) diffusion model is used. Results of the investigation on real networks and realistic synthetic benchmarks show that the proposed method can identify nodes with high influence better than other centrality methods.
In this article, Swendsen-Wang-Wolff algorithms are extended to simulate spatial point processes with symmetric and stationary interactions. Convergence of these algorithms is considered. Some further generalizations ...
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In this article, Swendsen-Wang-Wolff algorithms are extended to simulate spatial point processes with symmetric and stationary interactions. Convergence of these algorithms is considered. Some further generalizations of the algorithms are discussed. The ideas presented in this article can also be useful in handling some large and complicated systems.
The problem of virtual machine (VM) live migration in self-driving systems targets reallocating resources among VMs running different self-driving services for load balance. It is of great importance to enable a runni...
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The problem of virtual machine (VM) live migration in self-driving systems targets reallocating resources among VMs running different self-driving services for load balance. It is of great importance to enable a running VM to be moved to another physical machine (host) seamlessly. Due to the nature of self-driving applications, it is important to select an optimal set of hosts to place VMs within an interactive time. The VM placement problem can be formalized as a bin packing problem, which is proved to be NP-hard. To solve the problem, we develop a cluster-based genetic algorithm that outputs an approximation result of the bin pack problem. In particular, our proposed algorithmclusters the population of current generation and selects individuals from different groups with reduced crossover operations. The number of crossover operations is directly related to the algorithm efficiency. We use the run-time features to evaluate the preference of VMs on hardware resources, which is utilized to generate initial solutions and avoid overload. Experimental results show that our approach is able to outperform the tradition genetic algorithm regarding both accuracy and efficiency. (C) 2020 Elsevier B.V. All rights reserved.
We present sample OpenACC programs of the Swendsen-Wang multi-cluster spin flip algorithm. OpenACC is a directive-based programming model for accelerators without requiring modification to the underlying CPU code itse...
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We present sample OpenACC programs of the Swendsen-Wang multi-cluster spin flip algorithm. OpenACC is a directive-based programming model for accelerators without requiring modification to the underlying CPU code itself. In this paper, we deal with the classical spin models as with the sample CUDA programs (Komura and Okabe, 2014), that is, two-dimensional (2D) Ising model, three-dimensional (3D) Ising model, 2D Potts model, 3D Potts model, 2D XY model and 3D XY model. We explain the details of sample OpenACC programs and compare the performance of the present OpenACC implementations with that of the CUDA implementations for the 2D and 3D Ising models and the 2D and 3D XY models. Program summary Program title: SWspin_OpenACC Catalogue identifier: AEXU_v1_0 Program summary URL: http://***/summaries/AEXU_v1_*** Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://***/licence/*** No. of lines in distributed program, including test data, etc.: 2898 No. of bytes in distributed program, including test data, etc.: 9729 Distribution format: *** Programming language: C, OpenACC. Computer: Any computer with an OpenACC-enabled accelerator (tested on NVIDIA GPU). Operating system: No limits (tested on Linux). RAM: About 1MiB for the parameters used in the sample programs. Classification: 23. Nature of problem: Monte Carlo simulation of classical spin systems. Ising model, q-state Potts model, and the classical XY model are treated for both two-dimensional and three-dimensional lattices. Solution method: Swendsen-Wang multi-cluster spin flip Monte Carlo method. The OpenACC implementation for the cluster-labeling is based on the work by Kalentev et al. [J. Parallel Distrib. Comput. 71 (2011) 615-620]. Restrictions: The system size is limited depending on the memory of an accelerator. Running time: A few minutes per each program for the parameters used in th
We present new versions of sample CUDA programs for the GPU computing of the Swendsen-Wang multi-cluster spin flip algorithm. In this update, we add the method of GPU-based cluster-labeling algorithm without the use o...
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We present new versions of sample CUDA programs for the GPU computing of the Swendsen-Wang multi-cluster spin flip algorithm. In this update, we add the method of GPU-based cluster-labeling algorithm without the use of conventional iteration (Komura, 2015) to those programs. For high-precision calculations, we also add a random-number generator in the cuRAND library. Moreover, we fix several bugs and remove the extra usage of shared memory in the kernel functions.
We study, via Monte Carlo simulation, the dynamic critical behavior of the Chayes-Machta dynamics for the Fortuin-Kasteleyn random-cluster model, which generalizes the Swendsen-Wang dynamics for the q-state Potts ferr...
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We study, via Monte Carlo simulation, the dynamic critical behavior of the Chayes-Machta dynamics for the Fortuin-Kasteleyn random-cluster model, which generalizes the Swendsen-Wang dynamics for the q-state Potts ferromagnet to non-integer q >= 1. We consider spatial dimension d = 2 and 1.25 <= q <= 4 in steps of 0.25, on lattices up to 1024(2), and obtain estimates for the dynamic critical exponent z(CM). We present evidence that when 1 <= q less than or similar to 1.95 the Ossola-Sokal conjecture z(CM) = >= beta/nu is violated, though we also present plausible fits compatible with this conjecture. We show that the Li-Sokal bound z(CM) >= alpha/nu is close to being sharp over the entire range 1 <= q <= 4, but is probably non-sharp by a power. As a byproduct of our work, we also obtain evidence concerning the corrections to scaling in static observables.
An overview of cluster analysis techniques from a data mining point of view is given. This is done by a strict separation of the questions of various similarity and distance measures and related optimization criteria ...
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An overview of cluster analysis techniques from a data mining point of view is given. This is done by a strict separation of the questions of various similarity and distance measures and related optimization criteria for clusterings from the methods to create and modify clusterings themselves. In addition to this general setting and overview, the second focus is used on discussions of the essential ingredients of the demographic cluster algorithm of IBM's Intelligent Miner, based Condorcet's criterion.
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