Results 261 to 270 of about 3,839,662 (304)

Conditional Diagnosability of Alternating Group Networks Under the PMC Model

IEEE/ACM Transactions on Networking, 2020
Fault diagnosis of processors has played an essential role when evaluating the reliability of multiprocessor systems. In many novel multiprocessor systems, their diagnosability has been extensively explored. Conditional diagnosability is a useful measure for evaluating diagnosability by adding a further condition that all neighbors of every node in the
Nai-Wen Chang 0002, Sun-Yuan Hsieh
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The Strong Diagnosability of Multiprocessor Systems Under the PMC Model

International Journal of Foundations of Computer Science
The diagnosability is a prominent parameter to estimate the fault diagnosis capability of multiprocessor systems, which is the maximum number of faulty processors that can be identified by the system. Compared to traditional diagnosability, strong diagnosability proposed by Lai et al.
Xuemin Wu, Liqiong Xu, Chuanye Zheng
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Diagnosis of faulty units in regular graphs under the PMC model

2009 12th International Symposium on Design and Diagnostics of Electronic Circuits & Systems, 2009
The paper presents a system-level diagnosis based on a general symmetric diagnostic model - the PMC model. In particular, the one-step diagnosis of t-diagnosable systems and its Boolean formalization extension are presented. This extension transforms a syndrome decoding process to solving Boolean expressions.
Miroslav Manik, Elena Gramatová
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PMC-Based Power Modelling with Workload Classification on Multicore Systems

2014 43rd International Conference on Parallel Processing Workshops, 2014
In this paper, we propose a PMC-based power modelling methodology that utilizes workload classification. Unlike traditional approaches which use a limited set of benchmarks, our methodology uses a single well-designed micro-benchmark to collect samples of PMCs and power values for training the power estimation model.
Jason Mair, Zhiyi Huang 0001, David M. Eyers, Haibo Zhang 0001   +3 more
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A New Measure for Locally t-Diagnosable Under PMC Model

2021
PMC model is the test-based diagnosis which a vertex performs the diagnosis by testing the neighbor vertices via the edges between them. Hsu and Tan proposed two structures to diagnose a vertex. But these structures don’t always exist for any vertex.
Meirun Chen, D. Frank Hsu, Cheng-Kuan Lin   +2 more
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PMC model fault analysis and artificial intelligence

Proceedings of the 32nd Midwest Symposium on Circuits and Systems, 2003
If a system is composed of n independent processors, and if each processor tests a subset of other processors, it is possible to diagnose t of these processors which are permanently faulty. Using the concept of automated reasoning and making use of 2/sup t-1/ subgraphs, it is shown that it is possible to identify the faulty and nonfaulty sets of ...
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The pessimistic diagnosability of Split-Star Networks under the PMC model

Information Processing Letters, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Fault Diagnosability of Regular Networks Under the Hybrid PMC Model

2021
Large scale multiprocessor systems or multicomputer systems, taking interconnection networks as underlying topologies, have been widely used in the big data era. System level diagnosis is a primary strategy to identify the faulty processors in multiprocessor systems. To enhance the robustness of networks against processors and links fail simultaneously,
Jiafei Liu 0001, Qianru Zhou, Zhengqin Yu, Shuming Zhou   +3 more
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Improved acoustic modeling based on selective data-driven PMC

IEEE International Conference on Acoustics Speech and Signal Processing, 2002
This paper proposes an effective method to remedy the acoustic modeling problem inherent in the usual log-normal PMC intended for achieving robust speech recognition. In particular, the Gaussian kernels under the prescribed log-normal PMC cannot sufficiently express the corrupted speech distributions.
Wooil Kim, Hanseok Ko
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An Efficient Pmc Model-Based Local Diagnosis Structure and Algorithm

Theoretical Computer Science
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Meirun Chen, Cheng-Kuan Lin, Stafen Wang
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