Failure is a systemic error that affects overall system performance and may eventually crash across the entire *** Real-Time Systems(RTS),deadline is the key to successful completion of the *** tasks effectively meet ...
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Failure is a systemic error that affects overall system performance and may eventually crash across the entire *** Real-Time Systems(RTS),deadline is the key to successful completion of the *** tasks effectively meet the deadline,it means the system is working in pristine ***,missing the deadline means a systemic fault due to which the system can crash(hard RTS)or degrade inclusive performance(soft RTS).To fine-tune the RTS,tolerance is the critical issue and must be handled with extreme *** article explains the context of fault tolerance with improvised Joint EDF-RMS algorithm in *** backup method has been derived to prevent the system from being recursively migrating the same *** any task migrates three times,this migrated task will get shifted to the backup *** backup queue assigns the task to a backup processor and is destined for final *** performance evaluation purposes,a relative graph between fault and failure rates,failure and total processor utilization along with other averages have been ***,these archived results are compared with fault-tolerant Earliest Deadline First(EDF)and Rate Monotonic Scheduling(RMS)algorithms independently in relatively similar *** comparisons show better performance against overloading conditions.
We investigate the statistical behavior of gradient descent iterates with dropout in the linear regression model. In particular, non-asymptotic bounds for the convergence of expectations and covariance matrices of the...
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We investigate the statistical behavior of gradient descent iterates with dropout in the linear regression model. In particular, non-asymptotic bounds for the convergence of expectations and covariance matrices of the iterates are derived. The results shed more light on the widely cited connection between dropout and ℓ2-regularization in the linear model. We indicate a more subtle relationship, owing to interactions between the gradient descent dynamics and the additional randomness induced by dropout. Further, we study a simplified variant of dropout which does not have a regularizing effect and converges to the least squares estimator.
A common assumption for real-world, learnable data is its possession of some low-dimensional structure, and one way to formalize this structure is through the manifold hypothesis: that learnable data lies near some lo...
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A common assumption for real-world, learnable data is its possession of some low-dimensional structure, and one way to formalize this structure is through the manifold hypothesis: that learnable data lies near some low-dimensional manifold. Deep learning architectures often have a compressive autoencoder component, where data is mapped to a lower-dimensional latent space, but often many architecture design choices are done by hand, since such models do not inherently exploit mathematical structure of the data. To utilize this geometric data structure, we propose an iterative process in the style of a geometric ow for explicitly constructing a pair of neural networks layer-wise that linearize and reconstruct an embedded submanifold, from finite samples of this manifold. Our suchgenerated neural networks, called Flattening Networks (FlatNet), are theoretically interpretable, computationally feasible at scale, and generalize well to test data, a balance not typically found in manifold-based learning methods. We present empirical results and comparisons to other models on synthetic high-dimensional manifold data and 2D image data. Our code is publicly available.
The Salp Swarm Algorithm (SSA) is a recently proposed swarm intelligence algorithm inspired by salps, a marine creature similar to jellyfish. Despite its simple structure and solid exploratory ability, SSA suffers fro...
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The Salp Swarm Algorithm (SSA) is a recently proposed swarm intelligence algorithm inspired by salps, a marine creature similar to jellyfish. Despite its simple structure and solid exploratory ability, SSA suffers from low convergence accuracy and slow convergence speed when dealing with some complex problems. Therefore, this paper proposes an improved algorithm based on SSA and adds three improvements. First, the Real-time Update Mechanism (RUM) underwrites the role of ensuring that excellent individual information will not be lost and information exchange will not lag in the iterative process. Second, the Communication Strategy (CMS), on the other hand, uses the multiplicative relationship of multiple individuals to regulate the exploration and exploitation process dynamically. Third, the Selective Replacement Strategy (SRS) is designed to adaptively adjust the variance ratio of individuals to enhance the accuracy and depth of convergence. The new proposal presented in this study is named RCSSSA. The global optimization capability of the algorithm was tested against various high-performance and novel algorithms at IEEE CEC 2014, and its constrained optimization capability was tested at IEEE CEC 2011. The experimental results demonstrate that the proposed algorithm can converge faster while obtaining better optimization results than traditional swarm intelligence and other improved algorithms. The statistical data in the table support its optimization capabilities, and multiple graphs deepen the understanding and analysis of the proposed algorithm.
For the current strained layer superlattice (SLS) based FPAs mesa structures are used to define the pixels. For those SLS based FPAs with scaled pixel size making the mesa structures is challenging due to the need for...
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In this paper, the time-of-arrival (TOA) localization problem in the presence of non-line-of-sight (NLOS) errors is addressed. We propose a robust localization method based on the maximum correntropy criterion with va...
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The present study introduces a novel binary meta-heuristic optimizer, referred to as the Binary Growth Optimizer (BGO), which is specifically developed to address discrete optimization problems. The BGO utilizes trans...
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The transformation of traditional electric power systems into smart grids has allowed for improved monitoring and control capabilities. However, this evolution has also brought about new challenges in the form of malw...
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Uncertain data is widely used in many practical applications, such as data cleaning, location-based services, privacy protection, and so on. With the development of technology, data has a tendency to high-dimensionali...
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The medical device industry has significantly advanced by integrating sophisticated electronics like microchips and field-programmable gate arrays (FPGAs) to enhance the safety and usability of life-saving devices. Th...
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