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Evaluating Gaussian processes for matched-field processing localization using minimum mean squared error criterion [PDF]
JASA Express LettersGaussian processes (GPs) can densify and denoise sparsely sampled signals and have been applied in matched-field processing (MFP) localization to improve localization accuracy and robustness.
Shanru Lin +4 more
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A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm
JOIV: International Journal on Informatics Visualization, 2023 A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept.
Salam Abdulkhaleq Noaman +2 more
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Architectures for block Toeplitz systems [PDF]
, 1995 In this paper efficient VLSI architectures of highly concurrent algorithms for the solution of block linear systems with Toeplitz or near-to-Toeplitz entries are presented.
Bouras, Ilias +2 more
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Data Science Journal, 2010 An alternative ratio-cum-product estimator of population mean using the coefficient of kurtosis for two auxiliary variates has been proposed. The proposed estimator has been compared with a simple mean estimator, the usual ratio estimator, a product ...
Rajesh Tailor +2 more
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Results in Control and OptimizationAn error-minimizing estimator is always preferred in model fittings. However, each error-minimizing estimator minimizes error differently. This paper combines four error-minimizing estimators, which are root mean-squared error, mean absolute error, root ...
Razik Ridzuan Mohd Tajuddin
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Estimating a Bounded Normal Mean Relative to Squared Error Loss Function [PDF]
Journal of Sciences, Islamic Republic of Iran, 2011 Let be a random sample from a normal distribution with unknown mean and known variance The usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible ...
A. Karimnezhad
doaj
Comparative Analysis Using Multiple Regression Models for Forecasting Photovoltaic Power Generation
EnergiesEffective machine learning regression models are useful toolsets for managing and planning energy in PV grid-connected systems. Machine learning regression models, however, have been crucial in the analysis, forecasting, and prediction of numerous ...
Burhan U Din Abdullah +5 more
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Size Constrained Clustering With MILP Formulation
IEEE Access, 2020 Clustering is one of the essential tools for data mining since it reveals the natural structures of the unlabeled data. Many clustering algorithms have been proposed in the last decades.
Wei Tang +3 more
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Convergence of statistical moments of particle density time series in scrape-off layer plasmas [PDF]
, 2015 Particle density fluctuations in the scrape-off layer of magnetically confined plasmas, as measured by gas-puff imaging or Langmuir probes, are modeled as the realization of a stochastic process in which a superposition of pulses with a fixed shape, an ...
O. E. Garcia, Pecseli H. L., R. Kube
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Media StatistikaAnalysis of gene expression data, particularly in cancer data, often faces challenges due to the presence of missing values. One approach to overcome this is data imputation.
Mastika Mastika +2 more
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