He explained that real-time visualization is done via smart sampling, which he called the "secret sauce" of the function.
The fused behavior of the master-ranked bread characteristics behavior was smart sampled with GRNN-crowdsourcing and robust analysis.
This paper investigates the possibility of reducing the amount of communications and subsequently the power consumption of a sensor network by a smart sampling of data.
This paper introduces an innovative method to make a smart sampling from sensors, in order to avoid unneeded measurements and, consequently, to reduce power consumption.
Smart sampling is used to consolidate various sources of product/process uncertainties by deploying the effect-ranking capabilities of the general-regression neural networks.
In this article, we extensively evaluate the smart sampling algorithm (SSA) developed by Garud et al. (2017a) for constructing multidimensional surrogate models.
The proposed procedure also embeds a smart sampling technique that will help monitor and record the system behavior effectively for the provisioning of new software services.
Beyond the massively parallelized and distributed approaches exploiting hardware architectures and programming frameworks that have received increasing interest these last few years, several variants of the Stochastic Gradient Descent (SGD) method based on "smart" sampling procedures have been designed for accelerating the model fitting stage.
The cleaned reads from the OV and SMART samples were specifically trimmed (respectively the first 12 and three bases in respect of the manufacturer's recommendations).
The three-part sequential processing routine requires: 1) a regressive data-compression preprocessing, 2) a smart-sample generation using general-regression neural networks (GRNN), and 3) a screening power prediction using 'reverse' swarm intelligence (SI).
