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large data
For the multiring and hypercube, a method of conflictless realization of an arbitrary permutation of "large" data items that can be divided into many "smaller" data blocks was considered, and its high efficiency was demonstrated.
      
A general method of conflictless arbitrary permutation of "large" data elements that can be divided into a multitude of "smaller" data blocks was considered for switches structured as the Cayley graphs.
      
The system unified the operation of various sets of equipment (radiation monitoring, radiometric, wave, materials science, and magnetic) and allowed the transfer of large data arrays from detectors located on the outer surface of the station.
      
To date, a large data set on the mitochondrial DNA (mtDNA) sequence variation in human populations has been accumulated.
      
The high-speed compression of large data streams in ultrasonic diagnostics
      
The problem of high-speed compression of large data streams in ultrasonic diagnostics is considered.
      
This provides a possibility of processing large data arrays and considerably improves their statistical significance.
      
The present paper deals with the application of classical and fuzzy principal components analysis to a large data set from coastal sediment analysis.
      
We applied the Cocktail method to a large data set of 4 117 relevés of all Slovak vegetation types with the aim to create formalised definitions of all Slovakian mire plant associations.
      
In this analysis we made use of a large data base of individuals insured by large American corporations to estimate the impact of managed care provision on hospital care for depression.
      
The aim of the present retrospective study is to evaluate prognostic factors and the influence of treatment on outcome of differentiated thyroid cancer from a large data base.
      
We conducted systematic experiments using large data sets including over 500,000 newspaper articles and confirmed our system outperformed other ones.
      
The comparison of various multivariate mathematical-statistical methods indicated that principal component analysis followed by two dimensional non-linear mapping is the most appropriate method for the evaluation of large data matrices in RP-HPLC.
      
Here, we tested this claim by reanalysing a large data set (N = 26) on grasping in the Ebbinghaus illusion.
      
Although the incidence of injury data are limited, this study presents an extremely large data set from human head impacts that provides valuable insight into the lower limits of head acceleration that cause mild traumatic brain injuries.
      
To ensure an accelerated convergence of the calculation, the pertinent schemes require this rather large data set to be stored and mixed within 3-6 iteration steps.
      
The importance of diverse elements of these models was empirically supported by the results of a survey of creativity conditions in a Japanese university, using multiple criteria decision making for knowledge acquisition from large data bases.
      
Exploiting the sparse structure of these problems opens up a whole range of new possibilities for multivariate smoothing on large data sets via ANOVA-type decomposition and partial linear models.
      
Two large data sets, the Canadian Trial of Physiological Pacing (CTOPP) and the Mode Selection Trial (MOST) both similar in design, largely failed to clearly establish such a significant difference.
      
Discretizing the misorientation and employing multiscale statistical analysis on large data sets allows (relative) grain boundary energy as a function of boundary character to be extracted from triple junction geometry.
      
 

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