Sentence examples for speed classification from inspiring English sources

In the computational parametric study, wind speed classification is derived based on Physiological Equivalent Temperature (PET) to evaluate the effect of wind speed on outdoor thermal comfort.

The parameters measured by the system were vehicle count, speed, classification, weight, length, headway, vehicle and axis gap.

Since it also has advantages in terms of simplicity, learning speed, classification speed, storage space and incrementality its use should perhaps be considered more often".

Avlund et al. [ 53] compared 'tiredness' and 'reduced speed' classifications, and found that the reduced speed scale was more effective in reducing ceiling effects.

We used a syllabic version of the speeded classification paradigm, whereby response latencies to the first (target) syllable of spoken word-like stimuli are slowed down when the second (irrelevant) syllable varies from trial to trial.

We hypothesized and showed that in a speeded classification task male names are processed faster when they are presented in a black typeface (Exp. 1) or a dark color (Exp. 2) than when they are presented in white or a light color, with the opposite pattern for female names.

Speeded classification data from different information-processing tasks (univariate and correlated) were predicted by distance between stimuli in similarity space but not by redundancy.

Synesthetic associations in neurocognitively-normal individuals have typically been studied by means of the speeded classification paradigm, in which participants have to classify a series of stimuli in one sensory modality while trying to ignore concurrent task-irrelevant stimuli presented in a second modality [2], [13].

Participants were instructed to hold the digits in memory while performing the attention task, making speeded classification responses indicating whether the target image was of a fruit or a household object and ignoring the distractor images.

In visual lexical decision (i.e. the speeded classification of a letter string as word or pseudoword) the existence of many neighbors' (i.e. words which share all but one letter with the target) slows down the answer to pseudowords (neighborhood size effect; [ 26, 27]).

An AdaBoost-based hypothesis is formulated to select a few hundreds of the numerous extracted features to speed up classification.