Sentence examples for augmented input from inspiring English sources

So, in this article, with the aid of the "load" parameter as an augmented input and using the fault patterns obtained at different part-load conditions, a fuzzy-based FDI system is proposed for an industrial two-shaft gas turbine with the ability to use in both the full and part-load operations.

Communication partners are encouraged to provide augmented input with the child, such as signing or pointing to symbols and codes as they communicate, including using the individual's communication system themselves.

The Ionian gyre reversal of 1987 and consequent augmented input of surface waters of North Atlantic origin in the Adriatic Sea was hypothesized, together with the general warming of the area, as possible explanations for the alteration in the copepod community.

Less intensive, near-to-nature land uses such as natural vegetation, forests, and pasture in the immediate vicinity inhibit input of NO3 - (and other nutrients) while intensively managed arable land tends to augment input of nutrients (Mayer et al. 2005).

Recently, extension of DNN-HMM is investigated by augmenting input with additional information, for example speaker adaptation using I-Vectors [28, 29] and noise-aware training using noise information [30].

Recently, Mimura et al. [29] augmented the input of the autoencoder based on long short-term memory (LSTM) [30] with phone-class information (denoted as pLSTM).

The metamodel is formulated to approximate the system response (metamodel output) with respect to both the design variables and the uncertain model parameters (augmented metamodel input), so that it can simultaneously support the uncertainty propagation and the design optimization, adopting a sequential approximate optimization (SAO) strategy for the latter.

The source of chance that could be used to augment the input of a computer for the purposes of the Monte-Carlo method of calculation may be chosen as the erratic emission of electrons from the cathode of an electronic tube and is frequently so chosen.

Improving DNN noise robustness was explored in [10] by augmenting DNN input vectors with an estimate of the noise present in the signal.

We do this by exploring and comparing two approaches in parallel: first, augmenting the input with multi-resolution features and therefore dealing with the locality outside the model, and second, exploiting the locality with a model that integrates that concept, all within the context of deep learning.

This study presents the development of fuzzy radial basis function-based neural networks augmented with virtual input variables.