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The hybrid system evaluations are made on the basis of a typical SOFC generator scheme without any modifications.
In this work, we consider a pseudorandom generator scheme that consists of a number of subgenerators, the first of which is a linear feedback shift register (LFSR).
Figure 2c shows the full-converter wind-turbine generator scheme, which consists of a permanent-magnet synchronous generator connected to the grid by means of a full-scale, power electronic converter with a rated power that is 110% of the generator's power.
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In this paper, a grid connected hydrokinetic power generation scheme with asynchronous generator coupled to Savonius rotor is presented.
Fig. 1 General scheme for BCL Conf conformation generator.
This generating scheme is straightforward because of the existence of fast generators for gamma random variables.
In [12], a distributed generator dispatching scheme for voltage support in distribution feeders was studied.
This algorithm is employed to design a generator rejection scheme (GRS) which acts according to the online instability prediction.
This generator-detector scheme is implemented with a single potentiostat.
Fig. 1 Learning Schemes Generator and Evaluator: This figure represents the learning scheme generator and evaluator component.
Our framework consists of two components: 1) Learning Scheme Generator and Evaluator and 2) Effort Prediction.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com