Small signal frequency domain model of an HVDC converter

2001 ◽  
Vol 148 (6) ◽  
pp. 573 ◽  
Author(s):  
C.M. Osauskas ◽  
D.J. Hume ◽  
A.R. Wood
Keyword(s):  
Frequency Domain ◽  
Domain Model ◽  
Signal Frequency ◽  
Small Signal

Small Signal Frequency-Domain Model of a LCC-HVDC Converter Based on an Infinite Series-Converter Approach

2019 ◽  
Vol 34 (1) ◽  
pp. 95-106 ◽  
Author(s):  
Yi Qi ◽  
Huanfeng Zhao ◽  
Shengtao Fan ◽  
Aniruddha M. Gole ◽  
Hui Ding ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Infinite Series ◽  
Domain Model ◽  
Signal Frequency ◽  
Small Signal

Differential carrier lifetime in InGaN-based light-emitting diodes obtained by small-signal frequency-domain measurements

2017 ◽  
Vol 121 (3) ◽  
pp. 035701 ◽  
Author(s):  
I. Reklaitis ◽  
F. Nippert ◽  
R. Kudžma ◽  
T. Malinauskas ◽  
S. Karpov ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Light Emitting Diodes ◽  
Carrier Lifetime ◽  
Signal Frequency ◽  
Small Signal ◽  
Light Emitting

scholarly journals Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3451 ◽  
Author(s):  
Sławomir Opałka ◽  
Bartłomiej Stasiak ◽  
Dominik Szajerman ◽  
Adam Wojciechowski
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Parameters Optimization ◽  
Alternative Methods ◽  
Signal Frequency ◽  
Eeg Signal ◽  
Mental Tasks Classification ◽  
Mental Tasks

Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this problem, this paper presents a multi-channel convolutional neural network architecture with adaptively optimized parameters. Our solution outperforms alternative methods in terms of classification accuracy of mental tasks (imagination of hand movements and speech sounds generation) while providing high generalization capability (∼5%). Classification efficiency was obtained by using a frequency-domain multi-channel neural network feeding scheme by EEG signal frequency sub-bands analysis and architecture supporting feature mapping with two subsequent convolutional layers terminated with a fully connected layer. For dataset V from BCI Competition III, the method achieved an average classification accuracy level of nearly 70%, outperforming alternative methods. The solution presented applies a frequency domain for input data processed by a multi-channel architecture that isolates frequency sub-bands in time windows, which enables multi-class signal classification that is highly generalizable and more accurate (∼1.2%) than the existing solutions. Such an approach, combined with an appropriate learning strategy and parameters optimization, adapted to signal characteristics, outperforms reference single- or multi-channel networks, such as AlexNet, VGG-16 and Cecotti’s multi-channel NN. With the classification accuracy improvement of 1.2%, our solution is a clear advance as compared to the top three state-of-the-art methods, which achieved the result of no more than 0.3%.

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