Beat Phenomenon Suppression Method Based on Repetitive Control for Reduced DC-link Capacitance IPMSM Drives

2021 ◽  
Author(s):  
Zekun Ren ◽  
Gaolin Wang ◽  
Dawei Ding ◽  
Nannan Zhao ◽  
Junya Huo ◽  
...  
Keyword(s):  
Repetitive Control ◽  
Beat Phenomenon ◽  
Suppression Method

A Suppression Method of Higher Harmonics Resonance in Distribution System

2011 ◽  
Vol 131 (9) ◽  
pp. 737-746 ◽  
Author(s):  
Shouji Sugimura ◽  
Tadashi Naitoh ◽  
Atsushi Toyama ◽  
Fumihiko Ohta
Keyword(s):  
Distribution System ◽  
Higher Harmonics ◽  
Suppression Method

High-Precision Tracking by Digital Repetitive Control Scheme with Periodic-Type Hold Function

1992 ◽  
Author(s):  
Shinji Hara ◽  
Hiroyuki Kawamura ◽  
Hak-Kyung Sung
Keyword(s):  
High Precision ◽  
Repetitive Control ◽  
Control Scheme ◽  
Precision Tracking

Repetitive control in the presence of multiple periodic disturbances

1992 ◽  
Author(s):  
EDWARD SOLCZ ◽  
RICHARD LONGMAN
Keyword(s):  
Repetitive Control ◽  
Periodic Disturbances

Robust Repetitive Control for a Class of Nonlinear Systems with Input Deadzone

2014 ◽  
Vol 39 (6) ◽  
pp. 908-912
Author(s):  
Sheng ZHU ◽  
Ming-Xuan SUN ◽  
Xue-Jie WANG ◽  
Yan-Jun LI ◽  
Zhi-Gang FANG
Keyword(s):  
Nonlinear Systems ◽  
Repetitive Control

Cluster-Type Photovoltaic Inverter Resonance Suppression Method Based on Adaptive Notch

2019 ◽  
Author(s):  
Shengqing Li ◽  
Zhichao Shen ◽  
Ziran Peng ◽  
Yuanming He ◽  
Xinpeng Gu ◽  
...  
Keyword(s):  
Suppression Method ◽  
Cluster Type

scholarly journals Disentangled generative adversarial network for low-dose CT

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wenchao Du ◽  
Hu Chen ◽  
Hongyu Yang ◽  
Yi Zhang
Keyword(s):  
Network Architecture ◽  
Low Dose ◽  
Noise Suppression ◽  
State Of The Art ◽  
Visual Quality ◽  
Ct Images ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network ◽  
Suppression Method

AbstractGenerative adversarial network (GAN) has been applied for low-dose CT images to predict normal-dose CT images. However, the undesired artifacts and details bring uncertainty to the clinical diagnosis. In order to improve the visual quality while suppressing the noise, in this paper, we mainly studied the two key components of deep learning based low-dose CT (LDCT) restoration models—network architecture and adversarial loss, and proposed a disentangled noise suppression method based on GAN (DNSGAN) for LDCT. Specifically, a generator network, which contains the noise suppression and structure recovery modules, is proposed. Furthermore, a multi-scaled relativistic adversarial loss is introduced to preserve the finer structures of generated images. Experiments on simulated and real LDCT datasets show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods.

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