A Suppression Method of Restricted Shock Separation in Overexpanded Rocket Nozzle

2006 ◽  
Author(s):  
Koichi Yonezawa ◽  
Yasuhide Watanabe ◽  
Tsuyoshi Morimoto ◽  
Yoshinobu Tsujimoto ◽  
Kazuhiko Yokota
Keyword(s):  
Rocket Nozzle ◽  
Restricted Shock Separation ◽  
Suppression Method

scholarly journals Suppressing Restricted Shock Separation in a Subscale Rocket Nozzle Using Contour Geometry

2016 ◽  
Vol 32 (5) ◽  
pp. 1298-1301 ◽  
Author(s):  
K. Schomberg ◽  
J. Olsen ◽  
A. Neely ◽  
G. Doig
Keyword(s):  
Rocket Nozzle ◽  
Restricted Shock Separation

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

Computational assessment of solar thermal rocket nozzle performance

1995 ◽  
Author(s):  
D Landrum ◽  
Robert Beard ◽  
J Pearson ◽  
Clark Hawk
Keyword(s):  
Solar Thermal ◽  
Rocket Nozzle

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.

Vibration suppression method of humanoid flexible arm based on model-free predictive control

2021 ◽  
pp. 1-9
Author(s):  
Qinjun Du ◽  
Chuanming Song ◽  
Wei Ding ◽  
Long Zhao ◽  
Yonggang Luo
Keyword(s):  
Predictive Control ◽  
Vibration Suppression ◽  
Model Free ◽  
Flexible Arm ◽  
Suppression Method
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