Few-shot Image Generation via Masked Discrimination

Few-shot image generation aims to generate images of high quality and great diversity with limited data. However, it is difficult for modern GANs to avoid overfitting when trained on only a few images. The discriminator can easily remember all the training samples and guide the generator to replicate them, leading to severe diversity degradation. Several methods have been proposed to relieve overfitting by adapting GANs pre-trained on large source domains to target domains with limited real samples. In this work, we present a novel approach to realize few-shot GAN adaptation via masked discrimination. Random masks are applied to features extracted by the discriminator from input images. We aim to encourage the discriminator to judge more diverse images which share partially common features with training samples as realistic images. Correspondingly, the generator is guided to generate more diverse images instead of replicating training samples. In addition, we employ cross-domain consistency loss for the discriminator to keep relative distances between samples in its feature space. The discriminator cross-domain consistency loss serves as another optimization target in addition to adversarial loss and guides adapted GANs to preserve more information learned from source domains for higher image quality. The effectiveness of our approach is demonstrated both qualitatively and quantitatively with higher quality and greater diversity on a series of few-shot image generation tasks than prior methods

Analysis on the aerodynamic performance of vertical axis wind turbine subjected to the change of wind velocity

AbstractReynolds averaged Navier-Stokes equations and Realizable kɛ− model were used in this paper, and the two dimensional unsteady flow field of the vertical axis wind turbine was simulated numerically at different wind velocity. The calculation results showed that the velocity in the region of wind turbine's rotation was much larger than the air flow of the upstream. The length of the wind turbine's downstream wake dispersion region was increased with the increase of the wind velocity. There is a much larger value of the eddy in the rear region of the wind turbine's rotational blades. And eddy existed in the downstream region of the wind turbine, and the larger velocity of cross flow, the larger value of the downstream flow's eddy. When the rotational speed was constant, with the increase in wind velocity, the variation of the wind turbine's total torque coefficient tended to smooth. The calculation results pointed out the direction for the follow-up study

Analysis on the influence of rotational speed to aerodynamic performance of vertical axis wind turbine

AbstractA two dimensional vertical axis wind turbine's model was established in this paper, and two dimensional unsteady incompressible N-S equations and Realizable kɛ− turbulence model were solved with software FLUENT. SIMPLC algorithm was applied, combined with the sliding grid technology; the influence of rotational speed to the flow structure of vertical axis wind turbine was discussed. The results showed that, the rotation of wind turbine had significant influence on wake, and higher the rotational speed, the greater reduction of the wake velocity. The wake velocity restored gradually away from the rotational part. There was much larger turbulent kinetic energy near the tail of the wind turbine's blade. The value of turbulent kinetic energy reduced gradually away from the rotational part, and the flow restored the stratospheric state gradually. With the increase of wind turbine's rotational speed, the value of turbulent kinetic energy in calculation domain increased too. The results showed that the flow structure of vertical axis wind turbine's rotational process could be revealed effectively by numerical simulation, provided theoretical reference for the engineering design of the vertical axis wind turbine

Maritime cognitive workload assessment

The human factor plays the key role for safety in many industrial and civil every-day operations in our technologized world. Human failure is more likely to cause accidents than technical failure, e.g. in the challenging job of tugboat captains. Here, cognitive workload is crucial, as its excess is a main cause of dangerous situations and accidents while being highly participant and situation dependent. However, knowing the captain’s level of workload can help to improve man-machine interaction. The main contributions of this paper is a successful workload indication and a transfer of cognitive workload knowledge from laboratory to realistic settings

POD-based reduced-order modeling study for thermal analysis of gas-cooled microreactor core

Small modular reactors require multi-physics coupling calculations to balance economy and stability, due to their compact structures. Traditional tools used for light water reactors are not effective in addressing the several modeling challenges posed by these calculations. The lumped parameter method is commonly used in the thermal analysis for its high computational speed, but it lacks accuracy due to the thermal model is one-dimensional. While computational fluid dynamics software (CFD) can provide high-precision and high-resolution thermal analysis, its low calculation efficiency making it challenging to be coupled with other programs. Proper Orthogonal Decomposition (POD) is one of the Reduced Order Model (ROM) methods employed in this study to reduce the dimensionality of sample data and to improve the thermal modelling of gas-cooled microreactors. In this work, a non-inclusive POD with neural network method is proposed and verified using a transient heat conduction model for a two-dimensional plate. The method is then applied to build a reduced order model of the gas-cooled micro-reactor core for rapid thermal analysis. The results show that the root mean square error of the reactor core temperature is less than 1.02% and the absolute error is less than 8.2°C while the computational cost is reduced by several orders of magnitude, shortening the calculation time from 1.5-hour to real-time display. These findings proved the feasibility of using POD and neural network in the development of ROMs for gas-cooled microreactor, providing a novel approach for achieving precise thermal calculation with minimized computational costs

N-acetylcysteine Protects against Apoptosis through Modulation of Group I Metabotropic Glutamate Receptor Activity

The activation of group I metabotropic glutamate receptor (group I mGlus) has been shown to produce neuroprotective or neurotoxic effects. In this study, we investigated the effects of N-acetylcysteine (NAC), a precursor of the antioxidant glutathione, on group I mGlus activation in apoptosis of glial C6 and MN9D cell lines, and a rat model of Parkinson's disease (PD). We demonstrated that NAC protected against apoptosis through modulation of group I mGlus activity. In glial C6 cells, NAC promoted phosphorylation of ERK induced by (s)-3,5- dihydroxy-phenylglycine (DHPG), an agonist of group I mGlus. NAC enhanced the group I mGlus-mediated protection from staurosporine (STS)-induced apoptosis following DHPG treatment. Moreover, in rotenone-treated MN9D cells and PD rat model, NAC protected against group I mGlus-induced toxicity by compromising the decrease in phosphorylation of ERK, phosphorylation or expression level of TH. Furthermore, the results showed that NAC prohibited the level of ROS and oxidation of cellular GSH/GSSG (Eh) accompanied by activated group I mGlus in the experimental models. Our results suggest that NAC might act as a regulator of group I mGlus-mediated activities in both neuroprotection and neurotoxicity via reducing the oxidative stress, eventually to protect cell survival. The study also suggests that NAC might be a potential therapeutics targeting for group I mGlus activation in the treatment of PD

Electrografting of amino-TEMPO on graphene oxide and electrochemically reduced graphene oxide for electrocatalytic applications.

4-Amino-2,2,6,6-tetramethyl-1-piperridine N-oxyl (4-amino-TEMPO), an electroactive nitroxide radical, was attached to the surface of graphene oxide (GO) and electrochemically reduced graphene oxide (ERGO) modified glassy carbon electrode by a simple, rapid and green electrografting method. The electroactive interfaces were analyzed by X-ray photoelectron spectroscopy (XPS) and cyclic voltammetry (CV). The calculated surface coverage for 4-amino-TEMPO is up to 1.55 × 10− 9 mol·cm− 2. The modified electroactive interface exhibited excellent electrocatalytic activity towards the electro-oxidation of reduced glutathione (GSH) and hydrogen peroxide (H2O2)