Mask-guided Style Transfer Network for Purifying Real Images

Recently, the progress of learning-by-synthesis has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However, due to the different distribution of synthetic images compared with real images, the desired performance cannot be achieved. To solve this problem, the previous method learned a model to improve the realism of the synthetic images. Different from the previous methods, this paper try to purify real image by extracting discriminative and robust features to convert outdoor real images to indoor synthetic images. In this paper, we first introduce the segmentation masks to construct RGB-mask pairs as inputs, then we design a mask-guided style transfer network to learn style features separately from the attention and bkgd(background) regions and learn content features from full and attention region. Moreover, we propose a novel region-level task-guided loss to restrain the features learnt from style and content. Experiments were performed using mixed studies (qualitative and quantitative) methods to demonstrate the possibility of purifying real images in complex directions. We evaluate the proposed method on various public datasets, including LPW, COCO and MPIIGaze. Experimental results show that the proposed method is effective and achieves the state-of-the-art results.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0582

An Unmixing-Based Multi-Attention GAN for Unsupervised Hyperspectral and Multispectral Image Fusion

Hyperspectral images (HSI) frequently have inadequate spatial resolution, which hinders numerous applications for the images. High resolution multispectral image (MSI) has been fused with HSI to reconstruct images with both high spatial and high spectral resolutions. In this paper, we propose a generative adversarial network (GAN)-based unsupervised HSI-MSI fusion network. In the generator, two coupled autoencoder nets decompose HSI and MSI into endmembers and abundances for fusing high resolution HSI through the linear mixing model. The two autoencoder nets are connected by a degradation-generation (DG) block, which further improves the accuracy of the reconstruction. Additionally, a coordinate multi-attention net (CMAN) is designed to extract more detailed features from the input. Driven by the joint loss function, the proposed method is straightforward and easy to execute in an end-to-end training manner. The experimental results demonstrate that the proposed strategy outperforms the state-of-art methods

FATIGUE LIFE PREDICTION FOR ROW ANGULAR CONTACT BALL BEARINGS UNDER THERMO-MECHANICAL COUPLING BASED ON FEM

The effect of thermo-mechanical coupling was considered in order to obtain the fatigue life of spilt torque transmission row angular contact ball bearings in helicopter. Based on the L-P fatigue life and Hertz contact theory,an mathematical model was established with the combination of Goodman formula,material P-S-N curves and EM modify rule. The friction calorific effect was calculated and the convective heat tranfer coefficient was determined. Then the steady thermal analysis was conducted and the steady maximum temperature under different radial forces and rotate speeds was discussed. Finally the bearing fatigue life was investigated under the effect of thermo-mechanical coupling. The results show that the fatigue life decrease obviously with the operationg temperature raising,so the effect of frictional heating on fatigue life can’t be neglected