This paper presents a new type of rotary valve engine, and discusses the basic mechanism and working principle of the machine. We design the combustion chamber structure and the cooling lubrication mode, calculate the compression ratio of the machine, design the gas distribution phase, and analyze the ventilation passing ability. It is summarized that the machine has the advantages of simple structure, few parts and strong ventilation capacity.
Deep convolutional neural networks (CNNs) are of great improvement for single image super-resolution (SISR). However, most existing SISR pre-trained models can only perform single image restoration and the upscale factors cannot be non-integers, which limits its application in real-world scenarios. In this letter, an enhanced dual branches network (EDBNet) in upsampling network is proposed to generate arbitraryscale super-resolution (SR) images. Specifically, the authors design a scale-guidance upsampling module (SGU) by adding the scale factors and pixel-level features to guide the weights of convolution. The SGU module performs discriminant learning for each instance in the same batch. Extensive experiments on four benchmark datasets show that the proposed method can achieve superior SR results.
Deep convolutional neural networks (CNNs) have great improvements for single image super resolution (SISR). However, most of the existing SISR pre-training models can only reconstruct low-resolution (LR) images in a single image, and their upsamling factors cannot be non-integers, which limits their application in practical scenarios. In this paper, we propose a multi-scale cross-fusion network (MCNet) to accomplish the super-resolution task of images at arbitrary scale. Specifically, we construct a multi-scale cross-fusion module (MSCF) to enrich spatial information and remove redundant noise, which uses deep feature maps of different sizes for interactive learning. A large number of experiments on four benchmark datasets show that the proposed method can obtain better super-resolution results than existing arbitrary scale methods in both quantitative evaluation and visual comparison.
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