In digital image inpainting tasks, existing deep-learning-based methods have achieved remarkable staged results by introducing structural prior information into the network. However, corresponding relationship between texture and structure is not fully considered, inconsistency appears in of current method. this paper, we propose a dual-branch network with branch assistance, which decouples low...

An abstract of the thesis of Andres Anibal Rustan for the Masters of Science in Electrical and Computer Engineering were presented November 1, 1999. Title: Improving Hardware Branch Predictors using Artificial Neural Networks This research shows that using an Artificial Neural Network as the hardware branch predictor of a microprocessor leads to performance as good as standard branch predictors...

In this paper, we present an exact solution procedure for the design of two-layer wavelength division multiplexing (WDM) optical networks with wavelength changers and bifurcated flows. This design problem closely resembles the traditional multicommodity flow problem, except that in the case of WDM optical networks, we are concerned with the routing of multiple commodities in two network layers....

Feature extraction from a signal is the most important step in signal-based fault diagnosis. Deep learning or deep neural network (DNN) an effective method to extract features signals. In this paper, novel vibration bearing diagnosis using DNN proposed. First, measured signals are transformed into new data form called multiple-domain image-representation. By transformation, task of transferred ...

The simplest water diversion method in irrigation systems is using intakes. Measuring the mean velocity is one of the essential hydraulic parameters in increasing the efficiency of the intake. In this study, the mean velocity was predicted for different width ratios of an intake using ANN-MLP neural network model. In order to do that, the flow field within a 90-degree intake was first simulated...