Convolutional neural networks (CNNs) with different layers have performed excellent results in watermark removal. However, how to extract robust and effective features via CNNs of black box removal is very important. In this paper, we propose an improved U-net (IWRU-net). Taking the robustness obtained information into account, a serial architecture designed facilitate useful for guaranteeing p...

Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a ...

Shortest path problem is solved by using the famous Dijkstra's algorithm, which would quickly provide a global optimization solution in most instances. But when the weight is not given, this method does not work. This research paper examines and analyzes the use of neural networks for finding the weights. A neural network is an artificial representation of human brain that tries to simulate the...

Time-Spatial data plays a crucial role for different fields such as traffic management. These data can be collected via devices such as surveillance sensors or tracking systems. However, how to efficiently analyze and visualize these data to capture essential embedded pattern information is becoming a big challenge today. Classic visualization approaches focus on revealing 2D and 3D spatial inf...

The increasing presence of intermittent renewables in modern power systems motivates the development methods for forecasting. More accurate forecasts may implicate less operational costs systems. In this context, paper proposes a family architectures based on fully convolutional neural networks wind speed prediction, ComPonentNet (CPNet) family. CPNet produces multi-site spatio-temporal forecas...

Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network which combines the strengths of residual learning and U-Net is proposed for road area extraction. The network is built with residual units and has similar architecture to that of U-Net. The benefits of this model is two-fold: firs...

In this paper, we introduce attention-based CNN matching net (ACM-Net), an end-to-end neural network for question answering. ACM-Net matches between the given passage, query and multiple answer choices, and then it extracts features from passage and choices based on query information. We also propose a two-staged CNN architecture and a query-based attention mechanism in our model. These two com...

This paper introduces a new real-time object detection approach named Yes-Net. It realizes the prediction of bounding boxes and class via single neural network like YOLOv2 and SSD, but owns more efficient and outstanding features. It combines local information with global information by adding the RNN architecture as a packed unit in CNN model to form the basic feature extractor. Independent an...

As airframe become more and more complex, and are called upon to perform increasingly stre~sful maneuvers, autopilots must be robust enough to adequately stabilize the airframe in the highly non-linear, strongly cross-coupled environments. Classic autopilot design can achieve stability throughout he flight envelope, but generally lack robustness for design and environmental ehange.~. Guidance a...