Basic Introduction or Principle: We all are aware with the term "Generator". A device which converts mechanical energy into electrical energy is known as generator. This generator makes rotate with the help of some kind of external energy. When this energy extract from the energy of steam, the plant is known as steam power plant. A simple steam plant works on Rankine cycle. In the first step, water is feed into a boiler at a very high pressure by BFP (boiler feed pump). This high pressurized water is heated into a boiler which converts it into high pressurized super heated steam. This high energized steam passes through steam turbine (a mechanical device which converts flow energy of fluid into mechanical energy) and rotate it. Owing to extract full energy of steam, three stage turbines is used which is known as LPT (Low pressure turbine), IPT (intermediate pressure turbine) and HPT (High pressure turbine). The turbine shaft is connected to the generator rot
PhD researchers at Nottingham University have developed a software programme that can turn a flat 2D image into a 3D selfie.
Their web application allows users to upload a single colour image, then transforms it into a 3D image that shows the physical shape of the face. It works using a Convolutional Neural Network (CNN) – artificial intelligence (AI) that applies machine learning which has been trained on a huge dataset of 2D pictures and 3D facial models. As well as being able to reconstruct 3D facial geometry, the CNN can also make predictions regarding the non-visible parts of the face.
“Our CNN uses just a single 2D facial image, and works for arbitrary facial poses [front or profile images] and facial expressions [smiling],” said Nottingham PhD student Aaron Jackson, the paper’s lead author.
According to the team, current techniques to create a 3D representation require multiple facial images, and face several challenges such as dense correspondences across large facial poses, expressions and non-uniform illumination. By applying neural networks, the Nottingham researchers believe they have found a more straightforward solution to these complex rendering problems.
“The main novelty is in the simplicity of our approach which bypasses the complex pipelines typically used by other techniques,” said research supervisor Dr Yorgos Tzimiropoulos. “We instead came up with the idea of training a big neural network on 80,000 faces to directly learn to output the 3D facial geometry from a single 2D image.”
As well as facial and emotional recognition applications, the 3D selfie software could be used to simulate the results of plastic surgery, or assist medical professionals in understanding conditions like autism. The technology could also help improve augmented and virtual reality and has potential for character personalisation within computer games.
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