Potential difference gradient along an energized insulator creates an electric field (E-field) that surrounds the insulator in concentric circles. An overview is also presented of some of the most common problems encountered on porcelain and composite line insulators, including moisture ingress, carbon tracking, surface contamination and manufacturing defects. Charles Jean of Positron Power in Canada reviews how this methodology evolved and claims it can prove effective in detecting even small defects that might not always be identified using other inspection methodologies. The methodology utilizes measurement of E-field distribution along insulators to detect presence of any conductive defects as well as record and display their severity and location.
Early detection of defective insulators avoids risk of significant problems and enables scheduled maintenance as well as maximum safety prior to live-line work. I have explained these networks in a very simple and descriptive language using Keras + Tensorflow(Backend).The electric field method has been used for over 30 years to test and verify the condition of energized porcelain and composite insulators on overhead transmission lines. Each architecture has a chapter dedicated to it. DCGAN, StackGAN, CycleGAN, Pix2pix, Age-cGAN, and 3D-GAN have been covered in details at the implementation level. Note : I had published a book(In 2019) on GANs titled “Generative Adversarial Networks Projects”, in which I have covered most of the widely popular GAN architectures and their implementations. And I represent Snapy, which is instant object detection and discovery tool for consumers. At Raven Protocol, we are building the world’s first decentralized and distributed Artificial Intelligence Platform. I am also a Co-founder of Raven Protocol. At Mate Labs, we are making demand forecasting easy for enterprises using automation in machine learning. Pros and Cons of GANs Evaluation Measures. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. Lower FID means smaller distances between synthetic and real data distributions It was introduced by Heusel et al in 2017. If you are using FID as your performance metric then try to minimize it. FID is based on the feature vectors of images. The lower FID score represents that the quality of images generated by the generator is higher and similar to the real ones. What is Frechlet Inception Distance(FID)?įID is a performance metric that calculates the distance between the feature vectors of real images and the feature vectors of fake images(Generated by the generator).
A Very Short Introduction to Frechlet Inception Distance(FID)