Predictive Modeling of Yarn Quality at Ring Spinning Machine using Resilient Back Propagation Neural Networks

Production of Core Spun Yarn with Ring & Siro Spinning System

A common problem in production of core–spun yarns on the Ring spinning frame is the slippage of the staple fibers as sheath fibers relative to the core. This effect is known as ‘strip-back’ or barberpole. This problem may lead to an incomplete core coverage and results in end–breaks in subsequent processing. In order to avoid this problem, in the present work, core spun yarns (acrylic sheath fi...

Quality Resilient Deep Neural Networks

We study deep neural networks for classification of images with quality distortions. We first show that networks fine-tuned on distorted data greatly outperform the original networks when tested on distorted data. However, finetuned networks perform poorly on quality distortions that they have not been trained for. We propose a mixture of experts ensemble method that is robust to different type...

Back - Propagation Neural Networks for

A back-propagation neural network is applied to a nonlinear self-tuning tracking problem. Traditional self-tuning adaptive control techniques can only deal with linear systems or some special nonlinear systems. The emerging back-propagation neural networks have the capability to learn arbitrary nonlinearity and show great potential for adaptive control applications. A scheme for combining back-...

Distribution of Power Requirements during Yarn Winding in Ring Spinning

scour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network

today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...