Automated grading system plays an important role in many industries. In ceramic industries, the grading of ceramic tiles is a difficult task as it has huge variations of surface properties. In this paper, automated surface grading system based on Discrete Wavelet Transform (DWT) and Gray Level Co-occurrence Matrices (GLCM) is presented. Texture information’s in ceramic tiles are effectively rep...

Abstract Preventing plunge pool scouring in hydraulic structures is crucial engineering. Although many studies have been conducted experimentally to determine relationship between the scour depth and water jets several fields, available equations deficiencies calculating exact due complexity of process. This study investigated local using Metaheuristic Artificial Bee Colony-Optimized Feed Forwa...

In this present paper, the machine learning approach is used to optimize, model, and predict factors during drilling Nimonic C263 under dry mode. tough aero alloys, it required find a predictive model optimize in alloy before actual machining process. It helps avoid cost material cost. Experimental trails are planned based on Taguchi analysis, L27 orthogonal array was chosen. Speed, feed, angle...

This paper introduces a novel two-stream deep model based on graph convolutional network (GCN) architecture and feed-forward neural networks (FFNN) for learning the solution of nonlinear partial differential equations (PDEs). The aims at incorporating both grid input representations using two streams corresponding to GCN FFNN models, respectively. Each stream layer receives processes its repres...

Iterative learning control is a feedforward control technique applied to systems or processes that operate in a repetitive fashion over a fixed interval of time to improve tracking/regulation performance in response to reference inputs/disturbance inputs that are repeatable in each cycle. In this paper, learning control is applied to coil-to-coil gauge and tension control during the thread-up p...

The present report summarizes the work conducted during the internship on Feedforward Control of the Magnetic Levitation Setup. Different feedforward strategies, specifically tailored for this setup, are developed and reviewed. These feedforward methods explicitly take the intrinsic position-dependent behavior of the magnetic levitation setup into account. Additionally, closed-loop stability of...

A comparative study of artificial neural network (ANN) and multiple regression is made to predict the fat tail weight of Balouchi sheep from birth, weaning and finishing weights. A multilayer feed forward network with back propagation of error learning mechanism was used to predict the sheep body weight. The data (69 records) were randomly divided into two subsets. The first subset is the train...

A new approach to robust estimation of signals, prediction of time{series and robust feedforward control is considered. Signal and system parameter deviations are represented as random variables, with known covariances. A robust design is obtained by minimizing the squared estimation error, averaged both with respect to model errors and the noise. A polynomial equations approach, based on avera...

An artificial neural network (ANN) modeling of gas drying by adsorption in fixed bed of composite materials is presented in this paper. The experimental investigations were carried out at two values of relative humidity and three values of air flow rate respectively. The experimental data were employed in the design of the feed forward neural networks for modeling the evolution in time of some ...