Experimental results on using artificial neural networks for accurate centroiding in Shack-Hartmann wavefront sensors with elongated spots

2016 ◽  
Author(s):  
Amokrane Berdja ◽  
Eduardo Garcés Santibañez ◽  
Christian Dani Guzmán
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Experimental Results ◽  
Wavefront Sensors ◽  
Artificial Neural

An Estimation of 3-Way Catalyst Performance Using Artificial Neural Networks During Idle Speed

2004 ◽  
Author(s):  
P. N. Botsaris ◽  
D. Bechrakis ◽  
P. D. Sparis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steady State ◽  
Expert Knowledge ◽  
Operating Conditions ◽  
Experimental Results ◽  
Outlet Temperature ◽  
Practical Applications ◽  
Idle Speed ◽  
Artificial Neural

The intelligent control as fuzzy or artificial is based on either expert knowledge or experimental data and therefore it possesses intrinsic qualities like robustness and ease implementation. Lately, many researchers present studies aim to show that this kind of control can be used in practical applications such as the idle speed control problem in automotive industry. In this study, an estimation of an automobile three-way catalyst performance with artificial neural networks is presented. It may be an alternative approach for an on board diagnostic system (OBD) to predict the catalyst performance. This method was tested using data sets from two kind of catalysts, a brand new and an old one on a laboratory bench at idle speed. The catalyst operation during the “steady state” phase (the phase that the catalyst has reached its operating conditions and works normally) is examined. Further experiments are needed for different catalyst typed before the methods is proposed generally. It consists of 855 elements of catalyst inlet-outlet temperature difference (DT), hydrocarbons (HC), and carbon monoxide (CO) and carbon dioxide (CO2) emissions. The simulation: detects the values of HC, CO, CO2 using the DT as an input to our network forms a neural network. Results showed serious indications that artificial neural networks (or fuzzy logic control laws) could estimate the catalyst performance adequately depending their training process, if certain information about the catalyst system and the inputs and output of such system are known. In this study the “steady state” period experimental results are presented. In this paper the “steady state” period experimental results are presented.

scholarly journals The Influence of Composite Laminate Stacking Sequence on Failure Load of Bonding Joints Using Experimental and Artificial Neural Networks Methods

2018 ◽  
Author(s):  
Bahadir Birecikli ◽  
Omer Ali Karaman ◽  
Selahattin Baris Celebi ◽  
Aydin Turgut
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Composite Laminate ◽  
Failure Load ◽  
Learning Algorithm ◽  
Fiber Composite ◽  
Material Design ◽  
Experimental Results ◽  
Stacking Sequence ◽  
Artificial Neural

The objective of this article was to forecast the ultimate failure load laminate stacking sequence combination on bonding joints which are exposed to tensile strength by using artificial neural networks. We have glass fiber composite materials with three different sequence combinations ([0°/90°], [±45°], [0°/90°/±45°]). Various adherend thicknesses and also ductile type adhesive was used in the experiment. The bonding geometry is a single lap and has four types of overlap angles 30°, 45°, 60°, 75° respectively. The experimental results demonstrate that composite laminate stacking sequence profoundly affects the bonding joints of failure load. Taking experimental results into account, Levenberg–Marquardt learning algorithm model was used by preferring a three layer forward on ANN so as to discipline network. In order to procure a precise ANN tool, an integrate methodology of experimental method has been used. The outcomes are used to ensure the experimental data’s to the ANN. The method of ANN permits surveying much adequately the probabilities of composite laminate stacking sequence combination using the prevalent ones which are [0°/90°], [±45°] and [0°/90°/±45°]. Testing data and training results were quite well 0.998, 0.997 and 0.998 in turn. Consequences acquired can be used by engineers who are interested in the composite material design to enhance failure load.

Technique for Intrusion Detection Based on NSST Domain Artificial Neural Networks

2015 ◽  
Vol 713-715 ◽  
pp. 2519-2522
Author(s):  
Yang Lei ◽  
Jing Ma
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intrusion Detection ◽  
Convergence Rates ◽  
Experimental Results ◽  
Shearlet Transform ◽  
Multi Scale ◽  
Novel Technique ◽  
Artificial Neural ◽  
Geometry Analysis

The issue of intrusion detection has been an active topic in both military and civilian areas, and a great many relevant algorithms and techniques have been developed accordingly. This paper addresses a novel technique based on non-subsampled shearlet transform (NSST) domain artificial neural networks (ANN) to solve the above problem, employing multi-scale geometry analysis (MGA) of NSST and the train characteristics of ANN together. Experimental results indicate that, compared with other existing conventional intrusion detection tools, the proposed one is superior to other current popular ones in both aspects of iteration numbers and convergence rates.

Sign in / Sign up

Export Citation Format

Share Document