Accurate quantum dynamics simulation of the photodetachment spectrum of the nitrate anion (NO3−) based on an artificial neural network diabatic potential model

2021 ◽  
Vol 154 (8) ◽  
pp. 084302
Author(s):  
Alexandra Viel ◽  
David M. G. Williams ◽  
Wolfgang Eisfeld
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Quantum Dynamics ◽  
Potential Model ◽  
Dynamics Simulation ◽  
Nitrate Anion ◽  
Artificial Neural ◽  
Photodetachment Spectrum

Artificial neural network approach for air brake pushrod stroke prediction in heavy commercial road vehicles

2018 ◽  
Vol 233 (10) ◽  
pp. 2467-2478 ◽  
Author(s):  
Radhika Raveendran ◽  
Apoorva Suresh ◽  
Vignesh Rajaram ◽  
Shankar C Subramanian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Confidence Interval ◽  
Prediction Error ◽  
Performance Degradation ◽  
Dynamics Simulation ◽  
Brake System ◽  
Road Vehicles ◽  
Prediction Scheme ◽  
Artificial Neural

In heavy commercial road vehicles, the air brake system is a critical vehicle safety system whose performance degradation increases the risk of accidents and hence requires periodic inspection and maintenance. The wear of brake pad lining and brake drum during operation leads to increase in the stroke of a component called pushrod whose ‘out-of-adjustment’ creates severe brake performance degradation. The fact that the driver does not receive a corresponding tactile feedback till it is too severe adds to the complexity of manual detection. Motivated by the increase in onboard sensing, electronics, and computation capabilities, this study proposes an artificial neural network–based approach to predict pushrod stroke based on measurement of brake chamber pressure. Here, a back propagation algorithm was used to train the multilayer feed-forward network. The effect of excessive pushrod stroke on vehicle braking response was first studied using a Hardware-in-Loop system that consists of brake system hardware and a commercial vehicle dynamics simulation software (IPG TruckMaker®). Experimental data collected from this system with manual slack adjuster and automatic slack adjuster have then been used to train and test the artificial neural network for pushrod stroke prediction. The performance of the prediction scheme has been tested over the entire range of brake operating conditions. The prediction error corresponding to manual slack adjuster was found to be within ±15% in 322 out of the entire test set of 328 instances (98.17%) and automatic slack adjuster within ±8% in all 57 test sets (100%). Statistical analysis based on confidence interval revealed a prediction error between −1.62% and −3.05% for manual slack adjuster and 0.43% and −1.62% for automatic slack adjuster for 99% confidence interval, which demonstrated the efficacy of the proposed prediction scheme.

scholarly journals Estimation of Vehicle Side-Slip Angle Using an Artificial Neural Network

2018 ◽  
Vol 166 ◽  
pp. 02001 ◽  
Author(s):  
Daniel Chindamo ◽  
Marco Gadola
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Point Of View ◽  
Dynamics Simulation ◽  
Slip Angle ◽  
Computational Point ◽  
Side Slip Angle ◽  
Model Complex ◽  
Active Safety Systems ◽  
Artificial Neural

In this work, a reliable and effective method to predict the vehicle side-slip angle is given by means of an artificial neural network. It is well known that artificial neural networks are a very powerful modelling tool. They are largely used in many engineering fields to model complex and strongly non-linear systems. For this application, the network has to be as simple as possible in order to work in real-time within built-in applications such as active safety systems. The network has been trained with the data coming from a custom manoeuvre designed in order to keep the method simple and light from the computational point of view. Therefore, a 5-10-1 (input-hidden-output layer) network layout has been used. These aspects allow the network to give a proper estimation despite its simplicity. The proposed methodology has been tested by means of the CarSim® simulation package, which is considered one of the reference tools in the field of vehicle dynamics simulation. To prove the effectiveness of the method, tests have been carried out under different adherence conditions.

Research on State-Space Mapping Algorithm from the Vehicle Unsteady Constraint Test System to the Independent Vehicle System

2011 ◽  
Vol 130-134 ◽  
pp. 326-331 ◽  
Author(s):  
Guo Ye Wang ◽  
Juan Li Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
State Space ◽  
Test System ◽  
Space Mapping ◽  
The State ◽  
Dynamics Simulation ◽  
Mapping Algorithm ◽  
Vehicle System ◽  
Artificial Neural

Project the vehicle unsteady constraint test system for testing vehicle ESP control performances safely and efficiently, set up the test system dynamics model. Based on the Matlab/Simulink establish the dynamics simulation system of the vehicle unsteady constraint test system for the Chery A3 car. Using the simulation model, we respectively simulate the stability control performances of the test system and the independent vehicle system on steady-state conditions of under steering and over steering. Research and verify the state-space mapping algorithm from the test system to the independent vehicle system using the artificial neural network. The study results indicate that the state-space mapping algorithm from the vehicle unsteady constraint test system to the independent vehicle system using the artificial neural network has ideal mapping performance, it will provide a theoretical basis and technical support for researching the vehicle ESP control performances based on the vehicle unsteady constraint test system.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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