Use of Neural Network and Discrete Wavelet Transformations in Estimation of Road Profile

Volume 2 ◽  
2004 ◽  
Author(s):  
Mohammad Durali ◽  
Alireza Kasaaizadeh
Keyword(s):  
Neural Network ◽  
Optimization Methods ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network ◽  
The Road ◽  
The Neural Network ◽  
Road Profile ◽  
Road Roughness ◽  
Coding Method ◽  
Dynamic Experiments

This paper presents a method for estimation of road profile for automotive research applications with more accuracy and higher speed. Dynamic response of a car equipped with position and velocity sensors and driving on a sample road is used as basic data. A feed-forward neural network, trained with outputs from a car model in ADAMS, is used as the car inverse model. The neural network is capable of estimating the road roughness from the car response during test drives. The ADAMS model is corrected and validated using a series of dynamic experiments on the car, performed on a hydro-pulse test rig. The only problem in this approach, like other identification and optimization methods, is the large volume of generated data in time domain, acquired from car response during road test. This problem is solved using wavelet methods to code the acquired data. Unlike all frequency methods that eliminate a large portion of the data details during processing, the wavelet coding method restores most of the details, while the volume of the stored data is kept to a minimum. The results show that this method can estimate the road profile accurately and with great savings in processing time and effort.

scholarly journals Research on Road Roughness Based on NARX Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yingjie Liu ◽  
Dawei Cui
Keyword(s):  
Neural Network ◽  
The Body ◽  
Identification Accuracy ◽  
Vertical Acceleration ◽  
Plane Model ◽  
The Road ◽  
The Neural Network ◽  
Road Roughness ◽  
Network Identification ◽  
Narx Neural Network

In order to solve the problem of road roughness identification, a study on the nonlinear autoregressive with exogenous inputs (NARX) neural network identification method was carried out in the paper. Firstly, a 7-DOF plane model of vehicle vibration system was established to obtain the vertical acceleration and elevation acceleration of the body, which were set as ideal input samples for the neural network. Then, based on the plane model, with common speed, the road roughness was solved as the ideal output sample of the NARX neural network, and the road roughness of B-level and C-level was identified. The results show that the proposed method has ideal identification accuracy and strong antinoise ability. The relative error of C-level road roughness is larger than that of B-level road roughness. The identified road roughness can provide a theoretical basis for analyzing the dynamic response of expressway roads.

Breast Cancer Classification using Discrete Wavelet Transformation and Deep Learning

2019 ◽  
Vol 13 ◽  
Author(s):  
Emmanuel Masa-Ibi ◽  
Rajesh Prasad
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Cancer Classification ◽  
Classification Model ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network ◽  
Cancer Data ◽  
Breast Cancer Classification ◽  
The Neural Network

Background: One of the most prevalent sicknesses these days is breast cancer which is common amongst women. This sickness has been in increase to an alarming rate due to the lack of accurate administration of diagnoses. Early and accurate detection is one of the safest ways to cure a breast cancer patient. Objectives: The objective of this study is to proffer a more effective way to accurately classify a cancer sample; whether is Benign or Malignant. Methods: The classification model is based on the data collected from the UCI machine learning repository acquired from Wisconsin hospital called Wisconsin breast cancer data (WBCD). In this study, we preprocessed the dataset using DWT and then test the efficiency of deep learning (DL) for breast cancer classification. The model is developed using a feed-forward neural network and the result is compared with the observed values. Results: The result of the experiment proved the effectiveness of the proposed classification technique. The new technique accomplishes 98.90% accuracy for classifying breast cancer. Conclusions: The result from the experiment shows that the importance of data preprocessing and the efficiency of the neural network over other classification algorithms.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

scholarly journals Effect of Road Profile on Suspension System of Heavy Truck

2019 ◽  
Vol 12 (2) ◽  
pp. 71-75
Author(s):  
Salem F. Salman
Keyword(s):  
Steering System ◽  
Suspension System ◽  
The Road ◽  
Road Profile ◽  
Road Roughness ◽  
Road Type ◽  
Heavy Truck ◽  
Main Factors ◽  
On The Road ◽  
The Stability

All vehicles are affected by the type of the road they are moving on it.  Therefore the stability depends mainly on the amount of vibrations and steering system, which in turn depend on two main factors: the first is on the road type, which specifies the amount of vibrations arising from the movement of the wheels above it, and the second on is the type of the used suspension system, and how the parts connect with each other. As well as the damping factors, the tires type, and the used sprungs. In the current study, we will examine the effect of the road roughness on the performance coefficients (speed, displacement, and acceleration) of the joint points by using a BOGE device.

Gray-Box Approach for Fault Detection of Dynamical Systems

2003 ◽  
Vol 125 (3) ◽  
pp. 451-454 ◽  
Author(s):  
Han G. Park ◽  
Michail Zak
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Fault Detection ◽  
Stochastic Model ◽  
Deterministic Model ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Simulated System ◽  
Auto Regressive ◽  
Nonlinear Components

We present a fault detection method called the gray-box. The term “gray-box” refers to the approach wherein a deterministic model of system, i.e., “white box,” is used to filter the data and generate a residual, while a stochastic model, i.e., “black-box” is used to describe the residual. The residual is described by a three-tier stochastic model. An auto-regressive process, and a time-delay feed-forward neural network describe the linear and nonlinear components of the residual, respectively. The last component, the noise, is characterized by its moments. Faults are detected by monitoring the parameters of the auto-regressive model, the weights of the neural network, and the moments of noise. This method is demonstrated on a simulated system of a gas turbine with time delay feedback actuator.

Contributions to Predict the Malfunction Probability of the Human-Machine-Environment System, Using Artificial Neural Networks

2015 ◽  
Vol 760 ◽  
pp. 771-776
Author(s):  
Daniel Constantin Anghel ◽  
Nadia Belu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Experiment ◽  
Manufacturing Processes ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Linear Relationships ◽  
The Neural Network ◽  
Artificial Neural

This paper presents the application of Artificial Neural Networks to predict the malfunction probability of the human-machine-environment system, in order to provide some guidance to designers of manufacturing processes. Artificial Neural Networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. We used, in this work, a feed forward neural network in order to predict the malfunction probability. The neural network is simulated with Matlab. The design experiment presented in this paper was realized at University of Pitesti, at the Faculty of Mechanics and Technology, Technology and Management Department.

Customer Usage Based on Pseudo Damage

2017 ◽  
Author(s):  
Craig T. Altmann ◽  
John B. Ferris
Keyword(s):  
Performance Monitoring ◽  
International Roughness Index ◽  
The Road ◽  
Road Profile ◽  
Road Roughness ◽  
Federal Highway ◽  
Roughness Index ◽  
Average Annual Daily Traffic ◽  
Road Type ◽  
Early Design Stages

Modeling customer usage in vehicle applications is critical in performing durability simulations and analysis in early design stages. Currently, customer usage is typically based on road roughness (some measure of accumulated suspension travel), but vehicle damage does not vary linearly with the road roughness. Presently, a method for calculating a pseudo damage measure is developed based on the roughness of the road profile, specifically the International Roughness Index (IRI). The IRI and pseudo damage are combined to create a new measure referred to as the road roughness-insensitive pseudo damage. The road roughness-insensitive pseudo damage measure is tested using a weighted distribution of IRI values corresponding to the principal arterial (highways and freeways) road type from the Federal Highway Administration (FHWA) Highway Performance Monitoring System (HPMS) dataset. The weighted IRI distribution is determined using the number of unique IRI occurrences in the functional road type dataset and the Average Annual Daily Traffic (AADT) provided in the FHWA HPMS data.

scholarly journals Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4946
Author(s):  
Tuan Pham Van ◽  
Dung Vo Tien ◽  
Zbigniew Leonowicz ◽  
Michal Jasinski ◽  
Tomasz Sikorski ◽  
...  
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Learning Rate ◽  
Accurate Estimation ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Artificial Neural ◽  
Stator Resistance

This paper presents a new approach method for online rotor and stator resistance estimation of induction motors using artificial neural networks for the sensorless drive. In this method, the rotor resistance is estimated by a feed-forward neural network with the learning rate as a function. The stator resistance is also estimated using the two-layered neural network with learning rate as a function. The speed of the induction motor is also estimated by the neural network. Therefore, the accurate estimation of the rotor and stator resistance improved the quality of the sensorless induction motor drive. The results of simulation and experiment show that the estimated speed tracks the real speed of the induction motor; simultaneously, the error between the estimated rotor and stator resistance using neural network and the normal rotor and stator resistance is very small.

Research on the Key Technologies of Intelligent Control for Cap-Shape Bending of Sheet Metal

2011 ◽  
Vol 239-242 ◽  
pp. 2867-2872
Author(s):  
Hong Lei Sun ◽  
Chun Jian Su ◽  
Rui Xue Zhai
Keyword(s):  
Neural Network ◽  
Control System ◽  
Real Time ◽  
Sheet Metal ◽  
Intelligent Control ◽  
Feed Forward Neural Network ◽  
Forming Process ◽  
Intelligent Control System ◽  
The Neural Network ◽  
Real Time Identification

The blueprint for an intelligent control system of cap-shape bending has been advanced in this paper using neural network technology, aiming at an accurate control of bending springback, the prominent problem during the forming process for the cap-shape bending of sheet metal. The feed-forward neural network of real-time identification for material performance parameters and the friction coefficient have been established. The neural network identifies the parameters for real-time needed material performance, which utilizes the measurability of the physical quantities, and predicts the parameters for optimum technology, so a satisfied accuracy of convergence has been achieved. The intelligent control experimentation system of cap-shape bending has been established, the validity of which has been tested for four kinds of materials. The result of the tests proves the feasibility of the blueprint of the intelligent control system.

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