On-Machine Measurement for Touch-Trigger Probes and its Error Compensation

2008 ◽  
Vol 375-376 ◽  
pp. 558-563 ◽  
Author(s):  
Xiao Ming Qian ◽  
Wen Hua Ye ◽  
Xiao Mei Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Measurement Error ◽  
Error Compensation ◽  
Error Modeling ◽  
Probe Measurement ◽  
Advantages And Disadvantages ◽  
Machining Center ◽  
Touch Trigger Probe ◽  
Artificial Neural

This paper advances a method to implement the on-machine measurement (OMM) with the touch-trigger probe, also called switching probes. Some of the advantages and disadvantages for touch-trigger probe are discussed. However, the touch-trigger probe errors exist and become one of the major errors for the measurement accuracy. Major factors that influence the probe measurement have been analyzed. The basic technique of probe measurement error modeling with artificial neural network was researched, and also the probe measurement error compensation with 3-layered backpropagation artificial neural network was presented. At last in the experimental system composed of DIXI 50 machining center, Fanuc 16i control system, Blum CNC P82.046 probe and PC, valid the correlated techniques. In addition, the connection and communication between the machining center equipped with probe system and the computer have been introduced. The experiment indicated that, using the touch-trigger probe makes on-machine measurement more automatic and efficient. And by using the back-propagation neural network for error compensation make on-machine measurement more precise.

scholarly journals Predicting the liquefaction potential of soil layers in Tabriz city via artificial neural network analysis

2021 ◽  
Vol 3 (7) ◽  
Author(s):  
Mohammad Alizadeh Mansouri ◽  
Rouzbeh Dabiri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Liquefaction ◽  
Data Sets ◽  
Liquefaction Potential ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Prediction Approach ◽  
Empirical Approaches

AbstractSoil liquefaction is a phenomenon through which saturated soil completely loses its strength and hardness and behaves the same as a liquid due to the severe stress it entails. This stress can be caused by earthquakes or sudden changes in soil stress conditions. Many empirical approaches have been proposed for predicting the potential of liquefaction, each of which includes advantages and disadvantages. In this paper, a novel prediction approach is proposed based on an artificial neural network (ANN) to adequately predict the potential of liquefaction in a specific range of soil properties. To this end, a whole set of 100 soil data is collected to calculate the potential of liquefaction via empirical approaches in Tabriz, Iran. Then, the results of the empirical approaches are utilized for data training in an ANN, which is considered as an option to predict liquefaction for the first time in Tabriz. The achieved configuration of the ANN is utilized to predict the liquefaction of 10 other data sets for validation purposes. According to the obtained results, a well-trained ANN is capable of predicting the liquefaction potential through error values of less than 5%, which represents the reliability of the proposed approach.

On the Neural Network Solution of One-Dimensional Wave Problem

2021 ◽  
Vol 5 (6) ◽  
pp. 1106-1112
Author(s):  
Aditya Firman Ihsan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wave Equation ◽  
Model Learning ◽  
One Dimensional ◽  
Advantages And Disadvantages ◽  
The Neural Network ◽  
Learning Capabilities ◽  
Artificial Neural ◽  
Dimensional Wave

Artificial neural network has become an emerging popular method to handle various problems, especially in case where it has deep multiple neural layers. In this study, we use a deep artificial neural network model to solve one-dimensional wave equation, without any external datasets. Different type of boundary conditions, i.e., Dirichlet, Neumann, and Robin, are used. We analyze the model learning capabilities in a set of settings, such as data setup and the model width and depth. We also present some discussions of advantages and disadvantages of the model in comparison with other matured existing techniques to solve wave equation.  

scholarly journals Artificial Neural Network Solution for Anomalous Operation Detection in Power Systems

2021 ◽  
Author(s):  
Nathan Elias Maruch Barreto ◽  
Ciro Monteiro Baer ◽  
Mateus Jaensen Daros ◽  
Marlon Alexsandro Fritzen ◽  
Guilherme Schneider de Oliveira ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Systems ◽  
Power System ◽  
Test System ◽  
Measurement Unit ◽  
Two Phase ◽  
Advantages And Disadvantages ◽  
Phasor Measurement ◽  
Artificial Neural

This paper presents an anomalous operation detection system for power systems using the artificial neural network approach while discussing its advantages and disadvantages. The initial data for the proposed technique is a set of simulated post-fault bus voltages and currents obtained in a sampling rate so as to emulate a phasor measurement unit network. Several types of faults are dealt with, such as three-phase to ground, two-phase, two-phase to ground and single-phase to the ground as well as line and load contingencies. All fault and steady-state simulations were performed on MATLAB using Graham Rogers’ Power System Toolbox. The artificial neural network was designed on MATLAB, using an architecture proper for pattern recognition with supervised learning and obtaining high accuracy predictions within a short amount of time. The test system used in all simulations is the IEEE 39-Bus New England Power System, which presents 10 generation units, 21 loads and three distinct areas alongside transient and sub transient models, with phasor measurement units in 14 buses. Future works are discussed, showing the possibilities for feature engineering in this type of problem, fault type detection and fault location in operation using analogous dataset and neural network structures.

FORECASTING THE INTENSITY OF SOLAR RADIATION BASED ON ARTIFICIAL NEURAL NETWORKS

2021 ◽  
Vol 43 (2) ◽  
pp. 60-67
Author(s):  
B.I. Basok ◽  
M.P. Novitska ◽  
V.P. Kravchenko
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Solar Radiation ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
Database Evaluation ◽  
Artificial Neural

The paper considers short-term forecasting of the intensity of solar radiation in the city of Odessa based on an artificial neural network. The artificial neural network was trained on the experimental data of the ground weather station (Davis 6162EU), which is installed on the roof of the educational building of the Odessa National Polytechnic University. Modeling, validation, and testing of experimental data were performed using the MATLAB software package, namely Neural Network Toolbox. The Levenberg-Markwatt model is used in this work. The analyzed data set was divided into proportions of 70%, 15%, 15% for neural network training, its validation, and testing, respectively. The results which the trained neural network gave during forecasting within the framework of the database and outside it are given. The deviation between real and forecast data is analyzed. The root-mean-square error on December 26, 2016 was 13.03 W / m2, and on December 27, 2016 - 9.44 W / m2 when forecasting outside the database. Evaluation of the accuracy of an artificial neural network has shown its effectiveness in predicting the intensity of solar radiation. To predict parameters based on artificial neural networks, experimental data that describe a real system are needed. Artificial neural networks, like other approximation methods, have both advantages and disadvantages.

Modeling and Simulation of Artificial Neural Network Controlled Soft Switched Push–Pull DC to DC Converter

2017 ◽  
Vol 14 (1) ◽  
pp. 585-590 ◽  
Author(s):  
S Devikala ◽  
V Sivachidambaranathan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prototype Model ◽  
Switching Power ◽  
Advantages And Disadvantages ◽  
Unique System ◽  
Control Scheme ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Storage Batteries

This paper presents the performance of DC/DC Push–Pull converter for storage batteries. Some of the DC/DC converters are analyzed for finding their advantages and disadvantages. Moreover, a unique system based on a Push–Pull converter associated with an active filter and superior controller is chosen. The main advantage is the possibility to minimize the ripple at the output, decrease the switching power losses, increase the power conversion efficiency and improve the transient and steady state response. This paper proposes a new filter, control scheme and Artificial Neural Network (ANN) controlled Push–Pull DC/DC converter. Simulation was done using MATLAB Simulink and designed biasing for the PIC 16F84 microcontroller. The performance of the proposed system has been verified through a 1 kW prototype model of the system for a 15 KHz, 48/12 V DC for battery. The simulation results are validated with experimental results.

Artificial Neural Network for Power System Static Security Assessment: A Survey

2013 ◽  
Vol 66 (1) ◽  
Author(s):  
I. S. Saeh ◽  
M. W. Mustafa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power System ◽  
Future Research ◽  
Security Assessment ◽  
Advantages And Disadvantages ◽  
Hybrid Techniques ◽  
Static Security ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

According to the growth rate of Machine Learning (ML) application in some power system subjects, this paper introduce a comprehensive survey of Artificial Neural Network (ANN) in Static Security Assessment (SSA). Advantages and disadvantages of using ANN in above mentioned subjects and the main challenges in these fields have been explained, too. We explore the links between the fields of SSA and NN in a unified presentation and identify key areas for future research. Recent developments in the solution methods for SSA are reviewed. Hybrid techniques in SSA are also discussed and reviewed and future directions for research are suggested. 

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