scholarly journals Analysis and classification of tem¬perature measurements during melting and casting of alloys using neural networks

2020 ◽  
Vol 63 (10) ◽  
pp. 856-861
Author(s):  
A. V. Fedosov ◽  
G. V. Chumachenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Human Factor ◽  
High Efficiency ◽  
Thermal Conditions ◽  
Second Stage ◽  
Three Stages ◽  
Fully Automatic ◽  
Artificial Neural

The article considers the issues of monitoring the thermal conditions of alloys melting and casting at foundries. It is noted that the least reliable method is when the measurement and fixing the temperature is assigned to the worker. On the other hand, a fully automatic approach is not always available for small foundries. In this regard, the expediency of using an automated approach is shown, in which the measurement is assigned to the worker, and the values are recorded automatically. This method assumes implementation of an algorithm for automatic classification of temperature measurements based on an end-to-end array of data obtained in the production stream. The solving of this task is divided into three stages. Preparing of raw data for classification process is provided on the first stage. On the second stage, the task of measurement classification is solved by using neural network principles. Analysis of the results of the artificial neural network has shown its high efficiency and degree of their correspondence with the actual situation on the work site. It was also noted that the application of artificial neural networks principles makes the classification process flexible, due to the ability to easily supplement the process with new parameters and neurons. The final stage is analysis of the obtained results. Correctly performed data classification provides an opportunity not only to assess compliance with technological discipline at the site, but also to improve the process of identifying the causes of casting defects. Application of the proposed approach allows us to reduce the influence of human factor in the analysis of thermal conditions of alloys melting and casting with minimal costs for melting monitoring.

Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra

1991 ◽  
Vol 45 (10) ◽  
pp. 1706-1716 ◽  
Author(s):  
Mark Glick ◽  
Gary M. Hieftje
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Glow Discharge ◽  
Multivariate Calibration ◽  
Emission Spectra ◽  
K Nearest Neighbor ◽  
The Neural Network ◽  
Calibration Sets ◽  
Artificial Neural

Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with different architectures and different training parameters. After the neural networks learned to discriminate correctly among alloy classes in the training set, their ability to classify samples in the testing set was measured. In general, the neural network approach performed slightly better than the K-nearest neighbor method, but it suffered from a hidden classification mechanism and nonunique solutions. The neural network methodology is discussed and compared with conventional sample-classification techniques, and multivariate calibration of glow discharge spectra is compared with conventional univariate calibration.

An Artificial Neural Network Classification of Prescription Nonadherence

2020 ◽  
pp. 487-501
Author(s):  
Steven Walczak ◽  
Senanu R. Okuboyejo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Healthcare Providers ◽  
Classification Model ◽  
Survey Method ◽  
Neural Network Classification ◽  
Treatment Plans ◽  
Ann Models ◽  
Artificial Neural

This study investigates the use of artificial neural networks (ANNs) to classify reasons for medication nonadherence. A survey method is used to collect individual reasons for nonadherence to treatment plans. Seven reasons for nonadherence are identified from the survey. ANNs using backpropagation learning are trained and validated to produce a nonadherence classification model. Most patients identified multiple reasons for nonadherence. The ANN models were able to accurately predict almost 63 percent of the reasons identified for each patient. After removal of two highly common nonadherence reasons, new ANN models are able to identify 73 percent of the remaining nonadherence reasons. ANN models of nonadherence are validated as a reliable medical informatics tool for assisting healthcare providers in identifying the most likely reasons for treatment nonadherence. Physicians may use the identified nonadherence reasons to help overcome the causes of nonadherence for each patient.

Fetal cardiotocography monitoring using Legendre neural networks

2019 ◽  
Vol 64 (6) ◽  
pp. 669-675 ◽  
Author(s):  
Abdulaziz Alsayyari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Uterine Contraction ◽  
High Efficiency ◽  
Fetal Monitoring ◽  
Data Sets ◽  
Legendre Series ◽  
Electronic Fetal Monitoring ◽  
A New Technique

Abstract A new technique for electronic fetal monitoring (EFM) using an efficient structure of neural networks based on the Legendre series is presented in this paper. Such a structure is achieved by training a Legendre series-based neural network (LNN) to classify the different fetal states based on recorded cardiotocographic (CTG) data sets given by others. These data sets consist of measurements of fetal heart rate (FHR) and uterine contraction (UC). The applied LNN utilizes a Legendre series expansion for the input vectors and, hence, has the capability to produce explicit equations describing multi-input multi-output systems. Simulations of the proposed technique in EFM demonstrate its high efficiency. Training the LNN requires a few number of iterations (5–10 epochs). The applied technique makes the classification of the fetal state available through equations combining the trained LNN weights and the current measured CTG record. A comparison of performance between the proposed LNN and other popular neural network techniques such as the Volterra neural network (VNN) in EFM is provided. The comparison shows that, the LNN outperforms the VNN in case of less computational requirements and fast convergence with a lower mean square error.

scholarly journals Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks

2010 ◽  
Vol 61 (4) ◽  
pp. 235-240 ◽  
Author(s):  
Perumal Chandrasekar ◽  
Vijayarajan Kamaraj
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Power Quality ◽  
Wavelet Transforms ◽  
Back Propagation ◽  
Voltage Sag ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Detection and Classification of Power Quality Disturbancewaveform Using MRA Based Modified Wavelet Transfrom and Neural Networks In this paper, the modified wavelet based artificial neural network (ANN) is implemented and tested for power signal disturbances. The power signal is decomposed by using modified wavelet transform and the classification is carried by using ANN. Discrete modified wavelet transforms based signal decomposition technique is integrated with the back propagation artificial neural network model is proposed. Varieties of power quality events including voltage sag, swell, momentary interruption, harmonics, transient oscillation and voltage fluctuation are used to test the performance of the proposed approach. The simulation is carried out by using MATLAB software. The simulation results show that the proposed scheme offers superior detection and classification compared to the conventional approaches.

A New Approach Hybrid Based in Artificial Neural Networks to Detection and Classification of Failures in Aeronautical Structures

2015 ◽  
Vol 763 ◽  
pp. 175-181
Author(s):  
Simone Silva Frutuoso Souza ◽  
Fernando Parra dos Anjos Lima ◽  
Fábio Roberto Chavarette
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
New Approach ◽  
Inspection Process ◽  
Artificial Neural ◽  
Aluminum Beam ◽  
First Time ◽  
Hybrid Methodology

In this paper presents a new hybrid methodology to perform fault detection and classification of aircraft structures using the tool as ARTMAP-Fuzzyand Perceptron multi-layer artificial neural networks. This method is divided into two steps, the first step performed by the multi-layer Perceptron neural network, which consists in the detection of abnormalities in the structure. The second step is performed by ARTMAP-Fuzzyneural network and consists of the classification of faults structural detected in the first time. The main application of this hybrid methodology is to assist in the inspection process of aeronautical structures in order to identify and characterize flaws as well, make decision-making in order to avoid accidents or air crashes. To evaluate this method, the modeling and simulation was carried out signals from a numerical model of an aluminum beam. The results obtained by the methodology demonstrating robustness and accuracy structural flaws.

Modification of BJT using Artificial Neural Network and implemented it on FPGA

2015 ◽  
Vol 3 (1) ◽  
pp. 55-69
Author(s):  
Hassan Fahad Khazal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Second Stage ◽  
Feed Forward Back Propagation ◽  
Input Signals ◽  
Third Stage ◽  
Field Programmable ◽  
Three Stages ◽  
Artificial Neural

       In this research the performance of the BJT has been improved using the "Feed Forward – Back Propagation Artificial Neural Network" (FFBPANN). The use of this type of networks led to improve the pre specified functions, by widening its bandwidth, improving its sensitivity to the minimum and maximum values of input signals, and reduce the effect of the rise of the temperature on its performance. The improvement done on the type "npn" of the  code "2N2222A /ZTX". The execution of this work passed through three stages using various types of computer's programs. The first step have been done using the "Orcad Pspice" program, the second stage; the collected data from the first stage have been introduced as the input data of the "FFBPANN" that represented using "MATLAB R2013b" and the third stage have been done using the (ISE, Project navigator (P.14.2)) in order to apply the results of second stage on the "Field Programmable Gate Array" chip (FPGA).

scholarly journals Faint Object Classification Using Artificial Neural Networks

1994 ◽  
Vol 161 ◽  
pp. 249-252
Author(s):  
M. Serra-Ricart
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Neural Network Classifier ◽  
The Neural Network ◽  
Faint Object ◽  
Artificial Neural ◽  
Astronomical Images

Artificial Neural Network techniques are applied to the classification of faint objects, detected in digital astronomical images, and a Bayesian classifier (the neural network classifier, NNC hereafter) is proposed. This classifier can be implemented using a feedforward multilayered neural network trained by the back-propagation procedure (Werbos 1974).

scholarly journals Classifying Descriptions of Goods with Artificial Neural Networks

2019 ◽  
Vol 1 (1) ◽  
pp. 53-57
Author(s):  
Vinicius Di Oliveira ◽  
Marcelo Ladeira
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
The Neural Network ◽  
Legal Document ◽  
Artificial Neural

The present study aims to evaluate the performance of an artificial neural network in the classification of merchandise descriptions indicated in electronic bills, legal document used to record all commercial transactions in Brazil. For this, a significant sample of the actual descriptions will be used as well as a overlook about the performance of the neural network with a KNN and a GBM algorithms forecasting the category of the merchandise each description refers. This paper brings a method for classifying descriptions of goods with Artificial Neural Networks. The descriptions are small non structured texts, maximum of 120 characters, relating to goods traded in commercial transactions.

scholarly journals Sistem Cerdas Deteksi Sinyal Elektrokardiogram (EKG) untuk Klasifikasi Jantung Normal dan Abnormal Menggunakan Jaringan Syaraf Tiruan (JST)

2019 ◽  
Vol 4 (1) ◽  
pp. 49-55
Author(s):  
Myza Rifali ◽  
Dessy Irmawati
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Neural Networks ◽  
Test Data ◽  
Acquisition Training ◽  
Statistical Characteristics ◽  
Ecg Signals ◽  
Final Project ◽  
Artificial Neural

This article aims to describe the accuracy of signal processing using neural networks. The design of this final project hardware consists of Arduino Uno, AD8232 module and electrodes. ECG signals obtained from respondents were used as test data for normal ECG signals, while for abnormal class test data the data used were obtained from the research website, namely physionet with atrial fibrillation class. The design process in this system includes the process of data acquisition, training, feature extraction, testing and classification with artificial neural networks. Based on the results of the performance of this device to record ECG signals on respondents obtained normal ECG signals because the results of recorded ECG signals have a similarity in the PQRST wave with a predetermined target. This system can detect the classification of the heart by recognizing the statistical characteristics of the two signal classes and is trained using neural networks. Based on the testing process using an artificial neural network obtained an accuracy of 76.9%.

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