scholarly journals Back-emf Based Rotor Position Estimation For Low Cost Pmsm Drive Using Fully Connected Cascade Artificial Neural Networks

2018 ◽  
Vol 23 (1) ◽  
pp. 69-77
Author(s):  
Gabriel Negri ◽  
Filipe Nazário ◽  
José Oliveira ◽  
Ademir Nied
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Low Cost ◽  
Position Estimation ◽  
Rotor Position ◽  
Artificial Neural ◽  
Rotor Position Estimation ◽  
Fully Connected

scholarly journals Prediction of Solar Irradiance Based on Artificial Neural Networks

Inventions ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 45 ◽  
Author(s):  
Waleed I. Hameed ◽  
Baha A. Sawadi ◽  
Safa J. Al-Kamil ◽  
Mohammed S. Al-Radhi ◽  
Yasir I. A. Al-Yasir ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Irradiance ◽  
Open Circuit Voltage ◽  
Low Cost ◽  
Short Circuit ◽  
Short Circuit Current ◽  
Open Circuit ◽  
Pv Module ◽  
Artificial Neural

Prediction of solar irradiance plays an essential role in many energy systems. The objective of this paper is to present a low-cost solar irradiance meter based on artificial neural networks (ANN). A photovoltaic (PV) mathematical model of 50 watts and 36 cells was used to extract the short-circuit current and the open-circuit voltage of the PV module. The obtained data was used to train the ANN to predict solar irradiance for horizontal surfaces. The strategy was to measure the open-circuit voltage and the short-circuit current of the PV module and then feed it to the ANN as inputs to get the irradiance. The experimental and simulation results showed that the proposed method could be utilized to achieve the value of solar irradiance with acceptable approximation. As a result, this method presents a low-cost instrument that can be used instead of an expensive pyranometer.

scholarly journals KLASIFIKASI BATIK RIAU DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS (CNN)

2020 ◽  
Vol 9 (1) ◽  
pp. 7-10
Author(s):  
Hendry Fonda
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
18Th Century ◽  
The Public ◽  
Artificial Neural ◽  
The Difference ◽  
Fully Connected

ABSTRACT Riau batik is known since the 18th century and is used by royal kings. Riau Batik is made by using a stamp that is mixed with coloring and then printed on fabric. The fabric used is usually silk. As its development, comparing Javanese  batik with riau batik Riau is very slowly accepted by the public. Convolutional Neural Networks (CNN) is a combination of artificial neural networks and deeplearning methods. CNN consists of one or more convolutional layers, often with a subsampling layer followed by one or more fully connected layers as a standard neural network. In the process, CNN will conduct training and testing of Riau batik so that a collection of batik models that have been classified based on the characteristics that exist in Riau batik can be determined so that images are Riau batik and non-Riau batik. Classification using CNN produces Riau batik and not Riau batik with an accuracy of 65%. Accuracy of 65% is due to basically many of the same motifs between batik and other batik with the difference lies in the color of the absorption in the batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning   ABSTRAK   Batik Riau dikenal sejak abad ke 18 dan digunakan oleh bangsawan raja. Batik Riau dibuat dengan menggunakan cap yang dicampur dengan pewarna kemudian dicetak di kain. Kain yang digunakan biasanya sutra. Seiring perkembangannya, dibandingkan batik Jawa maka batik Riau sangat lambat diterima oleh masyarakat. Convolutional Neural Networks (CNN) merupakan kombinasi dari jaringan syaraf tiruan dan metode deeplearning. CNN terdiri dari satu atau lebih lapisan konvolutional, seringnya dengan suatu lapisan subsampling yang diikuti oleh satu atau lebih lapisan yang terhubung penuh sebagai standar jaringan syaraf. Dalam prosesnya CNN akan melakukan training dan testing terhadap batik Riau sehingga didapat kumpulan model batik yang telah terklasi    fikasi berdasarkan ciri khas yang ada pada batik Riau sehingga dapat ditentukan gambar (image) yang merupakan batik Riau dan yang bukan merupakan batik Riau. Klasifikasi menggunakan CNN menghasilkan batik riau dan bukan batik riau dengan akurasi 65%. Akurasi 65% disebabkan pada dasarnya banyak motif yang sama antara batik riau dengan batik lainnya dengan perbedaan terletak pada warna cerap pada batik riau. Kata kunci: Batik; Batik Riau; CNN; Image; Deep Learning

COVID-19 CORONAVIRUS SCREENING ANALYSIS NEURAL NETWORK TECHNOLOGY

2021 ◽  
Vol 2 (64) ◽  
pp. 53-57
Author(s):  
S. Aloshyn ◽  
I. Khomenko ◽  
N. Fursova
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Low Cost ◽  
Signs And Symptoms ◽  
Multidimensional Space ◽  
Diagnostic Systems ◽  
Coronavirus Infection ◽  
Artificial Neural ◽  
High Degree

Low-cost, reliable and quick screening diagnosis of coronavirus can be implemented on the basis of intelligent technologies for analyzing a set of signs and symptoms with solving the problem of pattern recognition in the basis of artificial neural networks. The high degree of coronavirus infection diagnostic procedure uncertainty, the vector dimension of input factor-symptoms, fuzzy conditioning and poor formalizability of the subject condition connection with these symptoms require appropriate analytical tools. An analysis of the problem and possible solutions allows justifying the feasibilit y of implementing screening diagnostics as a solution to the problem of nonlinear optimization in a multidimensional space of high-dimensional factors and states. Artificial neural networks with compulsory training on a representative sample were chosen as a tool for implementing the project. The proposed technology brings diagnostics of coronavirus infection closer to full automation, robotization and intellectualization of complex monitoring (diagnostic) systems as the most promising technology for pattern recognition in systems with a high degree of entropy and allows you to solve the problem at the lowest cost and required performance indicators.

Interests and Limits of Machine Learning-Based Neural Networks for Rotor Position Estimation in EV Traction Drives

2018 ◽  
Vol 14 (5) ◽  
pp. 1942-1951 ◽  
Author(s):  
Wided Zine ◽  
Zaatar Makni ◽  
Eric Monmasson ◽  
Lahoucine Idkhajine ◽  
Bruno Condamin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Position Estimation ◽  
Rotor Position ◽  
Traction Drives ◽  
Rotor Position Estimation

A Survey of Partially Connected Neural Networks

1997 ◽  
Vol 08 (05n06) ◽  
pp. 535-558 ◽  
Author(s):  
D. Elizondo ◽  
E. Fiesler
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Extensive Survey ◽  
Recall Time ◽  
Biological Reality ◽  
Artificial Neural ◽  
Almost All ◽  
Fully Connected

Almost all artificial neural networks are by default fully connected, which often implies a high redundancy and complexity. Little research has been devoted to the study of partially connected neural networks, despite its potential advantages like reduced training and recall time, improved generalization capabilities, reduced hardware requirements, as well as being a step closer to biological reality. This publication presents an extensive survey of the various kinds of partially connected neural networks, clustered into a clear framework, followed by a detailed comparative discussion.

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