scholarly journals Identificación de figuras geométricas en un sistema de visión basándose en el entrenamiento de una red neuronal artificial convolucional utilizando Python

2019 ◽  
pp. 7-16
Author(s):  
Martha Isabel Aguilera-Hernández ◽  
Jorge Alan Velasco-Marín ◽  
Manuel Ortiz-Salazar ◽  
Jose Luis Ortiz-Simón
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Industry 4.0 ◽  
Vision System ◽  
Support Point ◽  
Process Systems ◽  
Geometric Figures ◽  
Different Types ◽  
Artificial Neural ◽  
Python Programming

The image processing projects through vision systems, are a great didactic support point in the mechatronics career, since they have wide application in the industry in the process lines primarily to perform assembly, inspection, selection and component placement. One of the methods used is to apply artificial neural networks for the identification of images and a factor to analyze is the evaluation of the learning capacities of these networks in the identification of geometric figures. In this article, the training of a convolutional artificial neural network using Python is presented. This type of work is focused on joining projects based on industry 4.0 that may contain link options with process systems based on these technologies. In this work, a vision system based on python programming was made and has its contribution in the libraries that were designed and can be linked to different types of applications within a manufacturing process.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

scholarly journals Toward Better PV Panel’s Output Power Prediction; a Module Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs

2019 ◽  
Vol 9 (18) ◽  
pp. 3670 ◽  
Author(s):  
Natsheh ◽  
Samara
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Output Power ◽  
Maximum Output Power ◽  
Maximum Output ◽  
Atmospheric Conditions ◽  
Power Prediction ◽  
Different Types ◽  
Artificial Neural ◽  
Nonlinear Autoregressive

Much work has been carried out for modeling the output power of photovoltaic panels. Using artificial neural networks (ANNS), one could efficiently model the output power of heterogeneous photovoltaic (HPV) panels. However, due to the existing different types of artificial neural network implementations, it has become hard to choose the best approach to use for a specific application. This raises the need for studies that develop models using the different neural networks types and compare the efficiency of these different types for that specific application. In this work, two neural network types, namely, the nonlinear autoregressive network with exogenous inputs (NARX) and the deep feed-forward (DFF) neural network, have been developed and compared for modeling the maximum output power of HPV panels. Both neural networks have four exogenous inputs and two outputs. Matlab/Simulink is used in evaluating the proposed two models under a variety of atmospheric conditions. A comprehensive evaluation, including a Diebold-Mariano (DM) test, is applied to verify the ability of the proposed networks. Moreover, the work further investigates the two developed neural networks using their actual implementation on a low-cost microcontroller. Both neural networks have performed very well; however, the NARX model performance is much better compared with DFF. Using the NARX network, a prediction of PV output power could be obtained, with half the execution time required to obtain the same prediction with the DFF neural network, and with accuracy of ±0.18 W.

scholarly journals Mengidentifikasi Tanaman Beracun pada Pola Daun dengan Jaringan Syaraf Tiruan Learning Vector Quantification

2019 ◽  
Vol 3 (1) ◽  
pp. 7
Author(s):  
Muhammad Jurnalies Habibie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Poisonous Plants ◽  
Toxic Plants ◽  
Network Method ◽  
Different Types ◽  
Artificial Neural Network Method ◽  
Artificial Neural

Technology nowadays is starting to go very fast, so that all people can use it. Toxic plants are very dangerous if consumed. Therefore to avoid undesirable events, an introduction to the community is needed to find out which plants are poisonous. Plants have many different types to recognize poisonous plants can be seen from the recognition of leaf patterns in these plants. For this reason, in order to determine the use of Learning Vector Quantification artificial neural networks. In this study, the use of input photos obtained from the camera. Photos will be processed later to extract the characteristics. Next, the process of pattern recognition can get the features in the photo. So that later it gets its characteristics. then the classification process uses the Learning Vector Quantification artificial neural network method. This research was conducted to be able to distinguish poisonous plants from those that are not. Which later the data is collected for grouping in accordance with the same data, so that information can be set about the plant.

scholarly journals Prediksi Nilai Tukar Dollar (USD) ke Rupiah (IDR) menggunakan Artificial Neural Network

2019 ◽  
Vol 2 (1) ◽  
pp. 1-7
Author(s):  
Ahmad Saparudin ◽  
Tiya Maulidina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Exchange Rates ◽  
Business Strategy ◽  
Training Dataset ◽  
Python Programming Language ◽  
The Future ◽  
Business Forecasting ◽  
Artificial Neural ◽  
Python Programming

Prediction (forecasting) is the activity of predicting events in the future. In terms of business forecasting has many uses, especially for the leadership of the company one of them i.e. to define its business strategy in the future. In this research, carried out the predictions of exchange rates dollar (USD) to Indonesian rupiah (IDR) on 11/03/2019 - 15/03/2019 using artificial neural networks (ANN) with a training dataset from 01/01/2018 - 08/03/2019. Establishment of ANN in the study formed in the Python programming language. Based on the research conducted, a decrease in the price of the exchange rate of USD to IDR on 11/03/2019 – 15/03/2019.

Artificial Neural Network Based Smell Simulator for Visualization

2015 ◽  
Vol 3 (4) ◽  
pp. 328-329
Author(s):  
R. Shifana Patcha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Video Game ◽  
Vision System ◽  
Laser Printer ◽  
Artificial Neural

The continued progress towards higher resolution screen Is a tr experience on the screen and Scentthat’s can be the powerful ashame simulator of memory and emotions. The idea of Smell Simulator for Visualization can make it possible. Smell-O-Vision was a system that released odor during the projection of a film so that the viewer could smell what was happening in the movie. A typical Smell synthesizer is essentially a box of various scented oils with some kind of spreading mechanism, like a fan. The box receives a signal from, for example a video game which tells to release an apple scent. Like in laser printer, this produces all the colours from the three basic colours (Red, Blue and Green). So, if single apple- scented oil isn’t avail would mix to create something close to that desired apple scent. Currently the smell-O-vision system can produce only one smell at a time and that too with increased delay and noise. We have aimed to produce our smell simulator using neural networks so that delay problem can be minimized. Smell Simulator for Visualization can become prevalent in consumer homes, and we can even literally stop and smell the roses in the game we play and shows we watch.

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

scholarly journals An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array

2021 ◽  
Vol 13 (11) ◽  
pp. 6194
Author(s):  
Selma Tchoketch_Kebir ◽  
Nawal Cheggaga ◽  
Adrian Ilinca ◽  
Sabri Boulouma
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Modeling Process ◽  
Photovoltaic Arrays ◽  
Mode Of Operation ◽  
Different Types ◽  
The Neural Networks ◽  
Operational Modes ◽  
Types Of Faults ◽  
Electrical Data

This paper presents an efficient neural network-based method for fault diagnosis in photovoltaic arrays. The proposed method was elaborated on three main steps: the data-feeding step, the fault-modeling step, and the decision step. The first step consists of feeding the real meteorological and electrical data to the neural networks, namely solar irradiance, panel temperature, photovoltaic-current, and photovoltaic-voltage. The second step consists of modeling a healthy mode of operation and five additional faulty operational modes; the modeling process is carried out using two networks of artificial neural networks. From this step, six classes are obtained, where each class corresponds to a predefined model, namely, the faultless scenario and five faulty scenarios. The third step involves the diagnosis decision about the system’s state. Based on the results from the above step, two probabilistic neural networks will classify each generated data according to the six classes. The obtained results show that the developed method can effectively detect different types of faults and classify them. Besides, this method still achieves high performances even in the presence of noises. It provides a diagnosis even in the presence of data injected at reduced real-time, which proves its robustness.

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