The Hybrid Classification Model Thanks to Artificial Neural Network and Artificial Immune Systems for Diagnosis of Epilepsy from Electroencephalography

Abstract An artificial neural network was used to build models caple of predicting and quantifying vodka adulteration with methanol and/or tap water. A voltammetric electronic tongue based on gold and copper microelectrodes was used, and 310 analyses were performed. Vodkas were adulterated with tap water (5 to 50% (v/v)), methanol (1 to 13% (v/v)), and with a fixed addition of 5% methanol and tap water varying from 5 to 50% (v/v). The classification model showed 99.5% precision, and it correctly predicted the type of adulterant in all samples. Regarding the regression model, the root mean squared error was 3.464% and 0.535% for the water and methanol addition, respectively, and the prediction of the adulterant content presented an R2 0.9511 for methanol and 0.9831 for water adulteration.

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Comprehensive Analysis of Classification Techniques Based on Artificial Immune System and Artificial Neural Network Algorithms

Algorithms for Intelligent Systems - Applications of Artificial Intelligence in Engineering ◽

10.1007/978-981-33-4604-8_68 ◽

2021 ◽

pp. 845-853

Author(s):

Kirti Bala Bahekar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Immune System ◽

Artificial Immune System ◽

Comprehensive Analysis ◽

Artificial Immune ◽

Network Algorithms ◽

Classification Techniques ◽

Artificial Neural

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Fault Diagnosis for UAV Blades Using Artificial Neural Network

Robotics ◽

10.3390/robotics8030059 ◽

2019 ◽

Vol 8 (3) ◽

pp. 59 ◽

Cited By ~ 21

Author(s):

Gino Iannace ◽

Giuseppe Ciaburro ◽

Amelia Trematerra

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Paper Tape ◽

Operating Conditions ◽

Classification Model ◽

Network Algorithms ◽

Substantial Modification ◽

Cargo Transport ◽

Artificial Neural ◽

Recreational Use

In recent years, unmanned aerial vehicles (UAVs) have been used in several fields including, for example, archaeology, cargo transport, conservation, healthcare, filmmaking, hobbies and recreational use. UAVs are aircraft characterized by the absence of a human pilot on board. The extensive use of these devices has highlighted maintenance problems with regard to the propellers, which represent the source of propulsion of the aircraft. A defect in the propellers of a drone can cause the aircraft to fall to the ground and its consequent destruction, and it also constitutes a safety problem for objects and people that are in the range of action of the aircraft. In this study, the measurements of the noise emitted by a UAV were used to build a classification model to detect unbalanced blades in a UAV propeller. To simulate the fault condition, two strips of paper tape were applied to the upper surface of a blade. The paper tape created a substantial modification of the aerodynamics of the blade, and this modification characterized the noise produced by the blade in its rotation. Then, a model based on artificial neural network algorithms was built to detect unbalanced blades in a UAV propeller. This model showed high accuracy (0.9763), indicating a high number of correct detections and suggests the adoption of this tool to verify the operating conditions of a UAV. The test must be performed indoors; from the measurements of the noise produced by the UAV it is possible to identify an imbalance in the propeller blade.

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Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Frequency Response Measurements

Energies ◽

10.3390/en14113227 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3227

Author(s):

Mehran Tahir ◽

Stefan Tenbohlen

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Frequency Response ◽

Power Transformer ◽

Classification Model ◽

Response Analysis ◽

Transformer Winding ◽

Artificial Neural ◽

Electrical Faults ◽

Artificial Neural Network Ann

Frequency response analysis (FRA) is a well-known method to assess the mechanical integrity of the active parts of the power transformer. The measurement procedures of FRA are standardized as described in the IEEE and IEC standards. However, the interpretation of FRA results is far from reaching an accepted and definitive methodology as there is no reliable code available in the standard. As a contribution to this necessity, this paper presents an intelligent fault detection and classification algorithm using FRA results. The algorithm is based on a multilayer, feedforward, backpropagation artificial neural network (ANN). First, the adaptive frequency division algorithm is developed and various numerical indicators are used to quantify the differences between FRA traces and obtain feature sets for ANN. Finally, the classification model of ANN is developed to detect and classify different transformer conditions, i.e., healthy windings, healthy windings with saturated core, mechanical deformations, electrical faults, and reproducibility issues due to different test conditions. The database used in this study consists of FRA measurements from 80 power transformers of different designs, ratings, and different manufacturers. The results obtained give evidence of the effectiveness of the proposed classification model for power transformer fault diagnosis using FRA.

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