A Novel Intelligent Bifurcation Classification Model Based on Artificial Neural Network (ANN)

2018 ◽  
pp. 53-61 ◽  
Author(s):  
Hasmat Malik ◽  
Tushar Sharma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Classification Model ◽  
Model Based ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

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

Energies ◽  
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.

scholarly journals An Efficient Techniques used for Diagnosis of Mitral Valve Regurgitation Severity

2019 ◽  
Vol 9 (1) ◽  
pp. 486-491
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Image Segmentation ◽  
Mitral Valve ◽  
Mitral Valve Regurgitation ◽  
Classification Model ◽  
Mitral Valve Stenosis ◽  
Valve Regurgitation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Mitral valve diseases are more common nowadays and might not show up any symptoms. The earlier diagnosis of mitral valve abnormalities such as mitral valve stenosis, mitral valve prolapses and mitral valve regurgitation is most important in order to avoid complex situation. Many existing methodologies such as heart sound investigation model, 3-layered artificial neural network (ANN) of phonocardiogram recordings, 3-layer artificial neural network (ANN) of phonocardiogram recording, echocardiography techniques and so on are there for Mitral Valve diagnosis, but still most of the methods suffer from inefficient image segmentation and misclassification problems. In order to address this issue, this paper proposes two techniques namely 1) Deep Learning based Convolutional Neural Network (CNN) model for Mitral Valve classification model meant for diagnosis and edge detection-based segmentation model to enhance the classifier accuracy. 2) Watershed Segmentation for Mitral Valve identification and image segmentation and Xception model with Random Forest (RF) classifier for training and classification. The proposed models are evaluated in terms of three parameters namely accuracy, sensitivity and specificity, which proved that the proposed models are efficient and appropriate for Mitral Valve diagnosis.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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