Classification of Power Quality Disturbances using Hilbert Huang Transform and a Multilayer Perceptron Neural Network Model

2019 ◽  
Author(s):  
Miguel Angel Rodriguez ◽  
John Felipe Sotomonte ◽  
Jenny Cifuentes ◽  
Maximiliano Bueno-Lopez
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Network Model ◽  
Neural Network Model ◽  
Multilayer Perceptron ◽  
Hilbert Huang Transform ◽  
Power Quality Disturbances

scholarly journals Vibration-based classification of road damages: gyroscope data and a simple neural network model

2018 ◽  
Vol 195 ◽  
pp. 012068
Author(s):  
Fergyanto E. Gunawan ◽  
Herriyandi ◽  
Benfano Soewito ◽  
Tuga Mauritsius ◽  
Nico Surantha
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Simple Neural Network

Artificial Neural Network Model of Discharge Lamps in the Power Quality Context

2013 ◽  
Vol 24 (3) ◽  
pp. 272-285 ◽  
Author(s):  
André Luiz Andreoli ◽  
Denis Vinicius Coury ◽  
Mario Oleskovicz ◽  
Paulo José Amaral Serni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Quality ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Artificial Neural

scholarly journals Classification of neovascularization using convolutional neural network model

2019 ◽  
Vol 17 (1) ◽  
pp. 463 ◽  
Author(s):  
Wahyudi Setiawan ◽  
Moh. Imam Utoyo ◽  
Riries Rulaningtyas
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model

scholarly journals Malware Classification with Improved Convolutional Neural Network Model

2020 ◽  
Vol 12 (6) ◽  
pp. 30-43
Author(s):  
Sumit S. Lad ◽  
◽  
Amol C. Adamuthe
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Personal Information ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Scale ◽  
Malware Classification

Malware is a threat to people in the cyber world. It steals personal information and harms computer systems. Various developers and information security specialists around the globe continuously work on strategies for detecting malware. From the last few years, machine learning has been investigated by many researchers for malware classification. The existing solutions require more computing resources and are not efficient for datasets with large numbers of samples. Using existing feature extractors for extracting features of images consumes more resources. This paper presents a Convolutional Neural Network model with pre-processing and augmentation techniques for the classification of malware gray-scale images. An investigation is conducted on the Malimg dataset, which contains 9339 gray-scale images. The dataset created from binaries of malware belongs to 25 different families. To create a precise approach and considering the success of deep learning techniques for the classification of raising the volume of newly created malware, we proposed CNN and Hybrid CNN+SVM model. The CNN is used as an automatic feature extractor that uses less resource and time as compared to the existing methods. Proposed CNN model shows (98.03%) accuracy which is better than other existing CNN models namely VGG16 (96.96%), ResNet50 (97.11%) InceptionV3 (97.22%), Xception (97.56%). The execution time of the proposed CNN model is significantly reduced than other existing CNN models. The proposed CNN model is hybridized with a support vector machine. Instead of using Softmax as activation function, SVM performs the task of classifying the malware based on features extracted by the CNN model. The proposed fine-tuned model of CNN produces a well-selected features vector of 256 Neurons with the FC layer, which is input to SVM. Linear SVC kernel transforms the binary SVM classifier into multi-class SVM, which classifies the malware samples using the one-against-one method and delivers the accuracy of 99.59%.

scholarly journals A new protection scheme for PQ events prediction and classification in distribution system: A SSO-ANN strategy

2019 ◽  
Vol 52 (5-6) ◽  
pp. 449-461 ◽  
Author(s):  
K Karthikumar ◽  
V Senthil Kumar ◽  
M Karuppiah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Quality ◽  
Distribution System ◽  
Swarm Optimization ◽  
Protection Scheme ◽  
Event Prediction ◽  
Power Quality Disturbances ◽  
Artificial Neural

Increased utilization of nonlinear loads and fault event on the power system have resulted in a decline in the quality of power provided to the customers. It is fundamental to recognize and distinguish the power quality disturbances in the distribution system. To recognize and distinguish the power quality disturbances, the development of high protection schemes is required. This paper presents an optimal protection scheme for power quality event prediction and classification in the distribution system. The proposed protection scheme combines the performance of both the salp swarm optimization and artificial neural network. Here, artificial neural network is utilized in two phases with the objective function of prediction and classification of the power quality events. The first phase is utilized for recognizing the healthy or unhealthy condition of the system under various situations. Artificial neural network is utilized to perceive the system signal’s healthy or unhealthy condition under different circumstances. In the second phase, artificial neural network performs the classification of the unhealthy signals to recognize the right power quality event for assurance. In this phase, the artificial neural network learning method is enhanced by utilizing salp swarm optimization based on the minimum error objective function. The proposed method performs an assessment procedure to secure the system and classify the optimal power quality event which occurs in the distribution system. At that point, the proposed work is executed in the MATLAB/Simulink platform and the performance of the proposed system is compared with different existing techniques like Multiple Signal Classification-Artificial Neural Network (MUSIC-ANN), and Genetic Algorithm - Artificial Neural Network (GA-ANN). The comparison results demonstrate the superiority of the SSO-ANN technique and confirm its potential to power quality event prediction and classification.

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