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The novel discovered disease coronavirus popularly known as COVID-19 is caused due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and declared a pandemic by the World Health Organization (WHO). An early-stage detection of COVID-19 is crucial for the containment of the pandemic it has caused. In this study, a transfer learning-based COVID-19 screening technique is proposed. The motivation of this study is to design an automated system that can assist medical staff especially in areas where trained staff are outnumbered. The study investigates the potential of transfer learning-based models for automatically diagnosing diseases like COVID-19 to assist the medical force, especially in times of an outbreak. In the proposed work, a deep learning model, i.e., truncated VGG16 (Visual Geometry Group from Oxford) is implemented to screen COVID-19 CT scans. The VGG16 architecture is fine-tuned and used to extract features from CT scan images. Further principal component analysis (PCA) is used for feature selection. For the final classification, four different classifiers, namely deep convolutional neural network (DCNN), extreme learning machine (ELM), online sequential ELM, and bagging ensemble with support vector machine (SVM) are compared. The best performing classifier bagging ensemble with SVM within 385 ms achieved an accuracy of 95.7%, the precision of 95.8%, area under curve (AUC) of 0.958, and an F1 score of 95.3% on 208 test images. The results obtained on diverse datasets prove the superiority and robustness of the proposed work. A pre-processing technique has also been proposed for radiological data. The study further compares pre-trained CNN architectures and classification models against the proposed technique.

A running machine generates multi-frequency vibration signals which can be captured by accelerometers. Empirical mode decomposition, wavelet decomposition, and wavelet packet decomposition are the commonly used methods to decompose the multi-frequency signal. Quick fault classification, accurate signal decomposition, and fault size detection are still a problem in machines with rotary components. In the proposed work, fault diameter in rotary part of machine is detected and classified using the machine learning methods. In the first stage, we have employed empirical mode decomposition (EMD) for high-frequency noise removal. Residue signal is obtained by removing first IMF from base signal considering first IMF as a high-frequency noisy signal, followed by wavelet decomposition. Entropy of the wavelet coefficient obtained from 3rd level decomposition of residue signal is calculated which acts as an input parameter to the machine learning techniques to determine the diameter for fault. Three different sets have been taken for inner race, outer race, and ball race correspondingly. The proposed method classifies and detects the fault diameter up to 99.5%. The proposed method can be used for different types of continuous as well as discrete wavelets. [ABSTRACT FROM AUTHOR]

This article presents an extreme learning machine based fast and accurate adaptive distance relaying scheme for transmission lines in the presence of a static synchronous series compensator. The ideal trip characteristics of the distance relay is greatly affected by pre-fault system conditions, ground fault resistance, and zero-sequence voltage. The proposed research develops an extreme learning machine based adaptive distance relaying scheme for two-terminal transmission networks with static synchronous series compensators when a single-line-to-ground fault situation is most likely to occur. The study includes an analytical approach, including a steady-state model of static synchronous series compensator with detailed simulation on MATLAB/Simulink (The MathWorks, Natick, Massachusetts, USA) and open real-time simulation software with MATLAB (OPAL-RT) platform (OPAL-RT Technologies, Montreal, Quebec, Canada). The proposed extreme learning machine based adaptive distance relaying scheme is extensively validated on the two terminal transmission lines with static synchronous series compensators, and the performance is compared with the existing radial basis feed-forward neural network based adaptive distance relaying scheme. The results on simulation and real-time platform show significant improvements in the performance indices, such as speed, selectivity, and reliability of the digital relay. [ABSTRACT FROM PUBLISHER]

The paper presents an on-line sequential extreme learning machine (OS-ELM) based fast and accurate adaptive distance relaying scheme (ADRS) for transmission line protection. The proposed method develops an adaptive relay characteristics suitable to the changes in the physical conditions of the power systems. This can efficiently update the trained model on-line by partial training on the new data to reduce the model updating time whenever a new special case occurs. The effectiveness of the proposed method is validated on simulation platform for test system with two terminal parallel transmission lines with complex mutual coupling. The test results, considering wide variations in operating conditions of the faulted power network, indicate that the proposed adaptive relay setting provides significant improvement in the relay performance. [ABSTRACT FROM AUTHOR]