Dynamic analysis and drilling degrees of freedom

Finite element methods for dynamic analysis employing elements with drilling degrees of freedom are presented. The formulation is based on a variational principle in which displacements and rotations are interpolated independently. The issue of zero masses corresponding to rotational degrees of freedom is addressed and techniques for defining consistent and lumped rotational mass matrices are presented.     

A knowledge-based system for sensor selection

This paper presents a Knowledge-based system (KBS) developed to allow users, who may not be knowledgeable about sensors, to select sensors suitable for their specific needs. The KBS runs on a micro-computer. The selection criteria are user specified and are based on the desired measurement parameters. The system output includes all of the operational and dimensional parameters of the recommended sensor, price, and vendor information.     

Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks

Anomaly detection in resource constrained wireless networks is an important challenge for tasks such as intrusion detection, quality assurance and event monitoring applications. The challenge is to detect these interesting events or anomalies in a timely manner, while minimising energy consumption in the network. We propose a distributed anomaly detection architecture, which uses multiple hyperellipsoidal clusters to model the data at each sensor node, and identify global and local anomalies in the network. In particular, a novel anomaly scoring method is proposed to provide a score for each hyperellipsoidal model, based on how remote the ellipsoid is relative to their neighbours. We demonstrate using several synthetic and real datasets that our proposed scheme achieves a higher detection performance with a significant reduction in communication overhead in the network compared to centralised and existing schemes.     

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall.     

A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19

The Journal of Supercomputing - This paper designs and develops a computational intelligence-based framework using convolutional neural network (CNN) and genetic algorithm (GA) to detect COVID-19...     

Rotary flexible joint control by fractional order controllers

In this paper, a fractional order control law is proposed and implemented for the evaluation of trajectory tracking performance of a rotary flexible-joint system. A state feedback based fractional integral control scheme is used in this proposed method. In this scheme, state feedback is responsible for stabilizing the system. The compensator, in series with the fractional integrator leads to obtain a similar closed-loop transient response like Bode’s ideal transfer function. The effectiveness of the proposed controller in tracking and being robust against parameter uncertainties is demonstrated through simulation. In addition, to show the usefulness of the proposed control scheme, the fractional controller is compared to an integer state feedback control by simulation and through experimentation on the Quanser’s rotary flexible-joint system.     

A Multiscale/stabilized Formulation of the Incompressible Navier–Stokes Equations for Moving Boundary Flows and Fluid–structure Interaction

This paper presents a multiscale/stabilized finite element formulation for the incompressible Navier–Stokes equations written in an Arbitrary Lagrangian–Eulerian (ALE) frame to model flow problems that involve moving and deforming meshes. The new formulation is derived based on the variational multiscale method proposed by Hughes (Comput Methods Appl Mech Eng 127:387–401, 1995) and employed in Masud and Khurram in (Comput Methods Appl Mech Eng 193:1997–2018, 2006); Masud and Khurram in (Comput Methods Appl Mech Eng 195:1750–1777, 2006) to study advection dominated transport phenomena. A significant feature of the formulation is that the structure of the stabilization terms and the definition of the stabilization tensor appear naturally via the solution of the sub-grid scale problem. A mesh moving technique is integrated in this formulation to accommodate the motion and deformation of the computational grid, and to map the moving boundaries in a rational way. Some benchmark problems are shown, and simulations of an elastic beam undergoing large amplitude periodic oscillations in a viscous fluid domain are presented.     

High dielectric permittivity and percolative behavior of polyvinyl alcohol/potassium dihydrogen phosphate composites

A new kind of anhydrous, transparent, and flexible potassium dihydrogen phosphate (KH₂PO₄ or KDP)/polyvinyl alcohol (PVA) composite in the form of film (0.10 mm) has been prepared by solution casting technique. KDP is well dispersed in the polymer matrix as observed from the microstructural studies. Frequency and temperature dependent dielectric properties of the composites have been studied with varying KDP concentrations. The PVA/KDP composite films exhibited extraordinarily high relative permittivity ε′ ∼ 430 (80 times higher compared with pure PVA and even higher than KDP) near the percolation threshold (ϕ_C = 2.5 wt % KDP) with low dielectric losses (∼ 0.15) at 1 kHz and room temperature. Such flexible, low loss and high dielectric permittivity material has enormous importance for application in devices. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Prospects of non-edible neem (Azadirachta indica) oil in Bangladesh: performance and emission evaluation in a direct injection diesel engine

Transition to alternative energy is receiving serious attention globally and in this very way, biodiesel from inedible neem can be an alternative to conventional liquid fuels as it contains 30–45%?wt of oil. In this study, esterification and transesterification reaction of methanol and triglycerides at a molar ratio of 7:1 was done and maximum 96% by volume biodiesel was extracted. Physiochemical properties of neem biodiesel lies within ASTM standards and Fourier transform infrared spectroscopy analysis of neem biodiesel reveals the presence of strong esters and the alkane group. The maximum efficiencies were found to be 22.8% and 22.3%, whereas minimum brake specific fuel consumptions were observed to be 0.57 and 0.59?kg/kW-hr for diesel and B20, respectively. Moreover, B20 provides less emission of HC, CO_x and increase in NO_x. Taking all into consideration, if at least 60% of the arid lands is utilised to cultivate neem trees then it will minimise the import of fossil fuel to an extent of 8.75%.