Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Department of Control and Automation

Autism spectrum disorder (ASD) is an abnormal condition of brain development characterized by impaired cognitive ability, speech and human interactions, in addition to a set of repetitive and stereotyped patterns of behaviours. Although no cure for autism exists, early medical intervention can improve the associated symptoms and quality of life. Several manually executed screening tools help to identify the ASD-related behavioural traits in the children that assists the specialist in diagnosing the disease accurately. The quantitative checklist for autism in toddlers (QCHAT) is one of the efficient screening tools used worldwide for ASD screening. ASD diagnosis requires many different manually administered procedures; hence long delay is encountered in getting final results. In recent years, deep neural network (DNN) popularity has been immensely increasing due to its supremacy in solving complex problems. The objective of this research is to apply algorithms, based on the deep neural network (DNN) to identify patients with ASD from the QCHAT datasets. We have used two datasets, the QCHAT and QCHAT-10, in our study. The results obtained show that related to contemporary techniques, the proposed method brings better performance.

School of Electrical Engineering

Vellore Campus

World University root node

Journal of Autism and Developmental Disorders

A Deep Neural Network-Based Model for Screening Autism Spectrum Disorder Using the Quantitative Checklist for Autism in Toddlers (QCHAT)

A new biologically inspired derivative-free global optimization algorithm called Firebug Swarm Optimization (FSO) inspired by reproductive swarming behaviour of Firebugs (Pyrrhocoris apterus) is proposed. The search for fit reproductive partners by individual bugs in a swarm of Firebugs can be viewed naturally as a search for optimal solutions in a search space. This work proposes a mathematical model for five different Firebug behaviours most relevant to optimization and uses these behaviours as the basis of a new global optimization algorithm. Performance of the FSO algorithm is compared with 17 popular <a href="https://www.sciencedirect.com/topics/computer-science/heuristic-algorithm">heuristic algorithms</a> on the Congress of Evolutionary Computation 2013 (CEC 2013) benchmark suite that contains high dimensional multimodal as well as shifted and rotated functions. Statistical analysis based on Wilcoxon Rank-Sum Test indicates that the proposed FSO algorithm outperforms 17 popular state-of-the-art heuristic global optimization algorithms like Guided Sparks Fireworks Algorithm (GFWA), Dynamic Learning <a href="https://www.sciencedirect.com/topics/engineering/particle-swarm-optimization">PSO</a> (DNLPSO), and <a href="https://www.sciencedirect.com/topics/computer-science/artificial-bee-colony">Artificial Bee Colony</a> Bollinger Bands (ABCBB) on the CEC 2013 benchmark.

Expert Systems with Applications

A new biologically inspired global optimization algorithm based on firebug reproductive swarming behaviour

The current study challenges the multi-objective optimization of electric discharge machining (EDM) parameters. EDM is used for creating profiles by machining of workpiece that are difficult to machine by conventional method. In the current work four responses such as material removal rate (production rate), tool wear rate, surface roughness (quality) and circularity (profile) are collectively investigated with varying controlling parameters. The human decision for best combination of controlling parameters for highest performance has uncertainties, which results in inferior solution. The multiple responses along with uncertainties and impreciseness can be addressed by combining a neuro-fuzzy system with particle swarm optimization (PSO). To illustrate the superiority of the proposed approach a set of experiment have been conducted in EDM process using AISI D2 tool steel as workpiece and brass tool. The experimental plan was made according to the Box-Behnken response surface methodology design with four process parameters namely discharge current, pulse-on-time, duty factor, and flushing pressure. The four response parameters such as material removal rate, tool wear rate, surface roughness, and circularity of machined components were optimized simultaneously. One unique Multi-response Performance Characteristic Index was obtained by combining the four responses using the proposed neuro-fuzzy technique. A regression model was developed on single response and optimized by PSO to obtain the optimal parameter setting. An experiment was conducted on optimal parameter to test the optimum performance. It is observed that the EDM responses were affected significantly by discharge current and pulse-on-time. The increase in pulse-on-time leads to larger surface cracks and more micro-pores on the machined surface.

Fulltext

SN Appl. Sci.

Fuzzy based multi-response optimization: a case study on EDM machining process

This paper deals with the fractional-order modeling, stability analysis and control of robotic manipulators, namely a single flexible link robotic manipulator (SFLRM) and 2DOF Serial Flexible Joint Robotic Manipulator (2DSFJ). The control law is derived using Pole Placement (PP) method. This paper uses Mittag–Leffler function for the analysis of SFLRM in the time domain. The stability analysis of the fractional model is carried in a transformed Ω -Domain, and from the analysis, it is observed that the response of the fractional model of SFLRM robotic manipulator is stable. The main motive behind this analysis is to understand the fractional behavior of robotic manipulators, and it is well known from literature that most of the real-world systems have their own fractional behavior. Furthermore, a real-time SFLRM and 2DSFJ setups are considered to validate the results obtained and it is found that the control law suggested by PP method improves the settling time of the robotic manipulators. © 2021, King Fahd University of Petroleum & Minerals.

Arabian Journal for Science and Engineering

Modeling and Control of Robotic Manipulators: A Fractional Calculus Point of View

ECTI Transactions on Electrical Engineering, Electronics, and Communications

Bearing Fault Detection in Induction Motors Using Line Currents

This paper focuses on developing a torque and stator temperature prediction model for permanent magnet synchronous motors using neural networks. The model can predict torque and four other temperature parameters at the permanent magnet surface, stator's yoke, tooth, and winding. The motor's torque and temperatures are predicted without installing any additional sensors into it. Using the training dataset with Levenberg-Marquardt optimization and Bayesian regularization algorithms, the predicted model has the best performance with the least mean square error and the best R2 values. Also, the prediction of testing data shows that the estimated model follows closely with actual values. This is true for all the five output parameters. © 2021 IEEE.

3rd International Conference on Energy, Power and Environment: Towards Clean Energy Technologies, ICEPE 2020

Torque and Temperature Prediction for Permanent Magnet Synchronous Motor Using Neural Networks

Wind Energy Conversion System (WECS) using a Doubly Fed Induction Generator (DFIG) is popular due to its control flexibility and higher conversion efficiency, but maintaining the operational stability and optimal efficiency under dynamic wind conditions is still a control challenge. In this paper, a nonlinear mathematical model for a DFIG based WECS was developed from fundamentals and its characteristics near the operating point were studied. A Proportional Integral (PI) controller and a Linear Quadratic Regulator (LQR) controller were designed to control the system and the behavior of the closed-loop system with these controllers was studied. While the designed PI controller failed to ensure stability, the LQR controller was giving stability but an LQR controller is vulnerable to loss of stability under uncertainties due to parameter variations or changes in operating points. A suboptimal H∞ controller was then synthesized to obtain robust control. The closed-loop system performance of the DFIG system with the proposed controller was found to be stable and superior to PI and LQR controllers in terms of performance. © 2021, The Korean Institute of Electrical Engineers.

Journal of Electrical Engineering and Technology

Robust Control of DFIG Based Wind Energy System Using an H∞ Controller

Nipple is a vital landmark in the breast lesion diagnosis. Although there are advanced computer-aided detection (CADe) systems for nipple detection in breast mediolateral oblique (MLO) views of mammogram images, few academic works address the coronal views of breast ultrasound (BUS) images. This paper addresses a novel CADe system to locate the Nipple Shadow Area (NSA) in ultrasound images. Here the Hu Moments and Gray-level Co-occurrence Matrix (GLCM) were calculated through an iterative sliding window for the extraction of shape and texture features. These features are then concatenated and fed into an Artificial Neural Network (ANN) to obtain probable NSA’s. Later, contour features, such as shape complexity through fractal dimension, edge distance from the periphery and contour area, were computed and passed into a Support Vector Machine (SVM) to identify the accurate NSA in each case. The coronal plane BUS dataset is built upon our own, which consists of 64 images from 13 patients. The test results show that the proposed CADe system achieves 91.99% accuracy, 97.55% specificity, 82.46% sensitivity and 88% F-score on our dataset. © The Author(s) 2020.

Ultrasonic Imaging

Nipple Localization in Automated Whole Breast Ultrasound Coronal Scans Using Ensemble Learning

The multilevel inverters (MLI) are resourceful in producing a voltage waveform with superior-quality staircase counterfeit sinusoidal and depressed harmonic distortion (THD). Several conventional topologies are proposed to realize the MLI however, the limitations of these topologies may involve more DC sources and power-switching devices, and less THD, which in turn, increases the cost and size of the inverter. These drawbacks can be eliminated with the proposed hybrid Cascaded H-Bridge Multilevel Inverter with reduced components topology. As compared with the established MLI topologies the recommended topology having a reduced number of DC sources, power-switching devices, component count level factor, lesser TSV, more efficient, lesser THD, and cost-effective. The proposed MLI is a blend of a single-phase T-Type inverter and an H-Bridge module made of sub switches. This article incorporates the design and simulation of the multilevel inverter with staircase PWM technique. Further, the 9-level and 17-level MLI is examined with different combinational loads. The proposed inverter is stable during nonlinear loads, and it is well suited for FACTS and renewable energy grid-connected applications. An operational guideline has been explained with correct figures and tables. The Output voltage wave is realized in numerical simulation. Finally, the experimental demonstrations were performed by implementing a hardware prototype setup for both linear and nonlinear loads using the dSPACE controller laboratory. © 2013 IEEE.