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作者： Murshid, Shadab Tayyab, Mohammad Sarwar, Adil Tariq, Mohd Al-Durra, Ahmed Tomar, Anuradha Nanyang Technological University Singapore Department of Electrical Engineering ZHCET Aligarh Muslim University Aligarh India Department of Electrical and Computer Engineering Khalifa University of Science Technology Abu Dhabi United Arab Emirates Instrumentation and Control Engineering Netaji Subhas University of Technology Delhi India

Multilevel inverters (MLI) are gaining wider popularity owing to their increasing power handling capability and improving power quality output. With an inclining trend towards renewable energy utilization, they find a vital application in wind and solar power generation systems. This article proposes a new multilevel dc-ac inverter. The proposed MLI produces a twenty five level output and offers an inherent capacitor self-balancing capability. The inherent self-balancing eliminates the requirement of auxiliary sensor circuit or capacitors voltage balancing algorithms. Dual asymmetric voltage sources and switched capacitors along with their proper charging/discharging pattern are utilized for the generation of output levels. A voltage gain of 2 and a total standing voltage (TSV) of 10 is achieved though the proposed inverter. In order to realize the merit of the proposed MLI over recently developed topologies, a comparative analysis in terms of system components such as numbers of power electronic switches, gate driver circuits, diodes, and capacitors as well as system parameters such as voltage gain, TSV and power loss is presented. The proposed MLI is developed in the laboratory and is controlled through the digital signal processor TMS320F28379D using nearest level control technique. The experimental results are acquired for different loads and under varying modulation index to validate the feasibility of the proposed inverter. © 1972-2012 IEEE.

关键词： Solar power generation

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Optimization of Energy Storage Systems for Efficient Energy Management in Microgrid

Optimization of Energy Storage Systems for Efficient Energy ...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Neeraj Pankaj Gupta Anuradha Tomar Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology Delhi India

Microgrids have become a promising decentralized and effective energy distribution alternative in modern power systems. Energy storage systems (ESS) management is a crucial component of microgrid operation to maintain efficient energy utilization and grid stability. The Energy management system is vital in achieving sustainability and cost-efficiency in microgrid systems. This work deals with the challenges of optimizing energy storage systems to manage energy efficiently within microgrids. The paper suggests a method based on an optimization approach to achieve the best possible performance from energy storage systems to minimize the power drawn from the grid. The proposed system optimally manages solar panels, grid power, and battery storage to meet variable load demands by integrating data-driven and physics-based models with MATLAB and Simulink. Two optimization techniques are compared to analyze their impact on cost and grid utilization. The results determine the effectiveness of the optimization approaches in achieving energy and cost savings.

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Artificial Neural Network-based State of Charge (SOC) Estimation of a Lithium-Ion Battery under Different Temperatures Conditions

Artificial Neural Network-based State of Charge (SOC) Estima...

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IEEE Power India Conference

作者： Swapnil Tiwari Bhavnesh Kumar Arjun Tyagi Department of Electrical Engineering Netaji Subhash University of Technology New Delhi India Department of Instrumentation and Control Engineering Netaji Subhash University of Technology New Delhi India

ISBN: (纸本)9781665459310

Lithium-Ion batteries are rechargeable batteries used to store energy. State of Charge estimation is necessary to increase the operational viability and reliability of the battery to reduce cost and for proper maintenance. Using a feed-forward backpropagation neural network (FFBPNN) technique combined with the Levenberg Marquardt Training (LM) algorithm, an artificial neural network (ANN) model is proposed in this research that can estimate the state of charge of a lithium-ion battery with greater precision. In the TANSIG transfer function's first layer, the quantity of hidden neurons is compared. The ANN model has been trained and achieved MSE is 0.00039609, RMSE is 0.019902, and R2 of 0.99815 for training, validation, and testing. Using MATLAB software, the effectiveness of the proposed ANN Model scheme has been evaluated for some statistical tools that are signal-based condition indicators based on time domain variables, such as Root Mean Square Error (RMSE), Gradient, Mu, Variance, error histogram, and Regression plot.

关键词： Training Lithium-ion batteries Temperature distribution Neurons Transfer functions Artificial neural networks Mathematical models

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Thermistor linearization on FPGA using hoge equations

Thermistor linearization on FPGA using hoge equations

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2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020

作者： Srivastava, Jaya Rana, K.P.S. Kumar, Vineet Netaji Subhas University of Technology Instrumentation and Control Engineering Department India

ISBN: (纸本)9781728150703

This paper investigates fixed point implementations of five thermistor linearization techniques, namely, Hoge 1, Hoge 2, Hoge 3, Hoge 4 and Hoge 5 on field programmable gate array (FPGA) in LabVIEW environment. The relative performance assessment, on the basis of residual nonlinearity and hardware utilization, has been carried out and presented so as to enable the user to choose a particular linearization technique for his/her application depending on the available FPGA hardware resources. The presented experimental investigations validated the residual nonlinearities in ascending order for Hoge 2, Hoge 3, Hoge 4, Hoge 1 and Hoge 5 techniques as 4.53812E-08, 1.80728E-06, 6.14227E-05, 5.86377E-04 and 2.87804E-02, respectively for an industrial grade thermistor (Model No.: TJ36-44004-1/4-12, M/S Omega engineering). Further, hardware utilizations have been recorded using the number of flip-flops, 4 input LUTs, slices occupied and multiplexers for Hoge 3, Hoge 2, Hoge 4, Hoge 1 and Hoge 5 in descending order. The reported results, in this work, are expected to be of industrial importance as thermistor is a common sensor which finds lots of applications. © 2020 IEEE.

关键词： Linearization

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SEMI-FND: Stacked Ensemble Based Multimodal Inference For Faster Fake News Detection

arXiv

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arXiv 2022年

作者： Singh, Prabhav Srivastava, Ridam Rana, K.P.S. Kumar, Vineet Instrumentation and Control Engineering Department Netaji Subhas University of Technology Sector-3 Dwarka New Delhi110078 India

Fake News Detection (FND) is an essential field in natural language processing that aims to identify and check the truthfulness of major claims in a news article to decide the news veracity. FND finds its uses in preventing social, political and national damage caused due to misrepresentation of facts which may harm a certain section of society. Further, with the explosive rise in fake news dissemination over social media, including images and text, it has become imperative to identify fake news faster and more accurately. To solve this problem, this work investigates a novel multimodal stacked ensemble-based approach (SEMI-FND) to fake news detection. Focus is also kept on ensuring faster performance with fewer parameters. Moreover, to improve multimodal performance, a deep unimodal analysis is done on the image modality to identify NasNet Mobile as the most appropriate model for the task. For text, an ensemble of BERT and ELECTRA is used. The approach was evaluated on two datasets – Twitter MediaEval and Weibo Corpus. The suggested framework offered accuracies of 85.80% and 86.83% on the Twitter and Weibo datasets respectively. These reported metrics are found to be superior when compared to similar recent works. Further, we also report a reduction in the number of parameters used in training when compared to recent relevant works. SEMI-FND offers an overall parameter reduction of at least 20% with unimodal parametric reduction on text being 60%. Therefore, based on the investigations presented, it is concluded that the application of a stacked ensembling significantly improves FND over other approaches while also improving speed. © 2022, CC BY.

关键词： Social networking (online)

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Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

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Computers, Materials & Continua 2021年第5期67卷 1679-1696页

作者： Siddhant Banyal Rinky Dwivedi Koyel Datta Gupta Deepak Kumar Sharma Fadi Al-Turjman Leonardo Mostarda Department of Instrumentation and Control Netaji Subhas University of TechnologyNew Delhi110078India Department of Computer Science&Engineering Maharaja Surajmal Institute of TechnologyNew Delhi110058India Department of Information Technology Netaji Subhas University of TechnologyNew Delhi110078India Research Center for AI and IoT Near East UniversityNicosiaMersin10Turkey Computer Science Department Camerino UniversityCamerino62032Italy

The COVID-19 outbreak initiated from the Chinese city of Wuhanand eventually affected almost every nation around the globe. From China,the disease started spreading to the rest of the world. After China, Italybecame the next epicentre of the virus and witnessed a very high death *** nations like the USA became severely hit by SARS-CoV-2 virus. TheWorld Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the worldhas instituted various policies like physical distancing, isolation of infectedpopulation and researching on the potential vaccine of SARS-CoV-2. Toidentify the impact of various policies implemented by the affected countrieson the pandemic spread, a myriad of AI-based models have been presented toanalyse and predict the epidemiological trends of COVID-19. In this work, theauthors present a detailed study of different articial intelligence frameworksapplied for predictive analysis of COVID-19 patient record. The forecastingmodels acquire information from records to detect the pandemic spreadingand thus enabling an opportunity to take immediate actions to reduce thespread of the virus. This paper addresses the research issues and correspondingsolutions associated with the prediction and detection of infectious diseaseslike COVID-19. It further focuses on the study of vaccinations to cope withthe pandemic. Finally, the research challenges in terms of data availability,reliability, the accuracy of the existing prediction models and other open issuesare discussed to outline the future course of this study.

关键词： COVID-19 diagnosis deep learning forecasting models machine learning metaheuristics prediction big data pandemic

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DRISHYAM: Real-Time Text to Braille Conversion and Realisation 17

DRISHYAM: Real-Time Text to Braille Conversion and Realisati...

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17th IEEE India Council International Conference, INDICON 2020

作者： Gautam, Himanshu Gaur, Prerna Netaji Subhas University of Technology Department of Instrumentation and Control Engineering Delhi India

ISBN: (纸本)9781728169163

For a blind person it is a challenge to read normal text. In this paper a device enabled with mobile application is proposed that enables the visually impaired person to read the text. The mobile application 'Eyes of DRISHYAM' captures images through camera and interface with hardware unit i.e. Braille cell via Bluetooth, controlled through Arduino Uno. The study approaches towards the development of a Braille cell under 50 which will be portable as well as affordable. © 2020 IEEE.

关键词： Mobile computing

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OSEPCloud: Opportunistic scenario-based Security and Privacy enhanced data forwarding framework for Cloud computing 17

OSEPCloud: Opportunistic scenario-based Security and Privacy...

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17th IEEE India Council International Conference, INDICON 2020

作者： Banyal, Siddhant Bhardwaj, Kartik Krishna Gupta, Riyanshi Verma, Isha Gaur, Prerna Netaji Subhas University of Technology Department of Instrumentation and Control Engineering New Delhi India

ISBN: (纸本)9781728169163

The recent technological advances in networking capabilities has led to an asymmetric shift towards harnessing cloud-based technologies. Amidst cloud technologies, there has been an emerging trend to integrate the growing IoT- based devices with the expanding cloud infrastructure. In our paper, we consider a mobile cloud-based infrastructure where contact opportunities are developed in an opportunistic manner so as to facilitate efficient data forwarding and secure process handling. We propose, OSEPCloud, a probabilistic framework that facilitates data forwarding between cloudlet and nodes in the network coupled with a 2-tier 3-phase trust scheme for secure data delivery in our cloud-based network. The proposed scheme is evaluated with T-CAFE protocol over INFOCOM06 SIGCOMM09 databases against performance metrics that show improved performance of OSEPCloud. © 2020 IEEE.

关键词： Cloud computing

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Hyper Smoothing Function based Edge Detection for Prediction of Fat Content in Marble Meat

Hyper Smoothing Function based Edge Detection for Prediction...

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2019 International Conference on Computing, Power and Communication Technologies, GUCON 2019

作者： Navdeep, Navdeep Singh, Vijander Rani, Asha Goyal, Sonal Netaji Subhas Institute of Technology Delhi University Department of Instrumentation and Control Engineering Delhi India

ISBN: (纸本)9789353510985

Intramuscular fat is one of the crucial quality indices in meat grading. The traditional intramuscular fat detection methods like chemical, visual, nuclear magnetic resonance etc. are tedious, costly and time-consuming whereas edge detection techniques are fast and non-destructive testing methods. In this paper hyper smoothing function-based edge detection technique is proposed for intramuscular fat detection. The proposed method utilizes an improved counting scheme and the relationship between surrounding pixels and centric pixel. The scheme calculates fat percentage, fat fleck size distribution and homogeneity. The quantitative analysis of results shows that the fat percentage measured by HLBP is more accurate as compared to black and white method and energy and skewness based fat detection method. © 2019 Galgotias University.

关键词： Feature extraction

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Glaucoma Detection and Segmentation as Computer Aided Design: A review and study 17

Glaucoma Detection and Segmentation as Computer Aided Design...

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17th IEEE India Council International Conference, INDICON 2020

作者： Vasudeva, Jayesh Rastogi, Pranshu Yadav, Jyoti Netaji Subhas University of Technology Department of Instrumentation and Control Engineering New Delhi India India

ISBN: (纸本)9781728169163

Glaucoma is the leading cause of irreversible blindness in India, little is known about the prevention of glaucoma, early detection and preventive treatment is the best way to keep one falling prey to the disease. In this paper, we present a review of the latest techniques developed to detect and segment glaucoma in an eye. Glaucoma is hard to diagnose with a single test, opthalmologists perform various tests to classify a patient as glaucomatous or not. Consequently, early detection can prove to be helpful for the patients as well as the experts who are studying the field. The techniques we discuss explores the intriguing properties of retinal images. Most of the papers reviewed are focussed on extracting cup-to-disc ratio(CDR), CDR is commonly used to analyse the damage to optic nerves, larger CDR indicates higher risk of glaucoma. These methods are inspired form deep learning, feature engineering methods like thresholding, pixel-classification.. © 2020 IEEE.

关键词： Computer aided design

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