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technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

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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A Machine Learning based Fault Identification Framework for Smart Grid Automation

A Machine Learning based Fault Identification Framework for ...

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Bhavya Dhingra Abhilasha Saini Anuradha Tomar Department of Electrical Engineering Netaji Subhas University of Technology Delhi-78 India Department of Inst. and Control Engg. Netaji Subhas University of Technology Delhi-78 India

A smart grid is an automated electric grid that frequently monitors the working of a power system to control it and one of the most essential parts of a smart grid is a transmission line, which is used to carry a large amount of generated power in the power system. However, due to their exposure to the environment, these lines may experience a flow of anomalous electric current or faulty current, which can disrupt the regular operation of the power system and cause equipment failure. using machine learning, this research provides a novel automated framework for identifying which type of fault is occurring in the system without having to visit the actual fault location. With an area under the curve (AUC) score of 99.95 % and an accuracy of 99.15 %, the suggested model combines quadratic discriminant analysis coupled with pre-processing techniques like feature engineering to detect if the system has defects and the type of faults. The suggested model generates all of these results in less than 0.015 seconds. Knowing which type of problem is occurring in the power system using voltage and current data can improve power system cost savings by lowering the use of relays and creating effective ways to automate fault handling for smart grids.

关键词： Renewable energy sources Power transmission lines Hydrogen Machine learning Fault location Feature extraction Power systems

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Comparative Analysis Between Bi-LSTM and Uni-LSTM Algorithms for Wind Speed Estimation

Comparative Analysis Between Bi-LSTM and Uni-LSTM Algorithms...

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International Conference on Computer Applications In Electrical engineering-Recent Advances (CERA)

作者： Sarvendra Kumar Singh Shyama Kant Jha Rahul Gupta Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi India Electrical and Electronics Engineering G L Bajaj Institute of Technology and Management Greater Noida India

The integration of sustainable energy sources into the conventional power grid depends on accurate wind speed estimates since it affects grid operations, stability, and overall planning. Owing to its advantages for the environment and ability to lessen dependency on fossil fuels, wind power is highly valued. Accurate wind speed prediction enables grid managers to estimate energy generation and make in-the-moment changes to ensure a steady and dependable power supply. It is becoming increasingly important to comprehend and utilize wind power’s potential as the globe moves toward a more sustainable energy future. The use of deep learning algorithms like Unilateral Long Short-Term Memory (Uni-LSTM), Bilateral Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Unit (GRU) networks is one of the novel ways being investigated in research to improve wind speed forecasts. These algorithms examine past wind speed data and forecast future wind speeds by utilizing their innate memory and flexibility. This study focuses on the Jaisalmer area in Rajasthan, India, where the energy mix may be considerably influenced by wind power. To ensure the data is properly scaled for input into the deep learning models, a preprocessing step is conducted using the Min-Max scaler approach on a dataset downloaded from the NASA website. The analysis shows that the GRU model outperforms the Uni-LSTM and Bi-LSTM models. The “RELU” activation function is used in these models. The model’s performance is analyzed by comparing MAE and RMSE error values with $R^{2}$ accuracy values. The GRU model gives rich $R^{2}$ value performances better than other models.

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EV Integrated PV Water Pumping System with Priority-Based control

EV Integrated PV Water Pumping System with Priority-Based Co...

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Sustainable Energy and Future Electric Transportation (SEFET), International Conference on

作者： Anuradha Tomar Department of Instrumentation & Control Engineering Netaji Suhas University of Technology Delhi India

Solar photovoltaic-based water pumping system (PVWPS) is emerging as a promising solution for addressing the water needs in rural/remote regions. However, there is a need to increase the utilization of installed PV capacity in such PVWPS as water pumping is required only a few hours a week. To address this issue, in this paper an electric vehicle integrable PVWPS with priority-based control is proposed. It will enable alternative utilization of installed PV capacity and thus would result in improved return on investment. The proposed system is validated through MATLAB simulation. The satisfactory performance of the proposed system has been observed under various operating scenarios. The proposed PVWPS with optimized configuration and design parameters could be further explored in future.

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A Comprehensive Study of Various Bypass Switches and their Applications on PV Array for Hotspot Reduction 1

A Comprehensive Study of Various Bypass Switches and their A...

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1st International Conference on Industrial Electronics Research and Applications, ICIERA 2021

作者： Verma, Abhishek Pathak, Diwaker Gaur, Prerna Netaji Subhas University of Technology Department of Instrumentation and Control Engineering New Delhi110078 India Sandip University Department of Electrical Engineering Madhubani847235 India

ISBN: (纸本)9781665435420

The paper shows a comprehensive study of various bypass topologies to mitigate hotspot problem due to partial shading conditions, cracking of solar modules, de-soldering of PV cells, etc. The hot spot created in solar cells are a result of high charge accumulation rate which reflects as counter leakage current. To reduce the severity of hot spot different topologies utilizing bypass diode, overlapped bypass diode, switches like MOSFET, IGBT, etc. are studied which are employed to mitigate the hotspot problem. © 2021 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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Coordinated control of DC-Nanogrid Cluster

Coordinated Control of DC-Nanogrid Cluster

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2022 IEEE International Conference on "Power Electronics, Smart Grid, and Renewable Energy", PESGRE 2022

作者： Parajuli, S. Tomar, Anuradha Nguyen, Phuong Hong Department of Electrical Engineering Eindhoven University of Technology Eindhoven5612 AZ Netherlands Department of Inst. and Control Engg. Netaji Subhas University of Technology Delhi 110078 India

ISBN: (纸本)9781665448376

In this paper a coordinated control algorithm for improving the power sharing among DC-nanogrid cluster is proposed which takes input from the forecasted load demand and the photovoltaic (PV) generation for the next 24 hours. The forecast data is used together with the battery state of charge (SOC) in the nanogrids to efficiently share the power among the nanogrids. The load and PV forecasting is obtained with Gated Recurrent Unit methodology. The proposed coordinated control algorithm is implemented on the developed DC nangorid cluster and is simulated through OPAL-RT. The simulation results show that, with the proposed coordinated control algorithm, if a nanogrid is in power deficit and the cluster in excess, the power received by the nanogrid in deficit can increase up to 100%. Similarly, if a nanogrid is in excess while the cluster is in deficit, the power shared by the nanogrid can decrease up to 40% depending on the forecast values and SOC levels. © 2022 IEEE

关键词： Forecasting

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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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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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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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