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8th International Conference on Signal Processing and Integrated Networks, SPIN 2021

作者： Prabha, Anju Yadav, Jyoti Rani, Asha Signh, Vijander Dept. Instrumentation and Control Engineering Netaji Subhas Institute of Technology University of Delhi New Delhi India

ISBN: (纸本)9781665435642

The Corona virus disease 2019 (COVID-19) pandemic has caused substantial increase in distress among people all over the world. This work aims to study depression during the COVID-19 among the educational sector and to develop a novel stroop test based depression detection system by analyzing the response time (RT) for normal stroop test (ST) and emotional stroop test (EST). The data for this work is collected from 44 participants. It is found that 66% of the participants have depression. The analysis of RT for ST and EST before and after showing video stimulus, indicates that there is a significant difference in change of response time (dRT) for both normal and depressive cases. Further this feature along with the physiological parameters (PhyP) of the participants are given to support vector machine (SVM) and extreme gradient boost (XGBoost) classifier to develop depression detection systems. The XGBoost provides highest accuracy of 85.71% with PhyP + ST dRT data and an accuracy of 71.43% with PhyP + EST dRT data. Therefore, the proposed systems may serve as a screening tool for depression during this pandemic situation. © 2021 IEEE

关键词： Support vector machines

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Optimization Algorithm-Based Artificial Neural Network control of Nonlinear Systems 3rd

Optimization Algorithm-Based Artificial Neural Network Contr...

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3rd International Conference on Innovative Computing and Communication, ICICC 2020

作者： Srivastava, Vishal Srivastava, Smriti Department of Instrumentation and Control Engineering Netaji Subhas Institute of Technology Sector-3 DwarkaNew Delhi India

ISBN: (纸本)9789811551123

For control of various nonlinear systems, many controllers have been developed over a period of time. Most of the plants are either nonlinear or their parameters vary with time. PID controller has limitations as it is unable to handle complex nonlinearities as well as parameter variations in plants. Therefore, a suitable controller needs to be designed for these plants. Artificial neural network (ANN) has capability to handle these complex issues. In this paper, teacher learning-based algorithm (TLBO) has been used with ANN which optimizes the controller parameters and adapts the nonlinearities present in the plants. To analyze the performance, TLBO with ANN controller is used on various systems, i.e., inverted pendulum and a plant discussed in [1]. The simulation results show the plant output versus mean square error (MSE). © 2021, Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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A Novel PID-AW based MPPT technique for SPV system 4

A Novel PID-AW based MPPT technique for SPV system

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4th International Conference on Recent Developments in control, Automation and Power engineering, RDCAPE 2021

作者： Goswami, Yagyadatta Rana, K.P.S. Kumar, Vineet University Of Delhi Department Of Instrumentation And Control Engineering Netaji Subhas Institute Of Technology New Delhi India Department Of Instrumentation And Control Engineering New Delhi India

ISBN: (纸本)9781665414296

Various of MPPT (maximum power point tracking) techniques have been used to draw the maximum power output from solar photovoltaic (SPV) systems. Two popular classical MPPT techniques e.g. perturb and observe (PO) and incremental conductance (IC) have applied for best optimized results. Shortcoming of PO and IC is of oscillations around the maximum power point (MPP). A proportional integral derivative -based dp/dv with Anti-windup (PID-AW) controller is proposed to counter the oscillations around MPP. The proposed technique is based on the classical model of the PID controller that leads to integral saturation. PID controller requires anti-windup (AW) that limit the controller's integral action to avoid the undesirable windup phenomenon. The performance parameters of the PID-AW controller are ripples in power output, offset and ITAE under standard test conditions (STC). The proposed technique is used to strengthen the capability of classical PID, PO and IC algorithm. Presented MATLAB®/Simulink® based simulations proves the superiority of PID-AW MPPT technique in comparison to the classical PID, conventional PO and IC algorithm. © 2021 IEEE.

关键词： controllers

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A study on the estimation of global horizontal irradiance via deep learning technique

A study on the estimation of global horizontal irradiance vi...

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Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON), International Conference on

作者： Manisha Kumari Chandan Kumar Smriti Srivastava Department of Electrical Engineering Netaji Subhas University of Technology New Delhi India Department of Electrical Engineering Indian Institute of Technology Kanpur Kanpur India Department of Control and Instrumentation Engineering Netaji Subhas University of Technology New Delhi India

For grid-connected photovoltaic (PV) systems, accurate solar irradiance forecasting is crucial, especially in cases of intermittent environmental conditions, in order to ensure grid operation, scheduling, and grid energy management. Solar irradiance, temperature, and other meteorological factors significantly influence PV generation and also make it intermittent in nature. Time series analysis provides an efficient way to map temporal patterns, like hourly, monthly, yearly, and seasonal variations, and within solar irradiance data. In this paper, time series forecasting of 24hourly future observations of global horizontal irradiance (GHI) is done using a multilayer perceptron (MLP) of deep learning framework. Different hyperparameters like hidden layers, hidden units of each layer, activation function, optimizer, and lag observations/steps are tuned to develop the best model that fits the GHI historical data. Further dropout regularization is used to avoid overfitting the model. The model is validated using validation data that the model has never seen before. RMSE, MSE, MAE, and NRMSE errors, aiming at minimizing forecasting errors on test data, are used to select the optimal value of hyperparameters.

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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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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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Review of Clustering Techniques in control System 1

Review of Clustering Techniques in Control System

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1st International Conference on Smart Sustainable Intelligent Computing and Applications, ICITETM 2020

作者： Singh, Saumya Srivastava, Smriti Instrumentation and Control Engineering Division Netaji Subhas University of Technology Delhi-78 India

Data clustering is an important tool in data mining, that helps to retrieve useful data from large amount of available data. In this digital era data is available in abundance, but finding useful data has become a challenging task. For this, data clustering is an effective and common approach where we can group data by seeing some pattern or inherent data similarity in one group. Clustering is an unsupervised learning method of linearly separable and nonlinearly separable clusters widely used for different nature of application [1]. Data clustering finds application in classification of patterns in different areas such as artificial intelligence, summarization, learning, segmentation, speech recognition, pattern recognition, image segmentation, biology, marketing, data mining, modelling and system identification etc [5][24][25]. No one clustering technique can be said as best or better than other, because different clustering algorithms co-exists and are application specific. This paper majorly emphasises on critical review of clustering algorithms used in control systems, but a brief overview is also given about all major algorithms. © 2020 The Authors. Published by Elsevier B.V.

关键词： Cluster analysis

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Home Automation using TV Whitespace technology 17

Home Automation using TV Whitespace technology

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17th IEEE India Council International Conference, INDICON 2020

作者： Jain, Abhinav Goswami, Archit Gaur, Prerna Netaji Subhas Institute of Technology Instrumentation and Control Engineering New Delhi India

ISBN: (纸本)9781728169163

The research aims at using the TV whitespace technology for home automation in which the Set-top box remote is modified as a unified remote control for all the home appliances. The channel numbers lying vacant in between any two designated channels can be integrated with particular appliance and the switching on/off of the appliance can be controlled by tuning to those channel frequencies. The conceptualization of the use of TV whitespaces has long been a theoretical or simulation approach. However, through this project, we aim to develop an integrated setup to utilize the unused channel frequencies to control appliances. Hardware prototype for the same has been designed and developed as a part of the project. © 2020 IEEE.

关键词： Domestic appliances

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A modified incremental conductance maximum power point technique for standalone PV system 7

A modified incremental conductance maximum power point techn...

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7th International Conference on Signal Processing and Integrated Networks, SPIN 2020

作者： Chauhan, Urvashi Rani, Asha Singh, Vijander Kumar, Bhavnesh University of Delhi Division of Instrumentation and Control Engineering Netaji Subhas Institute of Technology New Delhi110078 India

ISBN: (纸本)9781728154756

The main aim of this work is to design an effective MPPT algorithm so that the power utilization efficiency of PV array is enhanced. Therefore improved incremental conductance MPPT controller i.e. INC-PID MPPT is proposed which utilizes the concept of traditional incremental conductance algorithm and proportion-integral-derivative controller. The conventional MPPT methods i.e. incremental conductance and perturb & observation have simple structure and may be implemented easily. However these algorithms suffer from low tracking efficiency and steady state oscillations. The proposed technique overcomes these drawbacks and also preserves the simple structure. The controller parameters are optimized using grey wolf optimization (GWO). The proposed algorithm is applied to multi crystal solar panel, KC130GT by M/S Kyocera, and power tracking capability of INC-PID MPPT is evaluated for various patterns of irradiance. The suggested technique proves to be superior than existing techniques in terms of settling time, undershoot and rise time. © 2020 IEEE.

关键词： controllers

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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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