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检索条件"机构=Instrumentation and Control Engineering Netaji Subhas University of Technology"

共 350 条记录，以下是21-30 订阅

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An NFNet-Based Approach for Enhanced Bearing Fault Classification Using Vibrational Signal Analysis 14

An NFNet-Based Approach for Enhanced Bearing Fault Classific...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Jain, Vidhi Dixit, Mayank Misra, Vihaan Bhutani, Geetansh Singh, Vijander Netaji Subhas University of Technology Department of Electrical Engineering Delhi India Netaji Subhas University of Technology Department of Instrumentation and Control Engineering Delhi India

ISBN: (纸本)9798350335095

The present work proposes a novel approach for detecting bearing faults in induction motors using vibrational signals. The proposed method employs a Normalizer-Free ResNet (NFNet) architecture to extract relevant features from 2D image representations of raw vibration data and a classification model to identify the presence of a fault. We convert the vibrational signals into 2D images by applying the Short-Time Fourier Transform (STFT) technique, which offers a time-frequency analysis of the signals. The proposed method improves accuracy over conventional approaches along with reduced computational overhead by using high-performance NFNet architecture for classification. The effectiveness of the proposed method is evaluated on the Case Western Reserve university (CWRU) and Intelligent Maintenance Systems (IMS) datasets of vibration signals from induction motors, experiencing various bearing faults. The results indicate that the suggested framework outperforms the existing methods in terms of accuracy and efficiency, achieving state-of-the-art results. This method can be used for real-time monitoring of induction motors to detect bearing faults and prevent potential equipment failures, making it highly useful for industrial applications. © 2023 IEEE.

关键词： Computer vision

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A Comparative Study on Metaheuristic-Based Reconfiguration Strategies for Non-uniformly Shaded PV Array 2nd

A Comparative Study on Metaheuristic-Based Reconfiguration S...

引用

2nd International Conference on Signals, machines, and Automation, SIGMA 2022

作者： Dhariwal, Reeva Kumar, Bhavnesh Department of Electrical Engineering Netaji Subhas University of Technology Delhi India Department of Instrumentation and Control Engineering Netaji Subhas University of Technology Delhi India

ISBN: (纸本)9789819909681

In photovoltaic power plants, operating environmental conditions have a profound effect on the behavior and the quality of the photovoltaic (PV) energy produced. The non-homogenous insolation reduces the power producing capacity, introduces multiple peaks in the PV curve, and produces hot spots. Metaheuristic algorithms have emerged as an effective tool in reconfiguring the panels to disperse the shading uniformly. The actual locations of the modules remain unchanged during reconfiguration while the electrical connection is changed. Therefore, this paper presents a comprehensive study on two different metaheuristic optimization algorithms, namely particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA). These optimization tools provide a connecting matrix for the new electrical connection that gives high output power to the same PV array. In addition, a comparison of these reconfiguration methods is done to assess their suitability of these methods. Based on the result, PSO gives better output as compared to GOA both in terms of power produced as well as the PV curve. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar panels

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NARX Model Based Estimation of State of Charge of Lithium-Ion Batteries in Electric Vehicles under Varying Temperature Ranges 14

NARX Model Based Estimation of State of Charge of Lithium-Io...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

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

ISBN: (纸本)9798350335095

Lithium-ion batteries are a viable option for automotive applications due to their high energy density and low self-discharge rate. The device's longevity and security may be estimated based on its state of charge, which reflects the available energy. A Dynamic Nonlinear Auto-Regressive model with Exogenous Input Neural Network (NARX) Model is proposed and tested to estimate and predict the state of charge (SOC) of the Lithium-Ion Battery, which affects EV performance, range, and safety. In this research, we examine the efficacy of several model-training techniques based on the input characteristics they utilize to build models. The NARX Model with 5 Input Features and Levenberg Marquardt Training (LM) algorithm is selected to train the given model with the least Mean Square Error (MSE) of 1.79E-06 with 606 epoch value. © 2023 IEEE.

关键词： Lithium-ion batteries

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Clustering Approach Using Multiobjective Non-Dominated Sorting Teaching Learning Based Optimization with Kernel Fuzzy C-Means Algorithm (NSTLBO-KFCM)

Clustering Approach Using Multiobjective Non-Dominated Sorti...

引用

2023 International Conference on Recent Advances in Electrical, Electronics and Digital Healthcare Technologies, REEDCON 2023

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

ISBN: (纸本)9781665493826

Clustering has evolved over a period and has become more sensitive and precise with the outcomes. No clustering algorithm is suited for all types of data sets, but partitional clustering lays foundation for almost all-important clustering algorithms. Single solution can be achieved in case of single objective clustering, but in multiobjective clustering the solution becomes set of solution vector. Multiobjective clustering has made the pattern recognition possible in more than one dimension. The solution strategy of Multiobjective clustering is conceptualized on Pareto dominance. A multiobjective NSTLBO-KFCM is implemented in this paper using non-dominated sorting technique. The clustering algorithm is then compared with multiobjective non dominated sorting genetic algorithm third generation-based kernel fuzzy C-means (NSGAIII-KFCM) algorithm and multiobjective particle swarm optimization-based kernel fuzzy C-means (MPSO-KFCM) algorithm. The algorithm is also compared with non-dominated sorting teaching learning-based optimization with fuzzy C-means (NSTLBO-FCM) algorithm and the results show that NSTLBO-KFCM is superior clustering algorithm. © 2023 IEEE.

关键词： Clustering algorithms

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Grid-Connected Fuel Cell with Upgraded Voltage Profile 2nd

Grid-Connected Fuel Cell with Upgraded Voltage Profile

引用

2nd International Conference on Signals, machines, and Automation, SIGMA 2022

作者： Saxena, Purvaa Jha, S.K. Department of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi India

ISBN: (纸本)9789819909681

In this work, an electrical network connected to proton exchange membrane fuel cell (PEMFC) is considered. The voltage profile of the system is enhanced by connecting the fuel cell with the boost converter and a seven-level cascaded multilevel inverter. The traditional PID controller does not responds to the nonlinearities of the system. Hence, PID controller is dynamically tuned by the Whale Optimization Algorithm (WOA). The whole architecture is developed in SIMULINK. For testing the effectiveness of the proposed model, the transients are given to the system for short duration and the results are then compared. It is observed that the proposed model has improved voltage response and reduced harmonics. The analysis for the power quality enhancement is confirmed through Total Harmonic Distortion (THD). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Three term control systems

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Emotion Classification Using Triple Layer CNN with ECG Signals 6th

Emotion Classification Using Triple Layer CNN with ECG Signa...

引用

6th International conference on communication and computational technologies, ICCCT 2024

作者： Puniya, Gaurav Patel, Tanishq Kumar, Harshit Giri, Chaitanya Nandini, Durgesh Yadav, Jyoti Agrawal, Alok Instrumentation and Control Engineering Department Netaji Subhas University of Technology Sector-3 New Delhi Dwarka India

ISBN: (纸本)9789819774227

The growing interest in recognizing human emotions from physiological signals gathered by smart wearable devices is covered in this research study. Although these gadgets have made it feasible to discreetly and continuously record physiological signals, readings can be influenced by user activity, which makes it difficult to create reliable models for wearable sensor-based emotion identification. Though EEG provides the best signal capture to identify human emotions, it significantly interferes with daily activities and is inconvenient for daily use. Electrocardiogram (ECG) signals have been shown to have potential use in emotion identification in recent studies. In this work, a triple-layered 1-D Convolutional Neural Network (CNN) model is applied to improve emotion recognition performance using electrocardiogram (ECG) signals by utilizing hyperparameter tuning methods. We have systematically explored two well-established hyperparameter tuning methods: grid search and random search. In addition, we have harnessed Bayesian optimization, a more advanced technique acknowledged for its superior performance compared to grid and random search. Bayesian optimization has been employed in conjunction with Optuna to augment its efficacy further. Upon careful evaluation, it became evident that Bayesian optimization significantly outperforms the other two techniques. Consequently, we have implemented the model utilizing Bayesian optimization with Optuna. Our results show that the triple-layered 1-D CNN model using Bayesian optimization in conjunction with Optuna performs significantly better than other neural network architectures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Emotion Recognition

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An Overview of Bi-Directional Charging Converter Topologies for Electric Vehicles and Fast Charging Stations analysis using Neural Network 1

An Overview of Bi-Directional Charging Converter Topologies ...

引用

1st International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2023

作者： Khan, Mohammad Shahbaz Agrawal, Alok Netaji Subhas University of Technology Department of Electrical Engineering New Delhi India Netaji Subhas University of Technology Department of Instrumentation and Control Engineering New Delhi India

ISBN: (纸本)9798350338027

Power Electronics circuit are the vital part of any electrical vehicle (EV) system. It consists of DC-AC (or AC) Converters and DC-DC (or DC) Converters. The AC Converters are responsible for supplying the high power to electric motor and other miscellaneous systems, whereas the DC Converters are responsible for supplying power to DC loads in car and integrating the EV battery. This paper presents a detailed knowledge about the various charging converters used in the plug-in Battery Electric Vehicles (BEV's) and Hybrid Electric Vehicle (PHEV's). The paper summarizes the different EV converter design approaches available in the literature which can be employed at fast charging stations, as well as on the on/off-board chargers and its application with neural network. © 2023 IEEE.

关键词： Supercapacitor

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An improved Alternate-Odd-Even (AOE) reconfiguration technique for performance enhancement of SPV arrays

An improved Alternate-Odd-Even (AOE) reconfiguration techniq...

引用

2023 International Conference on Power, instrumentation, Energy and control, PIECON 2023

作者： Beg, Mirza Tabish Shah Rana, K.P.S. Kumar, Vineet Netaji Subhas University of Technology Department of Instrumentation and Control Engineering New Delhi India

ISBN: (纸本)9798350399769

The paper deals with an Alternate-Odd-Even (AOE) static reconfiguration technique for the power enhancement and performance improvement of the solar photovoltaic (SPV) array under partial shading conditions (PSC). The modules of the chosen 4×4 SPV array are connected electrically in a total-cross-tied (TCT) but repositioned in the SPV array using the proposed AOE reconfiguration technique. The proposed scheme is analyzed and compared with the primary 4×4 SPV array topologies, namely the total-cross-tied (TCT), honeycomb (HC), bridge-link (BL), and series-parallel (SP) for the performance analysis. This technique leads to the redistribution of shade throughout the SPV array. Due to the lower current and voltage outputs, the simple series and parallel configurations have not been considered for this analysis. The proposed technique gives an enhanced performance of the SPV array in terms of higher peak power, lower losses due to shading, improved fill factor and execution ratio, and smooth output characteristics. The proposed technique allows the researchers to design a less complex controller for tracking peak power for the SPV array under PSCs. © 2023 IEEE.

关键词： Solar power generation

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Optimized Multi-model Machine Learning Approach for Residential Load Prediction 5

Optimized Multi-model Machine Learning Approach for Resident...

引用

5th International Conference on Energy, Power, and Environment, ICEPE 2023

作者： Tomar, Anuradha Netaji Subhas University of Technology Department of Instrumentation & Control Engineering Delhi78 India

ISBN: (纸本)9798350313123

Accurate prediction of the residential load is important to increase the utilization of available local energy resources, to address over/under voltage problems, and overloading issues in low voltage (LV) distribution grids. The need for an optimized prediction model has been observed that performs well even when the dataset has characteristic deviations. This paper aims to present an optimized model for residential electrical load prediction. Linear regression, regression tree, ensemble, and gaussian process regression (GPR) methods are considered, and based on the error metrics evaluation, a prediction model with minimum error is selected for load prediction. Calendar and weather-based features are selected based on the principal component analysis. A case study has been performed, considering the measured smart meter data from a multi-storied residential house in Houston, Texas, USA. A comparison of predicted and measured residential electrical load shows that the suggested approach performs satisfactorily. © 2023 IEEE.

关键词： Energy resources

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2DOF PID-Based controller for Chemotherapeutic System 2nd

2DOF PID-Based Controller for Chemotherapeutic System

引用

2nd International Conference on Signals, machines, and Automation, SIGMA 2022

作者： Mondal, Disha Rani, Asha Singh, Vijander Department of Instrumentation and Control Engineering Netaji Subhas University of Technology New Delhi India

ISBN: (纸本)9789819909681

This paper aims to analyze the capability of 2DOF PID controller while dealing with multiple and conflicting objectives problem. A basic PID control system fails to track the reference input and reject the disturbance simultaneously. This problem may be better dealt with the use of a two-degree of freedom controller which combines the effect of two controllers in separate loops to meet the two criteria. The problem considered in this study is the drug dose control in chemotherapy. In this study, multi-objective GA and multi-objective swarm optimization algorithms are implemented to obtain the optimum amount of the drug delivery for chemotherapeutic treatment. The designed controller is compared with the conventional PID controller. Various analyses like step response analysis, bode analysis, parameter perturbation analysis and disturbance analysis are carried out to justify the performance of the designed controller. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： controllers

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