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Performance Analysis of Single Ended Primary Inductor Converter with Adaptive and Static Proportional Integral Control Variants 11

Performance Analysis of Single Ended Primary Inductor Conver...

引用

11th International Conference on Electrical engineering, computer science and Informatics, EECSI 2024

作者： Panawan, A. Nur Annisa Alif Samman, Faizal Arya Sahali, Ida Rachmaniar Smedley, Kayue M. Hasanuddin University Dept. of Electrical Engineering Gowa Indonesia University of California Dept. of Electrical Engineering and Computer Science IrvineCA United States

ISBN: (纸本)9798350355314

The Single-Ended Primary-Inductor Converter (SEPIC) is one type of DC-DC converter that has been widely utilized. In certain conditions, this DC-DC converter may exhibit inappropriate behavior regarding stability and reliability, thus requiring a controller to ensure that the voltage, current, or power values they produce align with the desired one. In this research, we examine and simulate the performance of the SEPIC converter using a Proportional Integral (PI) controller and compare the use of tuning methods: Ziegler-Nichols (ZN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Fuzzy Logic in regulating the PI control parameters. Simulations will be performed through MATLAB. From the simulation results, it is observed that the PI tuning methods using GA, PSO, and Fuzzy Logic are superior to the ZN method, and with the advantage of adaptive process for PI tuning, the fuzzy logic method outperforms in terms of convenience to the other three methods. © 2024 IEEE.

关键词： MATLAB

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Smart Meter-based Outage Detection and Fault Location in a Microgrid in Gaidouromantra, Greece 14

Smart Meter-based Outage Detection and Fault Location in a M...

引用

14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Bangali, Sanjeev Athanasios, Vasilakis Dimitrios, Lagos Arya, Prasoon Singhai, Jigyasa Das, Sarasij Dept. of Electrical Engineering Indian Institute of Science Bengaluru India Dept. of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (纸本)9781837242689

In the existing literature, Digital relay-based protection schemes are proposed for microgrids (MGs). Digital relays are expensive and uneconomical for practical microgrids. However, in most MGs, smart meters (SMs) with communication facilities are available at the consumer end for energy measurements. These SMs can measure the electrical quantities and communicate the measurements to the local server. This paper demonstrates that SMs can also be used for outage detection and fault location in practical MGs. Gaidouromantra MG is one such MG in Kythnos island, Greece, with SMs and communication facilities. This paper proposes two algorithms to detect outages and fault locations. The first algorithm is a voltage-based algorithm which assumes that SM communication is intact. The second algorithm extends the first. It uses current measurements along with voltage measurements to tackle the problem of SM communication disconnection after the fault. The algorithms use measurements from the SMs to detect the outage in the feeder and locate the fault with the guidance of the MG's topology. Additionally, the extended algorithm can be used to indicate SM communication failure and no-load/disconnection status of houses on the island, if any occur. The algorithms are validated using the Gaidouromantra MG model on Simulink and hardware-in-loop simulations of the Gaidouromantra MG. The proposed algorithms can generally be applied to MGs with radial structures consisting of a single grid-forming inverter and multiple grid-following inverters, as in Gaidouromantra. © The Institution of engineering & Technology 2024.

关键词： Microgrids

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Multi-Trajectory Drone Surveillance: Amplifying Monitoring Capabilities in Disaster Management

Multi-Trajectory Drone Surveillance: Amplifying Monitoring C...

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Mourya, Gojja Sree Jami, Abhinav Ajith, Abhinav Seikh, Ayan Habib Kumar, J. Sathish Motilal Nehru National Institute of Technology Dept. of Computer Science and Engineering Allahabad India

ISBN: (纸本)9798350348460

In the disaster-affected area, whether it's a search and rescue operation, mock drill, managing food distribution, issuing warnings or anything else, the key to successfully carrying out these activities is preparation. To ensure that plans are accurate, one needs to know what actually happened in the disaster-affected area and for this purpose thoroughly and quickly the entire area need to be studied. To address this issue, we use UAV or drone navigation autonomously in required trajectories. By using UAVs you can cover surveillance across the vast acres of land quickly and know what happened and plan rescue operations accordingly. In this paper, we presented our work on moving the drones in multiple trajectories and swarm, thus amplifying surveillance capabilities. Each trajectory is maneuvered autonomously and a corresponding trajectory-algorithm running at the back end is presented along with the simulation results. © 2024 IEEE.

关键词： Disaster management Drones Multi-Agent systems Surveillance Swarm Trajectories Unmanned Aerial Vehicles (UAV)

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Enhancing Decentralized Finance for Scalability, Interoperability, and User Experience 8

Enhancing Decentralized Finance for Scalability, Interoperab...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Nalini, N. Rao, Vishak Rao, Karthik Shobha, P. Nitte Meenakshi Institute of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331505462

This research explores Decentralized Finance (DeFi) through the lens of enhanced applications, introducing the concept of Decentralized Finance Enhanced (DeFine). It examines the transformative potential of DeFi, analyzing its evolution and the innovative solutions driving its progress. The objective is to investigate how DeFine addresses scalability, interoperability, security, and user experience challenges in the DeFi ecosystem. Through case studies and analysis, this proposed work provides insights into the implications, opportunities, and risks associated with the adoption and maturation of DeFi enhancements. © 2024 IEEE.

关键词： Decentralized finance

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Machine Learning based Real Estate Price Prediction 8

Machine Learning based Real Estate Price Prediction

引用

8th International Conference on Inventive systems and Control, ICISC 202

作者： Mukherjee, Anupam Nambiar, Rtwik Choudhury, Deepjyoti Manipal University Jaipur School of Computer Science and Engineering Dept. of Aiml Jaipur India

ISBN: (纸本)9798350386578

The dynamic nature of real estate markets gives rise to huge challenges for buyers and sellers in accurately predicting property prices. This paper presents a detailed study on real estate price predictions, focusing on using machine learning techniques to forecast property prices effectively. Using a dataset from Bengaluru, as a case study, it explored the intricacies of Real Estate markets and developed a predictive model to estimate the property prices. Our studies consist of data preprocessing, featuring engineering, model selection, and evaluations, giving insights into the factors that affect property prices and the performances of various regression algorithms. After performing multiple experiments, we showcase the efficiency of our approaches in predicting property prices. Existing techniques that have tried to predict house prices have often struggled to deal with nonlinear relationships present among the factors. Recent techniques such as Linear regression, Ridge and Lasso Regression and Support Vector Machines(SVM) have come up short of being perfect when it comes to dealing with computational intensity and these techniques require extensive hyperparameter tuning. However, our predictive model is able to deal with these challenges by leveraging the strengths of Random Forest regression. This is done with high accuracy and the implications for real estate stakeholders are discussed, subsequently. This paper also highlights the various avenues through which the performance of real estate estimation models can be improved in the future. © 2024 IEEE.

关键词： Support vector regression

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Classifying Alzheimer's Disease Stages via Bootstrap Aggregation of CNN Architectures on MRI Scans 1

Classifying Alzheimer's Disease Stages via Bootstrap Aggrega...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Neeharika, S. Rao, D. Rajeswara Nagaveni, V. Lalitha V.R. Siddhartha Engineering College Computer science Dept. Vijayawada India

ISBN: (纸本)9798350365016

Alzheimer's disease (AD) presents a pressing challenge in healthcare globally, necessitating timely and accurate diagnosis for effective management. Magnetic Resonance Imaging (MRI) holds promise for AD detection due to its ability to reveal structural brain changes. However, manual MRI analysis is labor-intensive and prone to human error. To tackle these issues, this study proposes a novel method for AD classification using ensemble learning with Convolutional Neural Network (CNN) architectures-specifically, CNN, Inception V3,DenseNet121, and MobileNet-leveraging bootstrap aggregation. This ensemble approach diversifies training data subsets, leading to enhanced accuracy. The proposed model achieved an impressive 84% accuracy with bootstrapping and 94 % without. Additionally, specificity values underscore the model's robustness, with 0.93 and 0.97 for bootstrapped and non-bootstrapped ensembles, respectively. This innovative approach not only improves accuracy but also addresses scalability and generalization challenges, offering promise for AD diagnosis and management based on neuroimaging data. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine Learning-based sEMG Signal Classification for Hand Gesture Recognition

Machine Learning-based sEMG Signal Classification for Hand G...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Aarotale, Parshuram N. Rattani, Ajita Wichita State University Dept. of Biomedical Engineering United States Uni. of North Texas Dept. of Computer Science and Engineering United States

ISBN: (纸本)9798350386226

EMG-based hand gesture recognition uses electromyographic (EMG) signals to interpret and classify hand movements by analyzing electrical activity generated by muscle contractions. It has wide applications in prosthesis control, rehabilitation training, and human-computer interaction. Using electrodes placed on the skin, the EMG sensor captures muscle signals, which are processed and filtered to reduce noise. Numerous feature extraction and machine learning algorithms have been proposed to extract and classify muscle signals to distinguish between various hand *** paper aims to benchmark the performance of EMG-based hand gesture recognition using novel feature extraction methods, namely, fused time-domain descriptors, temporal-spatial descriptors, and wavelet transform-based features, combined with the state-of-the-art machine and deep learning models. Experimental investigations on the Grabmyo dataset demonstrate that the 1D Dilated CNN performed the best with an accuracy of 97% using fused time-domain descriptors such as power spectral moments, sparsity, irregularity factor and waveform length ratio. Similarly, on the FORS-EMG dataset, random forest performed the best with an accuracy of 94.95% using temporal-spatial descriptors (which include time domain features along with additional features such as coefficient of variation (COV), and Teager-Kaiser energy operator (TKEO)). © 2024 IEEE.

关键词： Gesture recognition

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An Empirical Study of Precision Agriculture

An Empirical Study of Precision Agriculture

引用

2024 IEEE Students Conference on engineering and systems, SCES 2024

作者： Tirumalasetti, Gnana Kartheek Kandula, Ajay Kumar Shaik, Abdul Basheer Yendluri, Baladithya Kalluri, Hemantha Kumar SRM University AP Dept. of Computer Science Engineering Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350374711

The demand for food production has led to advancements in precision agriculture, aiming to enhance crop yield and quality. This study investigates the application of deep learning algorithms, including GoogLeNet, RESNET-50, MobileNet-v2, VGG-16, and ShuffleNet, for automated plant disease detection. The research utilizes a dataset comprising images of citrus diseases to train and evaluate the models. Results show promising accuracy rates, highlighting the potential of deep learning in optimizing resource utilization and facilitating timely interventions in agriculture. © 2024 IEEE.

关键词： Citrus diseases Deep learning Image classification Plant disease detection Precision agriculture

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Real-Time Stress Detection Using Facial Images and Convolutional Neural Networks

Real-Time Stress Detection Using Facial Images and Convoluti...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Sindu, M. Ashwin, T. Shyam Sunder, V. Shivasankaran, S. Poongothai, P. Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India Chennai Institute of Technology Dept. of Computer Mathematics Tamil Nadu India

ISBN: (纸本)9798331535476

Stress is one of the prevailing issues in today's world, deeply impacting the actual well-being of individuals. In this paper, we introduce an innovative method to detect stress that deviates from the mainstream approaches, which rely solely on physiologic signals and self-reporting. Our non-invasive, efficient, and scalable real-time system, built on the facial image data processed by CNNs, makes real-time human stress detection possible. The system captures facial expressions through video or static images, which it then processes within a CNN model to identify stress-related features. CNNs are particularly innovative in that they are adept.at automatically extracting and learning hierarchical features from extremely complex, unstructured data like facial images. Facial expression datasets were used to train and test the proposed model in different stress conditions. The model achieved a very good accuracy level in differentiating stressed from non-stressed states, thus putting it on par with professionals. Real-time testing of the system's performance was done on edge devices such as smartphones to prove its potential for use in real-life applications related to mobile health. As facial image data is utilised by the system, thereby killing the need for wearable sensors or invasive physiological monitoring, the approach would be more accessible for detecting stress levels. This research showed that CNN models developed based on facial images can transform stress monitoring. Its implications are wide, applicable in health care, workplace settings, and even self-manageable personal stress. In future work, the model should be generalised for diverse populations, the system's real-time capabilities enhanced, and multimodal data further integrated to increase detection accuracy and reliability. © 2024 IEEE.

关键词： Convolutional neural networks

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Multispectral Imaging Supervised by Optical Spectrometry for Close Acquisition in Precision Agriculture

Multispectral Imaging Supervised by Optical Spectrometry for...

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2024 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2024

作者： Scutelnic, Dumitru Muradore, Riccardo Daffara, Claudia University of Verona Dept. of Computer Science Verona Italy University of Verona Dept. of Engineering for Innovation Medicine Verona Italy

ISBN: (纸本)9798350355444

Satellite and airborne multispectral imaging has shown huge potential for many agricultural applications, primarily for plant indices. Imaging close to plants allows further expansion of this potential, such as early identification of plant disease or sufficient maturity status for harvest. To this aim, the joint use of optical spectrometry and multispectral imaging is shown effective: the most informative spectral bands for a given application can be identified so that the number of filters could be reduced to be mounted on a dedicated multispectral imaging system. To validate the on-field approach, we developed a compact, modular multispectral imaging system equipped with four sensors in the visible and near- IR band, a sensor in the far-IR, and a set of sensors to measure environmental parameters (T, RH, CO2, lux). This system collects only the most informative data needed for specific tasks from which spectral indices or processing methods can be implemented to improve automated analysis also through artificial intelligence approaches. © 2024 IEEE.

关键词： Deep Learning Multispectral imaging Plant close imaging Precision Agriculture Spectrometry

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