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Adaptive Control for Shunt Active Power Filter under Stochastic Solar Photovoltaics Behavior

IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Prakash, Surya Alkhatib, Mohamed Sindi, Hatem F. Alghamdi, Sultan Behera, Ranjan Kumar Muduli, Utkal Ranjan Nit Jamshedpur Dept. of Electrical Engg Jharkhand Jamshedpur India Uae University Al Ain Dept. of Electrical and Communication Engg Coe Abu Dhabi15551 United Arab Emirates King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering Jeddah21589 Saudi Arabia Indian Institute of Technology Patna Dept. of Electrical Engineering Bihta801103 India Khalifa University Apec Dept. of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates

The paper discusses the challenges in controlling a two-stage photovoltaic (PV)-based shunt active power filter (SAPF) under distorted grid conditions and stochastic PV system behavior. To address this, the paper proposes an adaptive proportional integral (API) regulator to regulate the DC-link voltage, which adapts its parameters based on the DC-link error voltage to provide low bandwidth for disturbance rejection and high bandwidth for dynamic performance enhancement. The paper also proposes an estimation method for the reference current using the sliding discrete Fourier transform and the instantaneous symmetrical component theory, which is suitable for non-ideal grid or load conditions. The accuracy of the proposed reference current estimation method is analyzed using time- and frequency-domain analysis. The performance of the PV-SAPF system with the proposed control algorithms is illustrated through simulation and real-time hardware validation under various grid conditions. Despite distorted grid conditions, the total harmonic distortion of the grid current is substantially within IEEE Std 519-2014. The proposed API regulator and reference current estimation method show promising results in improving the performance of the PV-SAPF system in the presence of internal and external disturbances. © 2013 IEEE.

关键词： Frequency domain analysis

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Cryptosight: Decrypting Future Trends with LSTM, PyCaret and Jinja 2.0

Cryptosight: Decrypting Future Trends with LSTM, PyCaret and...

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： Soham Motiram Mhatre Brajesh Kumar Mishra Ishan Kumar Raja Bigit Krishna Goswami Sneha Agrawal Pranjal Upadhyay dept. of Electronics and Communication Engineering Student National Institute of Technology Dimapur Nagaland India dept. of Mechanical Engineering Student National Institute of Technology Dimapur Nagaland India dept. of Electrical & Electronics Engineering Student National Institute of Technology Dimapur Nagaland India dept. of Computer Science and Engineering Student National Institute of Technology Dimapur Nagaland India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

The primary objective of this research is to develop and evaluate combined cryptocurrency price prediction models using Long Short-Term Memory (LSTM) neural networks along with traditional time series models and machine learning techniques like Random Forest and Linear Regression. The LSTM models will capture long-term dependencies in sequential cryptocurrency price data, while the other models provide complementary predictive capabilities. This multi-model approach aims to leverage the strengths of different techniques to improve overall prediction accuracy. Additionally, this research seeks to explore the practical application of these predictions in real-world cryptocurrency trading scenarios. The study will collect historical price data and relevant news for major cryptocurrencies from reputable sources. Data preprocessing, feature engineering, model training, hyper-parameter tuning, and evaluation using metrics like RMSE, MAE, and R-squared will be performed. The predictive performance of the LSTM-based models will be compared against baseline models, with statistical significance testing. Interpretability analysis will provide insights into the factors driving price movements. Furthermore, the research will investigate integrating the predictions into trading algorithms and evaluate their efficacy through back-testing on historical data. Limitations, challenges, and future research directions in cryptocurrency price prediction will also be discussed.

关键词： Measurement Training Renewable energy sources Time series analysis Predictive models Data models Cryptocurrency

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A Simple Approach to Analyze and Evaluate Image Segmentation Methods Based on Image Quality Metrics

A Simple Approach to Analyze and Evaluate Image Segmentation...

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International Conference on Applied Electromagnetics, Signal Processing and communication (AESPC)

作者： Ganesan P M. Ravichandran L.M.I. Leo Joseph B.S. Sathish M. Raiendra Prasad G. Sajiv Dept. of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad India Dept. of Computer Science Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. of Electronics and Communication Engineering SR University Warangal Telangana India Dept. of Computer Science Engineering AI Sri Venkateshwara College of Engineering Bengaluru India Saveetha School of Engineering SIMATS Chennai India

The role of image segmentation is inevitable in image analysis. There are many algorithms and methods for image segmentation. However it is tricky to choose best possible clustering procedure. The proper selection of clustering method leads to the successful outcome in image analysis. In segmentation process, data is fragmented into some reasonable regions (clusters). Elements (image pixels) in a particular cluster should be very analogous w.r.t some attributes like lightness, texture or color. In the proposed method, test image is clustered based on chrominance (color) using four segmentation methods, viz., FCM, PCM, PFCM and MFCM. The outcome of segmentation methods are transformed into binary image using otsu threshold method. The binary image of FCM is taken as a reference image because it is standard and fundamental one. The binary images of other methods are compared with reference image. The comparison is done using some image quality metrics such as dice, jaccard. The optimal segmentation method is decided based on the analysis of these image quality metrics.

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Global Asymptotic Stabilty Analysis of DC-DC Buck Converters

Global Asymptotic Stabilty Analysis of DC-DC Buck Converters

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Innovative engineering Sciences and Technological Research (ICIESTR), International Conference on

作者： Kanimozhi Kannabiran B. Raja Mohamed Rabi Booma Jayapalan Krismadinata Thangapalani Lakshmanan Rajalakshmi K Dept. of Electrical and Electronics Engg. NPR College of Engineering & Technology Natham India Dept. of Mechanical Engineering Rathinam Technical Campus Coimbatore India Dept. of Electronics and Communication Engg. PSNA College of Engineering and Technology Dindigul India Centre for Energy and Power Electronics Research Universitas Negeri Pedang Indonesia Dept. of Computer Science and Engineering Sri Sairam Institute of Technology Chennai India Dept. of Computer Science and Engineering NPR College of Engineering & Technology Natham India

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

Stability of power electronic converters is one of the most important problems related to their dynamics. The problems related to global asymptotic stability is discussed here. The design example used is DC-DC buck converter with Adaptive sliding mode control. An averaged small signal model is derived and transfer functions are obtained. Stability is also verified using conventional techniques by MATLAB simulation. Simulation results and mathematical proof shows that closed loop system designed is stable and a fast response is obtained. Simulated results show that the simulated converter system acquires the excellent robustness feature of achieving a stable steady-state and fast transient response under variable operating points. A prototype model has been developed and simulated results are compared with experimental results. The results validate the stability analysis of Dc-Dc buck converter

关键词： Asymptotic stability Transient response Buck converters Simulation Prototypes Stability analysis Mathematical models Robustness Steady-state Load modeling

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Next-Gen SDN Security: Quantum Keying Meets AI Analytics

Next-Gen SDN Security: Quantum Keying Meets AI Analytics

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Distributed systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： S. Virushabadoss Anithaashri T. P M. Nivedha S. Hari Krishnan Anand Karuppannan Dept. of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication and Engineering Arasu Engineering College Kumbakonam India Department of Electronics and Communication and Engineering Sanskrithi School of Engineering Puttaparthi India Department of Electronics and Communication and Engineering Gnanamani College of Technology Namakkal India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This paper suggests a complete security architecture that blends quantum key distribution (QKD) with cutting-edge machine learning methods to improve the Software-Defined Networking (SDN) control plane. By creating secure channels of communication through QKD, the framework ensures integrity and secrecy of control messages between the SDN controller and network devices. Furthermore, network traffic is continuously monitored and analyzed using machine learning techniques, enabling the detection of unusual behaviors that might be warning signs of upcoming security problems. Tests utilizing Mininet as the network interface and Open Daylight as the SDN controller proved the efficacy of the suggested method. The outcomes revealed a minimal computational overhead, exceptional detection accuracy up to 98.1%, and low false positive rates, substantiating the system's ability to effectively manage varied network conditions and traffic volumes. These results underscore the potential of integrating QKD with robust machine learning strategies to secure the SDN control plane against sophisticated threats.

关键词： Quantum computing Machine learning Telecommunication traffic computer architecture Quantum key distribution Network interfaces Cryptography Software defined networking computer security Monitoring

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A Quantitative Analysis and Mapping of Soluble Solids Content with Interior Regions of `Hayward' Kiwifruit from Centre to Pericarp Using Hyperspectral Imaging and Chemometric Algorithms

SSRN

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SSRN 2024年

作者： Francis, Jobin George, Sony Devassy, Binu M. George, Sudhish N. Dept. of Computer Science Norwegian University of Science and Technology Innlandet Gjøvik2815 Norway Dept. of Electronics and Communication Engineering National Institute of Technology Calicut Kerala Calicut673601 India

The Soluble Solids Content (SSC) of kiwifruit is an essential internal quality attribute that represents its consumption of eating and post-harvest shelf life. This paper presents the findings of a feasibility study that aimed at improving the mapping of SSC values with different regions at the interior part of the kiwifruit from its center to the outer pericarp. Hyperspectral images of 495 kiwifruit were acquired in reflectance mode between 400 and 1000 nm wavelengths. Further, the interior of each kiwifruit has been divided into five regions (Region I to Region V), from the center to the outer pericarp, and an algorithm is proposed to automatically select spectral points from those regions. A third-order tensor framework is employed in this work, where spectral measurements from the respective locations of the kiwifruit are effectively arranged into its frontal slices. This study then employed Partial Least Square Regression (PLSR) as a chemometric model and Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and second derivative (D2) as the pre-processing methods. Among these, Region III which comprises the fleshy part of the kiwifruit between the center and the pericarp delivers the calibration and prediction outcomes with MSC+D2+PLS combination as $\mbox{R}_{C}^2=0.485$, $\mbox{RMSEC}=1.216$ and $\mbox{R}_{P}^2=0.349$, $\mbox{RMSEP}=1.349$ respectively. This study also attempted to compare the evaluation results of SSC values with spectral measurements taken from hyperspectral images of kiwifruit with its skin. Overall, the proposed study can be effectively used to find the correlation of the SSC values of the kiwifruit with their spectral measurements and, thereby, can be employed for the non-invasive prediction of the SSC values of the kiwifruit. © 2024, The Authors. All rights reserved.

关键词： Mapping

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Review of Cloud AI for Real-Time Fraud Detection

Review of Cloud AI for Real-Time Fraud Detection

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International Conference on Autonomous Robots and Agents, ICARA

作者： Gulnara A.Abitova Miras Abalkanov Gulnara A. Abitova Gulnur Shuteyeva Elvira Aitmukhanbetova Korlan Kulniyazova Dep. Computer Engineeering Astana IT University Astana Kazakhstan Dept. Electical Engineering and Computer Science State Univerity of New York at Binghamton Binghamton USA Dept. System Anslysis and Managemnt Eurasian National University Astana Kazakhstan

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

The paper presents a comprehensive analysis of the integration of Cloud AI in real-time fraud detection, highlighting significant advancements and practical applications. It is identifying a key challenge such as data privacy and the need for scalable solutions, while also exploring the potential of innovative AI algorithms and strategic partnerships to enhance fraud detection capabilities. Through detailed case studies, the work is demonstrated the effective application of Cloud AI across various sectors. The study concludes that Cloud AI, despite not being a complete solution, plays a crucial role in strengthening fraud detection systems. Our findings suggest that strategic implementation of Cloud AI can lead to more adaptive and efficient systems, thereby significantly contributing to the economic sector's resilience against fraudulent activities. This paper underscores the transformative impact of Cloud AI in fraud detection, offering insights into its future potential and laying the groundwork for further advancements in this field.

关键词： Economics Training Data privacy Technological innovation Scalability Real-time systems Fraud

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Reliability Analysis of a Monitoring system for Extraterrestrial Habitats

Reliability Analysis of a Monitoring System for Extraterrest...

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IEEE International Conference on Wireless and Mobile Computing, Networking And communications (WiMob)

作者： Saurabh Band Florian Stechmann Malavika Unnikrishnan Shadi Attarha Christiane Heinicke Andreas Willig Anna Foerster dept. Sustainable Communication Networks University of Bremen Germany Center of Applied Space Technology and Microgravity University of Bremen Germany Dept. of Computer Science and Software Engineering University of Canterbury New Zealand

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

This paper presents a robust and dependable monitoring system designed for space missions where reliability is critical. Theoretically, we use the Markov chain model to evaluate the availability of the system, considering multiple instances of backup devices. The results indicate a substantial improvement in system availability with the inclusion of backup devices. The findings highlight the significance of redundancy in enhancing system dependability while considering practical constraints. Based on this insight, we design an algorithm to deploy backup devices in the system that would improve the system's availability without putting strain on the resources (e.g., channel congestion). It uses backup devices at the sensor node and gateway levels. The algorithm involves backup devices that actively monitor and take over in case of failures, enhancing the system's ability to recover lost or faulty data. We test the system in three failure scenarios: hard failures, sensor failures, and gateway failures. We perform the experiments on our hardware prototypes. Experimental results demonstrate that using backup devices significantly improves the Packet Reception Ratio (PRR) in failure scenarios. We observed an improvement of 30 - 34% in packet reception for different failure scenarios.

关键词： Wireless communication Wireless sensor networks Space missions Redundancy Prototypes Logic gates Reliability theory Reliability engineering Monitoring Strain

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Assessment of Enterobacteriaceae Bacteria and Antibiotic Resistance in Spring Water of Sikkim Using OPTICS Clustering Algorithm

Assessment of Enterobacteriaceae Bacteria and Antibiotic Res...

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Next Generation communication & Information Processing (INCIP), International Conference on

作者： Shobha Prabha Niranjan Dept. of Computer Science and Engineering Nitte Meenakshi Institute of Technology Benagaluru India Department of Electronics and Communication Engineering NMAM Institute of Technology Nitte (Deemed to be University) Nitte Karnataka India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Waterborne diseases pose meaningful public health risks, especially in regions reliant on natural spring water like Sikkim, India. This study aims to evaluate water quality in four districts of Sikkim, focusing on Enterobacteriaceae bacteria, indicators of fecal contamination and potential pathogens. Using selective culture techniques, Enterobacteriaceae were isolated and taxonomically characterized. Multiple species were identified, indicating a risk of waterborne diseases. Antibiotic exposure testing proven that Escherichia coli isolates were resistant to commonly used antibiotics and carried both virulence and resistance genes. The proposed study introduces OPTICS (Ordering Points to Identify the Clustering Structure) for bacterial strain clustering analysis The results highlight the critical need for robust water quality management strategies in Sikkim to reduce health risks linked to antibiotic-resistant bacteria and fecal contamination. This proposed study specifies critical insights into community health risks and offers a novel approach for clustering bacterial strains in water quality assessment. The escalating issue of antibiotic resistance in bacterial infections, a critical public health concern, is discussed in the proposed work. The study's primary objective is to analyze the correlation between antibiotic usage patterns and the development of resistance across various bacterial strains. By employing a combination of data analysis, laboratory experiments, and statistical modeling, the research seeks to identify key factors that drive the emergence of antibiotic-resistant bacteria. The findings reveal a strong association between the misuse and overuse of antibiotics and the rapid evolution of resistance in several common bacterial pathogens. The study highlights the role of specific antibiotics that contribute disproportionately to resistance, emphasizing the need for stricter regulation and more judicious use of these drugs. Moreover, the research ide

关键词： Antibiotics Water quality Optics Water pollution Public healthcare Springs Contamination Water resources Immune system Strain

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Recognition of Facial Stress system using Machine Learning with an Intelligent Alert system

Recognition of Facial Stress System using Machine Learning w...

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International Conference on Computing Methodologies and communication (ICCMC)

作者： S. Kanaga Suba Raja Durai Arumugam S S L R. Praveen Kumar J. Selvakumar Easwari Engineering College Chennai India Dept. of Information Technology Easwari Engineering College Chennai India Dept. of Electronics and Communication Engineering Easwari Engineering College Chennai India Dept. of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India

The primary component in this paper is to investigate the facial emotional states and EEG indicators, especially in pressure, for the duration of the interplay with games. The proposed paper identifies sure precise expressions in game enthusiasts whose facial feelings are segmented frames were separated into special areas, then the mind indicators are classified based on their frequencies, ranges are also analysed, and the signal value is *** on facial features are extracted from the localized areas, used fuzzy c-means class, and directed onto an emotion space. Then the EEG sign price is evaluated with the brink fee. After that, the strain data can be ship thru by using a SMS alert by using GSM module and buzzer alerts the use of Arduino micro controller. The cease results are accurate and robust.

关键词： Image recognition Psychology Human factors Games Machine learning Electroencephalography Software

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