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检索条件"任意字段=IEEE International Conference on Automation Science and Engineering"

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Impact of Electric Vehicle Integration on Power Grids: A Comprehensive Review

Impact of Electric Vehicle Integration on Power Grids: A Com...

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ieee Students' conference on Electrical, Electronics and Computer science (SCEECS)

作者： Sarathkumar D J. B. Sajin Murugesan Manivel T.Jayakumar S.Ragul Sabarish Ponnusamy Department of Electrical and Electronics Engineering Kongu Engineering College Tamil Nadu India Department of Mechanical Engineering Sree Buddha College of Engineering Kerala India Department of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore Tamilnadu India Department of Electrical and Electronics Engineering Nandha Engineering College Tamilnadu India Department of Electrical and Electronics Engineering Kongunadu College of Engineering and Technology Tamil Nadu India Department of Electrical and Electronics Engineering K. Ramakrishnan College of Technology (Autonomous) Samayapuram Tamil Nadu India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The rapid growth of electric vehicles (EVs) poses significant challenges and opportunities for modern power grids. This comprehensive review examines the impact of large-scale EV integration on power grid infrastructure, focusing on key areas such as load demand, grid stability, energy storage, and renewable energy integration. We analyze the effects of EV charging on peak loads, voltage regulation, and grid reliability, highlighting the need for advanced control strategies and smart grid technologies. Additionally, this paper explores bidirectional power flow, vehicle-to-grid (V2G) systems, and their potential to enhance grid resilience and support renewable energy adoption. Through a detailed examination of current research and emerging trends, we present potential solutions for mitigating integration challenges, including smart charging, demand response, and distributed energy resources. This review provides insights into how the synergy between EVs and power grids can lead to more sustainable and efficient energy systems.

关键词： Vehicle-to-grid Electric potential Renewable energy sources Reviews Power system stability Demand response Stability analysis Power system reliability Research and development Smart charging

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Machine Learning Induced Selective Identification of Dopamine Using Doped Carbon Quantum Dots 8

Machine Learning Induced Selective Identification of Dopamin...

引用

8th international conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Das, Ruma Paria, Abhirup Giri, P.K. Haldia721657 India Haldia Institute of Technology Department of Computer Science & Engineering Artificial Intelligence & Machine Learning Haldia721657 India Indian Institute of Technology Guwahati Department of Physics & Centre for Nanotechnology Guwahati781039 India

ISBN: (纸本)9798331520762

The present work highlights the selective identification of dopamine (DA) with machine learning (ML) classification. Dopamine is an essential organic neurotransmitter that acts a dynamic role. With the inhouse experimental da-ta, the ML models are trained with more than thirty classification algorithms for the efficient selection of DA among various metal ions and biomolecules that exist in human body fluids. The experimental data was collected by measuring the turn-off photoluminescence intensity of nitrogen-doped car-bon quantum dots in the presence of various metal ions and biomolecules. A promising agreement between predicted result and experimental consequences endorses the effectiveness of the proposed ML prediction. Additionally, confusion matrices and receiver operating characteristic (ROC) are introduced to validate the outcomes by visual analysis. © 2025 ieee.

关键词： Glycoproteins

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Privacy-Preserving Mechanisms in Resource-Constrained IoT-WSN Systems for Healthcare Applications

Privacy-Preserving Mechanisms in Resource-Constrained IoT-WS...

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Gaurav Tuteja Parul Nasra Kanegonda Ravi Chythanya Hasssan MuhamedAle Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The exponential growth of IoT-enabled healthcare systems has revolutionized patient monitoring and diagnosis through real-time data collection. However, the integration of wireless sensor networks (WSNs) in healthcare introduces significant privacy and security concerns, especially in resource-constrained environments. This paper explores contemporary privacy-preserving mechanisms tailored for IoT-WSN systems in healthcare. Through a critical analysis of recent advancements, we highlight various techniques, their strengths, limitations, and application domains. Our study emphasizes lightweight protocols, secure data aggregation, and end-to-end encryption techniques that address privacy concerns without overburdening system resources. We conclude by discussing open challenges and future directions in designing robust yet efficient privacy-preserving frameworks for healthcare IoT systems.

关键词： Wireless sensor networks Patient monitoring Protocols Federated learning Medical services Data aggregation Robustness Real-time systems Security Internet of Things

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Revolutionizing Energy Storage Management with Advanced Battery Algorithms

Revolutionizing Energy Storage Management with Advanced Batt...

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automation and Computation (AUTOCOM), international conference on

作者： Shubhansh Bansal Irshed Hussain Hitesh Kalra Manjunath H R V S Vijaya Krishna V Rakesh Kumar Yadav Centre for Interdisciplinary Research for Business and Technology Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Information Technology Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Physics JAIN (Deemed to be University) Bangalore Karnataka Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The growing demand for renewable electricity resources has improved the need for green and reliable electricity storage structures. Batteries have emerged as one of the number one solution for storing renewable electricity. However, the overall performance and lifespan of those batteries are limited due to factors including temperature fluctuations, charging and discharging costs, and aging. To address those demanding situations, there's a need for advanced battery management algorithms that can optimize battery performance while ensuring safety and prolonging its lifespan. This assignment aims to revolutionize electricity garage management with the aid of growing superior battery algorithms, which could, as it should be, eliminate battery degradation and optimize performance based on actual-time information. These algorithms will contain machine getting-to-know strategies to evolve to convert environmental situations and usage styles, thereby maximizing battery performance and storage capacity. Additionally, those algorithms permit the mixing of different types of batteries into an unmarried device, considering a more flexible and green energy storage answer. The implementation of these advanced battery algorithms is predicted to have a considerable impact on the renewable power region, making it extra dependable, price-powerful, and sustainable. It can boost the adoption of renewable power sources and make contributions to the worldwide intention of lowering carbon emissions.

关键词： Performance evaluation Degradation Fluctuations Temperature Electricity Green products Storage management Prediction algorithms Batteries Safety

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Smart Playground for Electricity Generation Using Swing 8

Smart Playground for Electricity Generation Using Swing

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8th international conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Mandal, Lopa Devang, Prathiksha Fathima, Hafiza Ahmed, Syed Faraz Duddukuri, Sridhar Alliance University Department of Computer Science and Engineering Bangalore India Alliance University Department of Information Technology Bangalore India

ISBN: (纸本)9798331520762

The world's energy demand is rapidly increasing due to rise in population. Energy generation is mainly dependent on fossil fuels, nuclear energy and renewable energy resources. Therefore, there is a need for a cleaner method to generate electricity which can act as a substitute for the existing methods. This paper proposes an innovative method of generating electricity using play equipment like swings. The motion of the swing has been used in the present work to move the shaft back and forth which will drive the sprockets and transfer the momentum to the spur gear arrangement. This runs the dynamo and generates electricity, and it can then be stored in batteries or kinetic energy storing equipment and then used later. The proposed method minimizes environmental impact, reduces energy consumption and promotes sustainability. © 2025 ieee.

关键词： Energy utilization

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Next-Gen Home automation: Sensor-Based Connectivity and Appliance Control 1st

Next-Gen Home Automation: Sensor-Based Connectivity and Appl...

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1st international Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Nithiyaraj, E. Emerson Srinivass, S. Mukilan, K. Muthukumar, M. Nanmaran, R. Srimathi, S. Department of Artificial Intelligence and Data Science Mepco Schlenk Engineering College Sivakasi India Department of ECE Ramco Institute of Technology Rajapalayam India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Biotechnology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9783031823824

Internet of Things (IoT) integrated smart home automation systems use sensor networks and communication modules to extend home energy efficiency, security, and convenience. IoT transforms houses into intelligent environment that are remotely monitored and controlled from anywhere using communication technologies. In this paper, a NodeMCU microcontroller and a variety of sensors, such as a PIR for motion detection, a gas sensor for monitoring hazardous gases, a flame sensor for fire detection, a DHT11 for temperature and humidity sensing, as well as smart locks, lights and fans for environmental control, are used to develop and implement a smart home system. With a user-friendly smartphone interface, the suggested system enables residents to monitor and operate their home appliances remotely in real- time, improving security, comfort, and energy efficiency. This article intends to further the development of smart home technology by providing a thorough overview of system design, sensor integration, and functionality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart homes

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Electroencephalographic Insights into Mental Stress Induced by Arithmetic Challenges

Electroencephalographic Insights into Mental Stress Induced ...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Anmol Rattan Singh Gurjinder Singh Komuravelly Sudheer Kumar Hasssan MuhamedAle Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Mental stress is a significant factor contributing to various health issues, including heart attacks and strokes. Accurately measuring stress is crucial for timely clinical interventions and preventing these diseases. This research explores the potential of using Electroencephalography (EEG) signals to distinguish between stressed and relaxed states during mental arithmetic tasks. Our research demonstrates distinct variations in EEG signals when comparing states of relaxation and stress across three tiers of arithmetic complexity, showing significant p-values of 0.03, 0.042, and 0.05. This study verifies the effectiveness of EEG in identifying levels of mental stress. Furthermore, by employing a support vector machine (SVM), we achieved stress detection accuracies of 94%, 85%, and 80% for the respective levels of arithmetic task difficulty.

关键词： Support vector machines Technological innovation Loading Human factors Machine learning Stroke (medical condition) Electroencephalography Reliability Arithmetic Stress measurement

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A Monitoring Method Based on EEG Data and Deep Learning

A Monitoring Method Based on EEG Data and Deep Learning

引用

Consumer Electronics and Computer engineering (ICCECE), 2021 ieee international conference on

作者： Zihao Gong Guoliang Liu School Of Information and Communication Guilin University of Electronic Technology Guilin Guangxi China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin Guangxi China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

In the context of modern education, the pursuit of effective teaching and learning has been constantly evolving. With the increasing emphasis on personalized education and the need to understand students' cognitive processes in real-time, the limitations of traditional teaching assessment methods have become more prominent. This paper presents a monitoring method for real-time assessment of students' classroom understanding based on EEG data and deep learning. By addressing the limitations of traditional teaching assessment methods, this approach aims to provide educators with more accurate and timely feedback. The proposed method utilizes specific EEG data, advanced data processing techniques, and deep learning algorithms to achieve high-accuracy monitoring.

关键词： Deep learning Accuracy Cognitive processes Educational technology Data processing Electroencephalography Real-time systems Monitoring Random forests Consumer electronics

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Quasi Polynomial and Interpolative Models for Tensor Approximation

Quasi Polynomial and Interpolative Models for Tensor Approxi...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jeongmin Chae Selin Bac Usama Saleem Shaama Mallikarjun Sharada Urbashi Mitra Department of Electrical and Computer Engineering University of Southern California Department of Chemical Engineering and Materials Science University of Southern California

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

A novel Tucker decomposition based tensor approximation is considered herein: observations based on only a few lateral slices and side structural information of a true tensor are exploited. This work is motivated by quantum chemistry problems wherein full Hessian computation is expensive, but partial computation is available. The proposed method successfully estimates the quasi-polynomial and interpolative structure of frontal and lateral slices given a priori knowledge of the true tensor. A theoretical error bound is provided, which characterizes the impact due to errors in the side information. To the best of our knowledge, this work proposes the first tensor approximation with side information and interpolation.

关键词： Interpolation Estimation error Tensors Quantum chemistry Signal processing Numerical simulation Polynomials Acoustics Speech processing

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The Evolution of Cloud Computing: A Study of Aspirational Technologies and Practical Achievements 14

The Evolution of Cloud Computing: A Study of Aspirational Te...

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14th ieee international conference on Communication Systems and Network Technologies, CSNT 2025

作者： Upreti, Kamal Jain, Rituraj Kumar, Raj Goyal, Kamal Deepika Gupta, Komal Dept. of Computer Sciecne CHRIST University DelhiNCR India Dept. of IT Marwadi University Gujarat Rajkot India Gautam Buddha University U.P Gautam Budh Nagar India Pimpri Chinchwad University Pune India Dept. of Computer Science & Engg. Maharishi MM Haryana Ambala India Accenture Bangalore India

ISBN: (纸本)9798331531935

Cloud computing has transformed the digital landscape, providing scalability, cost efficiency, and seamless access to computing resources. Yet, there is a gap between its theoretical aspirations and real-world achievements because of data privacy concerns, regulatory compliance, vendor lock-in, and performance bottlenecks. This paper critically examines these disparities through case studies, technological breakthroughs, and industry trends as a guide to understanding these impediments to cloud adoption. The study reviews the theoretical background of cloud computing, which include models related to deployment and service, and illustrates its successes in performance, reliability, and security. Persistent barriers, however, mean security and compliance are insecure while cost unpredictability remains a concern for organizations in using the maximum potential of a cloud. The study proposes strategic solutions that can serve to bridge the gap in terms of hybrid and multi-cloud adoption, AI-driven security frameworks, regulatory compliance automation, and various cost optimization techniques. Emerging trends like quantum computing, edge computing, and green cloud initiatives are shaping the future of cloud computing. By implementing these solutions, more is made out of the potential of cloud computing in securing a more efficient and sustainable digital infrastructure. © 2025 ieee.

关键词： Crime

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