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designing Organic Material Electrodes for Lithium-Ion Batteries:Progress,Challenges,and Perspectives

Electrochemical Energy reviews 2024年第1期7卷 484-527页

作者： Qiyu Wang Thomas O’Carroll Fengchun Shi Yafei Huang Guorong Chen Xiaoxuan Yang Alena Nevar Natallia dudko Nikolai Tarasenko Jingying Xie Liyi Shi Gang Wu dengsong Zhang Research Center of Nano Science and Technology College of SciencesShanghai UniversityShanghai 200444China State Key Laboratory of Space Power-Sources Technology Shanghai Institute of Space Power SourcesShanghai 200245China Department of Chemical and Biological Engineering University at BuffaloThe State University of New YorkBuffaloNY 14260USA B.I.Stepanov Institute of Physics National Academy of Sciences of Belarus 220072 MinskBelarus Head of the Inter-University R&D Marketing Centre Science and Technology Park of BNTU 220013 MinskBelarus Shanghai University(Zhejiang·Jiaxing)Emerging Industries Institute Jiaxing 314006ZhejiangChina

Organic material electrodes are regarded as promising candidates for next-generation rechargeable batteries due to their environmentally friendliness,low price,structure diversity,and flexible molecular structure ***,limited reversible capacity,high solubility in the liquid organic electrolyte,low intrinsic ionic/electronic conductivity,and low output voltage are the main problems they face.A lot of research work has been carried out to explore comprehensive solutions to the above problems through molecular structure design,the introduction of specific functional groups and specific molecular frameworks,from small molecules to polymer molecules,metal-organic frameworks(MOFs),covalent organic frameworks(COFs)and heterocyclic molecules;from simple organic materials to organic composites;from single functional groups to multi-functional groups;*** inevitable relationship between various molecular structure design and enhanced electrochemical properties has been illustrated in *** work also specifically discusses several approaches for the current application of organic compounds in batteries,including interfacial protective layer of inorganic metal oxide cathode,anode(metal lithium or silicon)and solid-state electrolyte,and host materials of sulfur cathode and redox media in lithium-sulfur *** overview provides insight into a deep understanding of the molecular structure of organic electrode materials(OEMs)and electrochemical properties,broadens people’s research ideas,and inspires researchers to explore the advanced application of electroactive organic compounds in rechargeable batteries.

关键词： Organic electrode materials Lithium-ion batteries Molecular structure design rechargeable batteries

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Flashover Identification of Aged Silicone rubber Insulators Using Partial discharge Signal Processing and Machine Learning random Forest Algorithm

Flashover Identification of Aged Silicone Rubber Insulators ...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Sankarganesh, r. Chandrasekar, S. Vinayaka Mission's Kirupananda Variyar Engineering College Department of Electrical and Electronics Engineering Tamilnadu Salem636308 India Sona College of Technology SonaPERT R&D Centre Tamilnadu Salem636005 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

due to a variety of factors including light weight, effortlessness of moving and installation, towering automatic power to heaviness percentage, destruction resistance, aesthetic appearance, and better padding piece, silicone rubber outdoor insulators have replaced ceramic units in distribution networks. The flashover analysis of long-term aged silicone rubber insulators is the subject of this research. The investigation is base on the limited release approach, which is a very effective and efficient method that is nearly universally used in high-voltage transmission and distribution systems. On aged polymeric insulators, laboratory experiments are conducted under ac voltage, at various humidity levels, and at various infectivity level with NACL impurity. The insulator was thermally aged for 100 days in the laboratory within the heat *** approach simulated the conditions encountered in real-time field applications. The partial discharge signals are collected using a Pd monitoring device that can capture Pd waveforms as well as phase resolved *** time domain and frequency domain features of Pd pulses are investigated in order to make a comparison between new and old polymeric insulators. To detect insulator flashover due to pollution, a machine learning-based random forest (rF) classifier technique is *** results show that the performance of outdoor polymeric insulators degrades with age, and that flashover of silicone rubber insulators may be detected using partial discharge pattern analysis and an rF classifier model. © 2022 IEEE.

关键词： Silicone rubber

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A High-Efficiency Compact rectifier Circuit design for Harvesting rF Signals in Low-Powered Wireless Communication Systems*

A High-Efficiency Compact Rectifier Circuit Design for Harve...

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IEEE Symposium on Wireless technology and Applications (ISWTA)

作者： Surajo Muhammad Osama Abuajwa Sufian Mitani Jun Jiat Tiang Research & Innovation Development Division Telekom Research & Development (TM R&D) TM Innovation Centre Cyberjaya Malaysia Futurist Unit Telekom Research & Development (TM R&D) TM Innovation Centre Cyberjaya Malaysia Faculty of Engineering Centre For Wireless Technology Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350351354

ISBN: (纸本)9798350351361

This paper presents a compact and efficient rectifier capable of harvesting GSM/900 radio frequency (rF) signals. The design uses an impedance-matching network (IMN) block with a series of short radial and curve stubs and an inductor. The rectifier is designed on rT/duroid substrate and has a total size of $22.10\ \text{mm}\times 8.30\ \text{mm}$ , using a single series diode configuration. The proposed design achieves a maximum power conversion efficiency (PCE) of 83.50% at 1 dBm input power. It can also detect and convert low rF input power below −40 dBm into a usable output dC source, making it suitable for low-powered wireless communication systems. Overall, this compact rectifier is a promising solution for harvesting ambient rF energy and can be used for long transmission with minimal path loss.

关键词： radio frequency Wireless communication Shunts (electrical) rF signals rectifiers Size measurement Propagation losses

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Predictive Maintenance in IoT Networks Using a Temporal Attention Mechanism Based Federated Convolutional Neural Network 4

Predictive Maintenance in IoT Networks Using a Temporal Atte...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Guduri, Naga Venkata ramakrishna Charaan, r.M. dilip Kalaiyarasi, r. Samuthira Pandi, V. reddy, d.Laxma Selvaprabhu, T. University of Technology and Applied Sciences Faculty of Engineering Al Musannah Oman Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad India Dhanalakshmi College of Engineering Department of Cse Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

In the era of Industry 4.0, edge devices in industrial settings generate vast amounts of data crucial for process optimization and Predictive Maintenance (PM). Traditional cloud-based approaches for handling this data are becoming untenable due to cost, latency, and privacy issues. Federated learning emerges as a viable solution, offering localized and efficient data processing while mitigating these challenges. This paper presents a Federated Convolutional Neural Network with Temporal Attention Mechanism (FedCNN-TAM) for PM tasks in Internet of Things (IoT) networks. This model leverages temporal features in sensor data to enhance predictive accuracy. Evaluated on the CMAPSS dataset, FedCNN-TAM outperforms representative models with a significant performance gain. This model exhibits the potential for broader applications in industrial IoT, setting the stage for future research on its scalability and adaptability to multi-modal data types. © 2024 IEEE.

关键词： Spatio-temporal data

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Experimental Evaluation of deep Learning Assisted Autism Spectrum disorder Prediction Scheme using Convolutional Classification Principle 5

Experimental Evaluation of Deep Learning Assisted Autism Spe...

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5th International Conference on data Intelligence and Cognitive Informatics, ICdICI 2024

作者： Kumar, Abhinav Thapak, Sonika Thatipudi, Jayendra Gopal rameshkumar, C. Krishnamoorthy, r. Muralitharan, divya Ies College of Technology Department of Computer Science and Engineering Bhopal India University of North Texas Dept. of Computer Science United States Sathyabama Institute of Science and Technology Department Physics India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of C.S.E Chennai India

ISBN: (纸本)9798350389609

Autism Spectrum disorder (ASd) is characterized by challenges in social communication and restricted or repetitive behaviors. With rising ASd incidence, early diagnosis and intervention become increasingly crucial for improving developmental trajectories and quality of life. Traditional diagnostic methods are often subjective and limited. This study evaluates a deep learning-based approach for ASd prediction using Convolutional Neural Networks (CNNs) and recurrent Neural Networks (rNNs). Leveraging the Autism Brain Imaging data Exchange (ABIdE) dataset, which includes neuroimaging, genetic, and behavioral data, the model integrates spatial and temporal features to enhance prediction accuracy. The combined feature set yielded the highest accuracy of 93.7%, demonstrating the efficacy of integrating multiple data types. The proposed Convolutional recurrent Hybrid (CrH) model achieved a superior accuracy of 98%, outperforming traditional models. This research highlights the potential of deep learning techniques in improving ASd diagnostics. © 2024 IEEE.

关键词： Convolutional neural networks

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The Future of Smart Grids: revolutionizing Energy distribution with Advanced IoT, and renewable Integration 5

The Future of Smart Grids: Revolutionizing Energy Distributi...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Avinash, S. Vinitha, J.C. dharinya, S. Sree Samuthira Pandi, V. Lakshmi Priya, J. Jaishika, S. St. Joseph's College of Engineering Department of Mba Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vit University School of Computer Science Engineering and Information Systems Vellore India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Saveetha Engineering College Department of Ece Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India

ISBN: (纸本)9798350376685

Smart grids, which integrate modern Internet of Things (IoT) technology and renewable energy sources to boost efficiency, sustainability, and dependability, are poised to bring about a dramatic shift in the future of energy distribution. Intelligent grids are expected to bring about this shift. Focusing on the role of the Internet of Things (IoT) in enabling real-time monitoring and control, as well as the incorporation of renewable energy to support a low-carbon future, this research investigates the potential for smart grids to change the energy sector. This study gives insights into how smart grids can be efficiently deployed to satisfy the growing energy needs of society by studying the technological breakthroughs, obstacles, and benefits of smart grids. Specifically, the study's focus is on smart grids. The research highlights the essential role that smart grids play in aiding the shift towards a more sustainable and resilient energy infrastructure by conducting an in-depth analysis of case studies, pilot projects, and industry trends all around the world. Providing recommendations for policymakers, industry stakeholders, and utilities to exploit the full potential of smart grids in determining the future of energy distribution, the findings contribute to the continuing discourse on the strategic development and implementation of smart grid technologies. Both of these topics are currently being discussed. © 2024 IEEE.

关键词： demand response Management distributed Generation Energy Efficiency Energy Storage Systems Grid resilience Low-Carbon Future real-Time Monitoring and Control

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Hybrid cloud storage system with enhanced multilayer cryptosystem for secure deduplication in cloud

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International Journal of Intelligent Networks 2023年第1期4卷 301-309页

作者： Mageshkumar, Nagappan Swapna, J. Pandiaraj, A. rajakumar, r. Krichen, Moez ravi, Vinayakumar Department of Computer Science and Technology Madanapalle Institute of Technology and Science Madanapalle India Department of Computer Science & Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computing Technology SRM Institute of Science and Technology Kattankulathur chennai India Al-Baha University Alaqiq 65779-7738 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar34754 Saudi Arabia Centre for Automation School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India

data deduplication is a crucial technique in the field of data compression that aims to eliminate redundant copies of recurring data. This technique has gained significant popularity in the realm of cloud storage due to its ability to effectively reduce storage requirements and optimize bandwidth utilization. To ensure the safeguarding of sensitive data while simultaneously facilitating deduplication, researchers have put forth the concept of convergent encryption as a potential solution. This technique involves encrypting the data prior to its outsourcing, thereby enhancing the confidentiality of the information. In this work, an earnest endeavor is undertaken to formally tackle the issue of authorized data deduplication, with the aim of enhancing data security. Our approach combines the diffie-Hellman algorithm and symmetrical external decision to protect and popularize information, ensuring end-to-end encryption to encourage user adoption of cloud storage. The proposed model employs block-level deduplication and guarantees the randomness of ciphertexts by generating encryption keys using the diffie-Hellman algorithm. This method effectively counters both internal and external brute-force attacks, enhancing data security while reducing computational costs. An extensive experimentation is carried out to demonstrate that our approach is particularly beneficial in scenarios with multiple privilege sets. Overall, the proposed model offers an elaborate framework that maintains data privacy and strengthens security measures, contributing to a more efficient and secure cloud-based document search. © 2023 The Author(s)

关键词： Cryptography

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Visionary Text recognition System for Assisting the Visually Impaired in Identifying Medicines and Products with Hybrid deep Learning 3

Visionary Text Recognition System for Assisting the Visually...

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3rd International Conference on Automation, Computing and renewable Systems, ICACrS 2024

作者： Begum, A. Yasmine Krishnamoorthy, r. Gaikwad, Anil T. Malarvizhi, N. rao, d. Sreenivasa Adinarayana, Nuthalapati Department of EIE Sree Vidyanikethan Engineering College Andhrapradesh India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India Institute of Management Kolhapur India Department of CSE Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology India Department of ECE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India KKR & KSR Institute of Technology & Sciences Vinjanampadu Narsapur India

ISBN: (纸本)9798331532420

This research focuses on developing an assistive technology for visually impaired individuals, enabling them to read text and recognize objects in their environment. The system utilizes a camera-based approach, employing a resNet model for object detection and text recognition. The proposed system aims to accurately identify and classify objects in real-time, providing audio feedback to the user. This technology can be particularly beneficial for visually impaired individuals in various daily life scenarios, such as reading medication labels, identifying objects in their surroundings, and navigating public spaces. By integrating advanced computer vision techniques, this research aims to improve the quality of life for visually impaired individuals by enhancing their independence and autonomy. © 2024 IEEE.

关键词： Assistive technology

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Machine Learning in recent technology: Shaping the Future of Intelligent Applications and data Analysis 5

Machine Learning in Recent Technology: Shaping the Future of...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Karpagam, P. Uma Maheswari, S. Kumar, A. Pandi, V.Samuthira Jose, P. Virushabadoss, S. Dhanalakshmi College of Engineering Department of Computer Science Engineering India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biosciences Chennai India Dhanalakshmi College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331530013

In the present day, machine learning (ML), which is a subset of artificial intelligence (AI), has emerged as a crucial technology that has revolutionized the way in which we interact with data and construct intelligent applications. The purpose of this study is to investigate the recent developments in machine learning and the profound impact that these breakthroughs have had on a variety of fields, such as the medical field, the financial sector, the transportation industry, and environmental science. Through an examination of cutting-edge machine learning techniques such as deep learning, reinforcement learning, and transfer learning, the study sheds light on the ways in which these methodologies are leading to the development of intelligent systems in the future. The purpose of this study is to investigate the function of machine learning in the process of data analysis, with a particular emphasis on its capacity to derive insights from intricate and extensive datasets. Additionally, it studies the difficulties that are linked with machine learning, such as the problem of data privacy, the interpretability of models, and the ethical implications of automated decision-making. The objective of this study is to identify major trends and future directions in machine learning by doing an exhaustive examination of the relevant literature, conducting case studies, and conducting experimental analysis. Based on the findings, it appears that the combination of machine learning with other technologies, such as edge computing and the Internet of Things (IoT), will result in intelligent systems that are more advanced and capable of adapting to their environment. The study highlights the significance of a multidisciplinary approach to machine learning, highlighting the necessity of collaboration between computer scientists, domain specialists, and ethicists in order to guarantee the development and deployment of machine learning technologies in a responsible manner. As machin

关键词： Adversarial machine learning

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A Sustainable renewable Energy Assessment Framework Using Novel Machine Learning Algorithm 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： das, Gurumukh Singh, Gurdeep Zindani, divya Shanmugasundar, G. Chohan, Jasgurpreet Singh Kalita, Kanak Uttar Pradesh Agra282 005 India Punjab Ludhiana141013 India College of Engineering Kalavakkam India Department of Mechanical Engineering Sri Sairam Institute of Technology Chennai600044 India Department of Mechanical Engineering University Centre for Research and Development Chandigarh University Mohali140413 India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi600062 India Jadara University Research Center Jadara University Irbid Jordan

ISBN: (数字)9783031733215

ISBN: (纸本)9783031733208

In the quest for sustainable energy solutions, accurate prediction of renewable energy outputs is essential for the seamless integration of these resources into the energy grid. This paper presents a Squirrel Search-Optimized Gradient Boosted decision Tree (SS-GBdT) model, designed to enhance the precision of renewable energy forecasts. The SS-GBdT model leverages the search capabilities of the squirrel search algorithm to optimize the parameters of gradient boosted decision trees, thereby improving their predictive performance. Comparative analyses against established forecasting methods such as dNN, BiLSTM, CNN-BiLSTM, and AB-Net are conducted, with the SS-GBdT model consistently demonstrating superior accuracy, lower error metrics, and an overall robust performance. The findings suggest that the SS-GBdT model could be an invaluable asset for energy sector stakeholders, aiding in the advancement of reliable and efficient renewable energy systems. This study contributes to the growing body of knowledge in renewable energy forecasting and underscores the potential of advanced machine learning techniques in fostering sustainable energy transitions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Clean energy

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