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Cellulose Elementary Fibrils as deagglomerated Binder for High-Mass-Loading Lithium Battery Electrodes

Nano-Micro Letters 2025年第5期17卷 281-296页

作者： Young-Kuk Hong Jung-Hui Kim Nag-Young Kim Kyeong-Seok Oh Hong-I Kim Seokhyeon ryu Yumi Ko Ji-Young Kim Kwon-Hyung Lee Sang-Young Lee Department of Chemical and Biomolecular Engineering Yonsei University50 Yonsei-RoSeodaemun-GuSeoul 03722Republic of Korea Advanced Analysis and Data Center Korea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Ulsan Advanced Energy Technology R&D Center Korea Institute of Energy Research(KIER)Ulsan 44776Republic of Korea Department of Battery Engineering Yonsei University50 Yonsei-RoSeodaemun-GuSeoul 03722Republic of Korea

Amidst the ever-growing interest in high-mass-loading Li battery electrodes,a persistent challenge has been the insufficient continuity of their ion/electron conduction ***,we propose cellulose elementary fibrils(CEFs)as a class of deagglomerated binder for high-mass-loading *** from natural wood,CEF represents the most fundamental unit of cellulose with nanoscale *** preparation of the CEFs involves the modulation of intermolecular hydrogen bonding by the treatment with a proton acceptor and a hydrotropic *** elementary deagglomeration of the cellulose fibers increases surface area and anionic charge density,thus promoting uniform dispersion with carbon conductive additives and suppressing interfacial side reactions at ***,a homogeneous redox reaction is achieved throughout the *** resulting CEF-based cathode(overlithiated layered oxide(OLO)is chosen as a benchmark electrode active material)exhibits a high areal-mass-loading(50 mg cm^(-2),equivalent to an areal capacity of 12.5 mAh cm^(-2))and a high specific energy density(445.4 Wh kg–1)of a cell,which far exceeds those of previously reported OLO *** study highlights the viability of the deagglomerated binder in enabling sustainable high-mass-loading electrodes that are difficult to achieve with conventional synthetic polymer binders.

关键词： Cellulose elementary fibrils deagglomeration Electrode binders Lithium batteries High-mass-loading

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Prediction of gender from structural MrI images using Multiscale ShuffleNet Extreme Learning Machine 3

Prediction of gender from structural MRI images using Multis...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Stanley, Berakhah.F. Jeen retna Kumar, r. Gnanaprakash, V. Bini Palas, P. Patturose, J Gold Beaulah Joel devadass daniel, d.J. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SRM Easwari Engineering College Department of Electronics and Communication Engineering Chennai India St. Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350372120

In recent years, the application of machine learning techniques to medical imaging data has shown promising results in various clinical tasks. One such task is the prediction of gender from structural MrI (magnetic resonance imaging) images, which holds potential implications for personalized medicine and understanding neurobiological differences between genders. In this study, we propose a novel approach utilizing Multiscale ShuffleNet Extreme Learning Machine (MSEL), a fusion of Multiscale feature extraction and ShuffleNet architecture with Extreme Learning Machine classifier. We demonstrate the effectiveness of our method on a large dataset of structural MrI images, employing state-of-the-art preprocessing techniques and feature extraction methods. Our results indicate significant accuracy and robustness in gender prediction, outperforming existing methodologies. Furthermore, we conduct comprehensive analyses to investigate the contribution of different components in our proposed framework, shedding light on the underlying mechanisms of gender-related brain structural differences. Overall, our study presents a promising avenue for utilizing advanced machine learning techniques in neuroimaging research, with potential applications in clinical diagnostics and personalized healthcare. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Post-Quantum Encryption for Mathematical Model Using Quantum Machine Learning Approach 1

Post-Quantum Encryption for Mathematical Model Using Quantum...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Kavitha, J. Gayatri, V. rajani, r. Sharmila, L. Harilakshmi, V.S. Misba, M. School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Geethanjali Institute of Science and Technology Department of CSE Andhra Pradesh India Department of Artifical Intelligence and Data Science Nagercoil India

ISBN: (纸本)9798331518943

Because of the increasing emphasis put on the aviation industry in as a main industry of the global transportation industry, a number of organizations depend on different flight companies to get in touch with parts of the world which may be an effective measure to control the disease from spreading to other areas of the world. However, severe conditions can actually influence airline services in a given area of operation most significantly, flight delays. In this case, service encounters took place between the airline as a large organizational sector and the individual traveling as customers. The working for this issue is to accurately expect such flight delays though enables enhanced performers while in travel in order to be fit for destinations. disability which will be occasioned by the existence of the aforementioned impediment. Quantum machine learning is used to examine the measures that were used in building models to analyze probability of flights. Problems associated with late and early arrival and;delays due to unfavorable weather conditions. In the initial the process of project management, in which at the planned stage of the work, we study the feasibility of integration of Support Vector Machine with Python-based linear regression. © 2024 IEEE.

关键词： Project management

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Investigation on Nail denting or Crumbling from Nail Psoriasis Patient Ailment data using the Machine Learning Algorithm and data Mining Techniques 8

Investigation on Nail Denting or Crumbling from Nail Psorias...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Janani, K. Santhoshkumar, S.P. Vikram, d. Priya, S. Vijayaraj, M. Priyanga, M. Manipal Institute of Technology Department of Data Science and Computer Applications Manipal India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India Rathinam Technical Campus Department of Artificial Intelligence and Data Science Coimbatore India Rathinam Technical Campus Department of Information Technology Coimbatore India

ISBN: (纸本)9798350367904

Nail denting or crumbling may result from nail psoriasis. A nail condition known as psoriatic onychodystrophy or psoriatic nails. Psoriasis sufferers frequently experience it;reported occurrences range from 10% to 78%. Psoriatic nails are more common in the elderly and in people with psoriatic arthritis. By examining the nails on a human hand, numerous illnesses can be found early in the diagnosing process. The nail color of an individual can help in the diagnosis of specific medical disorders. In this case, the recommended method results in the decision-making process for sickness diagnosis. The system is fed by human nail art. The technique recognizes nail features unique to a given condition by analyzing images of nails. The suggested technique makes advantage of the various distinctive characteristics of human nails to detect illness by changing the colour of the nails. The first training set data, which is derived from an image of a patient's nails with a particular ailment, is handled by the Weka tool. To get the desired results, the feature discoveries of the picture are compared to the training dataset. The distortion of the nail unit is known as nail disease. Nail units have their own disease class due to their own indications, symptoms, causes, and consequences, which may or may not be related to other medical disorders. Nail problems are still difficult and ambiguous to identify. A novel machine learning method for recognising and classifying nail problems from photographs is presented in this study. This framework combines CNN models with data mining techniques to extract characteristics. This research was also contrasted with certain other province algorithms (Support vector, ANN, K - nearest neighbors, and rF) evaluated on datasets and showed positive results. The accuracy numbers show how well the model can distinguish nail conditions. This ground-breaking discovery lays the groundwork for precise diagnosis in dermatology, medical image processing, an

关键词： Medical image processing

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Anomalous Sound detection Method Based On STgram-MFN Optimization 3

Anomalous Sound Detection Method Based On STgram-MFN Optimiz...

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Cheng, Shasha Shen, Hao Zhao, dexin Liu, Qing Jing, Guanjun Wang, Lei Yang, Jie Department of Big Data and Artificial Intelligence China Telecom Corporation Research Institute Beijing102209 China R&D Cloud Platform Operation Center China Telecom Corporation Research Institute Beijing102209 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Aiming at solving the problems of insufficient feature information extraction and low accuracy in conventional anomalous sound detection methods, this paper presents a new method for detecting anomalous sound based on STgram-MFN optimization. By fusing multiple attention mechanisms for feature recalibration, it can selectively emphasize features with high informative content and suppress less useful features, thereby improving the accuracy of anomalous sound detection. Experiments on the dCASE 2020 Challenge Task 2 dataset show that compared with the original STgram-MFN, Its AUC has reached 94.20%, 74.29%, 88.82%, 92.86%, 99.29%, 98.06% (ToyCar, Toycar, Fan, Pump, Slider, Valve). respectively, increased by 1.56%, 1.37%, 4.05%, 2.87%, 0.04% and 2.91%. In addition, the average AUC of our proposed method is improved by 2.13%. © 2023 SPIE.

关键词： Feature extraction

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Estimating Smart Grid Stability with Hybrid rNN+LSTM deep Learning Approach 12

Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Le...

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12th International Conference on Smart Grid, icSmartGrid 2024

作者： Oyucu, Saadin Sagiroglu, Seref Aksoz, Ahmet Bicer, Emre Faculty of Engineering Department of Computer Engineering Adiyaman Turkey Gazi University Artificial Intelligence and Big Data Analytics Security R&d Center Ankara Turkey Sivas Cumhuriyet University Mobilers Team Sivas Turkey Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas Turkey

ISBN: (纸本)9798350361612

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing recurrent Neural Networks (rNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of rNN to recognize relationships between consecutive data points with LSTM's capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently. © 2024 IEEE.

关键词： Smart power grids

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Total losses and dispersion effects management and upgrading fiber reach in ultra-high optical transmission system based on hybrid amplification system

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Journal of Optical Communications 2025年第2期46卷 289-298页

作者： ramkumar, Govindaraj Shahila, Ferlin deva Lingaraj, Vanitha Chandran, Prabhu Chidambaram, Vivek Arumugam, Parimala ramadan, Hegazy Mahmoud Saveetha University Tamil Nadu Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of ECE S.A. Engineering College Tamilnadu Chennai India Department of ECE Prathyusha Engineering College Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamilnadu Chennai India Department of ECE Sree Sastha Institute of Engineering and Technology Tamilnadu Chennai India Sina Institute of Technology Sina Egypt

This work demonstrated the total losses and dispersion effects management and upgrading the fiber reach in ultra-high optical transmission system based on hybrid amplification system. Based on the requirements of the system, a number of trade-offs must be made between the numerous interconnected performance characteristics of each component in the design of a high quality transmission link. The transmission distance is the greatest distance that an optical signal has to travel. It affects the choice of optical component, fiber type, and signal amplification techniques. Fiber amplifiers or signal regeneration may be required to compensate for signal loss over greater distances. reviewing these crucial aspects helps when selecting the best optical components and system layout. This ensures the optical link performs as expected. Hybrid amplification system unit (EdFA/raman/EdFA), (EdFA/raman), (BFA/PdFA) and (raman/EdFA) is employed in the transmission system to strength the signal and upgrade the system performance operation efficiency. Space division multiplexing scheme is also employed with the propagation scheme in order to upgrade and control of the total losses and dispersion effects. The optimum total losses/dispersion/repeater spacing are studied with the variations of the total number of links per fiber core channel. © 2024 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Amplification

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real Time Crop Health Monitoring Through Convolution Neural Networks 4

Real Time Crop Health Monitoring Through Convolution Neural ...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Sridevi, Bommagani Padma, Nanda Manjusha, Puram Tirunagari, Sharanya Hariharan, Shanmugasundaram Famila, S. Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics & Communication Engineering Tamil Nadu Chennai602001 India

ISBN: (纸本)9798331529635

The agriculture sector, also known as the primary sector in India, is currently facing significant challenges due to the increasing number of pests and insects. These pests can cause serious damage to crop fields, negatively affect crop health, reduce yields, and lead to substantial economic losses for farmers. Early detection of crop diseases can help minimize yield losses by 20-40%, depending on the specific crop type and its various growth stages. The paper discusses the important use of deep learning algorithms in agriculture, specifically focusing on developing an effective approach for the automatic detection of crop diseases through advanced image analysis techniques. The proposed Convolutional Neural Network (CNN) model architecture consists of multiple layers that extract meaningful features from images using convolution and pooling operations. After the feature extraction process, additional layers are used for accurately classifying the images into different disease categories. This model is trained using the Adam optimizer, which is known for its efficiency in handling large datasets and improving the overall training speed. It employs sparse categorical cross-entropy loss over 50 epochs, with a batch size of 32, to effectively optimize the learning process. during the training phase, the model's accuracy is continuously monitored to assess its performance and make any necessary adjustments to enhance its effectiveness. One of the key innovations of this approach is the ability to perform real-time crop disease detection using a camera. This advancement eliminates the need for manual picture collection from the fields, making the entire process faster and much more efficient. By enabling farmers to quickly identify diseases, this cutting-edge technology can significantly help improve crop management practices and increase overall agricultural productivity. Ultimately, the goal is to enhance food security and reduce economic losses in the agriculture sect

关键词： Convolutional neural networks

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Optimization of Path Planning for Automated Vehicles Using SLAM Algorithm 1st

Optimization of Path Planning for Automated Vehicles Using S...

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1st International Conference on data Engineering and Machine Intelligence, ICdEMI 2023

作者： Prinslin, L. Selvi, r. revathi, B. Yuvabharathi, E. Sharmila, L. Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Artificial Intelligence and Data Science Dhanalakshmi College of Engineering Chennai India Department of Information Technology Jeppiaar Institute of Technology Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819776153

SLAM (simultaneous localization and mapping) is a technique for automated vehicles that enables simultaneous map building and vehicle localization. The vehicle is able to map out uncharted terrain owing to SLAM algorithms. Engineers use the map information to carry out tasks such as path planning and obstacle avoidance. An autonomous machine needs the ability to plan the motion of a high-level command. For example, if a task is given to explore a particular area, the bot has to plan accordingly to complete the required task. To achieve this level of accomplishment, the path works on an algorithm that solves SLAM and path planning problems. The machine is designed in such a way that it will work as a grass cutter will complete the task in the given frame. The grass-cutting machine creates its own path in the frame and detects obstacle coming in the path. In this way, the SLAM robot is utilized in the application of agricultural field. In the current work, we have designed a 3-d model of a SLAM robot and its various components like the LIdAr Camera, Jetson Nano, Lithium-Ion Battery, Motor, and Body design machine. This model is capable of performing grass-cutting operations using a cutting blade and collection of waste in a sack attached to the machine. This machine works on automation based on path planning algorithms using rOS, this eliminates human interference. Various kinds of research papers were reviewed to compare different kinds of mechanisms and various components were analyzed for the final model *** check and verify that the author names are correctly recognized and presented in the correct sequence order, which is [given name, middle name/initial, family name] for the author(s). In addition, please verify that the name(s) and respective affiliation(s) shown on the metadata page are valid and make any necessary amendments if ***, have edited the affiliation of the last author. © The Author(s), under exclusive license to Springer Nat

关键词： SLAM robotics

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Vehicle Prediction for BUS Identification Output from our route Testing real Time Algorithm 5

Vehicle Prediction for BUS Identification Output from our Ro...

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

作者： Misba, M. ramya, r. dickson, Joel Sharmila, L. Kavitha, J. Udayakumar, K. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu India St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science Chennai India Bethlehem Institute of Engineering Tamil Nadu Karungal India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331530013

Traffic generation in Indian cities is of a mixed nature, comprising over a dozen types of vehicles that can be broadly categorized into slow-moving and fast-moving vehicles. This study highlights the challenges associated with the inefficient operation of the existing bus system. GPS and mobile networks form integral components of the current system, enabling data storage and management using PostgreSQL. The system provides robust and reliable data to fulfill requests from other software applications. Operating on cloud platforms, it is compatible with all major web browsers. The proposed algorithm continuously tracks the real-time location of buses and their corresponding WMS (Web Map Service) location, alerting users through marker notifications. This paper also explores measures to evaluate the efficiency and effectiveness of bus services. recognizing that an inadequate public transport system impedes economic growth and development, this study emphasizes the need to enhance the overall quality and accessibility of public transportation. The findings aim to improve the operational efficiency of bus services, contributing to a more effective and sustainable transport system. © 2024 IEEE.

关键词： Buses

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