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Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

Journal of Energy Chemistry 2024年第4期91卷 512-521页

作者： Liang Ma Jinpeng Tian Tieling Zhang Qinghua Guo Chunsheng Hu School of Mechanical MaterialsMechatronic and Biomedical EngineeringUniversity of WollongongWollongongNSW 2522Australia Department of Electrical and Electronic Engineering and Research Centre for Grid Modernization The Hong Kong Polytechnic UniversityKowloonHong Kong 999077China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia School of Advanced Interdisciplinary Studies Ningxia UniversityYinchuan 750000NingxiaChina

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured *** data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low *** response,this study integrates domain knowledge into deep learning to enhance the RUL prediction *** demonstrate accurate RUL prediction using only a single charging ***,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging *** parameters inform a deep neural network(DNN)to predict RUL with high accuracy and *** trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged *** data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,*** its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and *** inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.

关键词： Lithium-ion batteries Remaining useful life Physics-informed machine learning

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Leveraging Deep Learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart electrical Systems, ICSES 2024

作者： Suthanthiradevi, P. Revathy, G. Rejini, K. Muthu Lakshmi, V. School of Computing Srm institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering Chennai117 India Amrita college of Engineering and Technology Department of Computer Science Engineering Nagercoil India St. Joseph's College of Engineering Department of Computer Science and Engineering OMR Chennai India

ISBN: (纸本)9798331543617

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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High-Performance Intelligent System for Real-Time Medical Image Using Deep Learning and Augmented Reality 4th

High-Performance Intelligent System for Real-Time Medical Im...

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4th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2023

作者： Senthil, G.A. Prabha, R. Rajesh Kanna, R. Umadevi Venkat, G. Deepa, R. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Computer Science and Engineering Vels Institute of Science and Technology and Advanced Studies Chennai India

ISBN: (纸本)9789819965854

Evolving new diseases demand the need for technology to identify the disease in an effective way. Medical imaging in the field of disease identification helps to identify the disease by scanning the human parts, thereby preventing the increased rate of deaths. Deep learning algorithms make it easier to identify and analyze disease efficiently through medical imaging. The high performance of these models is needed for the disease to be predicted with accurate results. The prediction rate of the disease can be increased by the efficient use of deep learning modules and algorithms. This research involves the use of deep learning models in identifying brain hemorrhage and retinopathy diseases through deep learning algorithms. The deep learning algorithms AlexNet and convolutional neural network (CNN) with the accuracy of 90% and 96%, respectively, are employed for the detection of brain hemorrhage, and ResNet-50 and CNN with accuracy of 70% and 92%, respectively, are used for the identification of retinopathy. The output of the model is displayed using augmented reality (AR), which makes it interactive for the user to analyze the results. The AR display is achieved using the unity engine along with the Vuforia package and using the barracuda package for importing the deep learning model into unity. Thus, by increasing the accuracy rate of the system, this research demonstrates the high performance of the intelligent system. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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A First Approach on the Impact of Distributed Generation and Fault Impedance on studies on domain of frequency of Voltage Sags

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2023年 21卷 314-322页

作者： Ramos, A.C.L. Batista, A.J. Leborgne, R.C. Domingues, E.G. Calixto, W.P. EDP Transmission Goias S.A Brazil School of Electrical and Computer Engineering Federal University of Goias Brazil Department of Electrical Engineering Federal University of Rio Grande do Sul Brazil Nucleus of Studies Experimental and Technological Electrotechnical Department-Federal Institute of Goias Brazil

An analysis of the distributed generation (DG) and fault impedance on studies of voltage sags caused by system faults is presented on domain of frequency. The simulation of 62 case studies of phase-to-ground faults on 13.8, 69, 138 and 230 kV transmission lines were performed and the voltage of a 380 V sensitive industrial busbar client was monitored. These lines are part of the electrical system of the city of Goiania, Brazil. For each case study, different fault positions and impedance fault were simulated by considering different DG levels connected to the consumer busbar. Long-term simulation scenarios were obtained by the Monte Carlo method and analyzed based on their cumulative distribution functions and probability density curves of voltage sags. This is one major contribution of this work. © 2023, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Monte Carlo methods

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One-Step Plasma Jet Deposition and Self-Sintering of Gold Nanoparticle Inks on Low-Temperature Substrates

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第5期3卷 197-204页

作者： Manzi, Jacob Varghese, Tony Eixenberger, Josh Prakasan, Lakshmi Estrada, David Subbaraman, Harish Oregon State University School of Electrical Engineering and Computer Science CorvallisOR97331 United States Boise State University Department of Electrical and Computer Engineering Micron School of Material Science and Engineering BoiseID83725 United States Boise State University Micron School of Material Science and Engineering Center for Advanced Energy Studies BoiseID83725 United States Idaho National Laboratory Idaho FallsID83415 United States INFlex Labs LLC BoiseID83706 United States

Flexible electronics on low-temperature substrates like paper are very appealing for their use in disposable and biocompatible electronic applications and areas like healthcare, wearables, and consumer electronics. Plasma-jet printing (PJP) uses a dielectric barrier discharge plasma to focus aerosolized nanoparticles onto a target substrate. The same plasma can be used to change the properties of the printed material and even sinter in situ. In this work, we demonstrate the one-step deposition of gold structures onto flexible and low-temperature substrates without the need for thermal or photonic postprocessing. We also explore the plasma effect on the deposition of the gold nanoparticle ink. The plasma voltage is optimized for the sintering of the gold nanoparticles, and a simple procedure for manufacturing traces with increased adhesion and conductivity is presented, with a peak conductivity of 6.2\times 10{5} S/m. PJP-printed gold LED interconnects and microheaters on flexible substrates are developed to demonstrate the potential of this single-step sintered deposition of conductive traces on low-temperature substrates. © 2022 IEEE.

关键词： Sintering

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What Drives I4.0 Adoption? Establishing the Importance of Strategy

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IEEE engineering Management Review 2024年第5期52卷 8-16页

作者： Arcidiacono, Francesco Ancarani, Alessandro Di Mauro, Carmela Schupp, Florian University of Catania Department of Electrical Electronic and Computer Engineering Catania95124 Italy Schaeffler Automotive Bühl Bühl77815 Germany University of Catania Department of Civil Engineering and Architecture Catania95124 Italy Corvinus University of Budapest Corvinus Institute for Advanced Studies Budapest1093 Hungary Constructor University School of Business Social and Decision Sciences Bremen28759 Germany

This article investigates the adoption patterns of Industry 4.0 (I4.0) technologies in the automotive component sector and links I4.0 adoption to competitive strategies. Analysis of survey data concerning 288 automotive suppliers reveals that most firms currently focus on implementing real-time collection and analysis of production data (i.e., the Internet of Things, Big Data, and Artificial Intelligence), while other technologies, such as additive manufacturing and blockchain, are infrequently adopted. I4.0 adoption is associated with a simultaneous strategic emphasis on cost, quality, and delivery, thus suggesting automotive suppliers look at I4.0 as a mean to overcome performance tradeoffs. Findings also confirm that small- and medium-sized firms are less likely to adopt I4.0. Based on these results, implications for academics, managers, and policymakers are suggested. © 2023 IEEE.

关键词： Smart manufacturing

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Rural Road Pavement Panel Detection with Unmanned Aerial Vehicles 15

Rural Road Pavement Panel Detection with Unmanned Aerial Veh...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Maluya, Melody Mae O. Aleluya, Earl Ryan M. Alagon, Francis Jann A. Clar, Steve E. Salaan, Carl John O. Opon, Joel G. Bahinting, Maria Fe P. College of Computer Studies Department of Computer Applications Philippines College of Engineering Department of Computer Engineering and Mechatronics Philippines College of Engineering Department of Electrical Engineering Philippines Graduate School of Engineering Philippines College of Engineering Mindanao State University-Iligan Institute of Technology Department of Civil Engineering Iligan City9200 Philippines

ISBN: (纸本)9798350381177

Since they connect major population and economic hubs across borders, roads and transportation play a crucial role in a nation's economy. Crash and accident rates are a direct result of poor and substandard road conditions. Road inspectors can better plan repair and assessment tasks with the help of the Pavement Maintenance System (PMS). Distress detection surveys are still carried out manually in developing nations. However, manual road inspection techniques carried out on the ground are dangerous, time-consuming, and labor-intensive. Unmanned aerial vehicles (UAVs) offer the flexibility and manoeuvrability to swiftly visually inspect the damaged area and gather the required data without endangering human safety. Surveyors would therefore be better able to assess the state of road pavement in rural areas if pavement panel detection using unmanned aerial vehicles (UAVs) was implemented. In this study, the precision, recall, and latency of the YOLOv7 and YOLOv8 instance segmentation models for pavement panel detectors were examined. This paper also introduced a new dataset of UAV images from complex and unstructured road environments within the highway areas in the Philippines. The findings indicate that YOLOv8 performed better, with a latency of 84.65 ms, a precision of 97.36% ±0.002, and a recall of 98.71 %±0.004. Road users and managers may benefit from an accelerated pavement assessment process in rural areas thanks to the suggested pavement panel detection model. © 2023 IEEE.

关键词： Antennas

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Blockchain-based decentralized identifier in metaverse environment for secure and privacy-preserving authentication with improved key management and cryptosystem

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Peer-to-Peer Networking and Applications 2025年第4期18卷 1-21页

作者： S, Vijitha R, Anandan Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Tamil Nadu India

The metaverse is gaining popularity because it offers a virtual environment with social interactions that are similar to those in the real world. Because of this heightened attention, protecting privacy and security is becoming more and more crucial. Users can create a variety of avatars in the metaverse, which presents internal security issues if misused and leads to deceptive or dangerous behavior. Metaverse in e-learning platforms becomes an interesting aspect in the current scenario. However, users trying access to the metaverse are exposed to a number of external security threats because they interact with service providers through open channels. To overcome this issue, a novel privacy-preserving and secure authentication protocol using Improved Light Weight key management-based CryptoSystem (ILWKM-CS) is proposed. The proposed ILWKM-CS scheme for privacy-preserving and secure authentication comprises four distinct phases: User setup, User Registration, Login, and Avatar authentication. In the User setup phase, individuals establish their Decentralized Identifiers (DID), and a verifiable credential is issued by the central authority as evidence of the personal data of the user. The next step is user registration, where users create an avatar in the virtual space and register using their DID. Using the ILWKM system, the user and the service provider authenticate each other during the login phase. Lastly, avatars interact with other avatars within the virtual environment and authentication is ensured between them using the MECC technique in the Avatar authentication phase. Moreover, the optimal key is generated to encrypt the message via the proposed hybrid optimization TSAOO algorithm.

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Quantum Machine Learning Techniques based on Nurturing Agri-Ontology Framework in Agricultural Science 3

Quantum Machine Learning Techniques based on Nurturing Agri-...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Bennet Prabhu, A. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Drbccc Hindu College Department of Computer Applications Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Kone Elevator India Technology & Engineering Centre Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

The absence of a consistent ontological framework often impedes efficient knowledge organization and sharing within the agricultural community, despite the quantity of data created in agricultural research. The lack of defined vocabulary and conceptual frameworks hinders data integration, agricultural research, and innovation by preventing interoperability between databases and systems. To address these issues, this research work proposes the Nurturing Agri-Ontology Framework (NAOF), which offers a systematic approach to creating and updating agricultural ontologies. Ontologies that faithfully portray agricultural ideas and connections can be more easily created with the help of NAOF, which combines ideas from knowledge management, agricultural science, and ontology engineering. NAOF aims to ensure that the ontologies produced reflect the wide range of viewpoints and terminology used in agricultural research by encouraging cooperation among stakeholders, ontology engineers, and domain specialists. In this study, the improvement of knowledge organization, sharing and integration in the agricultural area is shown with the application of NAOF. Using this research study, scientists can build ontologies that improve agricultural decision-making, data interoperability, and the discovery of new information. Using quantum machine learning techniques, a strong foundation of knowledge can be laid for several agricultural applications, such as precision agriculture, crop management, and the formulation of agricultural policies. Ontology coverage, interoperability, scalability, and usability are established evaluation parameters that are used to assess the efficacy of NAOF in this research work. The analysis shows that NAOF effectively solves the problems and creates a better agricultural knowledge ecosystem by thoroughly evaluating it using these standards. © 2024 IEEE.

关键词： Ontology

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