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Deep neural network-enabled battery open-circuit voltage estimation based on partial charging data

Journal of Energy Chemistry 2024年第3期90卷 120-132,I0005页

作者： Ziyou Zhou Yonggang Liu Chengming Zhang Weixiang Shen Rui Xiong State Key Laboratory of Mechanical Transmission for Advanced Equipment&College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400000China Department of Vehicle Engineering School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China Department of Electrical Engineering Harbin Institute of TechnologyHarbin 150000HeilongjiangChina School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

Battery management systems(BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion *** main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage(OCV).However,acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation *** addressing these concerns,this study introduces a deep neural network-combined framework for accurate and robust OCV estimation,utilizing partial daily charging *** incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV *** analysis is employed to identify the optimal partial charging data,optimizing the OCV estimation precision while preserving exceptional *** validation results,using data from nickel-cobalt-magnesium(NCM) batteries,illustrate the accurate estimation of the complete OCV-capacity curve,with an average root mean square errors(RMSE) of less than 3 *** this level of precision for OCV estimation requires only around 50 s collection of partial charging *** validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed *** cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV *** method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.

关键词： Lithium-ion battery Open-circuit voltage Health diagnosis Deep learning

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VitalLink: NodeMCU IoT Solution for Vital Signs Surveillance 7

VitalLink: NodeMCU IoT Solution for Vital Signs Surveillance

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Ramitha Rajeswari, J. Chamanthi, Reddy Mary Posonia, A. Albert Mayan, J. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India Sathyabama Institute of Science And Technology School of Computing Chennai India

ISBN: (纸本)9798350359299

This research study proposes the development of comprehensive vital sign surveillance designed for real-time patient assessment. The system integrates multiple sensors, including the LM35 temperature sensor, Heartbeat Rate (HBR) and Blood Oxygen (SpO2) Sensor, DHT11 humidity and temperature sensor, OLED display, and gyroscope sensor, to provide a holistic approach to vital signs. The LM35 sensor is employed for accurate temperature measurement, providing crucial data for monitoring the patient's thermal status. This information is essential for detecting fever or abnormal temperature variations, aiding in the early identification of potential health issues. The HBR and SpO2 sensor plays a vital role in cardiovascular vital signs. The system can assess the patient's cardiac and respiratory well-being by measuring the heart rate and blood oxygen levels. Abnormalities in these parameters can trigger timely alerts, enabling swift medical intervention. The DHT11 sensor enhances the system's capabilities by monitoring ambient temperature and humidity. This additional environmental data contributes to a more comprehensive understanding of the patient's surroundings, ensuring that external factors are considered in the health assessment process. Integrating an OLED display serves as an intuitive user interface, providing real-time feedback on the measured parameters. This ensures that patients and healthcare providers can easily interpret and respond to the health data presented by the system. The display can showcase temperature, humidity, heart rate, blood oxygen levels, and other relevant information in a user-friendly format. Furthermore, the gyroscope sensor adds a layer of sophistication to vital sign surveillance by enabling the assessment of body movement and posture. This feature is particularly beneficial for patients with mobility issues or those prone to falls. The gyroscope data can be analyzed to detect sudden movements or abnormal postures, triggering alerts

关键词： Heart

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Unravelling State-wise Crime Patterns with Ensemble Machine Learning and Data Visualization for Public Safety 2

Unravelling State-wise Crime Patterns with Ensemble Machine ...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Sri Harsha, M.V.R. Akhil, M.H.S. Ronald Doni, A. Albert Mayan, J. Yovan Felix, A. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology School of Computing Chennai India

ISBN: (纸本)9798350349221

This study, detailed below, introduces a new method of handling crimes in particular states, using ensemble machine learning and data visualization techniques in particular, as a way to enhance public safety. Crime detection is a hard task for two concerns, the complexities of the crime and the enormity of the data. By means of random forest and AdaBoost ensemble learning techniques we can model sophisticated problems of the relationship between features such as crime data as a way to match predictions. The other point we stress is data visualization which allows us to comprehend the patterns and connections that have been derived from the ensemble learning process. In addition to the analysis of local crime data, the hybrid approach that we propose involves the inclusion of national crime data to make the identification of hidden crime patterns at state level possible. This move will enable the law enforcement agencies to gain a better understanding of the activities and to effectively respond to the crimes. The proposed methodology represents a, robust and efficient solution to statewise crime detection utilizing the advantages of machine learning and mapping tools. © 2024 IEEE.

关键词： Crime

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DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring 5

DL-Based Somnolence Detection for Improved Driver Safety and...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Neeraja, Peddinti. Kumar, R.G. Kumar, M. Sunil Liyakat, Kazi Kutubuddin Sayyad Vani, M. Sowmya School of Computing Department of Computer Application AP Tirupathi India Siddharth Institute of Engineering & Technology Computer Science and Engineering AP Puttur India School of Computing Department of Computer Science and Engineering AP Tirupathi India Brahmdevdada Mane Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India

ISBN: (纸本)9798350383522

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads. © 2024 IEEE.

关键词： Convolution neural network deep learning Drowsiness detection transfer learning

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Design and Development of Artificial Intelligence Framework to Forecast the Security Index Direction and Value in Fusion with Sentiment Analysis of Financial News

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SN computer science 2024年第6期5卷 1-14页

作者： Singh, Harmanjeet Malhotra, Manisha Singh, Supreet Sharma, Preeti Prabha, Chander University School of Computing Rayat Bahra University University Institute of Computing Chandigarh University School of Computer Science UPES Chitkara University Institute of Engineering and Technology Chitkara University

The domain of stock price prediction is extensively researched owing to its complex data structure and numerous influential factors. In the current epoch, many modern financial applications demonstrate non-linear and uncertain characteristics that exhibit temporal variability. As a result, there has been a notable rise in the need for resolutions to highly non-linear and time-variant issues. External factors, including public sentiment and political events, can influence stock market trends. The primary aim of this research is to propose a novel framework for predicting the blue-chip stock price in the future by combining historical stock data corresponding to the financial news published regarding stock in the newspaper. The initial step involves integrating sentiment and situational features into a machine-learning model. This integration aims to evaluate public sentiments’ influence on algorithms’ predictive precision thirty days ahead. Furthermore, regression models are utilized to analyze the inter-dependencies that exist among companies. To facilitate an experimental analysis, the researchers acquired historical data from the stock market through reputable sources such as the National Stock Exchange-India and collected news data about stock from the financial newspaper “EconomicTimes”. A series of machine learning models were tested to facilitate the classification of news sentiments of the stocks, followed by the fusion of resultant polarities with the stock’s historical data on the standard column “DATE” and then tested series of deep learning models to observe the impact of news on stock price and by taking reference of this, the authors successfully predicted the stock price on a random day. The findings unequivocally indicate that the proposed research design produces better results than other state-of-the-art models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Comparative analysis Economics Sentiment analysis Stock news Stock price forecast

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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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Harvesting Knowledge: An Innovative Ontology Framework for Agricultural Advancement using NPL methodologies 3

Harvesting Knowledge: An Innovative Ontology Framework for A...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Deepa, R. Das, Manisha Thilakavathy, P. Bennet Prabhu, A. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Andhrapradesh Rajam India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Kone Elevator India Technology & Engineering Centre Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

This paper introduces Agricultural Advancement using NLP (AA-NLP) techniques as a strategy to overcome the limitations of traditional agricultural management information systems. There is a lot of unstructured data available but this situation makes agricultural research inefficient because of its knowledge extraction and usage failure. This problem is addressed by combining an ontology framework developed for agricultural information management with Natural Language Processing (NLP) methods. The solution, AA-NLP, utilizes modern NLP algorithms which extract, classify and evaluate agricultural information from many textual sources that include research papers, reports and online repositories. AA-NLP achieves effective information retrieval and exploitation through named entity identification, sentiment analysis and semantic similarity as well as other techniques to convert unstructured input into structured ontological representations. Applying AA-NLP has several benefits such as increased discoverability of relevant agricultural information, automatic classification of scientific findings, and generation of actionable insights for decision making in farming practices, crop management and formulation of agricultural policies. The effectiveness of AA-NLP was analyzed in this paper by measuring the precision associated with knowledge extraction and ontology creation through various performance metrics including accuracy, sustainability farming practices, agricultural productivity and precision. © 2024 IEEE.

关键词： Semantics

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Performance Evaluation of Biometric Authentication Using Fragment Jaya Optimizer-Based Deep CNN with Multi-kernel SVM

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SN computer science 2024年第4期5卷 1-9页

作者： Umasankari, N. Muthukumar, B. Shanmuganathan, C. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing Department of CSE Sathyabama Institute of Science and Technology Chennai India Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

The earlier research clearly indicated that the bimodal authentication system has more efficiency than unimodal and multimodal. This is due to the reason for the best intact biometric traits of fingerprint and retina. There is a chance to additionally improve further performance of the proposed biometric trait combination by additional or alternate algorithms or methodologies. Therefore, in this research work, the multi-kernel support vector machine (MK-SVM), a machine learning classification approach, is proposed and is used for the implementation. In addition, a hybrid algorithm of fragment Jaya optimizer-based deep convolutional neural network (FJO-DCNN) approach is also used to improve the performance value for bimodal biometric authentication and classification. The recognition systems analyze both biometrics independently, and their conclusions are combined to determine whether to give or refuse access to the user in the end. According to the findings of the implementation, this work demonstrates more dependability than the cascaded biometric system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Bimodal biometric authentication Deep convolutional neural network Fragment Jaya optimizer convolutional neural network Multi-kernel support vector machine

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Credit Card Fraud Detection Using Machine Learning- A Comprehensive Review 3

Credit Card Fraud Detection Using Machine Learning- A Compre...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Jaswant, T. Venkata Manoj, G. Sai Vamisdhar, V. Aravind, A. Reddy, S. Vasudeva Patni, Jagdish Chandra Bahadure, Nilesh Kumar, Rohit Alliance School of Advanced Computing Alliance University Dept. of Computer Science Engineering Bengaluru India School of Technology Gsfc University Department of Computer Science and Engineering Gujarat India

ISBN: (纸本)9798331518592

Credit card fraud is an omnipresent threat in the digital era that cannot be effectively managed without complex detection and prevention strategies. Machine learning has become instrumental for the industry, principally, due to providing tools to analyze transactional information and identify patterns of fraud. The article dwells on the methods, problems, and prospects of their future use. The algorithms are analyzed according to their potential applicability, scalability, and interpretability for fraud detection. This study includes convolutional neural networks and recurrent neural networks are used for utilization of anomaly exposure detection methods to detect unseen fraud with models. © 2024 IEEE.

关键词： Convolutional neural networks

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Explore Statistical Properties of Undirected Unweighted Networks from Ensemble Models 27th

Explore Statistical Properties of Undirected Unweighted Netw...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Xunda Wu, Xing Wang, Jianjia School of Computer Engineering and Science Shanghai University Shanghai China Engineering and Science Shanghai Institute for Advanced Communication and Data Science School of Computer Shanghai University Shanghai China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9783031783975

Complex network theory has been widely demonstrated as a powerful tool in modeling and characterizing various complex systems. In the past, complex network theory has focused on the behaviors as well as the characteristics of the network nodes and edges. However, with the continuous evolution of society, traditional graph theory faces challenges due to the emergence of extermely large network structures. Recently, complex network method based statistics has attracted much attention. The new approach effectively manages very large networks and uncovers their intrinsic properties. In this paper, we present a complex network analysis model for undirected, unweighted networks based on a statistical analysis approach. This model is inspired by the ensemble model in thermostatistical physics. Based on the established mathematical model, we derive physical measures that reflect the intrinsic properties of the network, including Entropy, Free Energy, Temperature, and so on. In the experimental part, we first explored the mathematical characterization of these metrics. Then, we observed the performance of various network categories under the same metric. Finally, we applied these measures to the field of graph classification. Extensive experiments demonstrate the effectiveness and superiority of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Undirected graphs

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