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A Literature Review: Application of AI for Predictive Maintenance in Digital Twins Across Various Industries

A Literature Review: Application of AI for Predictive Mainte...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Ilhem Ben Hnaien Eric Gascard Zineb Simeu-Abazi Hedi Dhouibi NOCCS Laboratory Natl. Engr. School of Sousse University of Sousse Sousse Tunisia Univ. Grenoble Alpes CNRS Grenoble INP * G-SCOP * Institute of Engineering Univ. Grenoble Alpes Grenoble France LARATSI Laboratory High. Inst. of Appl. Sci. Technol. of Kairouan University of Kairouan Kairouan Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper provides a comprehensive literature review on the integration of Artificial Intelligence (AI) into Digital Twin (DT) systems for predictive maintenance across various industries. As industries adopt digital transformation, Digital Twins have emerged as powerful tools, linking physical assets with their digital counterparts for real-time monitoring, analysis, and diagnostics. By leveraging AI methods like machine learning and deep learning, predictive maintenance within DTs can identify and mitigate potential failures before they disrupt operations. This review synthesizes recent advancements in AI applications within DTs, examining sector-specific methods, applications, and the impact on operational efficiency. We explore challenges such as real-time data processing, model complexity, and system integration, presenting a comparative analysis across industries including manufacturing, healthcare, energy, and transportation.

关键词： Industries Deep learning Accuracy Transportation System integration Medical services Real-time systems Digital twins Predictive maintenance Systematic literature review

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Examining the Use of a Teacher Alerting Dashboard During Remote learning 22nd

Examining the Use of a Teacher Alerting Dashboard During Rem...

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22nd international conference on Artificial Intelligence in Education (AIED) - Mind the Gap - AIED for Equity and Inclusion

作者： Dickler, Rachel Adair, Amy Gobert, Janice Hussain-Abidi, Huma Olsen, Joe O'Brien, Mariel Sao Pedro, Michael Rutgers State Univ New Brunswick NJ 08901 USA Apprendis Berlin MA 01503 USA

ISBN: (纸本)9783030782702;9783030782696

Remote learning in response to the COVID-19 pandemic has introduced many challenges for educators. It is important to consider how AI technologies can be leveraged to support educators and, in turn, help students learn in remote settings. In this paper, we present the results of amixed-methods study that examined howteachers used a dashboard with real-time alerts during remote learning. Specifically, three high school teachers held remote synchronous classes and received alerts in the dashboard about students' difficulties on scientific inquiry practices while students conducted virtual lab investigations in an intelligent tutoring system. Quantitative analyses revealed that students significantly improved across amajority of inquiry practices during remote use of the technologies. Additionally, through qualitative analyses of the transcribed audio data, we identified five trends related to dashboard use in a remote setting, including three reflecting effective implementations of dashboard features and two reflecting the limitations of dashboard use. Implications regarding the design of dashboards for use across varying contexts are discussed.

关键词： Dashboard intelligent tutoring system Remote learning

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A Study on ARIMA-Based Melon Size Prediction and Monitoring System Design

A Study on ARIMA-Based Melon Size Prediction and Monitoring ...

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international conference on Software engineering Research, Management and Applications (SERA)

作者： Sangmin Lim Meong Hun Lee Hyeon O Choe Hyun Yoe Dept. Information and Communication Engineering Sunchon National University Suncheon si Jeollanam-do Dept. Smart Agriculture Major Sunchon National University Suncheon si Jeollanam-do

ISBN: (数字)9798350391343

ISBN: (纸本)9798350391350

This study introduces a pioneering monitoring system designed to forecast melon size using the ARIMA (Auto Regressive Integrated Moving Average) model, set against the backdrop of the Fourth Industrial Revolution's integration of advanced technologies into agriculture. The project underscores the escalating role of agtech, driven by the amalgamation of big data, machine learning, drone technology, and computer vision, aimed at enhancing agricultural productivity and meeting the rising food demand. Through the deployment of ARIMA models, this research endeavors to provide melon farmers with precise predictions of fruit size, thereby facilitating better crop management and production planning. In conclusion, this research provides significant insights into the application of ARIMA models for predicting melon size, demonstrating the potential to improve agricultural practices through precision farming. However, the study also acknowledges the limitations posed by the reliance on single-season data and the potential for overfitting, suggesting the need for further research into multivariate time series models and the incorporation of more extensive data sets to refine and enhance the predictive capabilities of the system.

关键词： Productivity Analytical models Computational modeling Time series analysis Predictive models data models Monitoring

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Face Recognition Smart Attendance System using Deep Transfer learning 25

Face Recognition Smart Attendance System using Deep Transfer...

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25th KES international conference on Knowledge-Based and intelligent Information & engineering Systems (KES)

作者： Alhanaee, Khawla Alhammadi, Mitha Almenhali, Nahla Shatnawi, Maad Higher Coll Technol Dept Elect Engn Technol Abu Dhabi U Arab Emirates

Face identification has been considered an interesting research domain in the past few years as it plays a major biometric authentication role in several applications including attendance management and access control systems. Attendance management systems are very important to all organization though they are complex and time-consuming for managing regular attendance log. There are many automated human identification techniques such as biometrics, RFID, eye tracking, voice recognition. Face is one of the most broadly used biometrics for human identity authentication. This paper presents a facial recognition attendance system based on deep learning convolutional neural networks. We utilize transfer learning by using three pre-trained convolutional neural networks and trained them on our data. The three networks showed very high performance in terms of high prediction accuracy and reasonable training time. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-nd license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES international.

关键词： Here Separated by semicolons

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On the Synergy of Homomorphic Blockchain and Deep Neural Network for Enhanced data Security: A Review 22

On the Synergy of Homomorphic Blockchain and Deep Neural Net...

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22nd OITS international conference on Information Technology, OCIT 2024

作者： Swanthana, K. Aravinth, S.S. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

ISBN: (纸本)9798331510404

data Security is a concern as we are in an age where the data becomes digitized and can be easily compromised more often being surrounded by cybercrime threats. Meanwhile, researchers have been investigating to solve this issue using various techniques and methods like differential privacy, federated learning, homomorphic encryption. The purpose of these techniques is to improve data security and at the same time, provide effective machine learning and data analysis. Utilizing a homomorphic blockchain approach, data can be encrypted and processed with complete security without compromising confidentiality of any kind by using deep neural network. This is possible through combination of following the best practice in AI using deep neural networks which have been very successful at solving difficult problems and following model security principles by leveraging homomorphic encryption as well as blockchain technology. These models will store the data in the cloud securely, fostering decentralized sharing that provides transparency and traceability. By following this framework with data analytics, companies can still leverage the power of their stored information while maintaining privacy and security over sensitive material. It also protects the data from malicious attacks and encrypted computation can keep both model-training and running over a set of secret databases in an almost fully-encrypted fashion. As a result, the risk of data leakage or abuse decreases and an additional security barrier is established. © 2024 IEEE.

关键词： Sensitive data

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Millimeter Wave Beam Tracking Scheme with Neural Network Embedded Kalman Filter 22

Millimeter Wave Beam Tracking Scheme with Neural Network Emb...

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22nd IEEE international conference on Communication Technology, ICCT 2022

作者： Chen, Jienan Wu, Tingyong Liang, Chentao Xiang, Wei University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Communications Chengdu China School of Computing Engineering and Mathmatical Science La Trobe University Melbourne Australia

ISBN: (纸本)9781665470674

With an ever-increasing demand for wireless access capacity, the Millimeter-wave (mmWave) has become a promising technology for the forthcoming B5G/6G communications. Unfortunately, consistent high accuracy beam tracking is still a challenging task due to the narrow beam of mmWave. To address this challenge, we propose an intelligent neural network (NN) embedded Kalman filter (KF) scheme. The KF filter can provide robust beam tracking but with a penalty of accuracy loss, if the state transition function deviates from the changing of beam direction in practical scenarios. In our proposed scheme, the acquisition of the state transition function in the KF is converted to a neural network training process by introducing variance Bayesian inference. Therefore, the proposed scheme is a hybrid model-data-driven solution, which improves the prediction accuracy while inheriting the KF robustness. To verify the effectiveness of our proposed solution, we implement the proposed scheme on a software-defined radio system and conduct field testing. Experimental results illustrate that the proposed algorithm outperforms the existing beam tracking methods in terms of mean square error of beam angle prediction. © 2022 IEEE.

关键词： Kalman filters

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Fast Provisioning of Virtual Machine Based on intelligent Virtual Machine Image Prefetching 19

Fast Provisioning of Virtual Machine Based on Intelligent Vi...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Fei, Haiqiang Zhang, Yindan Wang, Wei Li, Yubo Xu, Hui Zhu, Hongsong Hao, Zhiyu Li, Dahui University of Chinese Academy of Sciences School of Cyber Security Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Xidian University Academy of Advanced Interdisciplinary Research Shaanxi Xi'an China Zhongguancun Laboratory Beijing China

ISBN: (纸本)9798350346558

Virtual Machine (VM) provisioning is one of the most critical issues in cloud computing. However, the long delay in VM provisioning significantly degrades the customer experience. Especially, the time-consumption of transferring Virtual Machine Images (VMI) from storage nodes to selected compute nodes is one of the major factors that cause the delay in VM provisioning. Unfortunately, existing approaches usually make their efforts to improve efficiency at the cost of disk performance. In this paper, we propose an intelligent VMI prefetching approach to expedite VM provisioning. First, we identify the data blocks necessary for VMs startup from the whole VMI and record their access order. Then, we design a VMI prefetching mechanism to reduce data transmission, which transfers only a small amount of startup block to compute node during the pre-steps stage (i.e., the short time before startup). Next, we employ a VM request prediction model which leverages historical records to predict the number of requests for each VM type in the future period, so as to perform VMI prefetching in advance, thus reducing VM suspension caused by the preparation of requested blocks. Furthermore, we implement a prototype named PreVMI and perform a series of experiments to evaluate the performance of our approach. Experiments show that our approach can reduce VM provisioning latency by about 35%. © 2022 IEEE.

关键词： Virtual machine

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Quantifying the Impact of SevereWeather Conditions on Online learning During the COVID-19 Pandemic 22nd

Quantifying the Impact of SevereWeather Conditions on Online...

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22nd international conference on Artificial Intelligence in Education (AIED) - Mind the Gap - AIED for Equity and Inclusion

作者： Lagmay, Ezekiel Adriel Rodrigo, Ma Mercedes T. Ateneo Manila Univ Quezon City Metro Manila Philippines

ISBN: (纸本)9783030782702;9783030782696

From October to November 2020 the Philippines was struck by eight typhoons, two of which caused widespread flooding, utilities interruptions, property destruction, and loss of life. How did these severe weather conditions affect online learning participation of students pursuing their undergraduate and graduate studies in the midst of the COVID-19 pandemic? We used CausalImpact analysis to explore September 2020 to January 2021 data collected from the Moodle learning Management System data of one university in the Philippines. We found that overall student online participation was significantly negatively affected by typhoons. However, the effect on participation in Assignments and Quizzes were not significant. These findings suggested that students continued to invest their time and energy on activities that have a direct bearing on their final grades.

关键词： CausalImpact COVID-19 learning management system Typhoon Philippines

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Advancing Emotion Recognition through LLaMA3 and LoRA Fine-Tuning

Advancing Emotion Recognition through LLaMA3 and LoRA Fine-T...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Ibtissen Yacoubi Radhia Ferjaoui Warith Eddine Djeddi Anouar Ben Khalifa Université de Tunis El Manar Ecole Nationale d'Ingénieurs de Tunis Tunis Tunisie Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef Tunisie Université de Jendouba Institut Supérieur d'Informatique du Kef Le Kef Tunisie Université de Sousse Ecole Nationale. d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisie

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Emotion recognition from text is a key task in natural language processing (NLP), with applications ranging from interactive chat systems and mental health detection to consumer feedback analysis. While various machine learning and deep learning techniques have been explored, the success of Large Language Models (LLMs) presents new opportunities revolutionizing how we interact with text. In this study, we harness LLaMA-3-8B for this task by applying LoRA (Low-Rank Adaptation), a highly effective fine-tuning technique that reduces computational and memory costs while maintaining high performance. Experiments on the ISEAR, Emotion for NLP and SemEval 2019 datasets demonstrate significant improvements in accuracy and efficiency over traditional approaches. This work highlights the potential of Llama-3-8B, combined with LoRA, to excel in complex language understanding tasks, particularly in emotion recognition.

关键词： Deep learning Performance evaluation Emotion recognition Adaptation models Translation Large language models Mental health Natural language processing Distance measurement data models

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intelligent Pressure Reconstruction of Centrifugal Compressor Based on Gappy POD and Sparse Measurement

Intelligent Pressure Reconstruction of Centrifugal Compresso...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Xuhui Li Chenxing Hu Jingyu Peng School of Mechanical Engineering Beijing Institute of Technology Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

The capacity to reconstruct the coherent characteristics of fluid flows from limited observational data holds paramount significance for applications across the physical and engineering sciences. The present study conducted steady-state experiments on a centrifugal compressor, utilizing a densely distributed array of static pressure probes to measure the actual static pressure distribution within the flow field, thereby obtaining the aerodynamic performance characteristics of the centrifugal compressor. Furthermore, the study employed randomly distributed sparse pressure sensors and numerical model data of the centrifugal compressor, applying the Gappy POD method to achieve high-fidelity reconstruction of the static pressure distribution on the impeller shroud wall. By comparing the reconstructed results with the experimental measurements, the overall reliability of the data-driven algorithm was validated, and the differences in the subtle flow field characteristics were identified. This not only deepened the understanding of the complex flow dynamics and their underlying patterns within turbo machinery, but provided important theoretical foundations and technical support for the optimization design, performance prediction, and flow control of turbo machinery.

关键词： Measurement uncertainty Mean square error methods Aerodynamics Market research Numerical models Pressure measurement Steady-state Probes Machinery Image reconstruction

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