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Simulation Study on Nondestructive Testing of Winding Hot Spot Temperature Based on Ultrasonic Emission

IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2024年第4期31卷 2179-2188页

作者： Sun, Youliang He, Dongxin Geng, Pan Xu, Tao Wang, Xinbing Zang, Qingjing Su, Wenbin Shandong Power Equipment Co Ltd Jinan 250002 Shandong Peoples R China Shandong Univ Sch Elect Engn Shandong Prov Key Lab UHV Transmiss Technol & Equi Jinan 250061 Peoples R China Shandong Univ Sch Phys Jinan 250100 Peoples R China

The rise in hot spot temperature is a critical indicator of transformer winding defects, making its monitoring essential. Traditional temperature measurement methods, however, have certain constraints in obtaining hot spot information due to the complex structure of transformers. This article thus proposes a nondestructive testing for measuring the transformer winding hot spot temperature based on ultrasonic thermometry, without altering the transformer's internal structure. It also explores the potential application of ultrasonic temperature measurement technology inside transformers. The propagation pattern of ultrasound within a transformer is simulated, and the effect of hot spot faults at varying positions and temperatures on the spatiotemporal distribution of ultrasonic propagation is examined. The range of effective acoustic signals has been determined through comparison. An artificial intelligence-based hot spot temperature inversion method is proposed, leading to the establishment of an effective diagnostic technique for hot spot temperature within the hot spot region, accomplishing a preliminary inversion of hot spot faults. This research offers a novel approach to measuring the internal temperature of transformers.

关键词： Temperature measurement Oil insulation Acoustics Power transformer insulation Windings Temperature sensors Ultrasonic variables measurement artificial intelligence algorithms nondestructive testing ultrasonic propagation ultrasonic thermometry winding hot spot temperature

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A Filtering Model for Evidence Gathering in an SDN-Oriented Digital Forensic and Incident Response Context

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IEEE ACCESS 2024年 12卷 75792-75808页

作者： Jimenez, Maria B. Fernandez, David Rivadeneira, Jorge Eduardo Flores-Moyano, Ricardo Univ Politecn Madrid Dept Telematic Syst Engn ETSI Telecomunicac Madrid 28040 Spain Univ Coimbra Ctr Informat & Syst P-3000214 Coimbra Portugal Univ San Francisco Quito Dept Comp Sci Engn Quito 170901 Ecuador

Software-defined networking (SDN) architecture enables flexible and centralized network management from the controller, making it increasingly attractive in deploying telecommunications services. However, despite the many benefits of SDN, the vulnerabilities inherent in its architecture must be considered, and potential attacks must be discarded. When this occurs, not only the technical areas are interested in the source of the problem, but also the organizational areas, since attacks can violate terms of service and lead to legal actions. Despite the shared interest in cybersecurity event information, forensics and incident response processes often operate independently, impacting the root cause determination. Considering this concern, an architectural evolution for digital forensics and incident response (DFIR) management is introduced. This paper presents an event filtering model that serves as a trigger for initialing the DFIR process, which involves the detection of unusual traffic and unexpected behavior of SDN elements. The proposal applies artificial intelligence technology and showcases the performance of the model and the presentation of a proprietary dataset obtained from OpenFlow traffic.

关键词： Proposals Filtering Feature extraction Denial-of-service attack Support vector machines Digital forensics Recurrent neural networks SDN DFIR SDN forensics SDN attacks SDN cybersecurity DDoS attacks artificial intelligence algorithms SDN dataset

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Social entertainment robot based on artificial intelligence algorithm application in interactive mode of music classroom

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ENTERTAINMENT COMPUTING 2024年 51卷

作者： Song, Qianwen Xianyang Normal Univ Sch Mus Xianyang 712000 Shanxi Peoples R China

With the rapid development of artificial intelligence technology, in-depth research on the application of social entertainment robots in the field of education has become increasingly important. Therefore, this study aims to explore how to use social entertainment robots based on artificial intelligence algorithms to improve the interactive mode in music classrooms, in order to enhance learning effectiveness and classroom atmosphere. The study introduced the detection algorithm of social entertainment robots and designed a complete detection process for accurately locating and identifying social entertainment robots in music classrooms. A machine data classification detection algorithm has been proposed to accurately determine the behavior and function of social entertainment robots. The social entertainment robot teaching module guides students to actively participate in music classroom activities through simulated conversations and interactive demonstrations. Interactive tests were conducted in actual music classrooms to evaluate the effectiveness of social entertainment robots in the classroom and student feedback. Through experiments, it has been proven that social entertainment robots based on artificial intelligence algorithms can effectively enhance the interactivity and fun of music classrooms. It not only promotes communication and cooperation among students, but also enhances learning participation.

关键词： artificial intelligence algorithms Social entertainment robots Music classroom Classroom interaction

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artificial intelligence Applied to Drone Control: A State of the Art

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DRONES 2024年第7期8卷 296-296页

作者： Caballero-Martin, Daniel Lopez-Guede, Jose Manuel Estevez, Julian Grana, Manuel Univ Basque Country UPV EHU Grp Computat Intelligence San Sebastian 20018 Spain Univ Basque Country UPV EHU Fac Engn Alava C-Nieves Cano 12 Vitoria-gasteiz 01006 Spain Univ Basque Country UPV EHU Fac Engn Gipuzkoa Europa Plaza 1 San Sebastian 20018 Spain Univ Basque Country UPV EHU Fac Comp Sci Paseo Manuel de Lardizabal 1 Donostia San Sebastian 20018 Spain

The integration of artificial intelligence (AI) tools and techniques has provided a significant advance in drone technology. Besides the military applications, drones are being increasingly used for logistics and cargo transportation, agriculture, construction, security and surveillance, exploration, and mobile wireless communication. The synergy between drones and AI has led to notable progress in the autonomy of drones, which have become capable of completing complex missions without direct human supervision. This study of the state of the art examines the impact of AI on improving drone autonomous behavior, covering from automation to complex real-time decision making. The paper provides detailed examples of the latest developments and applications. Ethical and regulatory challenges are also considered for the future evolution of this field of research, because drones with AI have the potential to greatly change our socioeconomic landscape.

关键词： artificial intelligence algorithms drones cargo transport autonomous decisions

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Emerging strategies for addressing flood-damage modeling issues: A review

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2025年 116卷

作者： Redondo-Tilano, Sergio Andres Boucher, Marie-Amelie Lacey, Jay Univ Sherbrooke Dept Genie Civil & Genie Batiment Quebec City PQ Canada

Flood-damage models allow decision-makers to estimate potential flood impacts, yet they continue to face critical challenges that affect their accuracy. This review identifies and organizes these challenges, highlighting recent strategies proposed to address them. Key issues in flood-damage modeling include data scarcity, unquantified uncertainty, limited consideration of extended losses (e.g., secondary, intangible, and indirect damages), and limited model transferability. These issues can be caused by both data limitations and methodological choices and are also interconnected so they can influence each other. Strategies to overcome these issues fall into two categories: using alternative data sources other than historical damage records and enhancing existing models to deal with methodological gaps. Additionally, artificial intelligence (AI) algorithms, such as Random Forests and Bayesian networks, show promise in optimizing limited datasets and delivering more comprehensive damage estimates than traditional models. However, their performance remains inconsistent across different applications. This review emphasizes the need for future research to refine AI applications and improve damage data availability, aiming to develop more robust, transferable flood-damage models. By addressing these gaps, the review provides a framework to guide improvements in flood loss identification and estimation, benefiting government agencies, insurance sectors, and communities at risk.

关键词： Flood damages Uncertainty assessment Data scarcity Health impacts of floods Transferability Integrated flood risk management artificial intelligence algorithms

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Spatio-temporal simulation of net ecosystem productivity in the Tibetan Plateau region using multi-scale data assimilation for terrestrial ecosystem process model

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AGRICULTURAL AND FOREST METEOROLOGY 2025年 366卷

作者： Ma, Changhui Duan, Si-Bo Xu, Cong Qin, Wenhua Wang, Feng He, Lei Chinese Acad Agr Sci State Key Lab Efficient Utilizat Arable Land China Inst Agr Resources & Reg Planning Beijing 100081 Peoples R China Lanzhou Univ Coll Earth & Environm Sci Lanzhou 730000 Gansu Peoples R China MNR Fourth Topog Surveying Team Harbin 150025 Peoples R China

Accurately elucidating the spatio-temporal pattern of net ecosystem productivity (NEP) in grasslands on the Tibetan Plateau (TP) is essential for understanding the feedback mechanisms of the carbon cycle to climate and grazing. Parameter localization through data assimilation for terrestrial ecosystem process model is the dominant approach to accurately simulate NEP. However, current studies calibrate ecosystem process model by grassland types, neglecting the spatial variation of model parameters within the same type. Consequently, the calibrated model struggles to characterize the spatially diverse ecological mechanisms. Therefore, we propose a multi-scale parameterization scheme for the CENTURY model, described as follows. (1) Calibrate model parameters in terms of grassland type by assimilating NEP observations from eddy covariance (EC) stations. (2) Migrate the grassland type-scale model to discrete pixels within prohibited pastures, then fine-tune the model parameters with strong spatial divergence and sensitivity by assimilating remotely sensed NPP data on a per-pixel basis;(3) Use ensemble learning algorithms to construct spatial interpolation models driven by environmental factors for discrete parameters. The results confirmed that the calibrated CENTURY model has higher spatial generalization performance compared to model calibrated using previous parameterization scheme. Specifically, the RMSE (R2) for NEP simulations at all EC stations was reduced from 10.18 g C m2 mo-1 (0.58) to 7.58 g C m2 mo-1 (0.72). The CENTURY model was employed in the Selinco region to generate spatio-temporal datasets of grassland NEP from 1980 to 2020, incorporating various grazing intensity scenarios. The following conclusions were drawn from the spatio-temporal analyses. (1) Regardless of grazing scenarios, almost all grasslands functioned as carbon sinks, exhibiting a significant annual increase of net sinks. (2) The annual net sink decreased significantly with altitude. (3) A

关键词： Carbon cycle Net ecosystem productivity Terrestrial ecosystem process model Data assimilation artificial intelligence algorithms Tibetan plateau

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algorithms as Defendants?

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COMPUTER 2024年第7期57卷 11-14页

作者： Voas, Jeffrey Miller, Keith Univ Missouri St Louis MO 63121 USA

This EIC message ponders what can be legally done now (and in the future) to algorithms when they misbehave and create problems for the humans they were designed to serve.

关键词： Accountability Black Box Effective Algorithm Short Message artificial intelligence algorithms Deep Pockets Criminal Proceedings Good Source Of Information Issue For Years

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Revolutionizing prosthetic hand control using non-invasive sensors and intelligent algorithms: A comprehensive review

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Shah, Gautam Sharma, Abhinav Joshi, Deepak Rathor, Ajit Singh Univ Petr & Energy Studies Sch Engn Dept Elect & Elect Engn Dehra Dun 248007 Uttarakhand India Indian Inst Technol Delhi Ctr Biomed Engn New Delhi 110016 India All India Inst Med Sci AIIMS Dept Biomed Engn New Delhi 110029 India

Over the last few years, there has been significant growth in neurological diseases which drastically affect a person's ability to perform everyday tasks, reducing their overall well-being. The development of wearable hand gesture interfaces to restore and augment hand function is a potential approach that facilitates natural human-machine interaction. This article presents a comprehensive review of recent advancements in prosthetic hand control using gesture recognition greatly aided by the incorporation of non-invasive sensors and the use of intelligent algorithms. Surface EMG, IMU, MMG, and FMG are some of the most prominent biosensors used to extract forearm muscle activity, while sensor fusion has shown significant improvement in identifying user intentions and assisting robotic hands in pattern recognition (PR). The extraction of significant signal information in myoelectric control is facilitated by feature engineering which involves manual extraction of features and feature learning that uses algorithms to automatically extract features from raw data. The conventional control techniques estimate a limited number of hand gestures, therefore artificial intelligence (AI) based intelligent algorithms were explored to predict a wide number of hand gestures with improved dexterity. This article also examines the effectiveness of sensor fusion, and intelligent algorithms and identifies recent trends and gaps which suggest new directions for research in gesture recognition for myoelectric prosthetic hand control systems.

关键词： Gesture recognition Non-invasive sensors Feature extraction Myoelectric control Prosthetic hand artificial intelligence algorithms

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Blockchain-based privacy-preserving technology to secure shared data in vehicular communication

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INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS 2025年第3期31卷 346-371页

作者： Slama, Omessaad Dhifallah, Walid Zidi, Salah Lloret, Jaime Alaya, Bechir Tarhouni, Mounira Univ Gabes Hatem Bettaher Lab IRESCOMATH Gabes 6029 Tunisia Univ Politecn Valencia Integrated Management Coastal Res Inst Valencia Spain Univ Gabes Hatem Bettaher Lab IResCoMath Gabes 6029 Tunisia Qassim Univ Coll Business & Econ Dept Management Informat Syst & Prod Management Buraydah 51452 Saudi Arabia

In vehicular ad-hoc networks (VANETs), securely exchanging sensitive data like misbehaviour detection models faces significant security and privacy hurdles. Our solution, machine learning model blockchain-based privacy-preserving (MBPP), combines Blockchain technology and advanced cryptography to tackle this. MBPP ensures data confidentiality and integrity while improving detection model reliability. It involves securely storing ML models on the blockchain using cryptographic hash functions. Our study meticulously evaluates transactional time and computational costs, vital for smooth blockchain transactions. This research not only presents a conceptual framework for blockchain use in VANETs but also offers insights into managing transactions via smart contracts, addressing VANETs' security and privacy challenges effectively.

关键词： blockchain smart contract computational costs artificial intelligence algorithms VANETs vehicular ad-hoc networks data sharing privacy preservation cryptography hash function

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Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques

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CRITICAL CARE 2024年第1期28卷 1-10页

作者： de Haro, Candelaria Santos-Pulpon, Veronica Telias, Irene Xifra-Porxas, Alba Subira, Carles Batlle, Montserrat Fernandez, Rafael Murias, Gaston Albaiceta, Guillermo M. Fernandez-Gonzalo, Sol Godoy-Gonzalez, Marta Goma, Gemma Nogales, Sara Roca, Oriol Pham, Tai Lopez-Aguilar, Josefina Magrans, Rudys Brochard, Laurent Blanch, Lluis Sarlabous, Leonardo Parc Tauli Hosp Univ Inst Invest Innovacio Parc Tauli I3PT CERCA 1 Crit Care Dept Carrer Parc Tauli 1 Sabadell 08208 Spain Inst Salud Carlos III Ctr Invest Biomed Red Enfermedades Resp CIBERES Madrid Spain Inst Invest & Innovacio Parc Tauli I3PT CERCA Sabadell Spain Unity Hlth Toronto Li Ka Shing Knowledge Inst Keenan Res Ctr Biomed Sci Toronto ON Canada Univ Toronto Interdept Div Crit Care Med Toronto ON Canada Univ Hlth Network Dept Med Div Respirol Toronto ON Canada Sinai Hlth Syst Toronto ON Canada Althaia Xarxa Assistencial Univ Manresa Critial Care Dept Manresa Spain IRIS Catalunya Cent I Grp Recerca Malalt Crit Manresa Spain Hosp Britanico Crit Care Dept Buenos Aires Argentina Univ Oviedo Hosp Univ Cent Asturias Unidad Cuidados Intens Cardiol Oviedo Spain Inst Salud Carlos III Ctr Invest Biomed Red Salud Mental CIBERSAM Madrid Spain Univ Autonoma Barcelona Dept Med Bellaterra Spain Univ Paris Saclay Hop Bicetre AP HPServ Med Intens Reanimat DMU CORREVEFHU SEPSISGrp Rech Clin CARMAS Le Kremlin Bicetre France Univ Paris Sud Univ Paris Saclay Ctr Rech Epidemiol & Sante Populat Equipe Epidemiol Resp IntegratInserm U1018 Villejuif France Better Care SL Sabadell Spain

Background Flow starvation is a type of patient-ventilator asynchrony that occurs when gas delivery does not fully meet the patients' ventilatory demand due to an insufficient airflow and/or a high inspiratory effort, and it is usually identified by visual inspection of airway pressure waveform. Clinical diagnosis is cumbersome and prone to underdiagnosis, being an opportunity for artificial intelligence. Our objective is to develop a supervised artificial intelligence algorithm for identifying airway pressure deformation during square-flow assisted ventilation and patient-triggered breaths. Methods Multicenter, observational study. Adult critically ill patients under mechanical ventilation > 24 h on square-flow assisted ventilation were included. As the reference, 5 intensive care experts classified airway pressure deformation severity. Convolutional neural network and recurrent neural network models were trained and evaluated using accuracy, precision, recall and F1 score. In a subgroup of patients with esophageal pressure measurement (Delta P-es), we analyzed the association between the intensity of the inspiratory effort and the airway pressure deformation. Results 6428 breaths from 28 patients were analyzed, 42% were classified as having normal-mild, 23% moderate, and 34% severe airway pressure deformation. The accuracy of recurrent neural network algorithm and convolutional neural network were 87.9% [87.6-88.3], and 86.8% [86.6-87.4], respectively. Double triggering appeared in 8.8% of breaths, always in the presence of severe airway pressure deformation. The subgroup analysis demonstrated that 74.4% of breaths classified as severe airway pressure deformation had a Delta P-es > 10 cmH(2)O and 37.2% a Delta P-es > 15 cmH(2)O. Conclusions Recurrent neural network model appears excellent to identify airway pressure deformation due to flow starvation. It could be used as a real-time, 24-h bedside monitoring tool to minimize unrecognized periods of inappropriate pa

关键词： Airway pressure deformation Flow starvation Patient-ventilator interaction Asynchronies artificial intelligence algorithms

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