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A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning

IEEE Transactions on Transportation Electrification 2025年第3期11卷 7379-7388页

作者： Wu, Tao Dai, Peipei Xue, Gang Guo, Youguang Lei, Gang Zhu, Jianguo Wang, Yifei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Thermal modeling and analysis are critical for permanent magnet linear synchronous motors (PMLSMs), particularly in multi-physical analysis and motor design optimization. This paper proposes a new method for thermal steady state modeling and analysis based on transfer learning, which combines the thermal mechanism model with the data-driven model to achieve high-precision temperature analysis with small number of finite element analysis (FEA) samples. First, an equivalent thermal circuit (ETC) model is established for PMLSMs based on the structural parameters and heat transfer principles. Second, a hybrid model based on transfer learning is developed: the model is pre-trained by a large sample set generated from the ETC in the source field (mechanism analytical model), and then the trained model is transferred to the target field (FEA data-driven model). The parameters of the neural network in the target field are optimized by a few FEA sample sets with higher precision to improve the prediction accuracy of the target model. Experimental results demonstrate that the proposed method significantly boosts the performance of the deep neural network (DNN) model, particularly when training sets are small. The root mean square error (RMSE) of the DNN model decreases by 0.901 in the 5:5 (training/testing) dataset and 1.726 in the 2:8 dataset;and the corresponding R2 and maximal absolute error of motor performances are significantly decreased. The smaller the proportion of the training set, the better performance of the hybrid modeling will achieve. Furthermore, a temperature rise experiment conducted on a prototype motor validates the efficacy and precision of the proposed hybrid modeling methodology. © 2015 IEEE.

关键词： Permanent magnets

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Guest Editorial: EV charging impacts on power system

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IET GENERATION TRANSMISSION & DISTRIBUTION 2024年第6期18卷 1091-1094页

作者： [Anonymous] Department of Electrical Engineering and Automation Aalto University Espoo Finland School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Electrical Engineering São Paulo State University Ilha Solteira Brazil Electrical Engineering Department Sharif University of Technology Tehran Iran School of Industrial Engineering University of Castilla-La Mancha Ciudad Real Spain

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Conversational AI for Framed Autonomy in AI-augmented Business Process Management 22

Conversational AI for Framed Autonomy in AI-augmented Busine...

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Best Dissertation Award, Doctoral Consortium, and Demonstration and Resources Forum at 22nd International Conference on Business Process Management, BPM-D 2024

作者： Casciani, Angelo Department of Computer Control and Management Engineering Sapienza University of Rome Italy

AI-augmented Business Process Management Systems (ABPMSs) represent an emerging category of process-aware information systems driven by AI technology. These systems autonomously manage the execution flow of business processes (BPs) within predefined frames, encompassing procedural and declarative specifications, which may sometimes conflict. Despite operating autonomously within these boundaries, ABPMSs require dynamic conversations with human agents. These conversations not only respond to user queries but also initiate discussions to inform them of BP progression and provide recommendations for performance improvement. This research proposal aims to leverage Conversational AI to support the ABPMS’s framed autonomy, functioning as a Decision Support System (DSS). This involves explaining system’s choices and suggesting actions when constraints are violated. This technique enables intelligent, context-aware interactions with ABPMSs, fostering user trust. Our findings indicate that Conversational AI has the potential to significantly enhance the interpretability and usability of ABPMSs, thereby facilitating improved decision-making and process optimization. © 2024 Copyright for this paper by its authors.

关键词： Chatbots

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A Combination of Soft Attention-aided CNN Models using Dempster-Shafer Theory for Skin Cancer Classification

A Combination of Soft Attention-aided CNN Models using Demp...

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International Conference on Current Problems of Applied Mathematics and computer Systems, CPAMCS 2023

作者： Sarkar, Sujan Ray, Amartya Kaplun, Dmitrii Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9783031640094

Skin cancer is one of the most deadly forms of cancer in the world, causing hundreds of deaths every year. Researchers have been developing various computer-aided diagnosis (CAD) systems to help medical professionals classify the types of skin cancer using different image modalities. In this paper, we propose a model, which combines the outcomes of three separately trained attention-based convolutional neural networks (CNNs) using the Dempster-Shafer theory. The three base learners used in this paper are ResNet50, InceptionResNetV2 and Dense-Net201, each of which is aided by a soft attention module. For evaluating the proposed model, we have considered a publicly accessible skin cancer dataset, called HAM10000. This is a challenging dataset to work with as it is a class imbalanced dataset. Our proposed approach yields an accuracy of 0.932, which is better than many recently published methods found in the literature. The source code is available at: https://***/Cmatermedicalimageanalysis/DST_Combination © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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DanteLLM: Let's Push Italian LLM Research Forward! 30

DanteLLM: Let's Push Italian LLM Research Forward!

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Bacciu, Andrea Campagnano, Cesare Trappolini, Giovanni Silvestri, Fabrizio Department of Computer Control and Management Engineering Sapienza University of Rome Italy

ISBN: (纸本)9782493814104

In recent years, the dominance of Large Language Models (LLMs) in the English language has become evident. However, there remains a pronounced gap in resources and evaluation tools tailored for non-English languages, underscoring a significant disparity in the global AI landscape. This paper seeks to bridge this gap, specifically focusing on the Italian linguistic context. We introduce a novel benchmark, and an open LLM Leaderboard, designed to evaluate LLMs' performance in Italian, providing a rigorous framework for comparative analysis. In our assessment of currently available models, we highlight their respective strengths and limitations against this standard. Crucially, we propose "DanteLLM", a state-of-the-art LLM dedicated to Italian. Our empirical evaluations underscore Dante's superiority, as it emerges as the most performant model on our benchmark, with improvements by up to 6 points. This research not only marks a significant stride in Italian-centric natural language processing but also offers a blueprint for the development and evaluation of LLMs in other languages, championing a more inclusive AI paradigm. Our code at: https://***/RSTLess-research/DanteLLM. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Benchmarking

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Enhancing Automotive Embedded Applications: A Comprehensive Evaluation of control Flow Checking Methods

Enhancing Automotive Embedded Applications: A Comprehensive ...

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2024 IEEE International Conference on Design, Test and Technology of Integrated Systems, DTTIS 2024

作者： Solouki, Mohammadreza Amel Sini, Jacopo Violante, Massimo Politecnico di Torino Department of Control and Computer Engineering Turin Italy

ISBN: (纸本)9798350363128

Mitigating the risks posed by Random Hardware Failures (RHFs) is crucial to prevent data corruption and control Flow Errors (CFEs) in embedded systems. This paper addresses these concerns through the application of Software-Implemented Hardware Fault Tolerance (SIHFT) methods, emphasizing compatibility with high-level programming languages such as C. Current SIHFT methods, often implemented in low-level Assembly, present challenges in terms of overhead to code size and real-time execution. Our proposed approach focuses on pre-compilation application of SIHFT methods, specifically control Flow Checking (CFC), to identify CFEs within C-language-based code. We conducted a comparative analysis of two established software-based CFE detection methods in C, seamlessly integrating CFC methods into the application behavioral model. Our methodology ensures ISO26262 compliance, crucial for the automotive sector, offering a software-only strategy that aligns with safety and cost considerations. © 2024 IEEE.

关键词： C (programming language)

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Open-Set Recognition in Unknown DDoS Attacks Detection With Reciprocal Points Learning

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IEEE Access 2024年 12卷 56461-56476页

作者： Shieh, Chin-Shiuh Ho, Fu-An Horng, Mong-Fong Nguyen, Thanh-Tuan Chakrabarti, Prasun National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung82444 Taiwan Nha Trang University Department of Electronics and Automation Engineering Nha Trang650000 Viet Nam Sir Padampat Singhania University Department of Computer Science and Engineering Rajasthan Udaipur313601 India

The internet, a cornerstone of modern life, has profound implications across personal, business, and society. However, its widespread use has posed challenges, especially concerning privacy and cybersecurity. Besides, the threats on the internet are increasing in terms of danger, intensity, and complexity. Distributed denial-of-service (DDoS) attacks have emerged as a common and dangerous cybersecurity threat capable of disabling the network systems of targeted organizations and services. Therefore, various security strategies, such as firewalls and intrusion detection systems (IDS), are employed to protect against DDoS attacks. Enhancing the defensive capabilities of IDS systems through machine learning (ML) and deep learning (DL) technologies is a significant trend nowadays. However, despite notable successes, detecting DDoS attacks using ML and DL technologies still faces challenges, especially with Unknown DDoS Attacks. In this research, the primary goal is to address the unknown DDoS detection problem through efficient and advanced techniques. Our proposed method, CNN-RPL, integrates Convolutional Neural Network (CNN) with Reciprocal Points Learning (RPL), a novel Open-Set Recognition (OSR) technology. This model can effectively handle both known and unknown attacks. The CNN-RPL model demonstrates excellent results, achieving an accuracy exceeding 99.93% against known attacks in the CICIDS2017 dataset. Simultaneously, the model achieves a commendable average accuracy of up to 98.51% against unknown attacks in the CICDDoS2019 dataset. In particular, the CNN-RPL model simplifies the architecture of the deep neural network by significantly reducing the number of training parameters without compromising defense capabilities. Therefore, our proposed method is genuinely efficient, particularly flexible, and lightweight compared to traditional methods. This can equip organizations and businesses with a highly applicable yet powerful security approach against the evolv

关键词： Denial-of-service attack

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2D Posture Estimation of Human Arm 9

2D Posture Estimation of Human Arm

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9th International Conference on Mathematics and computers in Sciences and Industry, MCSI 2024

作者： Rosca, Sebastian Daniel Leba, Monica Sibisanu, Remus University of Petrosani Department of System Control and Computer Engineering Petrosani Romania

ISBN: (纸本)9798350377224

Human pose estimation and automatic detection is a common task in Human-computer Interaction but especially more capitalized in human-robot interaction inspired from biological form of human anatomy to realize a biomimetic control. In this sense, using human pose estimation it can be realized the whole human body posture estimation using anatomical human joints detection as inputs for specialized algorithms to calculate it based on key points obtained from a person Kinematic tree using regression, generation or part relation as models to estimate the optimal position from video or even from image. In this paper we propose a solution to estimate of 2D human arm posture of a subject using image capture as input for advanced machine learning algorithms to determine the subject joints position used to control in biomimetic manner a simulation of human like robotic arm with six degrees of freedom as a computer-aided model in Matlab-Simulink based on forward kinematics. © 2024 IEEE.

关键词： MATLAB

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A Task-Oriented Hybrid Cloud Architecture with Deep Cognition Mechanism for Intelligent Space

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computers, Materials & Continua 2023年第8期76卷 1385-1408页

作者： Yongcheng Cui Guohui Tian Xiaochun Cheng School of Control Science and Engineering Shandong UniversityJinanChina Computer Science Department Swansea UniversityWalesUK

Intelligent Space(IS)is widely regarded as a promising paradigm for improving quality of life through using service task *** the field matures,various state-of-the-art IS architectures have been *** of the IS architectures designed for service robots face the problems of fixedfunction modules and low scalability when performing service *** this end,we propose a hybrid cloud service robot architecture based on a Service-Oriented Architecture(SOA).Specifically,we first use the distributed deployment of functional modules to solve the problem of high computing resource ***,the Socket communication interface layer is designed to improve the calling efficiency of the function ***,the private cloud service knowledge base and the dataset for the home environment are used to improve the robustness and success rate of the robot when performing ***,we design and deploy an interactive system based on Browser/Server(B/S)architecture,which aims to display the status of the robot in real-time as well as to expand and call the robot *** system is integrated into the private cloud framework,which provides a feasible solution for improving the quality of ***,it also fully reveals how to actively discover and provide the robot service mechanism of service tasks in the right *** results of extensive experiments show that our cloud system provides sufficient prior knowledge that can assist the robot in completing service *** is an efficient way to transmit data and reduce the computational burden on the *** using our cloud detection module,the robot system can save approximately 25% of the averageCPUusage and reduce the average detection time by 0.1 s compared to the locally deployed system,demonstrating the reliability and practicality of our proposed architecture.

关键词： Service robot intelligent space cloud robot interactive robot system robot cloud service framework

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Assembling Solar Panels by Dual Robot Arms Towards Full Autonomous Lunar Base Construction

Assembling Solar Panels by Dual Robot Arms Towards Full Auto...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Nunziante, Luca Uno, Kentaro Diaz, Gustavo H. Santra, Shreya De Luca, Alessandro Yoshida, Kazuya Sapienza University of Rome Department of Computer Control and Management Engineering Italy Department of Aerospace Engineering Sendai980-8579 Japan

ISBN: (纸本)9798331531614

Since the successful Apollo program, humanity is once again aiming to return to the Moon for scientific discovery, resource mining, and inhabitation. Upcoming decades focus on building a lunar outpost, with robotic systems playing a crucial role to safely and efficiently establish essential infrastructure such as solar power generating towers. Similar to the construction of the International Space Station (ISS), shipping necessary components via modules and assembling them in situ should be a practical scenario. In this context, this paper focuses on the integration of vision, control, and hardware systems within an autonomous sequence for a dual-arm robot system. We explore a perception and control pipeline specifically designed for assembling solar panel modules, one of the benchmark tasks. Ad hoc hardware was designed and tested in real-world experiments. A mock-up of modular solar panels and active-passive connectors are employed, with the control of this grappling fixture integrated into the proposed pipeline. The successful implementation of our method demonstrates that the two robot manipulators can effectively connect arbitrarily placed panels, highlighting the seamless integration of vision, control, and hardware systems in complex space applications. © 2025 IEEE.

关键词： Space applications

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