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modeling and simulation of Induction Heating Litz Coil Based on the Litz Wire Loss Model

Modeling and Simulation of Induction Heating Litz Coil Based...

引用

Power Electronics, computer Applications (ICPECA), IEEE international conference on

作者： Shaoze Wang Chengbin Zhu Jun Lei Yunfeng Wang Long Li Yan Shi School of Electronic Engineering Xidian University Xi'an China GD Midea Small Domestic Appliances Division Foshan China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

The Litz wire is extensively utilized in the applications of induction heating and wireless power transfer due to its small alternating current (AC) resistance and low loss at high frequency. However, the accurate numerical simulation of Litz wire consumes tremendous computational resources and time owing to its intricate structure of multi-strand twisted in multiple levels, thus posing huge challenges for its design and optimization. To tackle this issue, this study proposes a semi analytical full wave method to highly efficiently simulate the Litz wire. As a demonstration, a planar spiral coil of the induction cooker is simulated and measured. The simulation results demonstrate strong consistency with the measurement, verifying the correctness and effectiveness of the proposed modelling approach.

关键词： Coils Resistance Spirals Power measurement simulation Windings Wireless power transfer Conductivity Wire Induction heating

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Industry 5.0 in Healthcare: An Integrated Framework for Human-Centered Prosthetics Design and Manufacturing 6

Industry 5.0 in Healthcare: An Integrated Framework for Huma...

引用

6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Ambrogio, Giuseppina Borgia, Carmine Bortolini, Marco Catapano, Gerardo Conforti, Domenico De Napoli, Luigi Gagliardi, Francesco Galizia, Francesco Gabriele Guido, Rosita Longo, Francesco Mirabelli, Federica Mirabelli, Giovanni Mundo, Domenico Regattieri, Alberto Saffioti, Maria Rosaria Scordamaglia, Gabriele Solina, Vittorio Umbrello, Domenico Veltri, Pierpaolo University of Calabria Department of Mechanical Engineering Energy Engineering and Management Engineering Italy University of Bologna Department of Industrial Engineering Italy University of Calabria Department of Computer Engineering Modeling Electronics and Systems Engineering Italy

Industry 5.0 emphasizes human-centric approaches in technological advancement, particularly crucial in healthcare applications such as prosthetics development. While individual technologies like additive manufacturing, machine learning, digital twins, and tissue engineering have shown promise in prosthetics innovation, their integration remains a significant challenge. This paper presents a novel multidisciplinary framework developed through the National Centre for High-Performance Computing (CN-HPC) project, which addresses the gap between individual technological capabilities and their coordinated application in personalized prosthetics development. The framework, implemented through collaboration between six research groups from two universities, employs the House of Quality methodology to align technological integration with patient needs. Our approach encompasses a comprehensive seven-step process, from initial patient requirements definition through virtual modeling, production process optimization, virtual sensing, machine learning enhancement, digital twin simulation, to advanced tissue engineering. A data model facilitates seamless information exchange between different technological domains and research groups. This research contributes to the field by providing a scalable, human-centered framework for technology integration in medical device development, potentially extending beyond prosthetics to other healthcare applications. The proposed framework represents a significant step toward realizing the full potential of Industry 5.0 in healthcare manufacturing through the systematic integration of advanced technologies for delivering personalized prosthetic solutions. © 2024 The Authors. Published by Elsevier B.V.

关键词： Tissue engineering

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A Combined GAN and DF Based Expansion and Prediction for Small Sample Data 16th

A Combined GAN and DF Based Expansion and Prediction for Sma...

引用

16th international conference on Modelling, Identification and Control, ICMIC 2024

作者： Wang, Li Lu, Junfeng Feng, Shanfang Wang, Xiaoyi Jin, Xuebo Yu, Jiabin Bai, Yuting Beijing Laboratory for Intelligent Environmental Protection School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing100048 China

ISBN: (纸本)9789819617760

The high cost and difficulty of data collection result in discrete intermittency and limited available data for most datasets, posing challenges to modeling and prediction needs. Thus, it is crucial to explore methodologies suitable for discrete small sample data. Concurrently, unsupervised learning methods are employed for small sample prediction to circumvent the issue of low prediction accuracy resulting from inadequate a priori knowledge. Current data modeling and prediction methods rely heavily on neural network deep learning models, which are mainly designed for continuous processes and show low accuracy for intermittent processes. This paper proposes a data expansion method based on Time Generative Adversarial Networks (TimeGAN) and a prediction method based on the combination of Generative Adversarial Networks (GAN) and Deep Forest (DF). Firstly, the TimeGAN model is proposed to learn small sample data for data expansion. Then, the GAN-DF model is constructed so that the model is used for data prediction of unsupervised discrete processes. On this basis, we introduce the DFGAN and LSTM-DFGAN models, which embed DF and LSTM-DF models as generators within the GAN framework to enhance prediction accuracy for small sample data. simulation validation using contaminant data from a food processing process demonstrates the efficacy of the TimeGAN method in data expansion. The results demonstrate that the TimeGAN method is capable of effectively expanding the data, while the LSTM-DFGAN model exhibits the most optimal combined prediction effect. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Analysis of Defects' Impact on the External Magnetic Field of the Turbogenerator

Analysis of Defects' Impact on the External Magnetic Field o...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Ekaterina P. Kurbatova Alexandr A. Kostenko Evgeniy D. Smirnov Anna V. Dyshina National Research University “MPEI” Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper presents an approach to monitoring the technical state of turbogenerator based on the control of an external magnetic field. The study had been performed for the synchronous turbogenerator T3F-160 and includes computer modeling and using neural networks. The effects of defects in form of winding current asymmetry and tooth fracture in the magnetic core, are examined. simulation results demonstrate the feasibility of defect identification through deviations in the magnetic flux density distribution. Based on the obtained data, a neural network was developed to classify machine states with defect-specific accuracy. The proposed method enhances the reliability of diagnostics and fault prediction, enabling integration into existing monitoring systems.

关键词： Solid modeling Computational modeling Magnetic cores computer network reliability Neural networks Windings Turbogenerators Stator windings Magnetic fields Monitoring

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modeling and simulation of Grid-Connected PV System Faults Using MATLAB/Simulink

Modeling and Simulation of Grid-Connected PV System Faults U...

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Learning and Technology conference (L&T)

作者： Mayar Alkhawaja Sandy Miguel Moutaz Abu Shabab Muhy Eddin Za’Ter Majd Ghazi Batarseh Electrical Engineering Department Princess Sumaya University for Technology Amman Jordan Now With the Electrical and Computer Engineering King Abdullah University of Science and Technology Saudia Arabia Now With the College of Engineering and Computer Science University of Central Florida Orlando USA Electrical and Computer Engineering University of Colorado Boulder Boulder USA

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

Grid-connected photovoltaic (PV) systems are crucial to modern renewable energy strategies, but various types of faults can significantly impact their performance. Understanding the behavior of these faults is essential for improving system reliability and reducing maintenance costs. This paper investigates the impact of common faults, including open-circuit, short-circuit, partial shading, and grid faults, by simulating them using MATLAB/Simulink. The focus is on modeling faults that lead to power output reduction. The simulation results demonstrate how different fault types affect critical system parameters, such as grid voltage and active power output. The study highlights the value of simulation as a flexible approach for exploring fault scenarios, providing insights into fault detection and mitigation strategies that can help optimize PV system performance and support the development of robust fault data collection, detection, and classification techniques.

关键词： Renewable energy sources Costs Fault detection Prevention and mitigation System performance simulation Voltage Real-time systems Maintenance MATLAB

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Multi-agent simulation of Complex Economic Systems Driven by Deep Reinforcement Learning

Multi-agent Simulation of Complex Economic Systems Driven by...

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international conference on computer Research and Development (ICCRD)

作者： Bo Chen Shiqi Ding Yukun Lin Guohong Chen College of Computer and Data Science Fuzhou University Fuzhou China School of Economics and Management Fuzhou University Fuzhou China

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

In economics, the study of macroeconomic laws often encounters challenges such as difficulties in data collection and low data quality. A complex economic system (CES) simulation model based on Multi-agent simulation (MAS) was designed and proposed to provide reliable, high-quality virtual data support for economic research to address these issues. To enhance the intelligence of agent behaviors in the simulation model, an economic agent decision-making model based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm was developed, and experimental results demonstrate the effectiveness of the TD3 algorithm in CES simulations. Additionally, the model's decision-making capability in complex economic environments was further improved by incorporating Long Short-term Memory (LSTM) networks to handle temporal relationships. The research presented herein explores the application effects of deep reinforcement learning in economic research, providing a reliable source of virtual economic data for theoretical economic research and offering new perspectives and methods for economic system simulation research.

关键词： Economics Computational modeling Decision making Reliability theory Deep reinforcement learning Reliability engineering Data models Systems simulation Long short term memory Research and development

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Joint Task Offloading and UAV Deployment for Collaborative HAP-UAV Systems

Joint Task Offloading and UAV Deployment for Collaborative H...

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Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Kun Wang Haotian Ma Luwei Fu Qinyong Li Zhiwei Zhao School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

In emergency scenarios such as natural disasters, unmanned aerial vehicles (UAVs) are widely employed to provide communication and computation services when ground infrastructure fails. However, UAVs face limitations in resources. High Altitude Platforms (HAPs), operating in the stratosphere, offer a promising solution with their wide coverage and substantial payload capacity. This paper proposes a collaborative task offloading scheme that combines UAVs and HAPs to minimize task delays, considering task types and the mobility of ground devices, which are typically overlooked. We jointly optimize UAV deployment and task scheduling, modeling deployment as a maximum clique problem and task scheduling as a device-UAV matching problem solved by a stable matching algorithm. Numerical simulations demonstrate the effectiveness of the proposed solution.

关键词： Processor scheduling simulation Collaboration Terrestrial atmosphere Autonomous aerial vehicles Numerical simulation Scheduling Numerical models Delays Payloads

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AI-Assisted Data modeling and High-Resolution Image Synthesis: An Interactive Framework with Latent Diffusion Model Visualization

AI-Assisted Data Modeling and High-Resolution Image Synthesi...

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Intelligent Control, Computing and Communications (IC3), international conference on

作者： Asha Rani Borah Archana K. Pushpa Rani Sajjad Ziara Sorabh Lakhanpal Chalasanvi Vidyadhari Department of Computer Science Engineering New Horizon College of Engineering Bangalore India Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Lovely Professional University Phagwara Punjab India Department of Information Technology GRIET Bachupally Hyderabad Telangana India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

In this research paper, we present the design and implementation of an AI assisted interactive framework for data modeling and high resolution image synthesis that leverages both state of the art latent diffusion models and visualisation of the real-time algorithm. The system proposed enables user to be able to better understand complex data modeling processes and creates high resolution images in the latent space using diffusion. The framework is both interactive, allowing modifications in parameters and real time visualization of change, making it a useful tool both for educational and for advanced AI research. In this paper, we also discuss the underpinning AI-assisted data modeling algorithms, and why they enable efficient data manipulation as well as high quality image synthesis then provide simulation results and practical applications to illustrate the capabilities of the framework of visualizing the latent diffusion process and generating high resolution images.

关键词： Image resolution Image synthesis Computational modeling simulation Data visualization Streaming media Diffusion models Real-time systems Data models Artificial intelligence

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Virtual Prototype Study of a Boom-Type Offshore Transmission for Wind Power Operations and Maintenance

Virtual Prototype Study of a Boom-Type Offshore Transmission...

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Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Tibing Xiao Yue Zhou Zheng Yang School of Electro-mechanical Engineering Guangdong University of Technology Guangzhou Guangdong P. R. China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

In order to solve the safety problems of non-contact operation and personnel transfer between operation and maintenance vessels and offshore wind turbines, a boom structure of the jib type offshore transmission device with a lift and sink compensation function is proposed. This paper takes the offshore transmission device and the six-degree-of-freedom motion platform as the research object, bases on the method of software co-simulation, uses Simcenter 3D motion to build its dynamics modelling, simulates the lifting and sinking motion caused by the waves in MATLAB, and then transfers it to the hydraulic system established by AMESim through the interface module to carry out the solution, so as to complete the virtual prototype design and simulation of the offshore transmission device. The simulation results show that the transmission device can substantially reduce the influence of waves on its motion under the effect of rise and sink compensation, which provides a theoretical basis for its physical model and an idea for the simulation of other complex electromechanical-hydraulic systems.

关键词： Solid modeling Three-dimensional displays simulation Prototypes Hydraulic systems Mathematical models Software Maintenance MATLAB Periodic structures

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Real-Time simulation of Complex 4D Wind Fields and Gusts for UAS Control System Development

Real-Time Simulation of Complex 4D Wind Fields and Gusts for...

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international conference on Unmanned Aircraft Systems (ICUAS)

作者： Surabhi Parab Artur Wolek Dipankar Maity Andrew Willis Electrical and Computer Engineering Charlotte NC USA Mechanical Engineering and Engineering Science University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This article describes a new suite of simulation plugins for the Gazebo 3D simulator to facilitate realistic simulation of time-varying 3D wind fields and gusts. The plugins integrate with ROS and Pixhawk PX4 Software-In-The-Loop (SITL) firmware to aid in the development of robust UAS control systems. Our approach features two main components: (1) real-time plugins for simulating environmental and sensed versions of complex, spatially varying wind velocity fields, using a Fourier-based compression of large CFD datasets, and (2) real-time plugins for modeling environmental and sensed versions of short-duration windblasts. By building on opensource Gazebo and ROS software, the developed framework provides high-accuracy physics simulation with support for multiple vehicles, fostering improved flight controller design and testing in cluttered or challenging atmospheric conditions.

关键词： Three-dimensional displays Atmospheric modeling Wind speed Urban areas Control systems Aerodynamics Real-time systems Vehicle dynamics Time-varying systems Testing

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