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Chinese Control Conference (CCC)

作者： Faxiang Zhang Guanbin Gao Jing Na Jiande Wu Faculty of Mechanical and Electrical Engineering and Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming University of Science and Technology Kunming P. R. China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming P. R. China

This paper solves the neural network (NN) tracking control problem for uncertain nonlinear servo system with time-varying parameters and uncertainties. To address the uncertain nonlinear function with unknown time-varying parameters (i.e., unknown nonlinear spatiotemporal function), the time-varying parameter extraction method is used to separate the time-varying parameters from uncertain nonlinear spatiotemporal function, which yields an unknown state-based boundary function. By the tools of NN and adaptive technology, an adaptive neural tracking controller is designed, which guarantees the uniformly ultimately bounded (UUB) performance of the resulting closed-loop system. The effectiveness of the designed method is verified by simulations.

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Advanced Battery Management for Electric Vehicles: A Deep Dive into Estimation Techniques Based on Deep Learning for the State of Health and State of Charge of Lithium-Ion Batteries

Advanced Battery Management for Electric Vehicles: A Deep Di...

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Electrical, Electronic and Information sciences (eStream), Open Conference of

作者： Saad El Fallah Jaouad Kharbach Jonas Vanagas Živile Vilkelytė Sonata Tolvaišienė Ghita Ikmel Assia Redouani Saloua Lagnaoui Khaoula Boumais Mohammed Ouazzani Jamil Systems and Sustainable Environment Laboratory Faculty of Engineering Sciences (FSI) Private University of Fez (UPF) Fez Morocco Université Sidi Mohamed Ben Abdellah Faculté des Sciences Dhar El Mahraz Laboratoire de Physique de Solide Fez Morocco Dept. Electrical Engineering Vilnius TECH Vilnius Lithuania Signals Systems Components Laboratory (LSSC) Faculty of Science and Technology Sidi Mohamed Ben Abdellah University Fez Morocco Laboratory Engineering Systems and Applications ENSA Sidi Mohamed Ben Abdellah University Fez Morocco Artificial Intelligence Data Science and Emerging Systems (IASSE) ENSA Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798350352412

ISBN: (纸本)9798350352429

The precision of state of charge (SoC) prediction prediction of the SoC is necessary to avoid deep discharging and remains an important challenge in the field of electric vehicles and overcharging, which can damage batteries and shorten their life. the energy storage industry. The SoC is the percentage of energy The degradation of lithium-ion batteries is a key area of research, available in a battery relative to its total capacity. A precise as these type of batteries are extensively employed in many sectors, in particular electric vehicles, electronic devices and renewable energies. Evaluation systems based on deep learning, like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), are becoming increasingly popular for their ability to process complex data and make accurate predictions. As lithium-ion batteries degrade over time, their reliability in storing and delivering energy is diminishing. To effectively monitor and manage this degradation, researchers are turning to evaluation systems based on deep learning. These approaches enable the prediction of battery state of health (SoH) and SoC, making it easier to optimize battery use and extend battery life. This article presents various techniques for predicting the SoH and SoC of batteries to evaluate the degradation of cells.

关键词： Lithium-ion batteries Degradation Deep learning Recurrent neural networks Estimation Battery management systems Electric vehicles

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Towards the development of a unified virtual population model in hypertrophic cardiomyopathy

Towards the development of a unified virtual population mode...

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2021 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2021

作者： Grigoriadis, Grigoris I. Pezoulas, Vasileios C. Roumpi, Maria Gkois, George Tachos, Nikolaos S. Prodanovic, Momcilo Prodanovic, Danica Stojanovic, Boban Mijailovich, Srboljub M. Filipovic, Nenad Fotiadis, Dimitrios I. Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering University of Ioannina IoanninaGR 45110 Greece University of Kragujevac Faculty of Engineering Kragujevac Serbia University of Kragujevac Faculty of Science Kragujevac Serbia Department of Biology Illinois Institute of Technology ChicagoIL United States BioIRC Prvoslava Stojanovica 6 Kragujevac34000 Serbia Department of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH Ioannina Greece The Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina IoanninaGR 45110 Greece

ISBN: (纸本)9781665403580

The SILICOFCM platform is an in-silico cloud computing platform which utilizes advanced computational workflows for drug development and optimized clinical therapy in the domain of hypertrophic cardiomyopathy (HCM). The current study presents the SILICOFCM's virtual population model (VPM) which can be used to generate high-quality virtual clinical data using both multivariate and machine learning methods along with virtual geometries for in-silico clinical trials. The proposed VPM workflow includes data quality management functionalities for outlier detection and similarity detection which are used to enhance the quality of the real patient data. In addition, the virtual clinical data generator which is part of the VPM includes both multivariate methods, such as, the multivariate normal distribution and machine learning methods, such as, the tree ensembles, the artificial neural networks, and the Bayesian networks. The VPM was utilized in a use-case scenario which included 592 records of patients with HCM towards the generation of clinical data for 1000 virtual patients. Our results suggest that the VPM was able to yield virtual distributions with an increased convergence with the real distributions, where the average goodness of fit was 0.038, the Kullback-Leibler (KL) divergence was 0.029 and the absolute correlation difference 0.0443 between the real and the virtual correlation matrices along with virtual geometries that mimic the real ones. © 2021 IEEE

关键词： Bayesian networks

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A Novel Ultra-Highly Stretchable Mxene-Doped Hydrogel for Stretch-Insensitive Low-Pass Filters

SSRN

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SSRN 2025年

作者： Fan, Qianxi Guo, Ying Tang, Ziyi An, Jia Zheng, Kaiyuan Liu, Feng Peng, Jincai Liu, Yufei Key Laboratory of Optoelectronic Technology & Systems Ministry of Education Chongqing University Chongqing400044 China Centre for Intelligent Sensing Technology College of Optoelectronic Engineering Chongqing University Chongqing400044 China College of Optoelectronic Engineering Chongqing University of Posts and Telecommunications Chongqing China Faculty of Science and Engineering Swansea University SwanseaSA2 8PP United Kingdom

MXene hydrogels have garnered significant attention in the field of flexible electronics owing to their exceptional mechanical and electrical properties. Although extensive research has been devoted to stimulus-responsive flexible electronics, the development of stimulus-insensitive systems is equally critical for expanding their practical applications. In this study, we engineered MXene-based double-network (DN) hydrogels by incorporating MXene, ethylene glycol (EG), and salt solutions. The resulting hydrogels demonstrated outstanding mechanical performance, achieving an elongation at break of up to 1210% and withstanding compressive deformations of 70% without irreversible damage. Furthermore, the hydrogels exhibited high electrical conductivity, reaching a maximum value of 1 S/m. The synergistic integration of mechanical robustness and electrical conductivity enabled the hydrogels to function as highly sensitive strain sensors, capable of detecting both small (1%–5%) and large (10%–50%) strains with a linear response. Leveraging these properties, we fabricated hydrogel-based resistors and capacitors through optimized component selection and structural design. These components were subsequently integrated into a hydrogel-based low-pass filter featuring a stretch-adjustable cut-off frequency, based on the classic RC filter circuit model. Remarkably, the filter maintained stretch-insensitive behavior under varying strains, demonstrating its potential for stable performance in dynamic environments. This work not only provides a novel strategy for designing stretch-insensitive flexible electronics but also offers valuable insights into their practical applications in next-generation devices. © 2025, The Authors. All rights reserved.

关键词： Low pass filters

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Reconstructing Human Pose from Inertial Measurements: A Generative Model-based Compressive Sensing Approach

arXiv

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arXiv 2023年

作者： Hieu, Nguyen Quang Hoang, Dinh Thai Nguyen, Diep N. Alsheikh, Mohammad Abu School of Information Technology and Systems University of Canberra CanberraACT2617 Australia School of Electrical Data Engineering University of Technology SydneyNSW2007 Australia Faculty of Science & Technology University of Canberra CanberraACT2617 Australia

The ability to sense, localize, and estimate the 3D position and orientation of the human body is critical in virtual reality (VR) and extended reality (XR) applications. This becomes more important and challenging with the deployment of VR/XR applications over the next generation of wireless systems such as 5G and beyond. In this paper, we propose a novel framework that can reconstruct the 3D human body pose of the user given sparse measurements from Inertial Measurement Unit (IMU) sensors over a noisy wireless environment. Specifically, our framework enables reliable transmission of compressed IMU signals through noisy wireless channels and effective recovery of such signals at the receiver, e.g., an edge server. This task is very challenging due to the constraints of transmit power, recovery accuracy, and recovery latency. To address these challenges, we first develop a deep generative model at the receiver to recover the data from linear measurements of IMU signals. The linear measurements of the IMU signals are obtained by a linear projection with a measurement matrix based on the compressive sensing theory. The key to the success of our framework lies in the novel design of the measurement matrix at the transmitter, which can not only satisfy power constraints for the IMU devices but also obtain a highly accurate recovery for the IMU signals at the receiver. This can be achieved by extending the set-restricted eigenvalue condition of the measurement matrix and combining it with an upper bound for the power transmission constraint. Our framework can achieve robust performance for recovering 3D human poses from noisy compressed IMU signals. Additionally, our pre-trained deep generative model achieves signal reconstruction accuracy comparable to an optimization-based approach, i.e., Lasso, but is an order of magnitude faster. © 2023, CC BY.

关键词： Compressed sensing

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SmartCHANGE: AI-based long-term health risk evaluation for driving behaviour change strategies in children and youth 3

SmartCHANGE: AI-based long-term health risk evaluation for d...

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3rd International Conference on Applied Mathematics and Computer science, ICAMCS 2023

作者： Rescic, Nina Alberts, Janna Altenburg, Teatske M. Chinapaw, Mai J. M. De Nigro, Antonio Fenoglio, Dario Gjoreski, Martin Gradisek, Anton Jurak, Gregor Kiourtis, Athanasios Kyriazis, Dimosthenis Langheinrich, Marc Mancuso, Elena Mavrogiorgou, Argyro Pechenizkiy, Mykola Pratola, Roberto Ribeiro, Jose Soric, Maroje Taj, Fawad Tammelin, Tuija H. Vastenburg, Martijn Vilanova, Anna Vrijkotte, Tanja G. M. Lustrek, Mitja Jožef Stefan Institute Department of Intelligent Systems Ljubljana Slovenia ConnectedCare Research & Development Nijmegen Netherlands Vrije Universiteit Amsterdam Amsterdam Public Health Research Institute Department of Public and Occupational Health Amsterdam Umc Amsterdam Netherlands Informatica S.p.A. Engineering Ingegneria Rome Italy Università della Svizzera Italiana Faculty of Informatics Lugano Switzerland University of Ljubljana Faculty of Sports Ljubljana Slovenia University of Piraeus Department of Digital Systems Piraeus Greece Eindhoven University of Technology Department of Mathematics and Computer Science Eindhoven Netherlands University of Porto Faculty of Sport Porto Portugal Jamk University of Applied Sciences Likes School of Health and Social Studies Jyväskylä Finland

ISBN: (纸本)9798350324266

The SmartCHANGE project, a Horizon-Europe Research & Innovation project that has been ongoing since May 2023 and is scheduled to conclude in April 2027, aims to develop AI-driven decision-support tools to identify and reduce the long-term risk of non-communicable chronic diseases (NCDs) in children and youth. NCDs have become a significant public health burden in developed countries, with common risk factors including obesity, low physical fitness, and unhealthy lifestyle habits. Childhood and adolescence are critical periods for establishing healthy habits, and early intervention can prevent or delay the onset of NCDs later in life. However, current tools for identifying high-risk individuals mainly focus on adults, leading to missed opportunities for early detection. The SmartCHANGE project aims to address this gap by creating trustworthy AI tools that accurately assess risk factors in children and youth and promote optimised strategies for risk reduction. The paper outlines the plan for the project from the project's proposed methodologies, including dataset utilisation, data cleaning, risk prediction models (basic and complex), and the implementation of federated learning. Additionally, it discusses user experience aspects such as participatory design, suggested applications, and the evaluation of project outcomes. © 2023 IEEE.

关键词： Health risks

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Enhancing Dental Caries Identification with Deep Learning: A Study of Convolutional Neural Networks and Transfer Learning Approaches

SSRN

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SSRN 2024年

作者： Mannan, Fayqa Adeel, Muhammad Arif, Erssa Li, Jianqiang Ullah, Amin Nawazish, Samra Mahmood, Tariq Rehman, Amjad Shoukat, Ijaz Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Government College University Faisalabad38000 Pakistan Department of Computer Science School of Science National Textile University Faisalabad37610 Pakistan Department of Computer Science Faculty of Computing Riphah International University Faisalabad38000 Pakistan Beijing Engineering Research Center for IoT Software and Systems 100124 China Department of Computer Science Faculty of IT and Computer Science University of Central Punjab Lahore Pakistan School of Computer Science and Engineering Southeast University Nanjing China Software Engineering Department University of Engineering and Technology Taxila Pakistan Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia Faculty of information Sciences University of Education Vehari Campus Vehari61100 Pakistan

Various deep learning techniques have been employed to diagnose dental caries using X-ray images. In this study, we utilized deep learning models, including Convolutional Neural Networks (CNNs) and transfer learning models such as Visual Geometry Group (VGG16), VGG19, ResNet50, and Inception V3, to identify dental caries in periapical radiographs. The traditional CNN model comprises three sets of 2D convolutional layers, followed by an activation layer, a max-pooling layer, a flatten layer, one dense layer, another activation layer, dropout, another dense layer, and a final activation layer. For the VGG models, ResNet50, and Inception V3, the top convolutional layers were frozen to prevent retraining of the model, with only the last layers trained to produce the required output. The accuracy achieved by CNN, VGG16, VGG19, ResNet50, and Inception V3 was 90%, 96%, 73%, 70%, and 73%, respectively. VGG16 exhibited the highest accuracy among the models. Hyperparameters were carefully selected using a grid search approach, and the results were validated using the Shuffle-Split-Cross (SSC) validation method. These findings underscore the effectiveness of transfer learning in analyzing medical images, particularly in the diagnosis of dental caries. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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2D-DOA for a Monostatic ULA EMVS-MIMO Radar Based on RC-PARAFAC

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IEEE Sensors Journal 2025年第12期25卷 21763-21774页

作者： Wang, Jianlong Shi, Junpeng Wen, Fangqing China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hvdroelectric Engineering College of Computer and Information Technology Yichang443002 China National University of Defense Technology College of Electronic Countermeasure Hefei230037 China City University of Macau College of Computer and Information Technology China Three Gorges University Faculty of Data Science 999078 China

The electromagnetic vector sensor (EMVS) embedded multiple-input multiple-output (MIMO) radar is an emerging technology capable of two-dimensional (2D) direction of arrival (DOA) estimation. In this paper, we propose a low-complexity algorithm for monostatic uniform linear array (ULA) EMVS-MIMO radar to rapidly estimate 2D-DOA. First, we decompose the received signal into a third-order tensor using parallel factor analysis (PARAFAC). Next, we eliminate data redundancy through a reduced complexity (RC) process. Then, we use the least squares method to extract ambiguous elevation angle information from the spatial response matrix and apply the vector cross-product (VCP) technique to the electromagnetic vector to achieve unambiguous 2D-DOA estimation. Finally, we combine these estimates to achieve high-resolution, unambiguous elevation angle estimation. The proposed algorithm is suitable for large-scale and sparse EMVS-MIMO radar systems. © 2001-2012 IEEE.

关键词： Direction of arrival

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AI-based Flexible Online Laboratory Learning System for Post-COVID-19 Era: Requirements and Design

AI-based Flexible Online Laboratory Learning System for Post...

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2021 International Mobile, intelligent, and Ubiquitous Computing Conference, MIUCC 2021

作者： Elmesalawy, Mahmoud M. Atia, Ayman Yousef, Ahmed Mohamed Fahmy El-Haleem, Ahmed M. Abd Anany, Mohamed G. Elmosilhy, Noha A. Salama, Abdullah I. Hamdy, Alaa Zoghby, Helmy M. El Din, Eman Serag El Helwan University Faculty of Engineering Electronics and Communications Engineering Department Cairo Egypt Helwan University HCI-LAB Faculty of Computers and Artificial Intelligent Computer Science Department Cairo Egypt Giza Egypt Fayoum University Faculty of Specific Education Education Technology Department Fayoum Egypt Electronics and Communications Engineering Department Cairo Egypt Helwan University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Helwan University Faculty of Engineering Electrical Power and Machines Engineering Department Cairo Egypt Network Planning Department Cairo Egypt

ISBN: (纸本)9781665412438

The worldwide outbreak due to COVID-19 pandemic has led to a great interest in e-learning. However, the lack of suitable online laboratory management systems has posed a particular challenge for sectors that need laboratory activities such as engineering, science and technology. In this paper, the requirements and design for a flexible AI-based laboratory learning system (LLS) that can support online laboratory experimentations are presented. The elicitation of the LLS design requirements is decided based on a conducted survey for a set of LLS features. The LLS is designed with the flexibility to support various types of online experimentations such as virtual or remote controlled experiments using either desktop or web applications. The virtualization technique is used to manage the laboratory resources and allow multiple users to access the LLS. Moreover, the proposed LLS introduces the use of AI techniques to provide efficient virtual lab assistant and adaptive assessment process. © 2021 IEEE.

关键词： COVID-19

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RTLAB-Based Performance Validation of PV- Powered Single-Phase Asynchronous Motor Pumps

RTLAB-Based Performance Validation of PV- Powered Single-Pha...

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International Conference on Advanced Electrical engineering (ICAEE)

作者： Fezazi Omar Abdelkader Semmak Hasan Köten Mohammed Abd El Djalil Djehaf Ayad Ahmed Nour El Islam Biswadip Basu Mallik ICEPS Laboratory (Intelligent Control & Electrical Power Systems) Djillali Liabes University Sidi Bel-Abbes Algeria Department of Automatic Control Faculty of Electrical Engineering Djillali Liabes University Sidi Bel-Abbes Algeria Mechanical Engineering Department Mechanical Engineering Department Engineering Faculty Istanbul Medeniyet University North Campus Uskudar Istanbul TURKEY Kasdi merbah university Ouargla Algeria Department of Basic Science and Humanities Institute of Engineering & Management University of Engineering & Management Kolkata West Bengal India

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349368

In this paper, a comprehensive investigation of the performance evaluation and validation of a single-phase asynchronous motor pump system coupled with a photovoltaic (PV) power source is presented. The purpose of this study was to evaluate the practicality and effectiveness of this configuration for pumping applications. The RTLAB platform, which enables real-time simulation and emulation of dynamic systems, includes important components, such as the use of a single-phase asynchronous motor as the pump's power source, the incorporation of a PV system to supply renewable energy, and the use of RTLAB for real-time validation. Through rigorous simulation and experimentation, the motor response performance, such as speed, torque, and current response, was analyzed under varying solar irradiance and load conditions. In addition, the PV power and DC bus voltage were supervised after motor driving. This research fills the knowledge gap between the theoretical analysis and real-world applications of single-phase asynchronous motor pump systems combined with PV sources. This approach, which validates the results using RTLAB, provides a validation method to evaluate the performance and guide design choices for effective and environmentally friendly pumping solutions. Keywords- Single-phase asynchronous motors, PV, RTLAB validation, SimPower System MATLAB.

关键词： Maximum power point trackers Solar irradiance Renewable energy sources Radiation effects Torque systems operation Motors Real-time systems Stability analysis Robustness

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