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2022 IEEE International Conference on Electrical Sciences and Technologies in Maghreb, CISTEM 2022

作者： Moussa, Intissar Zerzeri, Mouna Khedher, Adel Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Université de Sousse Institut Supérieur de Transport et de la Logistique de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia

ISBN: (纸本)9781665451680

In this paper, a Sliding Mode Observer (SMO) for flux and then speed-sensorless of three-phase Induction Motor (IM) as robust Wind Turbine Emulator (WTE) design is investigated. A Soft-Voltage Source Inverter (VSI) structure, which is controlled by a specific Space Vector Modulation (SVM), is applied to drive the IM. Hence, appropriate three vectors which synthesize the desired output voltage are selected to allow the proposed system to react as a real turbine considering the wind velocity, static and dynamic behaviors, and parametric variations. Mathematical models of each system part are described to highlight electrical, mechanical, and electromagnetic relations. Simulation results confirm that the presented control method provides good flux and speed estimations despite rotor resistance and load torque variations in terms of trajectory tracking. © 2022 IEEE.

关键词： Wind power

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Fisher-HHT: A Feature Extraction Approach For Hand Gesture Recognition With a Leap Motion Controller 6

Fisher-HHT: A Feature Extraction Approach For Hand Gesture R...

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6th International Conference on advanced Technologies for Signal and Image Processing, ATSIP 2022

作者： Bhiri, Nahla Majdoub Ameur, Safa Jegham, Imen Mahjoub, Mohamed Ali Ben Khalifa, Anouar Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia

ISBN: (纸本)9781665451161

In the last decades, Hand Gesture Recognition (HGR) has become one of the most prominent research topics, due to its wide range of applications in computer vision. Various approaches and techniques were suggested and evaluated to conduct significant results on HGR. However, even with the triumph reached on state-of-the-art methods, they have not considered the non-linearity and non-stationarity existing on time-series data including the Leap Motion Controller (LMC) raw data. In this paper, we propose a novel method for extracting and selecting pertinent and discriminant features using Hilbert Huang Transform (HHT) and fisher discriminant analysis. Therefore, the time-series signals of LMC are decomposed through an empirical mode decomposition. Then, the HHT is applied to generate the resultant Hilbert marginal spectrum. Next, the fisher discriminant analysis is proceeded for an efficient feature selection. Experimental results demonstrate the effectiveness of the proposed approach in term of hand gesture classification accuracy on the challenging LeapGestureDB dataset. © 2022 IEEE.

关键词： Hilbert-Huang transform

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Experimental investigations on structural improvement of morphing mechanism for nose cone of aerospace vehicle adopting a bionic redundant constraint strategy

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Science China(Technological Sciences) 2023年第4期66卷 1075-1095页

作者： ZHANG YuLing LI ChengXiang ZHANG Yan CAI QiaoYan YAN ShaoZe Division of Intelligent and Biomechanical Systems State Key Laboratory of Tribology in Advanced EquipmentDepartment of Mechanical EngineeringTsinghua UniversityBeijing 100084China China Academy of Launch Vehicle Technology Beijing 100071China

Aerospace vehicles have recently received great attention for their completely reusable in aviation and *** achieve the optimal aerodynamic performance of the aerospace vehicle,the nose cone needs to change its aerodynamic shape under different flight ***,the structural design of a morphing nose cone that is capable of changing aerodynamic shape adaptively and withstanding prescribed aerodynamic loads remains an ongoing *** overcome this issue and to further advance our previous work,the motion performance of a morphing nose cone inspired by the deformation mechanism of the honeybee abdomen was tested to evaluate the deformation ability and bearing *** dynamic prediction analysis of the morphing nose cone with a clearance joint and flexible links was then investigated to elucidate the crucial issues in the deformation *** improve the motion performance,a performance improvement scheme based on redundant constraints drawn from the muscle distribution of the honeybee abdomen was ***,the structural stiffness,dynamic analysis,and experimental testing of the improved morphing nose cone were *** experimental results indicate that the extension and contraction ratios and bending angles of the improved morphing nose cone under loads of 0,10,and 25 kg loads improve by 1.51%and 2.329°,2.78%and-1.902°,and 5.06%and 1.111°,respectively,verifying the rationality and effectiveness of the performance improvement *** work provides a new reference for the design of the morphing structure for aerospace vehicles.

关键词： morphing nose cone experimental investigation dynamic analysis performance improvement bionic design

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MixUp Data Augmentation for Handwritten Arabic Mathematical Symbols Recognition

MixUp Data Augmentation for Handwritten Arabic Mathematical...

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Workshop held at the 12th International Conference on Model and Data Engineering, MEDI 2023

作者： Hadj Ali, Ibtissem Ben Khalifa, Anouar Mahjoub, Mohamed Ali Université de Sousse Ecole Nationale d’Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Université de Jendouba Institut National des Technologies et des Sciences du Kef Le Kef7100 Tunisia

ISBN: (纸本)9783031557286

Handwritten mathematical expression and symbol recognition is a subfield of document image analysis that aims to convert images of handwritten mathematical formulas into a machine-readable format. Despite decades of research, recognition of handwritten mathematical expressions and symbols, particularly those written in Arabic, remains a challenging problem. To address this issue, we propose a Deep Neural Network (DNN)-based approach for Handwritten Arabic Mathematical Symbol Recognition. DNNs are powerful tools for data analysis and image classification, but they are susceptible to overfitting. Additionally, there are limited large-scale databases available for Arabic mathematical symbol recognition. To overcome this, we propose using MixUp data augmentation to increase the diversity of available data. MixUp involves training a CNN on convex combinations of pairs of examples and their corresponding labels. Furthermore, we integrate our new framework with both Cross Entropy loss and triplet loss on the augmented samples, which significantly improves the classification accuracy. The experimental results conducted on the Handwritten Arabic Mathematical Dataset (HAMF) [1] demonstrate that our proposed framework yields a significant improvement in accuracy compared to the standard CNN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cross Entropy Loss Handwritten Arabic Mathematical symbols MixUp augmentation Triplet Loss Triplet Network

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Optimal Sizing of an Off-Grid Hybrid Renewable Energy System: A Case Study in Tunisia

Optimal Sizing of an Off-Grid Hybrid Renewable Energy System...

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2023 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2023

作者： Mallat, Mariem Bel Hadj Brahim Kechiche, Olfa Hamouda, Mahmoud Université de Sousse École Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Université de Sousse ESSTHSousse HSousse4011 Tunisia HSousse4011 Tunisia

ISBN: (纸本)9798350325539

Hybrid systems are becoming increasingly attractive due to their operational flexibility and unmatched price, which is attracting considerable interest. These systems require laborious upstream sizing based on a thorough knowledge of the renewable energy sources on site and precise know-how of the electrical energy produced and stored downstream. The economical optimization of the installation is usually performed using very powerful software. In this paper, the HOMER software was chosen, which is the most used software for the simulation and optimization of hybrid energy systems in several experiments. In this context, the design, optimization and technical-economic profitability of the hybrid energy system (PV/Wind/Batteries), intended for the electrical needs of a plant located in the Bizerte site, will be detailed and discussed in this paper. This technical and economic feasibility study of the electrification project has succeeded in reducing the high costs of using renewable energy, energy dependence, costly maintenance, the short lifespan. © 2023 IEEE.

关键词： Hybrid systems

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Prospective Role of Foundation Models in Advancing Autonomous Vehicles

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Research 2025年第1期7卷 667-691页

作者： Jianhua Wu Bingzhao Gao Jincheng Gao Jianhao Yu Hongqing Chu Qiankun Yu Xun Gong Yi Chang H Eric Tseng Hong Chen Jie Chen School of Automotive Studies Tongji UniversityShanghai 201804China Frontiers Science Center for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China SAIC Intelligent Technology Shanghai 201805China College of Artificial Intelligence Jilin UniversityChangchun 130012China Research and Advanced Engineering Ford Motor CompanyDearbornMI 48124USA College of Electronic and Information Engineering Tongji UniversityShanghai 201804China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai 201210China

With the development of artificial intelligence and breakthroughs in deep learning,large-scale foundation models(FMs),such as generative pre-trained transformer(GPT),Sora,etc.,have achieved remarkable results in many fields including natural language processing and computer *** application of FMs in autonomous driving holds considerable *** example,they can contribute to enhancing scene understanding and *** pre-training on rich linguistic and visual data,FMs can understand and interpret various elements in a driving scene,and provide cognitive reasoning to give linguistic and action instructions for driving decisions and ***,FMs can augment data based on the understanding of driving scenarios to provide feasible scenes of those rare occurrences in the long tail distribution that are unlikely to be encountered during routine driving and data *** enhancement can subsequently lead to improvement in the accuracy and reliability of autonomous driving *** testament to the potential of FMs'applications lies in world models,exemplified by the DREAMER series,which showcases the ability to comprehend physical laws and *** from massive data under the paradigm of self-supervised learning,world models can generate unseen yet plausible driving environments,facilitating the enhancement in the prediction of road users'behaviors and the off-line training of driving *** this paper,we synthesize the applications and future trends of FMs in autonomous *** utilizing the powerful capabilities of FMs,we strive to tackle the potential issues stemming from the long-tail distribution in autonomous driving,consequently advancing overall safety in this domain.

关键词： autonomous driving computer visionthe artificial intelligence natural language processing deep learninglarge scale autonomous vehicles understand interpr enhancing scene understanding reasoningby

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Investigation on the Effect of Two and Three Level PWM Inverters in Terms of Acoustic Noise Emitted by Induction Machine

Investigation on the Effect of Two and Three Level PWM Inver...

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International Conference on advanced systems and Electric Technologies (IC_ASET)

作者： Bouyahi Henda Adel Khedher Universite de Sousse Ecole Nationale d'Ingenieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisia

ISBN: (数字)9798350384895

ISBN: (纸本)9798350384901

Three phase Voltage Source Inverters VSI provide non-sinusoidal voltages rich in high order harmonics. The feed of power induction machine 1M by a VSI increases the level of acoustic noise which becomes unacceptable especially for applications requires a moderate level such that electrical traction. It has been found that the noise level is closely related to the voltage and current harmonic of the source feeding the machine. In addition, the harmonic level depends on the control technique used to produce the control signals. Indeed, in this paper a comparative simulation between two algorithms: (Space Vector Pulse Width Modulation SVPWM and Random Pulse Width Modulation RPWM) for both two types of voltage inverter, two-level and three- level NPC are developed and demonstrated. The simulation results show the validity and the efficiency technique for reducing acoustic noise.

关键词： Space vector pulse width modulation Pulse width modulation inverters Voltage source inverters Software packages Simulation Switching frequency Harmonic analysis

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Artificial Neural Network PI-Based Compensator for DC-Link Current Control in Grid-Connected Photovoltaic Current Source Inverter

Artificial Neural Network PI-Based Compensator for DC-Link C...

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2023 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2023

作者： Ezzeddine, Kawther Chibani, Radhia Hamouda, Mahmoud Kanaan, Hadi Y. Al-Haddad, Kamal Université de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Écolecole Nationale d'Ingénieurs de Sousse Sousse4023 Tunisia 1008 Tunisia Saint Joseph University Beirut Lebanon MontréalQC Canada

ISBN: (纸本)9798350325539

Conventional proportional integral (PI) controllers are the most, commonly, known and used techniques in grid-connected photovoltaic (PV) applications. Indeed, PI-based controllers are, practically, easy to design and implement. Moreover, they provide good performance, i.e., high-quality output signals, in steady-state conditions. Nevertheless, these controllers are, mainly, designed for the control of linear systems. Therefore, when implemented with non-linear systems, they suffer from slow dynamic response. Accordingly, in this paper, a non-linear and advanced PI-based controller is proposed. The proposed controller consists of an auto-tuned PI-based controller using artificial neural network (ANN). The main objective is to enhance the dynamic response of the DClink inductor current in grid-connected PV current source inverter (CSI) and to ensure that the injected grid current is equal to that generated by the DC-bus, which implies that the entire power provided by PV generator is injected into the grid. A numerical simulation model of the grid-connected PV system, i.e., CSI, PI-controller, and MPPT algorithm, is realized using Simulink and PLECS environments to validate the accuracy of the proposed solutions. The numerical results prove that the ANN auto-tuned PI-based controller leads to superior dynamic response of the DC-link inductor current and better-quality of the injected grid current compared to the conventional PI-based controller. © 2023 IEEE.

关键词： Power quality

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Distributed Solver for Discrete-Time Lyapunov Equations over Dynamic Networks with Linear Convergence Rate

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IEEE Transactions on Cybernetics 2022年第2期52卷 937-946页

作者： Jiang, Xia Zeng, Xianlin Sun, Jian Chen, Jie Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

The problem of solving discrete-Time Lyapunov equations (DTLEs) is investigated over multiagent network systems, where each agent has access to its local information and communicates with its neighbors. To obtain a solution to DTLE, a distributed algorithm with uncoordinated constant step sizes is proposed over time-varying topologies. The convergence properties and the range of constant step sizes of the proposed algorithm are analyzed. Moreover, a linear convergence rate is proved and the convergence performances over dynamic networks are verified by numerical simulations. © 2013 IEEE.

关键词： Convex optimization

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