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2nd International Microwave and Antenna Symposium, IMAS 2024

作者： Ben Haddi, Souhaila Achraou, Soufiane El Bakkali, Moustapha El Khamlichi, Dahbi Taher, Nihade Zakriti, Alia Zugari, Asmaa Touhami, Naima Amar National School of Applied Sciences Department of Civil and Industrial Sciences and Technologies Laboratory of Sciences and Advanced Technology Tetuan Morocco Faculty of Sciences Department of physics Laboratory of Intelligent Systems Design Tetuan Morocco

ISBN: (纸本)9798350356359

This paper presents a compact microstrip bandpass filter for 6GHz, exhibiting excellent performance for Wi-Fi 6E. The aim of this work is to develop a triangular open-loop resonator utilizing a meander line to attain miniaturization. The present bandpass filter was designed using advanced Design System software on Rogers RT5880 substrate. The simulation results indicate high performance with low insertion loss and good impedance matching. Furthermore, the proposed filter is well-miniaturized, occupying only 7.3 x 6.7 mm, which confirms the effectiveness of the meander line in reducing the structure's size. © 2024 IEEE.

关键词： Wi-Fi

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Pattern Matching of Industrial Alarm Floods Using Word Embedding and Dynamic Time Warping

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1096-1098页

作者： Wenkai Hu Xiangxiang Zhang Jiandong Wang Guang Yang Yuxin Cai School of Automation China University of GeosciencesWuhan 430074Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsWuhan 430074 Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China

Dear Editor,This letter proposes a new pattern matching method based on word embedding and dynamic time warping(DTW)to identify groups of similar alarm ***,alarm messages are transformed into numeric values that represent alarms and also reflect the relationships between alarm ***,similarities between numerically encoded alarm flood sequences are calculated by DTW and groups of similar floods are identified via *** effectiveness of the proposed method is demonstrated by a case study with alarm&event data obtained from a public industrial simulation model.

关键词： Word Alarm DTW

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GF2PReID: A Novel Framework for Person Re-IDentification Using Generative Networks 22

GF2PReID: A Novel Framework for Person Re-IDentification Usi...

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22nd International Conference on Cyberworlds, CW 2023

作者： Baoues, Emna Ben Jegham, Imen Ameur, Safa Khalifa, Anouar Ben 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)9798350315653

Person Re-Identification is a critical component in modern video surveillance systems for locating individuals across cameras from various viewpoints. However, one of the significant challenges in person ReID arises when facial information is unavailable. To address this issue, we propose GF2PReID, a novel framework that leverages state-of-the-art deep learning-based ReID approaches to generate prior knowledge of the face region and provide detailed information about human body images. Our approach utilizes a deep residual network model trained with transfer learning to extract discriminative features from images with low discrimination, including low illumination and occlusion, collected from diverse datasets. Experimental results, on 2 challenging datasets: Market-1501 and CUHK03, demonstrate that our GF2PReID framework improves the datasets and significantly improves the performance of the Resnet-50 model, reaching the highest performance. © 2023 IEEE.

关键词： Generative adversarial networks

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Comparative Performance Analysis of Metaheuristic Optimization Algorithms for Parameter Identification of Photovoltaic Cell/Module

Comparative Performance Analysis of Metaheuristic Optimizati...

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2024 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2024

作者： Gaaloul, Yasmine Faris Amiri, Ahmed Bel Hadj Brahim Kechiche, Olfa Hamouda, Mahmoud Chouder, Aissa University of Sousse National Engineering School of Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia University of Sousse ESSTHSousse HSousse4011 Tunisia PO Box166 Ichebilia M'sila28000 Algeria HSousse4011 Tunisia PO Box166 Ichebilia M'sila28000 Algeria

ISBN: (纸本)9798350389838

PV (photovoltaic) cells are key elements in the conversion process of the sunlight into electrical energy. Therefore, the accurate modeling of PV cells or modules is of amount importance to not only predict their performance in terms of energy production but also to analyse their degradation process and detect their faults, which may be of different types. In this regard, various metaheuristic algorithms have been proposed over the past years to identify the parameters of the PV cells/modules model with the aim to accurately predict their behaviour under different operation conditions. This paper intends to compare three parameters extraction algorithms: Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO). From random weather conditions, the measured I-V curves are first translated into Standard Test Conditions (STC) of 1000 W/m2 and 25°C. Thereafter, the five standard parameters of the one-diode model are extracted using the aforementioned algorithms. A comparative study is carried out based on experimental data of an Isofoton 106W-12V monocrystalline PV module. It reveals the superiority of the GA in terms of accuracy. It shows also that the PSO outperforms the remaining algorithms from the point of view computational complexity. Moreover, the worst performances in terms of accuracy and computational complexity are obtained with the GWO. © 2024 IEEE.

关键词： Genetic algorithms

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Revolutionizing Thermal Imaging: GAN-Based Vision Transformers for Image Enhancement

Revolutionizing Thermal Imaging: GAN-Based Vision Transforme...

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IEEE International Conference on Image Processing

作者： Mohamed Amine Marnissi LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sfax Ecole Nationale d’Ingénieurs de Sfax Sousse Tunisie

In this paper, we propose a new architecture for thermal image enhancement in which we exploit the strengths of both architecture-based vision transformers and generative adversarial networks. Our approach includes the introduction of a thermal loss function, which is specifically employed to produce high quality images. In addition, we consider fine-tuning based on visible images for thermal image restoration, resulting in an overall improvement in image quality. The performance of our proposed architecture is evaluated using visual quality metrics. The results show significant improvements over the original thermal images and over other established enhancement methods on a subset of the KAIST dataset. The performance of the proposed enhancement architecture is also verified on the detection results by obtaining better performance with a considerable margin considering different versions of the YOLO detector.

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Pressure Pain Recognition for Lower Limb Exoskeleton Robot with Physiological Signals 1

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2nd International Conference on Cognitive Computation and systems, ICCCS 2023

作者： Ma, Yue Wu, Xinyu Wang, Xiangyang Li, Jinke Qin, Pengjie Yin, Meng Cao, Wujing Yi, Zhengkun Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems Guangdong Shenzhen China Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jining China

ISBN: (数字)9789819708857

ISBN: (纸本)9789819708840

Pain is a feedback mechanism the body uses to protect itself. The perception of human pain is still missing in lower limb exoskeleton robots, such as the pain caused by the pressure between the knee joint and the baffle when the lower limb exoskeleton stands up, or the pain caused by the mismatch between the gait and the body function of the wearer when walking. Aiming at the pain perception problem of the exoskeleton robot, this study designed a pressure pain data acquisition paradigm combined first considering the movement mode of the exoskeleton robot, and collected facial electromyography and electrocardiography signals, as well as five levels of pain. Then a deep learning neural Network based on Temporal Convolutional Network and Long Short-Term Memory is designed. The composite Loss of Center Loss, InfoNEC Loss and Softmax Loss is designed. The designed deep neural network model was evaluated using the thermal pain dataset BioVid, and the average recognition rate using only electrocardiography signals reached 86.44%. By Using the fusion signals of electrocardiography, electrocardiogram and electromyogram signals, the average recognition accuracy can reach 87.16%, which is superior to other similar methods, and verifies the effectiveness of the proposed method. The proposed network is also used to verify the collected pressure pain dataset, and the recognition accuracy can reach 87.5%, which proves the validity of the collected data set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electrocardiography

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Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation and Improved Depthwise Separable Convolution Network 26

Bearing Fault Diagnosis Under Variable Working Conditions Ba...

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26th International Conference on Industrial technology, ICIT 2025

作者： Kuang, Zehui Mao, Fan Zhao, Xingyu Wan, Xiongbo School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Future Technology China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798331521950

Domain adaptation methods and appropriate feature extractors are usually applied to solve the problem that the variable working conditions of bearings affect the effectiveness of the fault diagnostic framework. However, the existing feature extractors are too complex to be deployed in real industries. In this paper, we propose a lightweight framework called improved depthwise separable convolution network (IDSCN) for the diagnosis of bearing faults in variable operating conditions. IDSCN is built on wavelet convolution and depthwise separable residual convolution (DSRC), where the wavelet convolution is used to extract frequency features from the source and target domains, and DSRC guarantees the performance of feature extraction with reduced parameters and computation. To minimise the distribution difference between the source and target domains, the local maximum mean difference is incorporated into the proposed framework. The performance of the proposed framework is validated on the Paderborn University dataset, involving a total of 6 transfer tasks. The experiment shows that the proposed framework achieves satisfactory results and outperforms other frameworks in bearing fault diagnosis. © 2025 IEEE.

关键词： Bearing fault diagnosis Depthwise separable residual convolution Domain adaptation Wavelet convolution

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Sensorless Vector Control of Dual Star Induction Machine Using Sliding Mode Flux Observer

Sensorless Vector Control of Dual Star Induction Machine Usi...

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International Conference on advanced systems and Electric Technologies (IC_ASET)

作者： Fatma Zohra Latrech Asma Ben Rhouma Adel Khedher LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Ecole Nationale d'Ingénieurs de Sousse Sousse Tunisie

This paper presents the vector control of a double-star induction machine (DSIM) using a sliding mode flux observer (SMFO). The vector control strategy is often sensitive to changes in machine parameters, and the real-time measurement of these parameters increases the cost of the system. To overcome these drawbacks, a sensorless DSIM operation can be achieved using an SMFO, which provides robust control against parameter variations without requiring speed and stator flux sensors. Simulation tests were conducted in the MATLAB/Simulink environment to demonstrate the effectiveness of the proposed structure. Finally, the simulation results are recorded and analyzed.

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3D Brain Tumor Segmentation Using Modified U-Net Architecture 22

3D Brain Tumor Segmentation Using Modified U-Net Architectur...

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22nd International Conference on Cyberworlds, CW 2023

作者： Gammoudi, Islem Ghozi, Raja Mahjoub, Mohamed Ali Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Tunis El Manar Faculté des Sciences Mathématiques Physiques et Naturelles de Tunis Tunis2092 Tunisia American University of Bahrain College of Engineering RiffaP.O. Box 38884 Bahrain University of Tunis El Manar Utm Associate Research Meneber of U3S Enit Tunisia Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia

ISBN: (纸本)9798350315653

Brain tumor segmentation from MRIs has been a continuing challenge for radiology and neurology specialists. Therefore, a reliable method that captures the growing and refined data in this regard, is much needed. Among many deep learning techniques having been proposed for medical image analysis, U-Net-based variants are found to the most used models in multimodal medical image segmentation. Moreover, knowing that brain tumors can have various shapes, sizes, and appearances, the segmentation task, using simple block architectures, does not capture all the intricacies of the tumor boundaries and internal structures. Therefore, more complex architectures, such as U-Net and its 3D extensions, are better suited to handle these variations and provide better segmentation results. In this paper, we introduce an enhanced automated 3D brain tumor segmentation network built on the foundation of the 3D U-Net architecture. Through the combination of the advantages of UNet, ResNet, we created a new 3D UNet model with block modifications, based on simple ResNet block, BNet's, with batch normalization. We used the 2020 Multi-modal Brain Tumor Segmentation Challenge (BraTS2020) data sets to train and validate the proposed model. The BraTS2020 validation data set yielded dice values of 0.86, 0.82 and 0.85 for the Whole Tumor (WT), for Enhancing Tumor core (ET), and for Tumor Core (TC), respectively. The experimental results show that our model significantly outperforms the standard brain tumor segmentation methods. © 2023 IEEE.

关键词： Tumors

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Enhanced SVSF for SoC Estimation of Lithium Ion Batteries

Enhanced SVSF for SoC Estimation of Lithium Ion Batteries

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

作者： Lazreg, Meriem Ben Jemmali, Sabeur Manai, Bilal Hamouda, Mahmoud IntelliBatteries Company SoftTech Firm Incubator Technopole of Sousse Sousse4059 Tunisia Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Université de Sousse Sousse4023 Tunisia

ISBN: (纸本)9798350325539

The last decade has witnessed a spike in Lithium-ion batteries usage. This fact is due to the battery interesting characteristics. This technology needs careful monitoring to avoid overcharge and deep discharge and ensure user safety. This paper proposes an enhanced version of the Smooth Variable Structure Filter (ESVSF) to estimate the State of Charge (SoC) of lithium ion batteries. The ESVSF algorithm proposes a variable boundary layer with an online and dynamic covariance error determination. This technique is validated on LCO batteries of nominal capacity 2.1Ah and validated on a Random Walk (RW) discharge profile. The correct operation of the proposal is confirmed throw simulation results. © 2023 IEEE.

关键词： Lithium-ion batteries

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