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检索条件"机构=Laboratory of Automatic Signal and Image Processing"

共 85 条记录，以下是1-10 订阅

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Online Identification of Switched Arx-Laguerre Model Using Sliding Window Technique

Online Identification of Switched Arx-Laguerre Model Using S...

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IEEE SSD International Multi-Conference on Systems, signals and Devices

作者： Ibtissem Bengharat Abdelkader Mbarek Research Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Monastir Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

The switched ARX-Laguerre model is a very important reduced complexity representation of a class of linear hybrid systems modeled by a switched Auto Regressive eXogenous model (SARX). This model is obtained by expanding the coefficients of every ARX sub-model on two independent Laguerre bases. Then, the SARX-Laguerre model is characterized by several Laguerre poles whose optimal values guarantee a significant parametric reduction. To identify the parameters of the SARXLaguerre model, we propose in this article an analytical method to optimize the poles and a recursive method to identify the Fourier coefficients. To create an online parametric identification algorithm of the SARX-Laguerre model we propose to realize the identification procedure on a sliding window. To evaluate the performance of the proposed online parametric identification algorithm of the SARX-Laguerre model, we use a numerical example.

关键词： Analytical models Parameter estimation Switches Minimization Numerical models Complexity theory Object recognition Optimization

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Robust Optimization-based Neural Architectures Against Adversarial Attacks

Robust Optimization-based Neural Architectures Against Adver...

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IEEE SSD International Multi-Conference on Systems, signals and Devices

作者： Khaoula Ben Ali Seifeddine Messaoud Mohamed Ali Hajjaji Mohamed Atri Noureddine Liouane Laboratory of Automatic Signal Image Processing National Engineering School Monastir University Monastir Tunisia Faculty of Sciences of Monastir Monastir University Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse Sousse University Sousse Tunisia Computer Engineering Department College of Computer Science King Khalid University Abha Saudi Arabia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Recent years have seen a rapid development in Machine Learning, which has profoundly influenced many areas of science and engineering. Among them, computer vision takes the leading place, where important tasks are image classifications powered by CNNs. Despite the great performance of CNNs in complicated scenarios, they remain sensitive to so-called adversarial attacks, and deliberate perturbations leading them to incorrect predictions. Besides more innocuous consequences, this has serious security implications for critical applications, in-cluding medical diagnostics, where misclassifications might result in disastrous outcomes. This research work discusses adversarial attacks on CNNs and other DNNs in computer vision, studying a full range of the generation and detection methods with details while discussing intrinsic vulnerability and robustness. It also proposes a learning framework that will enhance the robustness and security of DNNs and CNNs against such adversarial perils. The ultimate goal is directed to an improvement in the reliability of such models in absolutely critical scenarios for safe deployment into applications where accuracy is crucial.

关键词： Computer vision Accuracy Perturbation methods Computer network reliability Machine learning Robustness Security Medical diagnosis Resilience image classification

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The Dynamic of the Grid with the Presence of WF and the AVR for Power Quality Enhancement

The Dynamic of the Grid with the Presence of WF and the AVR ...

引用

Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Hafaiedh Hajer Saoudi Yahia Benamor Anouar Chrifi-Alaoui Larbi Okba Taouali University of Tunis Higher National School of Engineering of Tunis (ENSIT) Tunis Tunisia Laboratory of Automatic Signal and Image Processing (LARATSI) National School of Engineers of Monastir University of Monastir Monastir Tunisia Laboratory of Control and Energy Management Laboratory (CEMLab) National engineering school of Sfax University of Sfax Sfax Tunisia Verne IUT of Amiens Avenue of Faculties Laboratory of Innovative Technologies of the University of Picardy Jules Amiens France Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

There are several issues with energy quality when wind energy is integrated into electrical networks. This study further classifies these problems, focusing particularly on harmonics and short-term voltage fluctuations resulting from the integration of two types of wind turbines: the first type is the Squirrel Cage Induction Generator (SCIG), and the second type is the Double-Fed Induction Generator (DFIG). Additionally, it explores methods and tools to mitigate these challenges. The article focuses on future opportunities and current trends in improving the integration of Wind Energy Conversion Systems (WECS) into power grids. Through steady-state time series simulations, the study presents case studies examining voltage fluctuations, an important concern for power quality. Using the IEEE 14-bus test, designed to accommodate various operating conditions and wind power penetrations, the study assesses the impact on power quality. These systems feature synchronous generators connected by automated voltage regulators (AVR) to buses 2 and 4.

关键词： Voltage fluctuations Regulators Fluctuations Power quality Time series analysis Synchronous generators Wind turbines Steady-state Voltage control Wind energy conversion

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Efficient RNN Models for IOT Intrusion Detection System

Efficient RNN Models for IOT Intrusion Detection System

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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Jablaoui, Rahma Liouane, Noureddine University of Monastir Laboratory of Automatic Signal and Image Processing Monastir Tunisia

ISBN: (纸本)9798350361025

Due to the growing number of network devices, traffics and services, designing robust Intrusion Detection System (IDS) become a crucial need in the face of complex and various network attacks as a protective measure from hackers and cybercriminals. However, the traditional Machine Learning (ML) approach shows success in many research topics but with the increase in the amount of data and the diversification of network threats methods, it seems to lack reliability and accuracy. Therefore, considering a large amount of real-world cyber traffic, Deep Learning (DL) may be able to extract big data features more effectively. In this paper, we suggest an intrusion detection system for the Internet of Things (IoT) network based on Deep Learning to recognize various assault types for both binary and multiclass classification using two variants of Recurrent Neural Network (RNN) models long short-term memory (LSTM) and Bidirectional LSTM (BiLSTM). We have experimented the models with CSE-CIC-IDS2018, which is the newest comprehensive network traffic dataset. Accuracy, precision, recall, and F1 score are a few performance criteria where the suggested approach clearly excels. After comparison, we can infer that Bi-directional LSTM outperforms LSTM and other existing efforts in the literature. The accuracy of the experimental results was high, coming in at 98.62%. © 2024 IEEE.

关键词： Computer crime

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An effective deep CNN-LSTM based intrusion detection system for network security

An effective deep CNN-LSTM based intrusion detection system ...

引用

2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Jablaoui, Rahma Liouane, Noureddine University of Monastir Laboratory of Automatic Signal and Image Processing Monastir Tunisia

ISBN: (纸本)9798350361025

Deep learning (DL) models have become prevalent and consistently exhibit outstanding performance across diverse domains, particularly in information security. As a subset of machine learning (ML), DL has proven adept at handling complex, unstructured, and large-scale data, making it well-suited to tackle the evolving challenges of cybersecurity. By leveraging DL techniques, we can develop robust intrusion detection systems (IDS) to counter a wide range of network attacks, serving as a crucial defense against hackers and cybercriminals. The integration of DL into IDS holds immense potential for significantly enhancing network security and mitigating cyber risks in various real-world scenarios. This work proposes an IDS architecture that integrates Convolutional Neural Network (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) for temporal feature extraction and sequential analysis to predict and classify malicious cyberattacks in IoT network traffic. Our experiments with the CSE-CIC-IDS2018 dataset, the latest comprehensive network traffic dataset, demonstrate the superiority of our approach in terms of accuracy, precision, recall, and F1 score. Our model achieves an unprecedented accuracy of over 99% in both binary and multiclass classification, surpassing existing efforts in the literature. © 2024 IEEE.

关键词： Long short-term memory

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Robust Adaptive Sliding Mode Control of Upper-Limb Rehabilitation Exoskeleton System with Uncertainties

Robust Adaptive Sliding Mode Control of Upper-Limb Rehabilit...

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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Hassen, Mouna Dali Jouirou, Raghda Bouguila, Nasreddine University of Monastir Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir 5019 Tunisia National School of Engineers of Monastir Laboratory of Automatic Signal and Image Processing 5019 Tunisia

ISBN: (纸本)9798350361025

Uncertain robotic manipulator systems, known for their high nonlinearities, unmodeled dynamics, and uncertainties, are the subject of this paper. Based on the terminal sliding mode control algorithm and its nonsingular design method, a new robust control scheme is introduced to ensure fast and finite time convergence to the desired state. The upper bounds of unknown system uncertainties are estimated using adaptive control. The stability of the proposed control strategy is confirmed by the Lyapunov theory, and its effectiveness is tested on a two-degrees-of-freedom robotic manipulator. Robustness and rapid convergence, as well as reducing the controller's high-frequency commutation effect, are proved. © 2024 IEEE.

关键词： Sliding mode control

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A Machine Learning-based Intelligent ID System for the Internet of Things

A Machine Learning-based Intelligent ID System for the Inter...

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2024 IEEE International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2024

作者： Sawssen, Bacha Noureeddine, Liouane Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Tunisia

ISBN: (纸本)9798350384895

The Internet of Things, or loT, is a rapidly expanding field that has been integrated into numerous different industries. Thanks to this technology, devices may send, receive, and analyze data without the assistance of a human. loT security and privacy concerns continue to be a significant obstacle, despite the fact that it has gained widespread acceptance in a number of important domains due to its ability to simplify human life and enhance service quality. To protect loT networks from different attacks, an anomaly-based intrusion detection system (IDS) can be included as a security feature. In order to combat various cyberattacks in Internet of Things environments, this study suggests an anomaly-based intrusion detection system (IDS). The suggested approach makes use of in order to enhance anomaly identification performance and reduce the dimension of the data characteristics.. © 2024 IEEE.

关键词： Intrusion detection

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Improved Distance vector based Kalman Filter localization algorithm for wireless sensor network

Improved Distance vector based Kalman Filter localization al...

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2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023

作者： Mani, Rahma Messous, Sana Campobello, Giuseppe Battaglia, Filippo Liouane, Noureddine University of Monastir Laboratory of Automatic Signal and Image Processing Monastir5019 Tunisia University of Messina Department of Engineering Messina Italy National Engineering School of Monastir University of Monastir Laboratory of Automatic Signal and Image Processing Tunisia

ISBN: (纸本)9798350347074

Localization is a crucial concern in many wireless sensor networks (WSN) applications. Moreover, getting accurate information about the geographic positions of nodes in the sensing field is necessary in order to make the collected data useful and meaningful. In this context, the heuristic distance vector called DV-Distance is widely used for range-free localization in multi-hop WSN thanks to its advantages such as its simplicity and good accuracy. However, its localization accuracy may be relatively low due to environmental conditions. In this paper, we propose an improvement of the DV-Hop algorithm based on distance computation via a received signal strength indicator (RSSI) and a simple polynomial approximation technique. The proposed technique consists of RSSI measurement for distance estimation between sensor nodes thanks to low cost and simplicity compared to other techniques based on time and angle of arrival, i.e. TOA, AOA and TDOA which require more specialized equipment. However, as RSSI values may be affected by conditions environment such as noise, we propose to use the Kalman Filter in order to deal with this issue and improve localization accuracy. Test results proved that the proposed technique has a higher localization accuracy than DV-Distance by up to 50% and by up to 18% when compared with the state-of-the-art algorithms cited above and other DV-Hop-based localization methods. © 2023 IEEE.

关键词： Kalman filters

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Optimal sliding mode control for a pendulum system

Optimal sliding mode control for a pendulum system

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2022 International Conference on Control, Automation and Diagnosis, ICCAD 2022

作者： Raghda, Jouirou Anouar, Benamor Wafa, Boukadida Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir 5019 Tunisia

ISBN: (数字)9781665497947

ISBN: (纸本)9781665497947

This paper is dedicated to study the performance of the optimal control for linear systems by sliding mode of the first order with the integration of a Linear Quadratic Regulator LQR, applied to the pendulum. We are interested in the design of an optimal and robust control law which is, derived, to satisfy the precision, the convergence, the speed of and the asymptotic stabilization of the system despite the perturbations and the uncertainties born and a multi-optimization. The goal is to minimize the phenomenon of chattering. Simulation, results have been presented to confirm the usefulness and the effectiveness of the proposed control strategy. © 2022 IEEE.

关键词： Pendulums

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Estimation of the order and the memory of volterra model from input/output observations

Estimation of the order and the memory of volterra model fro...

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作者： Chouchane, Safa Bouzrara, Kais Messaoud, Hassani Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir 5019 Tunisia

This paper proposes a new method to estimate, from input/output measurements, the structure parameters (order and memory) of Volterra models used for describing nonlinear systems. For each structure parameter (order and memory), the identification method is based on the definition, for increasing values of such parameter, of a specific matrix the components of which are lagged inputs and lagged outputs. This matrix becomes singular once the parameter value exceeds its exact value. The proposed method is tested in numerical examples, then it is used for modelling a chemical reactor and the results were successful. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Nonlinear systems

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