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On extended state Kalman filter-based identification algorithm for aerodynamic parameters

control Theory and technology 2024年第2期22卷 235-243页

作者： Wenyan Bai Ruizhe Jia Peng Yu Wenchao Xue Beijing Aerospace Automatic Control Institute Beijing100854China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing100083China Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China

In this paper, the problem of time-varying aerodynamic parameters identification under measurement noises is studied. By analyzing the key aerodynamic parameters that affect the aircraft control system, a system model with extended states for identifying equivalent aerodynamic parameters is established, and error parameters are extended to the system state, avoiding the difficulty caused by the unknown dynamic in the system. Furthermore, an identification algorithm based on extended state Kalman filter is designed, and it is proved that the algorithm has quasi-consistency, thus, the estimation error can be evaluated in real time. Finally, the simulation results under typical flight scenarios show that the designed algorithm can accurately identify aerodynamic parameters, and has desired convergence speed and convergence precision.

关键词： Aerodynamic parameters Parameter identification Extended state Kalman filter

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Sliding Mode control For Underactuated Nonlinear Mechanical Systems 3

Sliding Mode Control For Underactuated Nonlinear Mechanical ...

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3rd International Conference on Advanced Electrical engineering, ICAEE 2024

作者： Feddaoui, Anis Abdeldjalil, Djehaf Mohamed Bilal, Bouchelarm Youcef Islam, Djilani Kobibi Youcef, Djeriri Badji Mokhtar University Genie Electromechanique Laboratory Department of Electromechanical dept Annaba23000 Algeria Djillali Liabes University Faculty of Electrical Engineering Department of Automatic Control Sidi Bel-Abbes22000 Algeria Mustapha Stambouli University Faculty of Sciences and Technology Department of Electrical Engineering Mascara29000 Algeria Djillali Liabes University Faculty of Electrical Engineering Department of Electrical Engineering Sidi Bel-Abbes22000 Algeria

ISBN: (纸本)9798350349351

The Rotary Inverted Pendulum (RIP) serves as a quintessential benchmark in control theory, representing a challenging and unstable system of paramount importance. In this study, we focus on a Quanser platform to investigate the application and significance of sliding mode control in stabilizing the RIP. The importance of RIP lies in its portrayal of real-world control challenges encountered in industries ranging from manufacturing to robotics. Its inherent instability and underactuation make it an ideal testbed for advanced control strategies. Our research delves into the experimental application of sliding mode control, on the Quanser RIP platform. Through rigorous modeling, simulation, and practical implementation, we aim to demonstrate the effectiveness of this control strategy. This study contributes to the broader discourse on control systems and automation, showcasing the potential of sliding mode control in addressing complex and dynamic systems. The outcomes of our research not only advance the understanding of control theory but also have practical implications for industries where precise control and stabilization are critical. © 2024 IEEE.

关键词： Sliding mode control

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Traditional Chinese Medicine Health Status Identification with Graph Attention Network 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Fu, Amin Ma, Jishun Wang, Chuansheng Zhou, Changen Li, Zuoyong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona08034 Spain Research Base of Traditional Chinese Medicine Syndrome Fujian University of Traditional Chinese Medicine Fuzhou350122 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The Traditional Chinese Medicine Health Status Identification plays an important role in TCM diagnosis and prescription recommendation. In this paper, we propose a method of Status Identification via Graph Attention Network, named SIGAT, which captures the complex medical correlation in the symptom-syndrome graph. More specifically, we construct a symptom-syndrome graph in that symptoms are taken as nodes and the edges are connected by syndromes. And we realize automatic induction of symptom to state element classification by using the attention mechanism and perceptron classifier. Finally, we conduct experiments by using hamming loss, coverage, 0/1 error, ranking loss, average precision, macro-F1 score, and micro-F1 score as evaluation metrics. The results demonstrate that the SIGAT model outperforms comparison algorithms on Traditional Chinese Medicine Prescription Dictionary dataset. The case study results suggest that the proposed method is a valuable way to identify the state element. The application of the graph attention network classification algorithm in TCM health status identification is of high precision and methodological feasibility. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for Edge Devices

EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Fuquan Wu, Yu Xu, Xinxin Wang, Chuansheng Ma, Minjia Grau, Antoni Huang, Jiayan School of Computer and Big Data Minjiang University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of New Engineering Industry Putian University Putian China Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China College of New Engineering Industry Putian University Fujian Ruituo Information Technology Limited Company Putian China

ISBN: (纸本)9798350386226

Fatigue driving is a common potential traffic safety hazard. To detect and analysis the driver Fatigue degree in real-time, more attention have been increasing paid to application research of EEG fatigue detection device. However, existing methods suffer from complex model structure or large-scale model parameters. In the other words, they requires high computational consumption and large memory footprint, which creates a contradiction between model and portable EEG machine with limited hardware resources. In this work, we take both time and frequency domains of EEG signal into account and design an lightweight and efficient driver fatigue detection network based on GCN (EEG-GNet), which is available edge-end device application. Experiments on 4th Raspberry Pi on a public baseline dataset show that our EEG-GNet outperforms the state-of-the-art methods and takes good tradeoff between detection accuracy and resource consumption. Our EEG-GNet provides a new potential opportunity for future applications in practical portable/wearable (P/W) devices. © 2024 IEEE.

关键词： Electrocardiography

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Prototype Guided Personalized Federated Intrusion Detection System 10

Prototype Guided Personalized Federated Intrusion Detection ...

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10th IEEE Smart World Congress, SWC 2024

作者： Cheng, Long Yan, Huiru Zhou, Hanlin Wang, Ying Tang, Haichuan Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Ai Lab of Crrc Academy Beijing China

ISBN: (纸本)9798331520861

Preventing network attacks and protecting user privacy are consistently hot research topics in the Internet of Things (IoT) and edge computing fields. Recent advancements in Federated Learning (FL) have shown promise in addressing these challenges. FL allows various clients to collaboratively build an intrusion detection system (IDS) without sharing their private data. However, most existing methods train a single intrusion detection model for all clients and assume that the training and test data distribution are identical, leading to unsatisfactory detection accuracy and generalization abilities in practice. To overcome these challenges, this paper introduces a prototype-guided personalized FL approach named PG-FedIDS. We propose two novel mechanisms within this method. Firstly, we utilize class prototypes as auxiliary information carriers to generate personalized models for each client, rather than generating a single global model as in previous works. Secondly, we propose a prototype-guided ensemble learning strategy, which can leverage the global knowledge in prototypes to enhance detection accuracy and generalization abilities for each client. We conduct extensive experiments on two benchmark datasets with different evaluation test settings. The results demonstrate that our PG-FedIDS achieves promising detection accuracy and consistently outperforms other FL baselines. © 2024 IEEE.

关键词： Federated learning

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Research on emotional space for movie and TV drama videos

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The Journal of China Universities of Posts and Telecommunications 2022年第5期29卷 73-82页

作者： Li Yujie Zhang Jingjing Jiang Wei Wang Chunxiao Key Laboratory of Acoustic Visual Technology and Intelligent Control System Communication University of ChinaBeijing 100024China Beijing Key Laboratory of Modern Entertainment Technology Communication University of ChinaBeijing 100024China School of Information and Communication Engineering Communication University of ChinaBeijing 100024China School of Computer and Cyber Sciences Communication University of ChinaBeijing 100024China

Emotional space refers to a multi-dimensional emotional model that describes a group of subjective feelings or emotions. Since the existing discrete emotional space is mainly aimed at human’s primary emotions, it cannot describe the complex emotions evoked when watching movies. In order to solve this problem, an emotional fusion space for videos was constructed by selecting movies and TV dramas with rich emotional semantics as the research objects. Firstly, emotional words based on movie and TV drama videos are acquired and analyzed by using subjective evaluation and semantic analysis methods. Then, the emotional word vectors obtained from the above analysis are fused, reduced dimension by t-distributed stochastic neighbor embedding(t-SNE) algorithm, and clustered by bisecting K-means clustering algorithm to get a discrete emotional space for movie and TV drama videos. This emotional fusion space can obtain different categories by changing the value of the emotion classification number without re-labeling and calculation.

关键词： emotional space movie and TV drama videos subjective evaluation words semantic analysis fusion space

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Observer Design and Sensorless control in Electrical Machines: A Brief Overview

Observer Design and Sensorless Control in Electrical Machine...

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International Conference on sciences and Techniques of automatic control and computer engineering (STA)

作者： Omar Naifar Mourad Elloumi Hamdi Gassara Control and Energy Management Laboratory National School of Engineering Sfax University Sfax Tunisia Laboratory of Sciences and Technology of Automatic Control and Computer Engineering ENIS University of Sfax Sfax Tunisia

ISBN: (纸本)9781665482622

This review paper discusses the most used observation techniques applied to the electrical machines, namely the direct estimation technique and estimation model based on a mathematical technique. Without a deep investigation, this paper allow readers to take a general overview of the observer techniques utilized to control the electrical machines.

关键词： Computational modeling Estimation Sensorless control Observers Mathematical models Induction machines Standards

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Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

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computer Systems Science & engineering 2022年第10期43卷 87-102页

作者： Safaa El-Gazar Walid El-Shafai Ghada El-Banby Hesham F.A.Hamed Gerges M.Salama Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department of Electrical Engineering Faculty of EngineeringEgyptian-Russian UniversityCairoEgypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last *** achieves safe access to systems using biometrics instead of the traditional *** utilization of a biometric in its original format makes it usable only ***,a cancelable biometric template should be used,so that it can be replaced when it is *** biometrics aims to enhance the security and privacy of biometric *** encryption is an efficient technique to be used in order to generate cancelable biometric *** this paper,a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification *** considered biometric in this paper is the speech *** speech signal is transformed into its ***,the spectrogram is encrypted using two cascaded optical encryption *** first algorithm is the Optical Scanning Holography(OSH)for its efficiency as an encryption *** OSH encrypted spectrogram is encrypted using Double Random Phase Encoding(DRPE)by implementing two Random Phase Masks(RPMs).After the two cascaded optical encryption algorithms,the cancelable template is *** verification is implemented through correlation estimation between enrolled and test templates in their encrypted *** the correlation value is larger than a threshold value,the user is *** threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two *** implementation of optical encryption is adopted using its software rather than the optical *** efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation *** can improve the biometric data security without deteriorating the recognition *** results give close-to-zero This values for the Equal Error Rate(EER)and close-to-one values for the Area under Receiver Operator Charact

关键词： Cancelable biometrics spectrogram OSH DRPE EER AROC

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H control for General Conformable Polynomial Fuzzy Models

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International Journal of Fuzzy Systems 2025年 1-10页

作者： Laajimi, Malek Gassara, Hamdi Rhaima, Mohamed Mchiri, Lassaad Makhlouf, Abdellatif Ben Laboratory of Sciences and Techniques of Automatic control & Computer Engineering National School of Engineering of Sfax Sfax Tunisia Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia Panthéon-Assas University Paris II Paris France Department of Mathematics Faculty of Sciences Sfax University Sfax Tunisia

This research focuses on exploring the exponential $$H_\infty $$ stability of general conformable nonlinear system. In order to address the nonlinearity inherent in the system, a polynomial fuzzy (PF) method is employed. Modeling a general conformable nonlinear system within the polynomial framework reduces the number of fuzzy rules compared to the classical Takagi–Sugeno fuzzy (TSF). Furthermore, controlling such a complex system, which accounts for perturbations, employs a PF model instead TSF model to describe its nonlinear dynamics, and incorporates a general conformable derivative instead of an integer-order one, is significantly more challenging, and remains unaddressed in previous studies. In this paper, a PF controller is designed in the form of sum of squares (SOS) to enhance the resilience against perturbations and ensure the exponential stability of the proposed model. The proposed SOS can be solved numerically, and partially symbolically, using the recently developed SOSTOOLS. In order to ensure the $$H_\infty $$ performance, a generalized criterion is defined for the general conformable nonlinear system. To demonstrate the effectiveness of the proposed method, a numerical example is provided.

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