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63rd IEEE Conference on Decision and control, CDC 2024

作者： Wang, Lingfei Xing, Yu Johansson, Karl H. Kth Royal Institute of Technology and with Digital Futures Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350316339

This paper studies the formation of final opinions for the Friedkin-Johnsen (FJ) model with a community of partially stubborn agents. The underlying network of the FJ model is symmetric and generated from a random graph model, in which each link is added independently from a Bernoulli distribution. It is shown that the final opinions of the FJ model will concentrate around those of an FJ model over the expected graph as the network size grows, on the condition that the stubborn agents are well connected to other agents. Probability bounds are proposed for the distance between these two final opinion vectors, respectively for the cases where there exist non-stubborn agents or not. Numerical experiments are provided to illustrate the theoretical findings. The simulation shows that, in presence of non-stubborn agents, the link probability between the stubborn and the non-stubborn communities affect the distance between the two final opinion vectors significantly. Additionally, if all agents are stubborn, the opinion distance decreases with the agent stubbornness. © 2024 IEEE.

关键词： Graph theory

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On tuning of ADRC with competing design indices:a quantitative study

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control Theory and Technology 2023年第1期21卷 16-33页

作者： Yutao Zhao Yi Huang Zhiqiang Gao Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Department of Electrical Engineering and Computer Science Center for Advanced Control TechnologiesCleveland State UniversityClevelandOH 44115USA

controller optimization has mostly been done by minimizing a certain single cost *** practice,however,engineers must contend with multiple and conflicting considerations,denoted as design indices(DIs)in this *** to account for such complexity and nuances is detrimental to the applications of any advanced control *** paper addresses this challenge heads on,in the context of active disturbance rejection controller(ADRC)and with four competing DIs:stability margins,tracking,disturbance rejection,and noise *** this end,the lower bound for the bandwidth of the extended state observer is first established for guaranteed closed-loop ***,one by one,the mathematical formula is meticulously derived,connecting each DI to the set of controller *** our best knowledge,this has not been done in the context of *** formulas allow engineers to see quantitatively how the change of each tuning parameter would impact all of the DIs,thus making the guesswork *** example is given to show how such analytical methods can help engineers quickly determine controller parameters in a practical scenario.

关键词： Uncertain systems Active disturbance rejection control(ADRC) Design indices(DIs) Extended state observer(ESO)

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Generating Adversarial Patterns in Facial Recognition with Visual Camouflage

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Journal of Shanghai Jiaotong University (Science) 2024年 1-12页

作者： Bao, Qirui Mei, Haiyang Wei, Huilin Lü, Zheng Wang, Yuxin Yang, Xin Key Laboratory of Social Computing and Cognitive Intelligence of Ministry of Education School of Control Science and EngineeringSchool of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China Systems Engineering Institute Academy of Military Sciences Beijing100141 China

Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, are not universal, have cumbersome deployment processes, or lack camouflage and are easily detected by the human eye. In this paper, we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles. To achieve visual camouflage, we use a generative adversarial network (GAN). The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior, the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability, and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks. Sufficient visualization results demonstrate that compared with existing methods, the proposed method can generate samples with camouflage and adversarial characteristics. Meanwhile, extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models. © 2023, Shanghai Jiao Tong University.

关键词： Face recognition

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Differentially Private Online Federated Learning with Correlated Noise 63

Differentially Private Online Federated Learning with Correl...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Zhang, Jiaojiao Zhu, Linglingzhi Johansson, Mikael Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science StockholmSE-100 44 Sweden Chinese University of Hong Kong Department of Systems Engineering and Engineering Management Hong Kong

ISBN: (纸本)9798350316339

We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an (, δ) DP budget, we establish a dynamic regret bound over the entire time horizon, quantifying the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments confirm the efficacy of the proposed algorithm. © 2024 IEEE.

关键词： Federated learning

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A Moreau Envelope Approach for LQR Meta-Policy Estimation 63

A Moreau Envelope Approach for LQR Meta-Policy Estimation

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Aravind, Ashwin Toghani, Mohammad Taha Uribe, Cesar A. Iit Bombay Centre for Systems and Control MH Mumbai India Rice University Department of Electrical and Computer Engineering HoustonTX United States

ISBN: (纸本)9798350316339

We study the problem of policy estimation for the Linear Quadratic Regulator (LQR) in discrete-time linear timeinvariant uncertain dynamical systems. We propose a Moreau Envelope-based surrogate LQR cost, built from a finite set of realizations of the uncertain system, to define a meta-policy efficiently adjustable to new realizations. Moreover, we design an algorithm to find an approximate first-order stationary point of the meta-LQR cost function. Numerical results show that the proposed approach outperforms naive averaging of controllers on new realizations of the linear system. We also provide empirical evidence that our method has better sample complexity than Model-Agnostic Meta-Learning (MAML) approaches. © 2024 IEEE.

关键词： Cost functions

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SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

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Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

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Automated and controlled patch generation for enhanced fixing of communication software vulnerabilities

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Intelligent and Converged Networks 2024年第3期5卷 222-236页

作者： Shuo Feng Shuai Yuan Zhitao Guan Xiaojiang Du School of Control and Computer Engineering North China Electric Power UniversityBeijing 100029China Department of Finance Operationsand Information SystemsBrock UniversitySt.Catharines L2S3A1Canada Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA

Software is a crucial component in the communication systems,and its security is of paramount ***,it is susceptible to different types of attacks due to potential ***,significant time and effort is required to fix such *** propose an automated program repair method based on controlled text generation ***,we utilize a fine-tuned language model for patch generation and introduce a discriminator to evaluate the generation process,selecting results that contribute most to vulnerability ***,we perform static syntax analysis to expedite the patch verification *** effectiveness of the proposed approach is validated using QuixBugs and Defects4J datasets,demonstrating significant improvements in generating correct patches compared to other existing methods.

关键词： automatic program repair controlled text generation communication software security program language model

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Comprehensive multimodal approach for Parkinson’s disease classification using artificial intelligence: insights and model explainability

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Soft Computing 2025年第3期29卷 1845-1877页

作者： Balaha, Hossam Magdy Hassan, Asmaa El-Sayed Ahmed, Rawan Ayman Balaha, Magdy Hassan Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computer Science and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Obstetrics and Gynecology Faculty of Medicine Tanta University Tanta Egypt

Parkinson’s disease (PD) is a debilitating neurodegenerative disorder affecting millions worldwide. Early detection is vital for effective management, yet remains challenging. In this study, we investigated four distinct datasets for PD detection. Through comprehensive experimentation employing ensemble methods and feature selection, we achieved high classification accuracies across the datasets. For the Oxford Parkinson’s Disease Detection Dataset, an accuracy of 95.67%, precision of 97.59%, recall of 84.5%, specificity of 99.32%, and F1-score of 90.57% were achieved. For the Alzheimer Parkinson Diseases 3 Class Dataset, the "Stacking" approach surpasses individual models, reaching an accuracy of 99.85%, precision of 99.81%, recall of 99.81%, specificity of 99.86%, and F1 of 99.81%. For the NewHandPD dataset, Regarding the Spiral category, The "Base-P32-384" model surpasses others with an accuracy of 97.35%, precision of 96.50%, recall of 98.57%, and F1-score of 97.53%. The collective "Stacking" approach proves highly effective regarding the Circle category, achieving 100% across all performance metrics. Regarding the Meander category, the "Base-P16-224" model achieves an accuracy of 97.35%, precision of 99.26%, recall of 95.71%, specificity of 99.19%, and F1 of 97.45%. The Mobile Device Voice Recordings at King’s College London (MDVR-KCL) dataset contains two datasets. Regarding the "SpontaneousDialogue" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 94.03%, 92.83%, 90.78%, 100.0%, and 85.67%, respectively. Regarding the "ReadText" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 91.89%, 90.62%, 87.5%, 100.0%, and 81.25%, respectively. Our findings highlight the efficacy of leveraging diverse data sources and advanced machine learning techniques to enhance PD detection accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germa

关键词： Neurodegenerative diseases

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A Novel Random Pulse Width Modulation for PMSM Drive System Based on Unified Voltage Modulation 6

A Novel Random Pulse Width Modulation for PMSM Drive System ...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Kan, Jinpeng Meng, Yongqing Shi, Yixin Li, Fengjiao Chen, Wenjie Li, Changyi Xi'an Jiaotong University School of Electrical Engineering Xi'an China Avic Xi'an Flight Automatic Control Research Institute Department of Computer and Electronic Systems Engineering Xi'an China

ISBN: (纸本)9798350377798

The electromagnetic interference (EMI) problem caused by power electronic switching devices and pulse width modulation (PWM) affects the normal operation of the motor drive system. In this paper, a random PWM based on unified modulation is proposed for permanent magnet synchronous motor (PMSM) drive system. The theoretical basis of unified modulation and the implementation scheme of random modulation are analyzed. Finally, the effectiveness of the proposed random PWM is verified by simulation. © 2024 IEEE.

关键词： Pulse width modulation

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Prediction of Radiated Electromagnetic Interference Based on Four-module Motor Drive System 6

Prediction of Radiated Electromagnetic Interference Based on...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Shi, Yixin Chen, Wenjie Li, Fengjiao Kan, Jinpeng Meng, Yongqing Yang, Guang Xi'an Jiaotong University School of Electrical Engineering Xi'an China Avic Xi'an Flight Automatic Control Research Institute Department of Computer and Electronic Systems Engineering Xi'an China

ISBN: (纸本)9798350377798

In recent years, wide bandgap semiconductor devices such as silicon carbide (SiC) and gallium nitride (GaN) have been increasingly applied in electric drive systems, effectively enhancing system power density. However, the high-frequency and high-speed characteristics of SiC devices exacerbate electromagnetic interference (EMI) issues within the system. Additionally, the cables in electric drive systems exhibit significant antenna effects, radiating conducted EMI into the surrounding space, thereby affecting the normal operation of other sensitive equipment. Therefore, it is essential to research motor drive system topologies that can actively suppress EMI and predict radiated interference in motor drive systems. The topology of a four-module motor paired with parallel inverters can achieve active suppression of EMI at the source. This paper conducts a simulation-based prediction of radiated interference for a parallel inverter-driven four-module motor system. It analyzes the coupling mechanisms of common mode and differential mode radiated interference and validates the radiated interference from both DC and AC cables through simulations, predicting the impact on surrounding sensitive equipment in practical systems. © 2024 IEEE.

关键词： Gallium nitride

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