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

作者： Omar Naifar Hamdi Gassara Mourad Elloumi Assaad Jmal Electrical Engineering Department Control and energy management laboratory (CEM lab) Sfax University Engineering National School Sfax Tunisia Laboratory of Sciences and Technology of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Sfax Tunisia

ISBN: (纸本)9781665482622

A single study has addressed actuator failure reconstruction for the One-sided Lipschitz (OSL) family of nonlinear systems. The predicted fault vector in that work does not provide any insight into the underlying problematic physical characteristics of the system, which is a significant shortcoming. In this work, we offer a way for estimating the incorrect physical parameters of actuators using an adaptive observer strategy. To demonstrate the utility of the suggested method, a numerical example and simulation research are provided.

关键词： Actuators Adaptation models Additives Computational modeling Observers Numerical models Nonlinear systems

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One-Dimensional EEG Artifact Removal Network Based on Convolutional Neural Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 142-159页

作者： Xiong, Jun Meng, Xiang-Long Chen, Zhao-Qi Wang, Chuan-Sheng Zhang, Fu-Quan Grau, Antoni Chen, Yang Huang, Jing-Wei School of Computer and Data Science Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulongjiang North Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 1 Campus New Village Longjiang Street Fuqing China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fuzhou China

The electroencephalogram (EEG) serves as a significant tool in the realms of clinical medicine, cerebral investigation, and neurological disorders research. However, the EEG records we obtain are often easily contaminated by various artifacts, which can blur or distort the underlying EEG signals and make data interpretation difficult. Generally speaking, removing EEG artifacts is considered an essential step in brain signal analysis. Therefore, removing artifacts is crucial for obtaining accurate and reliable EEG signals for subsequent analysis. Recently, deep learning techniques have found widespread application across various domains for denoising tasks, including image denoising and EEG denoising. Many advanced algorithms have been developed in image denoising, which has achieved good results in enhancing low-quality images. Moreover, it has shown superior performance in EEG denoising. In contrast, few people have devoted themselves to studying EEG denoising, and existing convolutional neural network EEG denoising methods still have problems of overfitting and poor denoising effect in Electromyograph(EMG) and ElectroOculoGram(EOG) artifact removal. Therefore, this paper proposes a method called DWINet (De-artifacting with Image-based Network for EEG Signals) based on an image dehazing network DRHNet for removing artifacts from EEG signals. Specifically, our approach DWINet, addresses the de-artifacting issue in EEG signals by converting it as an image dehazing problem and utilizes the image dehazing capability of DRHNet to enhance the denoising performance of EEG signals. Experimental results demonstrate that the proposed method outperforms the compared algorithms in removing the ocular artifact in EEG signals and exhibits higher accuracy and robustness. © 2024, J. Network Intell. All rights reserved.

关键词： Image enhancement

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Handwritten Signature Recognition using Parallel CNNs and Transfer Learning for Forensics

Handwritten Signature Recognition using Parallel CNNs and Tr...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Jaouhar Fattahi Feriel Sghaier Mohamed Mejri Ridha Ghayoula Emil Pricop Baha Eddine Lakdher Department of Computer Science and Software Engineering Laval University Quebec Canada Carthage National Engineering School Carthage University Tunis Tunisia Faculty of Engineering University of Moncton Canada Computers & Electronics Department Automatic Control Petroleum-Gas University of Ploieşti Ploieşti Romania Faculty of Science University of Monastir Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Handwritten signatures hold paramount importance in legal, financial, and administrative domains, necessitating the development of robust signature recognition tools for forensic applications. This paper introduces a handwritten signature recognition (HSR) model employing Parallel Convolutional Neural Networks (CNN) tailored for forensic endeavors. Utilizing the parallel processing capabilities of CNN, our proposed approach adeptly analyzes and extracts discriminative features from handwritten signature images to facilitate precise recognition. In addition, we leverage several transfer learning techniques by parallelizing proven pre-trained CNNs. Extensive experimentation validates the efficacy of our approach on a standard dataset, demonstrating high accuracy and resilience in signature recognition tasks. The proposed approach exhibits substantial promise in augmenting forensic investigations by automating signature verification processes, thereby bolstering fraud detection efforts and upholding the integrity of legal documentation.

关键词： Deep learning Handwriting recognition Forensics Face recognition Transfer learning Feature extraction Fraud Convolutional neural networks Standards Resilience

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Autonomous driving of trucks in off-road environment

arXiv

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arXiv 2023年

作者： Caldas, Kenny A.Q. Barbosa, Filipe M. Silva, Junior A.R. Santos, Tiago C. Gomes, Iago P. Rosero, Luis A. Wolf, Denis F. Grassi, Valdir Department of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos Brazil Division of Automatic Control Department of Electrical Engineering Linköping University Linköping Sweden Institute of Mathematics and Computer Science University of São Paulo São Carlos Brazil

Off-road driving operations can be a challenging environment for human conductors as they are subject to accidents, repetitive and tedious tasks, strong vibrations, which may affect their health in the long term. Therefore, they can benefit from a successful implementation of autonomous vehicle technology, improving safety, reducing labor costs and fuel consumption, and increasing operational efficiency. The main contribution of this paper is the experimental validation of a path tracking control strategy, composed of longitudinal and lateral controllers, on an off-road scenario with a fully-loaded heavy-duty truck. The longitudinal control strategy relies on a Non-Linear Model Predictive controller (NMPC), which considers the path geometry and simplified vehicle dynamics to compute a smooth and comfortable input velocity, without violating the imposed constraints. The lateral controller is based on a Robust Linear Quadratic Regulator (RLQR), which considers a vehicle model subject to parametric uncertainties to minimize its lateral displacement and heading error, as well as ensure stability. Experiments were carried out using a fully-loaded vehicle on unpaved roads in an open-pit mine. The truck followed the reference path within the imposed constraints, showing robustness and driving smoothness. © 2023, CC BY.

关键词： controllers

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Home Care Assistance Solution based on a Software Multi-Agents System

Home Care Assistance Solution based on a Software Multi-Agen...

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International Conference on control Systems and computer Science (CSCS)

作者： Mohammad Rasras Iuliana Marin Şerban Radu Doctoral School of Automatic Control and Computers University POLITEHNICA of Bucharest Bucharest ROMANIA Dept. of Engineering in Foreign Languages University POLITEHNICA of Bucharest Bucharest ROMANIA Dept. of Computer Science University POLITEHNICA of Bucharest Bucharest ROMANIA

A smart home allows automated technology to control systems and appliances to assist daily life activities. In the current work, a prototype of a designed smart home health-aware system is presented. Three predefined ruled agents have been used to gather data, communicate between themselves, interpret the data based on several machine learning algorithms to predict abnormality for cardiac conditions. The paper also compares results obtained by running five machine learning algorithms. The agents function in accordance with predefined dataset describing residents ’ age, gender, height, weight, systolic and diastolic blood pressure, cholesterol, and glucose levels, smoking and alcohol consumption habits, and lastly, physical activity. Our plan is to simplify ubiquitous health monitoring for smart home’s residents. The proposed solution aims to monitor parametric measurements to alert health specialists in case of emergencies.

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Learning to control Linear Systems can be Hard

arXiv

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arXiv 2022年

作者： Tsiamis, Anastasios Ziemann, Ingvar Morari, Manfred Matni, Nikolai Pappas, George J. Automatic Control Laboratory ETH Zurich Switzerland School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Electrical and Systems Engineering University of Pennsylvania United States

In this paper, we study the statistical difficulty of learning to control linear systems. We focus on two standard benchmarks, the sample complexity of stabilization, and the regret of the online learning of the Linear Quadratic Regulator (LQR). Prior results state that the statistical difficulty for both benchmarks scales polynomially with the system state dimension up to system-theoretic quantities. However, this does not reveal the whole picture. By utilizing minimax lower bounds for both benchmarks, we prove that there exist non-trivial classes of systems for which learning complexity scales dramatically, i.e. exponentially, with the system dimension. This situation arises in the case of underactuated systems, i.e. systems with fewer inputs than states. Such systems are structurally difficult to control and their system theoretic quantities can scale exponentially with the system dimension dominating learning complexity. Under some additional structural assumptions (bounding systems away from uncontrollability), we provide qualitatively matching upper bounds. We prove that learning complexity can be at most exponential with the controllability index of the system, that is the degree of underactuation. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear systems

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MI3SE: A Multi-User Index-Based Searchable Symmetric Encryption Scheme for Improving Security of Data in Connected Electronic Devices

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IEEE Transactions on Consumer Electronics 2024年

作者： Soleymani, Seyed Ahmad Goudarzi, Shidrokh Anisi, Mohammad Hossein Xiao, Pei Mihaylova, Lyudmila Wang, Wenwu University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom University of West London School of Computing and Engineering LondonW5 5RF United Kingdom University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom GuildfordGU2 7XH United Kingdom GuildfordGU2 7XH United Kingdom

In the Industrial Internet of Things (IIoT), outdoor electronic devices serve crucial roles across sectors, providing vital data for decision-making. However, their exposure to open outdoor environments makes them vulnerable to unauthorized access, physical theft, or compromise, endangering both the device and its data. Ensuring the security of outdoor devices and their data is thus critical. This study addresses data security in outdoor IIoT devices by supporting the encryption of all IIoT-related data in device memory. Accessing and retrieving this data requires operations on encrypted data. Hence, we introduce a Searchable Symmetric Encryption (SSE) scheme called MI3SE, which ensures each device's encryption key is unique and valid for a period based on the device's security sensitivity. Moreover, MI3SE meets key security requirements, including confidentiality, integrity, forward secrecy, and backward secrecy. It is specifically designed to mitigate physical compromise and query pattern analysis through a two-keyword query approach and withstand various attacks, as validated by rigorous security analysis. Comparative evaluations against benchmark schemes underscore the efficacy of MI3SE in terms of both security and performance. Moreover, comprehensive non-mathematical security analysis and simulation experiments affirm the enhanced accuracy and efficacy of MI3SE in securing sensitive data stored in outdoor IIoT devices. © 1975-2011 IEEE.

关键词： Data Security IIoT Outdoor Electronic Devices Searchable Symmetric Encryption (SSE)

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Efficiency Optimization of Induction Motor Using a Modified Gray Wolf Optimization

Efficiency Optimization of Induction Motor Using a Modified ...

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

作者： Oussama Zeghba Salim Chakroun Ali Aillane Research Laboratory on the Electrical Engineering University of M'Sila Algeria Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering (Lab-STA) National School of Engineering of Sfax (ENIS) University of Sfax Sfax Tunisia

ISBN: (纸本)9781665482622

In this research, a method for improving the efficiency of induction motors based on the modified gray wolf optimizer (mGWO) is proposed and examined. In order to reduce the overall copper and iron losses of the machine, the efficiency improvement technique entails modifying the geometric dimensions with regard to the lower and upper bound of those parameters. A comparison between the findings achieved utilizing the mGWO and GAs approach is done, and some simulation performances are shown along with a comparison of efficiency, electric current, and torque. The approach mGWO provided good results and required less time to extract results once the simulation results were obtained using the aforementioned methods.

关键词： Energy consumption Induction motors Upper bound Torque Simulation Iron Data models

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Research on Data Communication Monitoring System of Master and Slave Equipment in Avionics System Based on 1553B Bus

Research on Data Communication Monitoring System of Master a...

引用

Networking Systems of AI (INSAI), International Conference on

作者： Zhitian Zhu Yanfang Fu Chengtao Lu Yongxing Qiao Zhipeng Cai School of Weapon Science and Technological Xi'an Technological University Xi'an China School of Computer Science and Engineering Xi'an Technological University Xi'an China North-Automatic Control Technology Institute Taiyuan China

When the terminal sub-device sends data to the main device of the peer avionics system, it faces the related problems of data loss, blurred transmission, and format parsing errors. In response to these problems, this paper combs the basic concept and basic topology structure of the airborne bus 1553B applied to the master-slave equipment of the avionics system, analyzes the application method of the 1553B bus in the avionics system for high-efficiency and high-precision data transmission, and proposes an industrial control system. The scheme of optimizing data transmission between master and slave devices through 1553B bus access to the avionics system provides a new research idea for the development of data communication between master and slave devices in the real avionics system. The data communication monitoring system of the master-slave equipment of the electrical system will avoid risks for future real scenarios in terms of data analysis, data flow, and whether the transmission and reception are successful on the ground.

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An Access control Scheme Based on Decentralized Identifiers and Verifiable Credentials in IoT

An Access Control Scheme Based on Decentralized Identifiers ...

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computer Science and Management Technology (ICCSMT), International Conference on

作者： Yunliang Li Yanfang Fu Zhiqiang Du Zhipeng Cai School of Armament Science and Technology Xi’an Technological University Xi’an China School of Computer Science and Engineering Xi’an Technological University Xi’an China North Automatic Control Technology Institute Taiyuan China

With the rapid development of information technology, the Internet of Things (IoT) has become more complex, and more and more devices and users are connected to the IoT system. While improving data interactivity and expanding the scope of data sharing, secure access to IoT resources is also facing great risks. Therefore, it is necessary to design an appropriate access control scheme. Firstly, this paper introduces Decentralized Identifiers (DID) and Verifiable Credentials. It is used for authentication and rights management of users and devices, respectively, which makes identity and authority unforgeable. Secondly, this paper uses blockchain technology to support DID and Verifiable Credentials, which can ensure the decentralization, invariance and traceability of data. Finally, using smart contracts enables automatic processing of requester authorization and access requests. The test results show that the scheme has good performance.

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