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Estimation of robust invariant set for switched linear systems using recursive state updating and robust invariant ellipsoid

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SICE Journal of Control, Measurement, and System Integration 2021年第1期14卷 97-106页

作者： Okajima, Hiroshi Fujinami, Kakeru Department of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan

This paper provides an analysis method of a robust invariant set for discrete-time linear switched systems with peak-bounded disturbances. In the case of a switched linear system, it is challenging to analyse the robust invariant set accurately than that of the linear time-invariant system. We propose a novel method to estimate a robust invariant set using a combination of a recursive state updating and an invariant ellipsoid for a common Lyapunov function. The effectiveness of the estimation accuracy by the proposed method is illustrated using numerical examples. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Linear control systems

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： El-Sheikh, Mohamed M. Bayoumy, Aya Ahmed, Nada M. Mohamed Eldeeb, Amany Rabea, Fatma I. Nawar, Amira A. Khattap, Mohamed G. Galal Eldeen Mohamed Ali Hassan, Hend Mostafa, Mariam Ibrahim, Nayra Ramadan, Reem Refaat, Yomna Khaled, Mennatallah Maher, Ali Abd Elaziz, Mohamed Gamal Abdellatif Ibrahim, Ahmed Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez435611 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Air Defense College Department of Electronics and Electrical Communications Engineering Alexandria Egypt

ISBN: (纸本)9798350351408

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively. © 2024 IEEE.

关键词： Contrastive Learning

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Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

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computer Modeling in engineering & sciences 2024年第2期138卷 1981-2004页

作者： Shaik Mahaboob Basha Victor Hugo Cde Albuquerque Samia Allaoua Chelloug Mohamed Abd Elaziz Shaik Hashmitha Mohisin Suhail Parvaze Pathan Department of Electronics and Communication Engineering N.B.K.R.Institute of Science&TechnologyVidyanagarAndhra Pradesh524413India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Department of Avionics Indian Institute of Space Science and TechnologyThiruvananthapuramKerala695547India Department of Veterinary Clinical Sciences University of MinnesotaSt.PaulMinnesota55108USA

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 ***,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning *** study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal *** information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering *** learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding *** ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different ***,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased *** analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered ***,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

关键词： Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis

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Formal Analysis of electrical Circuit Network Topologies Using Theorem Proving

Formal Analysis of Electrical Circuit Network Topologies Usi...

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Annual IEEE Systems Conference

作者： Kubra Aksoy Adnan Rashid Osman Hasan Sofiène Tahar Department of Electrical and Computer Engineering Concordia University Montreal Quebec Canada School of Electrical Engineering and Computer Science National University of Science and Technology Islamabad Pakistan

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

electrical circuit networks consist of various electrical components connected in different arrangements that are used to process electrical signals, such as voltages and currents. Moreover, they are widely used in many engineering and physical systems, such as control, embedded and communication systems. In this work, we propose to use higher-order-logic theorem proving for formally analyzing electrical circuit networks using network topology matrices. In particular, we formalize in the Is-abellelHOL theorem prover incidence and circuit (loop) matrices, which are widely used topology matrices. Our formalization is based on modeling the system as a network using the notion of directed graphs that are algebraically represented by matrices. Next, we use this formalization to formally analyze various network topologies, such as Δ- $Y$ and ∏ - $L$ topologies, where we perform Kirchhoff's current and voltage laws based analysis in a generic way.

关键词： Network topology Communication systems Directed graphs Bridge circuits Network analyzers Control systems Matrices Libraries Topology Integrated circuit modeling

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Improved Proximal Policy Optimization Algorithm for Multi-objective Disassembly Line Balancing Considering Hazardous Tasks 21

Improved Proximal Policy Optimization Algorithm for Multi-ob...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Guo, Zeyu Guo, Xiwang Wang, Jiacun Qin, Shujin Qi, Liang Kang, Qi Liaoning Petrochemical University Information and Control Engineering Fushun China Monmouth University Computer Science and Software Eng. Wes Long BranchNJ United States Shangqiu Normal University Economics and Management Shangqiu China Shandong Technology University Computer Science Department Qingdao China New Jersey Institute of Technology Electrical and Computer Engineering Newark United States

ISBN: (纸本)9798350365221

As disassembly lines evolve, keeping workers safe from all potential dangers has become paramount. This work proposes an improved approach, Proximal Policy Optimization for Disassembly Line(PPO-DL), to address Multi-objective Dis-assembly Line Balancing Problem(MDLBP) that integrates task switching time and hazardous tasks. The MDLBP model aims to optimize disassembly net profit, balance workstation utilization, and mitigate penalties from hazardous tasks. The proposed PPO-DL modifies the action space of PPO to accommodate MD LBP characteristics, ensuring efficient learning by restricting actions to valid ranges through action masking. Comparative experiments with PPO, Q-learning, and A2C demonstrate PPO-DL's superiority in finding near-optimal solutions across various scenarios. © 2024 IEEE.

关键词： Assembly machines

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ToMFIR and Auxiliary Signals based Active Incipient Fault Detection for Autonomous Underwater Vehicle Thrusters 43

ToMFIR and Auxiliary Signals based Active Incipient Fault De...

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43rd Chinese Control Conference, CCC 2024

作者： Zhu, Zefan Wu, Yunkai Zhou, Yang Zhu, Zhiyu Soh, YengChai Jiangsu University of Science and Technology College of Automation Zhenjiang212100 China Jiangsu University of Science and Technology School of Computer Science and Engineering Zhenjiang212100 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore Singapore

ISBN: (纸本)9789887581581

This article proposes an active fault detection method combining the Total Measurable Fault Information Residual (ToMFIR) and auxiliary signals to address the incipient faults of autonomous underwater vehicle (AUV) thrusters. Firstly, the Takagi-Sugeno (T-S) fuzzy medelling of AUV dynamics is establised. Secondly, an auxiliary signal and redundant actuator module are introduced. The auxiliary signal is capable of amplifying the active ToMFIR in the event of a fault, while the redundant actuator ensures that the auxiliary signal does not interfere with the active ToMFIR in fault-free scenarios. Performance indicators and fixed thresholds are provided to assist in designing the parameters of the redundant actuator. This is done by maximizing the impact of the auxiliary signal on ToMFIR signals and minimizing any disturbances. Finally, simulation results demonstrate the effectiveness of the proposed method in improving the sensitivity for incipient faults in AUV propulsion systems. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Autonomous underwater vehicles

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Tolerant Tracking Control Protocol for PMSM Based on Policy Iteration Algorithm and Fault Compensation 16th

Tolerant Tracking Control Protocol for PMSM Based on Polic...

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16th International Conference on Social Robotics, ICSR + InnoBiz 2024

作者： Yan, Shuya Li, Xiaocong Qian, Huaming Ma, Jun Al Mamun, Abdullah College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Guangzhou China Information Science and Technology Eastern Institute of Technology Ningbo China

ISBN: (纸本)9789819611508

In robotics applications, ensuring reliable performance in the presence of actuator faults is essential for maintaining system safety and reliability. This paper presents a tolerant tracking control method for permanent magnet synchronous motors (PMSMs) based on adaptive dynamic programming and fault compensation. The method simultaneously considers tracking accuracy and energy consumption through a policy iteration algorithm. In the optimality analysis of the algorithm, more relaxed conditions are provided to demonstrate that the performance function can converge to a near-optimal value within a finite number of iterations. In practical implementation, an actor-critic network is used to approximate the performance function and control protocol, alongside a fault detection mechanism based on an expanded time horizon, which achieves fault detection from arbitrary initial values. The effectiveness of the proposed algorithm is verified using a high-fidelity PMSM model in Simulink. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Permanent magnets

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RAPUF: A Novel Integration of Reversible Logic and Arbiter Physical Unclonable Functions for Enhancing IoT Security

RAPUF: A Novel Integration of Reversible Logic and Arbiter P...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Rizk, Dominick Rizk, Frederic Rizk, Patrick Rizk, Rodrigue The Catholic University of America Dept. of Electrical Engineering and Computer Science WashingtonDC United States University of Louisiana at Lafayette The Center for Advanced Computer Studies LafayetteLA United States University of Quebec at Trois-Rivières Dept. of Mechanical Engineering Drummondville Canada University of South Dakota Dept. of Computer Science VermillionSD United States

ISBN: (纸本)9798350394528

The Internet of Things (IoT) devices' proliferation underscores the critical importance of fortifying nanoscale circuit security against evolving cyber threats. This paper introduces an unprecedented paradigm to enhance IoT security through the synergistic integration of reversible logic gates and Arbiter Physical Unclonable Functions (APUFs). Reversible logic, known for its theoretical efficiency and energy conservation, is coupled with APUFs, leveraging their unique ability to exploit nanoscale manufacturing variations for robust hardware security. The novel integration is specifically tailored to address the distinctive security challenges prevalent in IoT ecosystems. The proposed methodology encompasses designing and implementing a secure nanoscale circuit system, tailored to the resource constraints of IoT devices. Through an exhaustive literature review, we establish the significance of reversible logic and APUFs in the broader context of nanoscale circuit design and IoT security. The experimental results demonstrate not only the feasibility of the integrated approach but also its effectiveness in fortifying IoT devices against unauthorized access and data breaches. This research contributes a fresh perspective to the field of IoT security, presenting a novel solution that combines energy-efficient reversible logic with the intrinsic security features of APUFs. The outcomes of this work hold promise for advancing the security paradigm of IoT devices ensuring their resilience against emerging threats in the ever-evolving landscape of cybersecurity and contributing significantly to the evolving field of quantum-enhanced IoT security. © 2024 IEEE.

关键词： Internet of things

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Pediatric Bone Age Prediction Using Deep Learning 26

Pediatric Bone Age Prediction Using Deep Learning

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Bin Habib, Al Zadid Sultan Islam, Md. Ekramul Bin Syed, Md Asif Ahamed, Md Younus Tasnim, Tanpia West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States Stamford University Bangladesh Department of Computer Science & Engineering Dhaka1217 Bangladesh West Virginia University Department of Industrial and Management Systems Engineering MorgantownWV26506 United States Green University of Bangladesh Department of Computer Science and Engineering Dhaka1461 Bangladesh

ISBN: (纸本)9798350359015

Pediatric bone age prediction is a crucial task in clinical practice that can help diagnose endocrine disorders and provide insight into a child's growth and development. However, conventional bone age prediction methods are often labor-intensive and require specialized radiological expertise. This paper presents a Deep Learning (DL)-based approach to pediatric bone age prediction using EfficientNet with Additive Attention, a state-of-the-art neural network architecture for image classification and regression tasks. The method utilizes over 12,000 X-ray images from the RSNA bone age dataset. It involves image preprocessing, transforming them into three-channel images, and training a Convolutional Neural Network (CNN) to automatically learn the features of hand bone images. This approach provides a more effective and accurate solution for predicting bone age, which is critical in diagnosing pediatric endocrine diseases. This work uses two variations of the EfficientNet model (B0 and B4), where EfficientNetB4 is also finetuned with the Additive Attention mechanism. These three models predict the age for the original age, and their comparison is shown in curves. The predicted ages depict that in most cases, EfficientNetB4 and EfficientNetB4 with Additive Attention (EN-AA) successfully predicted the bone ages more accurately regarding the original age, and their performance was better than the EfficientNetB0. Specific performance metrics are provided to underscore this improvement. Learning curves for training and validation loss confirm effective learning without overfitting or underfitting, further validating our approach's efficacy in pediatric endocrine disease diagnosis. © 2023 IEEE.

关键词： Additive Attention Deep Learning EfficientNet Image Regression Pediatrics RSNA Bone Age

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Conductive Atomic Force Microscopy to Shed New Light on Surface electrical Trees

Conductive Atomic Force Microscopy to Shed New Light on Surf...

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2024 IEEE Conference on electrical Insulation and Dielectric Phenomena, CEIDP 2024

作者： Davis-Amendola, Kerry L. Ortiz-Flores, Luis A. Abu Hurayra Lizu, K.M. Huey, Bryan Cao, Yang Electrical Insulation Research Center Institute of Materials Science University of Connecticut United States Materials Science and Engineering University of Connecticut United States Electrical and Computer Engineering University of Connecticut 25 King Hill Road StorrsCT06268 United States

ISBN: (纸本)9798350366679

Greenhouse gas emission reduction is underway worldwide to mitigate the effects of global warming. Among the new initiatives, electrification of passenger airplanes could yield enormous economic and environmental benefits although a few key technology barriers remain. One main challenge is related to the insulation system. This is because insulation systems required for electric aircraft will need to withstand higher local electric fields and higher temperatures. Unfortunately, all polymeric insulation systems are subject to an aging degradation mechanism called electrical treeing, which can lead to electrical breakdown. The defect is exacerbated in areas of local high field, making it a crucial issue to understand in aircraft insulation. While this mode of tree-aging has been studied for centuries, the mechanism has been debated in the literature. Using new characterization techniques to shed new light on parts of the electrical treeing mechanism is key to new understanding that can lead to mitigation solution. One such new characterization technique is conductive atomic force microscopy (c-AFM). By using a conductive tip on the AFM, the electrical tree can be studied with respect to the electrical conductance of the interior walls and their speculated contribution to electrical tree propagation. This work used c-AFM to study an electrical tree on the surface of an insulation material to establish a baseline procedure in using this technique to study the walls of electrical trees. It was found that the c-AFM can provide conductivity results within and around an electrical tree. © 2024 IEEE.

关键词： Emission control

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