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SSRN 2022年

作者： Sedik, Ahmed El-Hag, Noha A. El-Hoseny, Heba M. El-Banby, Ghada M. Khalaf, Ashraf A.M. Abd El-Samie, Fathi E. El-Shafai, Walid Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Egypt Menoufia University Egypt Department of Electronics and Electrical Communication Engineering Al-Obour High Institute for Engineering and Technology Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Deptartment of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh84428 Saudi Arabia Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtainedwith several modalities such as magnetic resonance (MR) and confocal microscopy need to be classified for anomaly *** MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, braintumors are represented as areas composed of abnormal cells that need to be detected. In addition, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify © 2022, The Authors. All rights reserved.

关键词： Ophthalmology

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New procedure of generation of action scenarios

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AIP Conference Proceedings 2017年第1期1863卷

作者： Michał Staniszewski Kamil Wereszczyński Agnieszka Michalczuk Daniel Fernes Jakub Segen Marek Kulbacki Konrad Wojciechowski aSilesian University of Technology Faculty of Automatic Control Electronics and Computer Science Institute of Informatics Akademicka 16 Gliwice Poland bPolish-Japanese Academy of Information Technology Koszykowa 86 02-008 Warszawa Poland

Algorithms of tracking and action recognition are still under development and many problems still have to be solved. New methods are usually tested on available benchmarks with defined actions and human behavior however such approach has many limitations. For that reason the authors proposed new procedure of generating random action instances using on graph-based scenarios. Such idea can be applied in creation of different datasets as well as in simulations.

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A Novel Sparse Adaptive Filter for Suppressing Impulsive Disturbance in Audio Signals

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SSRN 2024年

作者： Zhou, Lei Liu, Hongqing Gan, Lu Zhou, Yi Niedźwiecki, Maciej Truong, Trieu-Kien School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Lab of Mobile Communications Technology and Intelligent Speech and Audio Research Lab Chongqing University of Posts and Telecommunications Chongqing400065 China College of Engineering Design and Physical Science Brunel University LondonUB8 3PH United Kingdom Department of Automatic Control Faculty of Electronics Telecommunications and Computer Science Gdansk University of Technology Gdansk80-233 Poland Department of Information Engineering I-Shou University 84001 Taiwan

This work studies the sparse adaptive filter designs in the presence of impulsive disturbance for audio signal recovery. By using the sparse representation of desired signal and compressibility of impulsive disturbance, a joint sparse least mean p-norm (JSLMP) optimization, in which p-norm (1 ≤ p ≤ 2) measures the data fidelity and q-norm (0 ≤ q ≤ 1) enforces sparse solutions, is developed, termed as q-JSLMP. The filter weights update is derived based on the gradient descent, and the Adam and variable step size (VSS) are integrated to accelerate convergence and avoid potential local minima. In the special case of q = 1, namely 1-JSLMP, its convergence condition and mean square deviation (MSD) analysis are derived. Finally, the frame of application to corrupted audio signals is designed. The results from experiments demonstrate that the proposed approach outperforms other approaches in terms of objective measures on numerical simulation and speech datasets. © 2024, The Authors. All rights reserved.

关键词： Adaptive filters

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Distributed Learning Mechanisms for Anomaly Detection in Privacy-Aware Energy Grid Management Systems

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ACM Transactions on Sensor Networks 1000年

作者： Jia-Hao Syu Jerry Chun-Wei Lin Gautam Srivastava National Taiwan University Taiwan Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and IT Devices Silesian University of Technology Poland Mathematics and Computer Science Brandon University Canada and Research Centre for Interneural Computing China Medical University Taiwan and Computer Science and Mathematics Lebanese American University Lebanon

Smart grids have become an emerging topic due to net-zero emissions and the rapid development of artificial intelligence (AI) technology focused on achieving targeted energy distribution and maintaining operating reserves. In order to prevent cyber-physical attacks, issues related to the security and privacy of grid systems are receiving much attention from researchers. In this paper, privacy-aware energy grid management systems with anomaly detection networks and distributed learning mechanisms are proposed. The anomaly detection network consists of a server and a client learning network, which collaboratively learn patterns without sharing data, and periodically train and exchange knowledge. We also develop learning mechanisms with federated, distributed, and split learning to improve privacy and use Q-learning for decision-making to facilitate interpretability. To demonstrate the effectiveness and robustness of the proposed schemes, extensive simulations are conducted in different energy grid environments with different target distributions, ORRs, and attack scenarios. The experimental results show that the proposed schemes not only improve management performance but also enhance privacy and security levels. We also compare the management performance and privacy level of the different learning machines and provide usage recommendations.

关键词： Anomaly Detection Distributed Learning Federated Learning Split Learning Energy Grid Management

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Predicting of Uncertainty Facial Expressions in Children with Autism Spectrum Disorder Based on an Improved Fuzzy Convolutional Neural Network Model

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SSRN 2024年

作者： Abd-Alhalem, Samia M. Ali, Hesham A. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Takieldeen, Ali E. Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa35712 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Zagazig University Egypt Menoufia University Egypt

Facial expression recognition plays a pivotal role in comprehending emotions and social interactions, particularly in individuals grappling with autism spectrum disorder (ASD), who often encounter challenges in non-verbal communication. For some with ASD, even subtle shifts in their facial expressions can prove elusive, rendering it arduous to categorize their emotional states accurately. Consequently, the analysis and processing of facial expression data hold the potential to facilitate early autism diagnosis, thereby enabling timely therapeutic interventions and alleviating the economic and psychological burdens experienced by patients. In this study, we introduce an enhanced approach that leverages a fuzzy convolutional neural network (CNN) and an Improved Binary Genetic Algorithm (IBGA) for autism diagnosis based on facial expression data. Our model confronts the intricate task of recognizing facial images by selecting salient features employing the IBGA method. Moreover, it employs fuzzy weights to ascertain the probability of similarity within the classifier layer. Our proposed model has yielded impressive outcomes, boasting an average accuracy of 0.9405 for ASD detection and 0.9365 for the recognition of six distinct facial expressions. These results hold profound implications for the enhancement of emotion recognition systems and the refinement of interventions aimed at bolstering social interactions for individuals grappling with ASD. © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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Stand for testing the electrical race car engine

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IOP Conference Series: Materials science and Engineering 2015年第1期95卷

作者： M Baier J Franiasz P Mierzwa D Wylenzek Silesian University of Technology Faculty of Mechanical Engineering 18A Konarskiego Street 44-100 Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science 16 Akademicka Street 44-100 Gliwice Poland

An engine test stand created especially for research of electrical race car is described in the paper. The car is an aim of Silesian Greenpower project whose participants build and test electrical vehicles to take part in international races in Great Britain. The engine test stand is used to test and measure the characteristics of vehicles and their engines. It has been designed particularly to test the electric cars engineered by students of Silesian Greenpower project. The article contains a description how the test stand works and shows its versatility in many areas. The paper presents both construction of the test stand, control system and sample results of conducted research. The engine test stand was designed and modified using PLM Siemens NX 8.5. The construction of the test stand is highly modular, which means it can be used both for testing the vehicle itself or for tests without the vehicle. The test stand has its own wheel, motor, powertrain and braking system with second engine. Such solution enables verifying various concepts without changing the construction of the vehicle. The control system and measurement system are realized by enabling National Instruments product myRIO (RIO - Reconfigurable Input/Output). This controller in combination with powerful LabVIEW environment performs as an advanced tool to control torque and speed simultaneously. It is crucial as far as the test stand is equipped in two motors - the one being tested and the braking one. The feedback loop is realized by an optical encoder cooperating with the rotor mounted on the wheel. The results of tests are shown live on the screen both as a chart and as single values. After performing several tests there is a report generated. The engine test stand is widely used during process of the Silesian Greenpower vehicle design. Its versatility enables powertrain testing, wheels and tires tests, thermal analysis and more.

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Implementation of object-oriented programming in study of electrical race car

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IOP Conference Series: Materials science and Engineering 2016年第4期145卷

作者： M Nowak M Baier Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science 16 Akademicka Street 44-100 Gliwice Poland Silesian University of Technology Faculty of Mechanical Engineering 18A Konarskiego Street 44-100 Gliwice Poland

The paper covers issue of conducting advanced research of electrical race car participating in international competition called Sileverline Corporate Challenge. Process of designing race cars in Silesian Greenpower team is aided by a professional engine test stand built particularly in purpose of this research. Phase of testing and simulation is an important part of the implementation of new technologies. Properly developed solutions and test procedures are able to significantly shorten development time and reduce design costs. Testing process must be controlled by a modular and flexible application, easy to modify and ensuring safety. This paper describes the concept of object-oriented programming in LabVIEW and exemplary architecture of object-oriented control application designed to control engine test stand of the electrical race car. Eventually, the task of application will be to steer the electromagnetic brake and the engine load torque to perform according to data from the actual race track. During the designing process of the car, minimizing energy losses and maximizing powertrain efficiency are the main aspects taken into consideration. One of the crucial issues to accomplish these goals is to maintain optimal performance of the motor by applying effective cooling. The paper covers the research verifying the effectiveness of the cooling system.

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Predicting of Uncertainty Facial Expressions in Children with Autism Spectrum Disorder Based on an Improved Fuzzy Convolutional Neural Network Model

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SSRN 2024年

作者： Abd-Alhalem, Samia M. Ali, Hesham A. El-Shafai, Walid Algarni, Abeer D. Soliman, Naglaa F. El-Samie, Fathi E. Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa35712 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Zagazig University Egypt Menoufia University Egypt

关键词： Emotion Recognition

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New approach for the control and the determination of attractors for nonlinear systems

New approach for the control and the determination of attrac...

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International Conference on Systems and computer science (ICSCS)

作者： Amira Gharbi Catalin Petrescu Mohamed Benrejeb Pierre Borne Ecole Nationale d'Ingenieurs de Tunis Tunis TN Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Bucharest Romania Laboratoire de Recherche en AutomatiqueLA.R.A Ecole Nationale d'Ingénieurs de Tunis Le Belvedere Tunis Tunisia Laboratoire Automatique Génie Informatique Signal Ecole Centrale de Lille Villeneuve d'Ascq France

For the attractor determination, we propose in this paper a new approach based on the definition of a comparison system by special description of the linearized model of the process around an operating state which correspond to a diagonalization or if it is not possible to the description with a matrix on the arrow form, and the use of aggregation techniques. As an illustration, this approach is applied with success for the case of a third order non linear system.

关键词： Stability analysis Vectors Mathematical model Nonlinear systems Biological system modeling Equations computer science

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Performance Analysis of Dual-Hop Mixed FSO/mmWave Systems

Performance Analysis of Dual-Hop Mixed FSO/mmWave Systems

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IEEE International Conference on Communications Workshops, ICC

作者： Yan Zhang Jiayi Zhang Liang Yang Dinh-Thuan Do Bo Ai School of Electronic and Information Engineering Beijing Jiaotong University Beijing China College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Vietnam State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

The combination of free-space optical (FSO)-based backhaul links and millimeter wave (mmWave)-based access links is considered as a key enabling technique to provide enhanced mobile broadband in ultra dense networks. In this paper, the generalized end-to-end performance of a dual-hop mixed FSO/mmWave relaying system is evaluated. The FSO link is assumed to follow the Gamma-Gamma fading with pointing error impairments, while the mmWave link experiences the fluctuating two-ray fading. Both heterodyne detection and indirect modulation/direct detection are accounted for in the FSO link. Using both amplify-and-forward and decode-and-forward relaying schemes, novel closed-form expressions for the outage probability and average bit error probability (BER) are derived in terms of bivariate Fox's H-functions. Furthermore, an asymptotic analysis for the outage probability and the average BER is provided to show the diversity order and other important engineering insights. Finally, the accuracy of our derived results is validated through Monte-Carlo simulations.

关键词： Radio frequency Fading channels Signal to noise ratio Relays Optical transmitters Power system reliability Probability

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