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

作者： Vladimir Bespalov Alexey Vedyakov Anastasiia Vediakova Faculty of Control Systems and Robotics ITMO University St. Petersburg Russia Department of Computer Applications and Systems St.Petersburg State University St. Petersburg Russia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper presents an adaptive state observer for a nonlinear induction motor model that accounts for viscous friction. The problem is solved using a modified version of the Dynamic Regressor Extension and Mixing (DREMBAO) method. The main idea is to reduce the original model to a regression-like model, where the vector of unknowns contains unknown parameters and state variables. After this step, it becomes possible to obtain a set of scalar linear equations with respect to the unknown state variables and parameters. Using these equations, parameters are estimated with gradient descent estimator, and state estimation is obtained using the gradient observer. Simulation results of an adaptive observer are presented, which demonstrate the effectiveness of the proposed approach.

关键词： Adaptation models Induction motors Sensitivity Friction Simulation Rotors Observers Mathematical models Vectors Robustness

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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Model-based epidemic data reconstruction using feedback linearization

Model-based epidemic data reconstruction using feedback line...

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2022 IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Csutak, Balazs Polcz, Peter Szederkenyi, Gabor Pázmány Péter Catholic University Faculty of Information Technology and Bionics Budapest Hungary Institute for Computer Science and Control Systems and Control Laboratory Kende u. 13-17 BudapestH-1111 Hungary

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

In this paper, a model-based method is proposed for the reconstruction of non-measured epidemic data of the COVID-19 pandemic in Hungary. Only the data series showing the daily number of hospitalized people are used for the reconstruction together with a nonlinear dynamical model of epidemic spread containing 8 compartments. The unknown input of the model is the infection rate, which is computed through the solution of a feedback linearization-based asymptotic output tracking problem, where the reference is the actually observed number of hospitalized people. Computations show good match with of hospitalized people. Computations show good match with previous reconstruction results, and show a roughly 3.5-4-fold underdetection of infections until the Omicron wave. © 2022 IEEE.

关键词： Feedback linearization

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Numerical approach to design of low frequency magnetic field irradiation system for lab on chip experiments 14

Numerical approach to design of low frequency magnetic field...

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14th International Conference ELEKTRO, ELEKTRO 2022

作者： Radil, Roman Bajtos, Marek Carnecka, Lucia Hargasova, Klaudia Kamencay, Patrik University of Zilina Department of Electromagnetic and Biomedical Engineering Zilina Slovakia Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Systems Biology and Engineering Gliwice Poland

ISBN: (纸本)9781665467261

The article is focused on design of specific electromagnetic coil system using numerical modelling and simulation methods. The proposed solution would be capable of delivering magnetic field of desired strength/ flux density and homogeneity within the specific area to affect the cultivation process of single cells in lab on chip experiments. © 2022 IEEE.

关键词： Numerical models

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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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Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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Reference Tracking control of a Nonlinear Epidemiological Model with State Estimation

Reference Tracking Control of a Nonlinear Epidemiological Mo...

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

作者： Balázs Csutak Gábor Szederkényi Faculty of Information Technology and Bionics Pazmany Peter Catholic University Budapest Hungary Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, a reference tracking controller for an 8-compartment epidemic model is proposed. The dynamical model describing the disease spread and progression is given in nonlinear input-affine form. The manipulable input is the transmission rate, while the output to be tracked is the number of infected people. The model parameters correspond to the COVID-19 pandemic. The control design uses a simple SEIR model and it is based on feedback linearization combined with extended Kalman filter for state estimation. Simulation results show good tracking performance even with model mismatch and significant parameter uncertainty.

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A Non-Parametric Identification Scheme of SISO-LTI systems Using Blaschke-Products*

A Non-Parametric Identification Scheme of SISO-LTI Systems U...

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Mediterranean Conference on control and Automation (MED)

作者： Gergő Ungvári Alexandros Soumelidis Tamás Dózsa Department of Numerical Analysis Faculty of Informatics Eötvös Loránd University Budapest Hungary Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

In this manuscript, a novel algorithm is presented for the identification of single input single output linear time invariant (SISO-LTI) systems. The proposed method is able to find poles of the transfer function describing the system without any assumptions on the system order. The results presented in this paper generalize earlier identification schemes based on expansions of the transfer function using generalized rational orthogonal basis functions (GOBFs). These previous methods depended on a convergent sequence constructed from the coefficients of the orthogonal GOBF expansion. The first contribution of this study is showing that a wider family of functions may be used to construct similar pole finding algorithms. The aforementioned previous approaches can be interpreted as special cases of the results presented here. In addition, using this generalization a new pole identification scheme is proposed using finite Blaschke-products. Through experiments, the practical utility of this scheme is verified when compared to earlier variations of the pole finding scheme.

关键词： Automation Transfer functions

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On-Board Intelligent Decision Support System Development for Aviation Complexes Using Machine-Oriented Methods

On-Board Intelligent Decision Support System Development for...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Alexander Vorobev Ilya Elesin Alexander Shevadronov Faculty Computer Science Artificial Intelligence and Control Systems Bauman Moscow State Technical University Moscow Russian Federation

This paper considers the possibility of using an integrated distributed on-board intelligent decision support system development by a CAD-centric integrated pipeline approach. The problems and solutions of developing and integrating such pipeline into aviation complexes are explained. Developed demonstration for making onboard intelligent decision expert system step-by-step. Key features and use cases are listed for CAD pipeline. The need to split a single monolithic system across multiple CADs to meet various requirements is considered. CAD pipeline consists of: CAD "Expert", CAD "Message", CAD "Astra-DB" and CAD "Scenario". In paper explained improvements given by CAD-centric approach, which increases speed of development and its overall quality summarized.

关键词： Decision support systems Visualization Solid modeling Power engineering Instruments Pipelines Aerospace electronics

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Charging Load Prediction Method for Electric Vehicles using CNN-GRU-Attention model with SSA optimization strategy

Charging Load Prediction Method for Electric Vehicles using ...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Xinyu Fan Galaktion V. Shvedov Chuqiao Cai Electrical Power Systems Department National Research University “MPEI” Moscow Russian Federation Information Processing and Management Systems Department Faculty of Computer Science and Control Systems Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper proposes a CNN-GRU-Attention model for short-term charging load prediction at electric vehicle (EV) charging stations. The model leverages the strengths of Convolutional Neural Networks (CNN) for feature extraction and dimensionality reduction, while Gated Recurrent Units (GRU) enhance temporal sequence learning. The attention mechanism assigns dynamic weights to the extracted features, improving the prediction accuracy. Hyperparameters are optimized using the Sparrow Search Algorithm (SSA), and an improved version, ISSA, further enhances model performance. Experimental results demonstrate that the CNN-GRU-Attention model outperforms baseline models, including CNN, GRU, LSTM (Long Short-Term Memory), and BiLSTM (Bidirectional Long Short-Term Memory), with higher prediction accuracy and fewer trainable parameters. The model's predictive capabilities enable optimization of power supply strategies in smart grids. However, it faces challenges in long-term load prediction and computational resource demands during hyperparameter optimization.

关键词： Adaptation models Accuracy Computational modeling Predictive models Hyperparameter optimization Feature extraction Smart grids Convolutional neural networks Optimization Load modeling

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