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IEEE Transactions on Intelligent Vehicles 2023年第9期8卷 4223-4236页

作者： Westny, Theodor Oskarsson, Joel Olofsson, Bjorn Frisk, Erik Linköping University Department of Computer and Information Science Linköping581 83 Sweden Lund University Department of Automatic Control Lund221 00 Sweden Linköping University Department of Electrical Engineering Linköping581 83 Sweden

Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics. © 2016 IEEE.

关键词： Trajectories

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

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computer Systems Science & engineering 2023年第5期45卷 1469-1482页

作者： Doaa Sami Khafaga Amel Ali Alhussan Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mohamed Saber El-Sayed M.El-kenawy Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electronics and Communications Engineering Department Faculty of EngineeringDelta University for Science and TechnologyMansouraEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Arrhythmia has been classified using a variety of *** of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more *** with cardiac arrhythmias can benefit from competent monitoring to save their *** arrhythmia classification and prediction have greatly improved in recent *** are a category of conditions in which the heart's electrical activity is abnormally rapid or *** year,it is one of the main reasons of mortality for both men and women,*** the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)*** proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia *** proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s *** purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia *** method of comprehensive arrhythmia classification outperforms earlier methods presented in the *** achieved classification accuracy using the proposed approach is 99.8%.

关键词： Feature selection electrocardiogram metaheuristics dipper throated algorithm

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Rehabilitation of the Lower Limb Motor Skills for Patients Using Cable-Driven Robot 8th

Rehabilitation of the Lower Limb Motor Skills for Patients U...

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8th International Congress on Information and Communication Technology, ICICT 2023

作者： Salem, Abdallah Salahelden Khalaf, Ahmed Ali Fouda, Abdulrahman Kamal Ammar, Hossam Hassan Shalaby, Raafat SESC Research Center Nile University Giza Egypt School of Engineering and Applied Science Nile University Giza Egypt Automatic Control Department Faculty of Electronics Engineering Menofia University Menouf Egypt

ISBN: (纸本)9789819930425

As the technological advancements in felid such as electronic, robotics, and artificial intelligence continue to grow and flourish, the more the traditional methods of doing things starts to get absolute. This phenomenon cannot be more clearly observed in like medicine, technological advancements changed the ways things are done from the way that the doctors preform their surgical operations to the way they prescribe pills and after surgery treatments. The purpose of this work is to develop a low-cost cable-driven manipulator robot to be used in the rehabilitation of human lower limb problems caused by stroke, accidents, and cerebral palsy. The robot offers a lot of advantages, but some of which is that is the robot is easily deployable anywhere where a power source is present and that the therapist does not to be present with the patient in the same room to monitor the sessions. The robot consists of a stationary frame and an end-effector (splint) connected to four and up to eight wires and can conduct individual hip/knee motions. The paper starts with a look at rehabilitation of the lower limb, then moving on to the kinematics, workspace, and hardware structure. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Patient rehabilitation

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Different architectures of thin film bilayers based on TiO2 and CuO for green hydrogen generation

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International Journal of Hydrogen Energy 2025年 136卷 1073-1085页

作者： Banaś-Gac, Joanna Radecka, Marta Llobet, Eduard Partyka-Jankowska, Ewa Zakrzewska, Katarzyna Faculty of Computer Science Electronics and Telecommunications AGH University of Krakow Krakow Poland Faculty of Materials Science and Ceramics AGH University of Krakow Krakow Poland Department of Electronic Electrical and Automatic Engineering Rovira i Virgili University Tarragona Spain SOLARIS National Synchrotron Radiation Centre Jagiellonian University Krakow Poland

Green hydrogen becomes one of the most important of all other forms due to ecological issues, described in directives requiring the transformation to energy sources with the lowest possible carbon footprint. The application of the PhotoElectrochemical Cell where hydrogen is produced with the direct use of solar energy is the best solution. Bilayer and multilayered systems based on CuO, especially in combination with TiO2 seem to be the most frequently studied of all heterostructured nanomaterials when considering green hydrogen generation. The interface created between rough surface of CuO and smooth TiO2 surface ensures the highest photocurrent when the top layer is of about 150 nm. For CuO(top)/TiO2 heterojunction characterized by rough/rough interface, photoeletrochemical performance also improves with increasing thickness of the top layer up to 100 nm. The cathodic character of the photocurrent is observed for both TiO2(top)/CuO and CuO(top)/TiO2 bilayers. © 2025 The Authors

关键词： Solar energy

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Experimental Efficiency Comparison Between the LLC Resonant Converter and the Phase Shifted Full Bridge Converter Operating as Battery Chargers 50

Experimental Efficiency Comparison Between the LLC Resonant ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： López-Santos, Oswaldo Zambrano-Prada, David A. Valderrama-Blavi, Hugo Flores-Bahamonde, Freddy Torres Pinzón, Carlos A. Martínez-Salamero, Luis Department of Electrical Electronic and Automatic Control Engineering Universitat Rovira I Virgili Tarragona Spain Energy Transformation Center Engineering Faculty Universidad Andrés Bello Santiago Chile Faculty of Electronic Engineering Universidad Santo Tomás Bogotá Colombia

ISBN: (纸本)9781665464543

DC-DC conversion in electric vehicle chargers can require a single or multiple stages depending on both electrical architecture and amount of processed power. When isolation is provided by an output stage, a unidirectional isolated DC-DC converter is required. Among the possible candidates, the LLC resonant converter and the phase shifted full-bridge (FSFB) converter show several advantages which has made them preferred choices in recent literature. This paper compares the efficiency of both converters on equal basis under the variable load conditions imposed by a cycle of the conventional constant-current constant-voltage (CC-CV) charging protocol. To support the study, scale-down experimental prototypes of both converters have been implemented and evaluated in laboratory showing the superiority of the LLC resonant converter for almost the entire range of operation. © 2024 IEEE.

关键词： Resonant converters

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Improved Perceptual Representation of Isosurfaces From Volume Data Using Curvature-based Features

Improved Perceptual Representation of Isosurfaces From Volum...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Marius Gavrilescu Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

The representation of relevant information from volume data sets is a challenging task due to the high complexity of the structures and spatial features found in such data. The challenge is to represent such structures and features in a way that makes them easy to visually perceive without causing information overload in the resulting images. To this end, we propose a straightforward means of highlighting various regions from isosurfaces found in volume data, such that the visual perception of important surface details is improved. We use an approach based on curvature analysis to determine variations of the isosurface shape, allowing the accentuation of meaningful surface regions. We show that, while the resulting surface accents alone are enough to improve the display of surface details, combining our method with local illumination significantly contributes to a raised level of perception of the surface shape, as well as to the generation of more comprehensive representations of the underlying data. We present our results through illustrative images of medical CT volumes and perform an evaluation using several state-of-the-art no-reference image quality assessment methods. Additionally, our technique does not require precomputation and is easy to incorporate into existing volume rendering engines.

关键词： Image quality Shape Shape measurement Lighting Rendering (computer graphics) Visual effects Engines Isosurfaces Visual perception Light sources

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

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computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： Doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid El-Sayed M.El-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack *** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things

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Three-Dimensional Analysis of the Retinal Vessels 21st

Three-Dimensional Analysis of the Retinal Vessels

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21st Polish control Conference, PCC 2023

作者： Bieda, Robert Jaskot, Krzysztof Jaworski, Marcin Faculty of Automatic Control Electronics and Computer Science Department of Automatic Control and Robotics Silesian University of Technology ul. Akademicka 16 room 625 Gliwice44-100 Poland OPTOMED Ophthalmological Center for Children and Adults Chorzów Poland

ISBN: (纸本)9783031351723

The article is an introduction to quantitative analysis retinal blood vessels. The analysis uses an active contouring system to isolate blood vessels obtained by scanning with an Angio-OTC device. It should bring possibility to make therapeutic decisions and determine the treatment prognosis that are based on the results of quantitative analysis of neovascular changes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blood vessels

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