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arXiv 2025年

作者： Escaño, Juan Manuel Ridao-Olivar, Miguel A. Ierardi, Carmelina Sánchez, Adolfo J. Rouzbehi, Kumars Department of Systems Engineering and Automatic Control Universidad de Sevilla Sevilla41092 Spain Department of Engineering Universidad Loyola Andalucía Sevilla41704 Spain

This work has as main objective the development of a soft-sensor to classify, in real time, the behaviors of drivers when they are at the controls of a vehicle. Efficient classification of drivers’ behavior while driving, using only the measurements of the sensors already incorporated in the vehicles and without the need to add extra hardware (smart phones, cameras, etc.), is a challenge. The main advantage of using only the data center signals of modern vehicles is economical. The classification of the driving behavior and the warning to the driver of dangerous behaviors without the need to add extra hardware (and their software) to the vehicle, would allow the direct integration of these classifiers into the current vehicles without incurring a greater cost in the manufacture of the vehicles and therefore be an added value. In this work, the classification is obtained based only on speed, acceleration and inertial measurements which are already present in many modern vehicles. The proposed algorithm is based on a structure made by several Neurofuzzy systems with the combination of projected data in components of various Principal Component Analysis. A comparison with several types of classical classifying algorithms has been made. © 2025, CC BY.

关键词： Smartphones

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Oligopoly Game Stabilisation Through Multilayer Congestion Dynamics

arXiv

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arXiv 2024年

作者： Willis, Toby Punzo, Giuliano Department of Automatic Control and Systems Engineering University of Sheffield Street South Yorkshire Sheffield100190 United Kingdom

International trade and logistics are subject to factors including geopolitical instability, climate change, and black swan events such as the unforeseen closure of the Suez Canal. The problem of predicting local price change under modification of an underlying transport network or change in supply characteristics unites elements of game theory, network theory and transport. The Cournot Oligopoly models economic actors as rational players attempting to maximise profit by optimising supply quantities with analytical results now consolidated about equilibrium characteristics where transport conditions are fixed. Similarly, where supply and demand are fixed, the routing of goods in a transport network can be analytically solved through a traffic assignment problem. Hence we can solve the coupled Cournot-congestion problem by means of a 2-layer network. Where the layers are linked, inter-layer feedback wherein players attempt to maximise their utility occurs. In this respect we find players benefit from taking advantage of non-simultaneous responses to the market rather than moving to a new equilibrium. We draw conclusions about the nature of equilibria, finding that the concave utility curve property results in unique and stable equilibrium for each uncoupled layer, while linked layers have a non-unique stable equilibria for which general solutions are stated. © 2024, CC BY.

关键词： International trade

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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

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computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology 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 Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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Optimisation-Based Iterative Learning control for Distributed Consensus Tracking

Optimisation-Based Iterative Learning Control for Distribute...

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UKACC International Conference on control (control)

作者： Yueqing Zhang Bin Chen Bing Chu Zhan Shu Faculty of Engineering and Physical Sciences University of Southampton Southampton Department of Automatic Control and Systems Engineering University of Sheffield Sheffield U.K. Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

This paper addresses high-performance consensus tracking of repetitively operating networked dynamical systems using an iterative learning control (ILC) algorithm. It circumvents the need for precise model information in traditional methods and guarantees the high-performance by the predictive framework with a novel performance index that takes into account both current and future performance. The proposed algorithm ensures geometric convergence of the tracking error norm to zero and can be applied to both heterogeneous and non-minimum-phase systems. A distributed implementation of the algorithm is developed using the Alternating Direction Method of Multipliers, with detailed convergence analysis and numerical examples confirming its effectiveness.

关键词： System dynamics Heuristic algorithms Scholarships Predictive models Prediction algorithms Performance analysis Reliability

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Pose-graph optimization for efficient tie-point matching and 3D scene reconstruction from oblique UAV images

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 225卷 461-491页

作者： Xu, Zhihua Niu, Yongze Jiang, Jincheng Qin, Rongjun Cui, Ximin College of Geoscience and Surveying Engineering China University of Mining and Technology-Beijing Beijing100083 China North Automatic Control Technology Institute Shanxi Taiyuan030006 China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518107 China Department of Civil Environmental and Geodetic Engineering The Ohio State University 2036 Neil Avenue ColumbusOH43210 United States Department of Electrical and Computer Engineering The Ohio State University Dreese Laboratories 205 2015 Neil Ave ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University 1760 Neil Ave ColumbusOH43210 United States

Oblique photogrammetry using unmanned aerial vehicles (UAV) is crucial to 3D scene reconstruction. Nevertheless, oblique images are generally acquired with large overlaps, and sometimes with multiple views. Although this increases the level of data completeness, it introduces additional and sometimes redundant computations in tie-point matching, creating overly dense camera connections in the pose graph for bundle adjustment (BA). This study optimizes the pose graph of oblique UAV images by removing redundant image connections to guide tie-point matching. Assuming a five-camera system for oblique image collection, a pose graph called a topologically connected camera network (TCN) was initially constructed using position and orientation system (POS) data to determine the spatial connectivity among oblique images. Second, five geometric meta-parameters of overlapping images were constructed, and their influence on tie-point matching was analyzed using a data-driven approach to generate a weighted pose graph. Third, the weighted pose graph was simplified to a degree-bounded skeletal camera network (D-SCN) using the proposed two-stage multi-objective graph optimization approach. Finally, the D-SCN was embedded into a structure from motion (SfM) pipeline to produce a novel D-SCN–SfM method to reduce the required computations for tie-point matching. The proposed D-SCN-SfM method was tested using data from three large sites, each containing over 5,000 images. In addition, the D-SCN-SfM method was compared with three state-of-the-art methods. The experimental results indicate that our method can significantly reduce the required computations for tie-point matching to 1/14–1/20 as compared to a method that uses only topological constraints, that is, TCN, saving 89–92% of the time expenditure. Furthermore, the accuracy and completeness of the 3D geometry produced by the proposed method were comparable to those produced by standard SfM methods. The source code of our approach

关键词： Drones

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Automated Method for the Research of the Relay Protection Function Implemented in 6(10) kV Digital Instrument Transformers Using Real Oscillograms

Automated Method for the Research of the Relay Protection Fu...

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International Russian Automation Conference (RusAutoCon)

作者： Galina Filatova Dmitry Piskunov Alexey Evdakov Department of electric power systems automatic control Ivanovo State Power Engineering University (ISPU) Ivanovo Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

The increasing of automation and digitization of 6(10) kV distribution networks remains a relevant task. The solution of this task may be associated with the widespread implementation of digital instrument transformers and devices based on them. The article is dedicated to the development of a methodology of the function for determining the faulted section at a single-phase ground fault (SPGF) implemented in a digital instrument transformer 6(10) kV. The methodology involves the modeling of electromagnetic transient processes on the Real Time Digital Simulator (RTDS). The research is carried out on a model of a 10 kV electrical network area with the simulation of various types of single-phase ground faults (e.g. arc faults). The model parameters are varied automatically using a «script». Additionally, a database of real oscillograms containing single phase to ground faults is created to validate the function. The article discusses the main features of data collection and processing specific to the electrical energy sector. The oscillograms obtained from models and real energy objects will be used for conducting high-voltage laboratory tests of digital instrument transformers and the faulted section determination function. The test facility diagram is presented in the article.

关键词： Instrument transformers Automation Test facilities Sensitivity Computational modeling High-voltage techniques Distribution networks Real-time systems Steady-state Reliability

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Software-defined event-based camera and vision algorithms for digitizing highly dynamic manufacturing tasks

Software-defined event-based camera and vision algorithms fo...

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2024 Low-Cost Digital Solutions for Industrial Automation, LoDiSA 2024

作者： Walker, Emily Turner, Christopher J. Oyekan, John Department of Automatic Control & Systems Engineering The University of Sheffield SheffieldS1 3JD United Kingdom Surrey Business School The University of Surrey Surrey GuildfordGU2 7XH United Kingdom Real Time & Distributed Systems Group Department of Computer Science University of York Heslington YorkYO10 5GH United Kingdom

ISBN: (纸本)9781837242672

Event-based cameras are inspired by the way the retina in the eye process information. Rather than capturing image frames at a constant rate, event cameras generate data asynchronously, based on changes in pixel value between frames. As a result, these cameras have a high spatiotemporal accuracy, low power constraints and reduced artefacts from motion blur. Hence, they represent favourable properties for digitizing highly dynamic manufacturing tasks such as machine vibrations and rapid human assembly actions. However, the costs of event-based cameras are prohibitively high for small to medium sized enterprises (SMEs). In this research, we present the development of a rig that combines low-cost visual sensors with low-cost algorithms to perform action recognition for highly dynamic manual manufacturing tasks. We applied our algorithms to achieve an action classification system via a novel threshold-based approach and in parallel use a neural network to perform classification of objects in the video feed. The results achieved demonstrated that our approach was able to successfully identify various rapid manual tasks being performed on identified objects. In addition to SMEs, our approach would be suited for providing low-cost digitization on legacy machines. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/).

关键词： Machine vibrations

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An Interacting Multiple Model Approach Based on Maximum Correntropy Student's T Filter

An Interacting Multiple Model Approach Based on Maximum Corr...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Fethi Candan Aykut Beke Lyudmila Mihaylova Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Department of Control Systems Design ASELSAN Ankara Turkiye

This paper presents a novel approach called the Interacting Multiple Model (IMM)-based Maximum Correntropy Student's T Filter (MCStF), which addresses the challenges posed by non-Gaussian measurement noises. The MCStF demonstrates superior performance compared to the IMM algorithm based on Kalman Filters (KFs) in both simulation environments and real-time systems. The Crazyflie 2.0 nano Unmanned Air Vehicle (UAV) model is used in the simulation validation, and results from 3000 independent Monte Carlo runs are shown. After getting the simulation results under monotonously changed non-Gaussian distribution, their performance results have been compared to each other. The same scenario has been applied in the real-time system using Crazyflie 2.0. Next, results from real-time tests are presented in which the position of Crazyflie 2.0 is estimated online.

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Metaheuristic Optimization of Time Series Models for Predicting Networks Traffic

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computers, Materials & Continua 2023年第4期75卷 427-442页

作者： Reem Alkanhel El-Sayed M.El-kenawy D.L.Elsheweikh Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Department of Information Technology 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 Computer Science Faculty of Specific EducationMansoura UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction *** prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were *** evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.

关键词： Network traffic soft computing LSTM metaheuristic optimization

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Rough inference system for automated failure prediction in cooling devices 1

Rough inference system for automated failure prediction in c...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Kokosinski, Zbigniew Szydlowski, Piotr Cracow University of Technology Department of Automatic Control and Computer Engineering Kraków31-155 Poland Efento Sp. Z O.o. Kraków31-123 Poland

ISBN: (纸本)9798350328042

This article presents a versatile model of failure prediction in control systems of objects with large time constants. The basic model of a control unit consists of deterministic finite automaton (DFA) which states correspond to states of the system where system variables satisfy a given set of constraints. System failures can be predicted on the basis of time series of the selected signals. Further diagnosis is performed on the basis of rough set theory and original algorithms for attributes' reduction and decision rule generation. A versatile failure prediction algorithm is constructed that integrates all involved techniques. Cooling systems are application examples of the proposed methodology. © 2023 IEEE.

关键词： Approximation algorithms

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