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MLD-MPC Approach for Three-Tank Hybrid Benchmark Problem

computers, Materials & Continua 2023年第5期75卷 3657-3675页

作者： Hanen Yaakoubi Hegazy Rezk Mujahed Al-Dhaifallah Joseph Haggège Research Laboratory in Automatic Control(LARA) National Engineering School of Tunis(ENIT)University of Tunis El ManarTunis1002Tunisia Department of Electrical Engineering College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityWadi Alddawasir11911Saudi Arabia Control and Instrumentation Engineering Department King Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia Interdisciplinary Research Center(IRC)for Renewable Energy and Power Systems King Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia

The present paper aims at validating a Model Predictive control(MPC),based on the Mixed Logical Dynamical(MLD)model,for Hybrid Dynamic systems(HDSs)that explicitly involve continuous dynamics and discrete *** proposed benchmark system is a three-tank process,which is a typical case study of *** MLD-MPC controller is applied to the level control of the considered tank *** study is initially focused on the MLD approach that allows consideration of the interacting continuous dynamics with discrete events and includes the operating *** feature of MLD modeling is very advantageous when an MPC controller synthesis for the HDSs is *** the MLD model of the system is well-posed,then the MPC law synthesis can be developed based on the Mixed Integer Programming(MIP)optimization *** solving this MIP problem,a Branch and Bound(B&B)algorithm is proposed to determine the optimal control ***,a comparative study is carried out to illustrate the effectiveness of the proposed hybrid controller for the HDSs compared to the standard MPC *** results show that the MLD-MPC approach outperforms the standardMPCone that doesn’t consider the hybrid aspect of the *** paper also shows a behavioral test of the MLDMPC controller against disturbances deemed as liquid leaks from the *** results are very satisfactory and show that the tracking error is minimal less than 0.1%in nominal conditions and less than 0.6%in the presence of *** results confirm the success of the MLD-MPC approach for the control of the HDSs.

关键词： Hybrid dynamic system model predictive control mixed logical dynamical model mixed integer programming three-tank hybrid system

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Observations of data characteristics and irregularities through domain-oriented transformations of attributes

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Procedia computer Science 2024年 246卷 2694-2703页

作者： Urszula Stańczyk Grzegorz Baron Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The paper presents research dedicated to observations of relations between attribute properties and discretisation. In the investigations described, the gradually increasing sets of features were discretised by selected approaches, and several variants of data were constructed. The continuous, partially discrete, and completely translated datasets were explored by the chosen classifiers and their performance studied in the context of a number of discretised attributes, discretisation procedures, and the way of processing of features and datasets. The stylometric problem of authorship attribution was the machine learning task under study. The experimental results enable to observe closer the specificity of style-markers employed as characteristic features, and indicate conditions for efficient recognition of authorship. They can be extended to other application domains with similar characteristics.

关键词： Attribute domain Discretisation Data representation Data irregularities Pattern recognition Stylometry Authorship attribution

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A Hybrid Intelligence Model Forecasts the SOC of Electric Vehicle’s Battery 19th

A Hybrid Intelligence Model Forecasts the SOC of Electric ...

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19th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Rubiños, Manuel Arcano-Bea, Paula Timiraos, Míriam Michelena, Álvaro Vega, Rafael Vega Andújar, José Manuel Casteleiro-Roca, José-Luis CTC Department of Industrial Engineering CITIC University of A Coruña EPEF Calle Mendizábal s/n Campus de Esteiro A Coruña Ferrol15403 Spain Department of Electronic Engineering Computer Systems and Automatic University of Huelva Campus de El Carmen Huelva21071 Spain

ISBN: (纸本)9783031750090

Battery technology is advancing due to the demand of safe energy storage in systems such as Electric Vehicles (EVs) and portable electronics systems. Estimating battery conditions and increasing the range of electric vehicles requires specific models and optimize control strategies. Anticipate battery performance is complicated due to several variables, such as temperature, deterioration, and usage models. Knowing battery performance entails an advanced understanding of time series analysis, particularly forecasting. This study proposes the use of Artificial Neural Networks (ANN) to create a Lithium Ion Battery (LIB) prediction model. The experiments were carried out in a 59.2 VDC, 120 Ah LIB, recording the voltage and temperature while simulating the charge and discharge. A hybrid model is developed with internal local models using clustering. The model prognostic the State of Charge (SOC) evolution and reach a Mean Squared Errors with values between 10-3 and 10-4. This research shows that hybrid models are adecuated instrument for predict the battery SOC. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Artificial intelligent Batteries Electric vehicles SOC prediction

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On-Device Learning with Raspberry Pi for GCN-Based Epilepsy EEG Classification

On-Device Learning with Raspberry Pi for GCN-Based Epilepsy ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： He, Zhuoli Wang, Chuansheng Huang, Jiayan Grau, Antoni Guerra, Edmundo Yan, Jiaquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University College of New Engineering Industry Putian China Minjiang University School of Computer and Big Data Fuzhou China

ISBN: (纸本)9798350386226

Epilepsy is a chronic brain disease characterized by recurrent and transient seizures, which is accompanied by super-synchronous abnormal discharge of electroencephalogram (EEG) signals. As a non-invasive auxiliary diagnostic technique, EEG is currently important means of seizure detection. However, due to ignoring the spatial topological relationship between electrodes, existing data-driver methods fail to fully reflect the interaction between signals. Meanwhile, their models usually be designed with a large number of redundant parameters, making it difficult to deploy to micro-embedded devices with limited-resources. In this paper, we propose a on-device learning with edge device for epilepsy EEG classification network based on GCN (oDLGCN-EEG). Specifically, to analyze the spatial relationships between various electrodes and their temporal dependencies, we design a Brain Topology Network (BTN) for the spatiotemporal dependency feature map construction. To capture the internal activity during epilepsy seizures, we design a Neural Feature Extraction Module (NFEM) for the neural activity feature map construction. Besides, we propose a pruning scheme to optimize the model, which successfully deploys the optimized oDLGCN-EEG on the embedded Raspberry Pi device for efficient and low-power consumption intelligent epilepsy classification. Experiments in comparison with state-of-the-art methods show that oDLGCN-EEG achieves the best classification accuracy and with the smallest parameter number on baseline EEG dataset. The code is available at https://***/cathnat/Epileptic-Classification. © 2024 IEEE.

关键词： Topology

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Steady-state analysis of networked epidemic models

arXiv

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arXiv 2023年

作者： Khong, Sei Zhen Su, Lanlan Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom

Compartmental epidemic models with dynamics that evolve over a graph network have gained considerable importance in recent years but analysis of these models is in general difficult due to their complexity. In this paper, we develop two positive feedback frameworks that are applicable to the study of steady-state values in a wide range of compartmental epidemic models, including both group and networked processes. In the case of a group (resp. networked) model, we show that the convergence limit of the susceptible proportion of the population (resp. the susceptible proportion in at least one of the subgroups) is upper bounded by the reciprocal of the basic reproduction number (BRN) of the model. The BRN, when it is greater than unity, thus demonstrates the level of penetration into a subpopulation by the disease. Both non-strict and strict bounds on the convergence limits are derived and shown to correspond to substantially distinct scenarios in the epidemic processes, one in the presence of the endemic state and another without. Formulae for calculating the limits are provided in the latter case. We apply the developed framework to examining various group and networked epidemic models commonly seen in the literature to verify the validity of our conclusions. Copyright © 2023, The Authors. All rights reserved.

关键词： Cell proliferation

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Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations

arXiv

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arXiv 2023年

作者： Jones, Morgan The Department of Automatic Control and Systems Engineering The University of Sheffield United Kingdom

This paper introduces a novel approach for learning polynomial representations of physical objects. Given a point cloud data set associated with a physical object, we solve a one-class classification problem to bound the data points by a polynomial sublevel set while harnessing Sum-of-Squares (SOS) programming to enforce prior shape knowledge constraints. By representing objects as polynomial sublevel sets we further show it is possible to construct a secondary SOS program to certify whether objects are packed correctly, that is object boundaries do not overlap and are inside some container set. While not employing reinforcement learning (RL) in this work, our proposed secondary SOS program does provide a potential surrogate reward function for RL algorithms, autonomously rewarding agents that propose object rotations and translations that correctly pack objects within a given container set. Copyright © 2023, The Authors. All rights reserved.

关键词： Containers

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Optimizing High Resolution Wide Field of View Optical Design for Long Wave Infrared Objective

Optimizing High Resolution Wide Field of View Optical Design...

引用

Electrical engineering (ICEENG), 12th International Conference on

作者： Hossam Aboalia Sherif Hussein Alaaeldin Mahmoud Control Systems Department Optoelectronics and Automatic Military Technical College Cairo Egypt Artificial Intelligence Department Computer Engineering Military Technical College Cairo Egypt

ISBN: (数字)9798350343427

ISBN: (纸本)9798350343434

Optical system is a basic component of infrared (IR) imaging systems that affect the image quality of the observed field of view (FOV). Responsible for collecting IR radiation from the scene and projecting it onto detector pixels. The optical system's characteristics, such as lens material, focal length, and optical diameter, critically impact the amount of transmitted IR radiation. The design process aims to eliminate and limit aberrations to enhance performance. Increasing spatial resolution and widening the FOV are crucial for image quality improvement. In this study, we present a unique high-resolution wide FOV optical design for an infrared objective operating in the long-wave infrared (LWIR) band $(\mathbf{8}-\mathbf{12}\ \boldsymbol{\mu} \mathbf{m})$ . Utilizing the time-tested Cooke triplet structure as the foundation, we enhance its capabilities through the implementation of a splitting methodology. This approach allows for improved control over aberrations and enhances the optical design's overall performance. Ansys Zemax OpticStudio software was employed to optimize radii of curvatures and thicknesses based on the proposed merit function operands. Our concept makes use of a small pixel size $(\mathbf{15}\ \mu \mathbf{m})$ to achieve an expansive FOV of 18° along with a high spatial resolution of 31 cycles/mm. This makes the design a highly suitable candidate for diverse remote sensing observation applications.

关键词： Integrated optics Image quality Optical design Optical detectors Optical imaging Size measurement Software

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Adaptive Neuro-Fuzzy Inference systems for Cognitive Sensor Communication in Smart Cities

Adaptive Neuro-Fuzzy Inference Systems for Cognitive Sensor ...

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2024 IEEE International Conference on Intelligent systems and Advanced Applications, ICISAA 2024

作者： Pradhan, Rahul Prathiba, Ch. Kholmukhamedov, Timur Patil, B.N. Mary, S. Suma Christal Raj, I Infant GLA University Department of Computer Engineering and Applications Mathura India Vignana Bharathi Institute of Technology Department of CSM Ghatkesar Hyderabad India Turin Polytechnic University Automatic Control and Computer Engineering Department Tashkent Uzbekistan Yeshwantrao Chavan College of Engineering Department of Applied Physics Nagpur India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K. Ramakrishnan College of Engineering Department of CSE Tamil Nadu Trichy India

ISBN: (纸本)9798331539948

Modern urban development depends on optimizing cognitive sensor communications in smart cities, which is the focus of this investigation. In the era of expanding sensor networks, efficient data transmission is paramount for the success of smart city initiatives. The current state of sensor communication faces challenges like suboptimal adaptability, inefficient energy usage, and limited fault detection. This study proposes a solution through the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS), identifying these challenges as opportunities for improvement. The novelty lies in ANFIS's introduction as an intelligent and adaptive solution. Its ability to learn from sensor data, adapt to changing conditions, and optimize communication parameters sets it apart in enhancing cognitive sensor communication. The research methodology involves a systematic approach, encompassing data collection, pre-processing, and ANFIS model development. The model is rigorously trained, validated, and tested for real-world effectiveness. Integration into the smart city communication system is explored, emphasizing applications in context-aware communication, energy-efficient protocols, and fault detection. Performance is evaluated using metrics like accuracy, reliability, and efficiency, demonstrating the model's efficacy in addressing drawbacks and enhancing overall sensor communication in smart cities. The findings contribute to advancing intelligent systems for urban infrastructure and services. © 2024 IEEE.

关键词： Fuzzy neural networks

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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation

Out-of-distribution Object Detection through Bayesian Uncert...

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International Conference on Information Fusion

作者： Tianhao Zhang Shenglin Wang Nidhal Bouaynaya Radu Calinescu Lyudmila Mihaylova Automatic Control and Systems Engineering The University of Sheffield UK Department of Electrical and Computer Engineering Rowan University USA Department of Computer Science University of York UK

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset.

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Pager Explosion:Cybersecurity Insights and Afterthoughts

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IEEE/CAA Journal of automatica Sinica 2024年第12期11卷 2359-2362页

作者： Chuan Sheng Wanlun Ma Qing-Long Han Wei Zhou Xiaogang Zhu Sheng Wen Yang Xiang Fei-Yue Wang Swinburne University of Technology Hawthorn VIC 3122Australia IEEE the School of Computer and Mathematical Sciences the University of AdelaideAdelaideSouth Australia 5005Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

PAGER EXPLOSION:THE *** 2024 Lebanon pager explosions represent one of the most unexpected and devastating technological incidents in recent *** September 17 and 18,2024,thousands of pagers and walkie-talkies exploded simultaneously across Lebanon and parts of Syria,resulting in 42 deaths and more than 3500 *** handheld communication devices,previously regarded as secure and low-profile,were rigged with concealed explosives and remotely triggered by attackers.

关键词： injuries thoughts explosion

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