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International Conference on control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)

作者： Nikolaos Kounalakis Emmanouil Kalykakis Dimitrios Kosmopoulos John Fasoulas Michael Sfakiotakis Control Systems and Robotics Laboratory (CSRL) School of Engineering Hellenic Mediterranean University Heraklion Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

We present an integrated framework for intelligent automated plant monitoring, towards early disease and pest detection in greenhouse tomato crops. The framework combines the use of a robotic mobile platform to autonomously collect multi-spectral images of the plants, with a tool that utilizes Faster R-CNN to detect regions that signify the presence of a disease or pest. The robot is based on a modified mobile vertical mast lift platform, and integrates a 6-dof robotic arm that is used to position the plant imaging multi-spectral camera. The robot can navigate autonomously inside the greenhouse via a magnetic guidance sensor. Results from a series of experiments demonstrate the validity and effectiveness of the implemented framework.

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Neuro-Symbolic Empowered Denoising Diffusion Probabilistic Models for Real-Time Anomaly Detection in Industry 4.0: Wild-and-Crazy-Idea Paper

Neuro-Symbolic Empowered Denoising Diffusion Probabilistic M...

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Forum on Specification, Verification and Design Languages, FDL

作者： Luigi Capogrosso Alessio Mascolini Federico Girella Geri Skenderi Sebastiano Gaiardelli Nicola Dall'Ora Francesco Ponzio Enrico Fraccaroli Santa Di Cataldo Sara Vinco Enrico Macii Franco Fummi Marco Cristani Dept. of Engineering for Innovation Medicine University of Verona Italy Dept. of Control and Computer Engineering Polytechnic of Turin Italy Dept. of Regional and Urban Studies and Planning Polytechnic of Turin Italy Dept. of Computer Science University of North Carolina at Chapel Hill USA

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before.

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Integration of a multi-rate electron density profile observer in the plasma control system of TCV 49

Integration of a multi-rate electron density profile observe...

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49th EPS Conference on Plasma Physics, EPS 2023

作者： Pastore, F. Felici, F. Sauter, O. Bosman, T.O.S.J. Galperti, C. Vincent, B. Pau, A. Poels, Y. Ravensbergen, T. Vu, N.M.T. Lausanne Switzerland DIFFER - Dutch Institute for Fundamental Energy Research Energy Systems and Control Group Eindhoven Netherlands Eindhoven University of Technology Dept. of Mechanical Engineering Control Systems Technology Group Eindhoven Netherlands Eindhoven University of Technology Dept. of Mathematics and Computer Science Data Mining Group Eindhoven Netherlands ITER Organization Route de Vinon-sur-Verdon St. Paul Lez Durance13067 Cedex France

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Discrete Shuffled Frog Leading Algorithm for Multiple-product Human-robot Collaborative Disassembly Line Balancing Problem

Discrete Shuffled Frog Leading Algorithm for Multiple-produc...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Fan, ChenYang Wang, JiaCun Guo, XiWang Qin, ShuJin Zhou, MengChu Qi, Liang Engineering of Liaoning Petrochemical University School of Information and Control Fushun China Engineering of Monmouth University Dept. Computer Science and Software West Long Branch United States Liaoning Petrochemical University Fushun China Monmouth University West Long Branch United States College of Economics and Management of Shangqiu Normal University Shangqiu China Dept. Electrical and Computer Engineering of New Jersey Institute of Technology Newark United States Tech of Shandong University of Science and Technology Dept. Computer Intelligent Sci. & Qingdao China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

With the rapid development of recycling and remanufacturing technologies, disassembly line balancing problems (DLBP) have drawn great attention. Considering the limitation of disassembly by humans or robots alone, this paper focuses on human-robot collaborative disassembly lines. Specifically, this work proposes a multi-product human-robot collaborative disassembly line balancing model to tackle the inflexibility of single product disassembly and inconsistency in recovery values of different product components. Its objective is to maximize disassembly profit. As a commercially available solver, IBM's CPLEX is used to obtain the exact solution of DLBP and verify the correctness of the proposed mathematical model. A discrete shuffled frog leading algorithm is newly designed to solve the sizable problems. Experimental results show that the proposed algorithm has a fast convergence rate and can find solutions consistent with those with CPLEX but requires much less time than the latter, thus advancing the field of disassembly automation. © 2022 IEEE.

关键词： Robots

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Vehicle Classification in Intelligent Transportation Systems Using Deep Learning and Seismic Data 20

Vehicle Classification in Intelligent Transportation Systems...

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20th IEEE International Conference on Intelligence and Security Informatics, ISI 2023

作者： Hashima, Sherief Saad, Mohamed H. Hatano, Kohei Rizk, Hamada Computational Learning Theory Team RIKEN-AIP Fukuoka819-0395 Japan Egyptian Atomic Energy Authority Engineering Dept Cairo13759 Egypt Egyptian Atomic Energy Authority Ncrrt Radiation Engineering Dept Cairo Egypt Kyushu University Department of Informatics Fukuoka819-0395 Japan Graduate School of Information Science and Technology Osaka University Suita565-0871 Japan Tanta University Computer and Control Engineering Department Tanta31733 Egypt

ISBN: (纸本)9798350337730

Intelligent transportation systems have become increasingly important for efficient traffic management and road safety. Vehicle classification is a fundamental task in these systems, enabling various applications such as traffic monitoring, congestion management, and accident prevention. Traditional methods for vehicle classification heavily rely on visual or sensor-based data, such as images or radar signals. However, these methods may encounter limitations in adverse weather conditions, poor lighting, or occlusion scenarios. To address these limitations, this paper introduces a novel approach for vehicle classification using seismic data, which captures the vibrations generated by vehicles and is less susceptible to environmental factors. The proposed approach leverages the fractional wavelet domain to extract both time and frequency signatures of vehicles from the seismic data effectively. These signatures are then used to train a recurrent neural network that captures the temporal features of the input, thereby facilitating vehicle classification. Additionally, the proposed approach incorporates various modules to improve the generalization and robustness of the deep model against noise. The implementation of the proposed approach on realistic data demonstrated its ability to classify vehicles with an accuracy of 98%, improving upon the state-of-the-art techniques. © 2023 IEEE.

关键词： Vehicles

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Computationally Efficient Predictive control Based on ANN State-Space Models

Computationally Efficient Predictive Control Based on ANN St...

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IEEE Conference on Decision and control

作者： Jan H. Hoekstra Bence Cseppento Gerben I. Beintema Maarten Schoukens Zsolt Kollár Roland Tóth Dept. of Electrical Engineering at the Eindhoven Control Systems group University of Technology The Netherlands Dept. of Measurement and Information Systems Budapest University of Technology and Economics Hungary Institute for Computer Science and Control Budapest Hungary

Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for the identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinear model predictive control (NMPC), the resulting nonlinear optimization problem is often overly complex due to the size of the network, requires the use of high-order observers to track the states of the ANN model, and the overall control scheme does not exploit the available autograd tools for these models. In this paper, we propose an efficient approach to auto-convert ANN statespace models to linear parameter-varying (LPV) form and solve predictive control problems by successive solutions of linear model predictive problems, corresponding to quadratic programs (QPs). Furthermore, we show how existing deep-learning methods, such as SUBNET that uses a state encoder, enable efficient implementation of MPCs on identified ANN models. Performance of the proposed approach is demonstrated by a simulation study on an unbalanced disc system.

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Load Demand Prediction for Electric Vehicles Smart Charging through Consensus-based Federated Learning

Load Demand Prediction for Electric Vehicles Smart Charging ...

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Mediterranean Conference on control and Automation (MED)

作者： Danilo Menegatti Antonio Pietrabissa Sabato Manfredi Alessandro Giuseppi Dept. of Computer Control and Management Engineering (DIAG) University of Rome ”La Sapienza” Rome Italy Dept. of Electrical Engineering and Information Technology University of Naples ”Federico II” Naples Italy

Having access to a reliable and accurate prediction of the short-term power demand is a fundamental step for the widespread adoption of Electric Vehicles (EVs), as their charges may have a significant impact on the power system balancing. In this direction, we propose a short-term load demand predictor, based on distributed Long Short-Term Memory Networks, that employs consensus and fully-decentralized Federated Learning (FL) algorithms to seek cooperation among multiple points of charge without the requirement of sharing any user-related data.

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Robotic Teleoperation control Using PSO-Tuned Fractional Order Active Disturbance Rejection

Robotic Teleoperation Control Using PSO-Tuned Fractional Ord...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Omar Gad R. Fareh J. Ghommam M. Nasir S. Khadraoui M. Bettayeb Dept. of Electrical Engineering University of Sharjah UAE Research Inst. of Sciences and Engineering University of Sharjah UAE Dept. of Electrical and Computer Engineering Sultan Qaboos University Oman Control and Energy Management Laboratory (CEM Lab) Ecole National d'Ingenieur de Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Bilateral teleoperation systems have become essential for enabling precise remote manipulation in fields such as robotic surgery and hazardous material handling. However, these systems face significant challenges, including time delays, environmental uncertainties, and nonlinear dynamics, which can affect their stability and performance. This research aims to investigate the application of Active Disturbance Rejection control (ADRC) to address these challenges. ADRC is chosen for its real-time estimation and compensation for disturbances, even with limited knowledge of the system model. Additionally, the study incorporates enhancements such as Fractional-Order control and optimization techniques, including Particle Swarm Optimization (PSO), for parameter tuning. A simulation study is conducted to assess the performance of the proposed controller in tracking control of bilateral teleoperation systems with 3-DOF and 4-DOF manipulators. Results illustrate the superiority of the fractional order ADRC in disturbance rejection and trajectory tracking compared to linear ADRC, making it a promising solution for teleoperation applications.

关键词： Robust control Uncertainty Trajectory tracking Simulation 3-DOF Surgery Manipulators Stability analysis Particle swarm optimization Tuning

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Intelligent Operational Decision Support System for Sugar Factory Resource Efficiency 5

Intelligent Operational Decision Support System for Sugar Fa...

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Lutska, Nataliia Zaiets, Nataliia Vlasenko, Lidiia Junge, Stefan National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine Dept. of Automation and Robotic Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine Berlin University of Applied Sciences Dept. of Packaging Technology Berlin Germany

ISBN: (纸本)9798350359787

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. The paper proposes a neural network model of sugar production. The model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. The structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%. © 2023 IEEE.

关键词： Decision support systems

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Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0

arXiv

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arXiv 2023年

作者： Capogrosso, Luigi Mascolini, Alessio Girella, Federico Skenderi, Geri Gaiardelli, Sebastiano Dall’Ora, Nicola Ponzio, Francesco Fraccaroli, Enrico Di Cataldo, Santa Vinco, Sara Macii, Enrico Fummi, Franco Cristani, Marco Dept. of Engineering for Innovation Medicine University of Verona Italy Dept. of Control and Computer Engineering Polytechnic of Turin Italy Dept. of Regional and Urban Studies and Planning Polytechnic of Turin Italy Dept. of Computer Science University of North Carolina Chapel Hill United States

关键词： Anomaly detection

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