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A latent representation of brain networks based on EEG ⁎

IFAC-PapersOnLine 2024年第15期58卷 414-419页

作者： Lucia Falconi Giulia Cisotto Mattia Zorzi Automatic Control Laboratory ETH Zurich Physikstrasse 3 8092 Zurich Switzerland Dept. of Informatics Systems and Communication University of Milano-Bicocca Milan Italy Dept. of Information Engineering University of Padova Italy Dept. of Information Engineering University of Padova Italy

Electroencephalography (EEG) is one of the most popular techniques to investigate normal as well as pathological cerebral mechanisms, as it allows to measure, non-invasively and in real-time, the brain activity. However, modeling EEG is still extremely challenging, because of its high-dimensionality, low signal-to-noise ratio, and high individual variability. This paper proposes a novel latent representation to study brain networks using EEG by means of a robust dynamic factor analysis (RDFA) approach. We investigate the ability of this latent representation to discriminate between two groups of subjects, i.e. alcoholic and healthy. By RDFA, we can extract a limited number of highly explanatory factors, as low as 8, significantly discriminating between the two groups. Also, we show that different brain patterns can be identified across different stimulation scenarios and EEG locations. Although preliminary, this work could give support to domain experts while providing some clinically-meaningful insights to identify common patterns as well as individual characteristics in different groups of healthy and pathological subjects.

关键词： EEG system identification dynamic factor analysis

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Tuning the feedback controller gains is a simple way to improve autonomous driving performance

arXiv

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arXiv 2024年

作者： Liang, Wenyu Baldivieso, Pablo R. Drummond, Ross Shin, Donghwan Dept. of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Dept. of Computer Science University of Sheffield Sheffield United Kingdom

Typical autonomous driving systems are a combination of machine learning algorithms (often involving neural networks) and classical feedback controllers. Whilst significant progress has been made in recent years on the neural network side of these systems, only limited progress has been made on the feedback controller side. Often, the feedback control gains are simply passed from paper to paper with little re-tuning taking place, even though the changes to the neural networks can alter the vehicle’s closed loop dynamics. The aim of this paper is to highlight the limitations of this approach;it is shown that retuning the feedback controller can be a simple way to improve autonomous driving performance. To demonstrate this, the PID gains of the longitudinal controller in the TCP autonomous vehicle algorithm are tuned. This causes the driving score in CARLA to increase from 73.21 to 77.38, with the results averaged over 16 driving scenarios. Moreover, it was observed that the performance benefits were most apparent during challenging driving scenarios, such as during rain or night time, as the tuned controller led to a more assertive driving style. These results demonstrate the value of developing both the neural network and feedback control policies of autonomous driving systems simultaneously, as this can be a simple and methodical way to improve autonomous driving system performance and robustness. © 2024, CC BY.

关键词： Feedback control

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Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks

Detection of Electric Motor Damage Through Analysis of Sound...

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Annual Conference of Industrial Electronics Society

作者： Waldemar Bauer Marta Zagórowska Jerzy Baranowski Dept. of Automatic Control & Robotics AGH University of Krakow Krakow Poland Dept. of Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Fault monitoring and diagnostics are important to ensure reliability of electric motors. Efficient algorithms for fault detection improve reliability, yet development of cost-effective and reliable classifiers for diagnostics of equipment is challenging, in particular due to unavailability of well-balanced datasets, with signals from properly functioning equipment and those from faulty equipment. Thus, we propose to use a Bayesian neural network to detect and classify faults in electric motors, given its efficacy with imbalanced training data. The performance of the proposed network is demonstrated on real life signals, and a robustness analysis of the proposed solution is provided.

关键词： Accuracy Fault detection Neural networks Training data Robustness Bayes methods Wind turbines Signal representation Time-domain analysis Electric motors

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Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks

arXiv

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arXiv 2024年

作者： Bauer, Waldemar Zagórowska, Marta Baranowski, Jerzy Dept. of Automatic Control & Robotics AGH University of Krakow Krakow Poland Dept. of Engineering Cybernetics Norwegian University of Science and Technology Trondheim Norway

关键词： Electric motors

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Opinion Evolution Model Based on Coordination between Information Networks and Social Networks

Opinion Evolution Model Based on Coordination between Inform...

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Cognitive engineering and Intelligent Interaction (CEII), Asia Conference on

作者： Meiyue Zhao Zhizhong Liu Xiaojun Zhang Quan Z Sheng dept. School of Computer and Control Engineering Yantai University Yantai China dept. School of Computing Macquarie University Sydney Australia

ISBN: (数字)9798331508760

ISBN: (纸本)9798331508777

Opinion dynamics is a popular process to model the evolution of group opinions, which provides technical support for public opinion-related decision issues. However, most existing works focus on studying the opinions evolution in social networks while overlooking the influence of information networks. Besides, in social networks, the rules for filtering authoritative individuals only consider public authorities, failing to consider the individuals' heterogeneity. To tackle the above issues, we propose an opinion evolution model based on collaboration between information networks and social networks (ISOE). Specifically, we first update the individual's opinion by judging the quality of the information obtained by the individual in the information network; then, we filter the trusted neighbor set for the current individual by quantifying the attributes of the individual's authority and closeness and update the individual's opinion after comprehensive weighting analysis of the trusted neighbor set; finally, we conduct information exchange between the social and information networks with the inter-layer information transfer mechanism. The above three steps are repeated until the group opinions reach a steady state. Extensive experiments have been carried out to prove the performance superiority of our proposed model.

关键词： Social networking (online) Asia Collaboration Information filters Steady-state Information exchange

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Adaptive control Using a Quaternion Wavelet Neural Network

Adaptive Control Using a Quaternion Wavelet Neural Network

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International Workshop on Robot Motion and control (RoMoCo)

作者： Gerardo Martínez-Terán Eduardo Bayro-Corrochano Dept. Electrical Engineering and Computer Science CINVESTAV Guadalajara Mexico Institute of Automatic Control and Robotics Poznan University of Technology

ISBN: (数字)9798350393965

ISBN: (纸本)9798350393972

Robotics and haptic systems have allowed new and diverse applications in the field of medicine, such as assisted surgery and teleoperation which have increasingly stringent requirements for accuracy, convergence, and low computational consumption. In this paper an adaptive PID control law (Proportional Integral Derivative controller, PID), of indirect architecture is presented for movement paths in a haptic system of open chain, where the identification of the plant is through a quaternionic wavelet neural network (Quaternion Wavelet Neural Network, QWNN) for tune the PID values, this allows the optimal movement into the regions of the workspace.

关键词： Uncertainty Quaternions Neural networks Surgery Robustness Haptic interfaces Topology

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Newton-Euler Modeling of Robot Dynamics Using Geometric Algebra

Newton-Euler Modeling of Robot Dynamics Using Geometric Alge...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Eduardo Bayro-Corrochano Daniel Alvarado-Mejia Institute of Automatic Control and Robotics Poznan University of Technology Dept. Electrical Engineering and Computer Science CINVESTAV Guadalajara Mexico

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

In this work, we present the modeling of the dynamics of a robot manipulator using the Newton-Euler algorithm in the conformal algebra framework. The modeling of the dynamics of robot manipulators is currently done using the Euler-Lagrange formulation which is a batch type of computation. In contrast, in this paper, we propose a recursive algorithm for the modeling of the dynamics of robot manipulators using the Newton-Euler algorithm in the conformal geometric algebra framework.

关键词： Algebra Heuristic algorithms Computational modeling Optimal control Motors Real-time systems Mathematical models

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Advanced UVM-Based Verification Methodology for Consumer Electronics HW Design Evaluation and Validation

Advanced UVM-Based Verification Methodology for Consumer Ele...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Marko Savić Nebojša Pjevalica Computing and Control Engineering Dept. Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798350349153

ISBN: (纸本)9798350349160

The rapid evolution of consumer electronics demands increasingly complex hardware devices supporting a wide range of consumer needs. Designing and verifying such solutions is becoming more challenging. This paper introduces an automated approach for assessing the efficacy of implemented digital systems. Leveraging these insights, the proposed methodology aims to guide HDL designers, enabling them to identify and rectify errors more effectively. The proposed methodology is tested on the example of a relatively simple digital design on a population of twenty designers. The achieved results expose a significant increase in performance and quality in a relatively short time interval.

关键词： Technological innovation Digital systems Sociology Hardware Hardware design languages Statistics Consumer electronics

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PI Passivity-based control of a Stand-alone Three-phase Current Source Inverter ⁎

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IFAC-PapersOnLine 2024年第13期58卷 362-367页

作者： Pau Boira-Pujol Romeo Ortega Arnau Dória-Cerezo Robert Griñó Institute of Industrial and Control Engineering Universitat Politècnica de Catalunya 08028 Barcelona Spain Departament of Electrical Engineering ITAM Ciudad de México Mexico Dept. of Electrical Engineering and Inst. of Industrial and Control Engineering Universitat Politècnica de Catalunya 08028 Barcelona Spain Dept. of Automatic Control and Inst. of Industrial and Control Engineering Universitat Politècnica de Catalunya 08028 Barcelona Spain

This paper presents the design of a Proportional-Integral Passivity-based controller (PI-PBC) for a current source inverter feeding a resistive load. Thanks to the definition of a new passive output, the closed-loop system is shown to be globally asymptotically stable. This result solves the internal stability problem reported for these power converters. To robustify the control algorithm, the paper also includes the design of a parameter estimation scheme for the parasitic resistances and the load conductance. Numerical simulations are carried out to validate the control algorithm. The simulation stage compares the behaviour using the averaged model of the power converter and a more realistic switching model, including the three-phase implementation.

关键词： Current source inverter Passivity-based control Proportional-Integral controllers parameter estimation

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Energy Demand Management in an Industrial Manufacturing Plant using MPC and Neurofuzzy Models

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IFAC-PapersOnLine 2023年第2期56卷 8738-8743页

作者： Javier Gómez William D. Chicaiza Juan M. Escaño Carlos Bordons Dept. System Engineering and Automatic Control Universidad de Sevilla Spain ENGREEN - Laboratory of Engineering for Energy and Environmental Sustainability Universidad de Sevilla Spain

An MPC controller is proposed to maximise the use of renewable energy in a manufacturing process. The strategy has been applied in a manufacturing system which has several machines, renewable generation resources, a combined heat and power (CHP) generator for power production, and a battery bank for energy storage. The work aims to maximise the use of renewable energy sources in this process, also taking into account the price of the electricity market, to reduce the cost. The use of neurofuzzy models for the prediction of the energy produced by renewable generators allows a dynamic prediction, using input values obtained from typical forecasting variables (wind speed, global irradiance, etc.).

关键词： Manufacturing processes Model Predictive control Neurofuzzy systems Energy management systems Genetic algorithms Intelligent control

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