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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Meng Xu Yuanhao Gong Sanja Dogramadzi Department of Automatic Control and Systems Engineering University of Sheffield S10 2TN Sheffield Faculty of Electronics and Information Engineering Shenzhen University Department of Automatic Control and Systems Engineering University of Sheffield S10 2TN

With the soaring interest in understanding the dynamics of human body skeletons for applications such as action recognition and video understanding, the significance of precise 3D key-point detection has become increasingly prominent. Despite the advancements, existing approaches struggle to address the issues of occlusions and limited annotated data. This paper proposes a novel framework integrating a multilevel attention mechanism and weakly supervised 3D key-point generation to tackle these prevalent issues, enhancing both the accuracy and efficiency of human pose estimation.

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Contribution to the Sensors Faults Diagnosis of a Variable Speed Wind Turbine: Multimodel Approach

Contribution to the Sensors Faults Diagnosis of a Variable S...

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International Conference on systems and control (ICSC)

作者： Yosra Sayahi Moez Allouche Mariem Ghamgui Sandrine Moreau Driss Mehdi Laboratory of Sciences and Techniques of Automatic Control & Computer Engineering (Lab-STA) National Engineering School of Sfax University of Sfax Sfax Tunisia Laboratory of Computer Science and Automatic Control for Systems University of Poitiers France

This paper highlights the importance of using a Doubly-Fed Induction Generators (DFIG) in the wind industry due to their ability to adapting for all variations in wind speed, thus providing increased efficiency and reliability. However, like any machine, DFIG are not immune to dysfunctional problems and faults (sensor faults, actuator faults and system faults) which affect energy production. To remedy this problem, we develop a Fault Detection and Insolation (FDI) system for sensors fault diagnosis in wind turbine. This work specifically addresses the use of observer's bench to detect and locate faults, such as intermittent sensor faults, inter-coil short circuits, emphasizing a multi-model approach. We use the Dedicated Observer Structure (DOS) and the Generalized Observer Structure (GOS) to solve the complex challenge of multiple and simultaneous sensor fault. Simulation results are presented to assess the effectiveness of the proposed diagnostic methods.

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Combined Filters Design, Using the Principle of Internal Models

Combined Filters Design, Using the Principle of Internal Mod...

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International Russian Automation Conference (RusAutoCon)

作者： Anatoly Gaiduk Darya Denisenko Nikolay Prokopenko Department of Automatic Control Systems Southern Federal University Scientific Design Office Robotics and Control Systems Taganrog Rostov-on-Don Russia Department of scientific research Department of Automatic Control Systems Don State Technical University Southern Federal University Rostov-on-Don Russia Department of Information Systems and Radio Engineering Don State Technical University Rostov-on-Don Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

To design active analog, discrete-analog and digital filters a new method is suggested, taking into account the conditions of selective invariance and using the internal models principle of the dynamic systems. For this purpose, the structure of each basic filter ensures the presence of an explicit spectral model of the filtered signal. Such a filter is selectively invariant, and this property depend only on the spectrum-setting parameters and do not depend on other parameters. It is suggest-ed to carry out the design of the combined selective filters by integration of the suggested basic second-order filters, focusing on the log magnitude plot of the latter. This makes possible to minimize the order of the combined both analog and digital filters. The combined selective filters are distinguished by high stability of selective properties and can be used in the systems for measurement, signal processing, data transmission, as well as automatic and automated control of various social, production and special facilities.

关键词： Automation Filtering Focusing Production Signal processing Stability analysis Data communication Digital filters Dynamical systems

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System Identification-informed Transparent and Explainable Machine Learning with Application to Power Consumption Forecasting

System Identification-informed Transparent and Explainable M...

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2023 International Conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Wei, Hua-Liang Dept. Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom Energy Institute The University of Sheffield Sheffield United Kingdom Centre of Machine Intelligence The University of Sheffield Sheffield United Kingdom Centre for Signal Processing and Cpmplex Systems The University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9798350322972

System identification (SysID) is the art and science of dealing with dynamic data modelling problems from systems science perspectives. It has been an active field and is still very active today, due to its wide range of applications, especially its basic principles of finding transparent, interpretable and parsimonious models for different purposes. The past decades have witnessed the explosive growth in machine learning (ML) and its applications in all areas of science and engineering. Meanwhile, there has been an increasing demand for the development of transparent, explainable and/or interpretable ML models. This paper proposes a new framework for developing System Identification-informed Transparent and Explainable MAchine Learning (SITEMAL) models. A case study, involving a real power consumption dataset, is presented to demonstrate the application of the proposed modelling framework and its performance for power consumption forecasting. © 2023 IEEE.

关键词： Electric power utilization

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Computing the explicit MPC solution in the constrained zonotope case

Computing the explicit MPC solution in the constrained zonot...

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IEEE Conference on Decision and control

作者： Florin Stoican Ştefan S. Mihai Martin Mönnigmann Bogdan D. Ciubotaru Department of Automatic Control and Systems Engineering University “Politehnica” of Bucharest 313 Splaiul Independenţei 060042 Sector 6 Bucharest Romania Martin Mönnigmann is with Automatic Control and System Theory Ruhr University Bochum Bochum Germany

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Solving the explicit model predictive control (MPC) problem entails enumerating a list of critical regions and their ancillary feedback laws. Unfortunately, their number and the time required to compute them increase exponentially with the problem size (state-space model dimension and length of the prediction horizon). We show that when, as it is often the case, the problem’s constraints take the form of boxes or zonotopes, the resulting feasible domain can be compactly described as a constrained zonotope. Subsequently, we investigate whether, and under which circumstances, the combinatorial structure of the constrained zonotope interpretation accelerates the computation of the explicit solution.

关键词： State-space methods Predictive control

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Path Planning for Autonomous Underwater Vehicles in Complex Environments Based on a PRM* Algorithm

Path Planning for Autonomous Underwater Vehicles in Complex ...

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

作者： Ali Arifi Raja Jarray Soufiene Bouallègue Research Laboratory in Automatic Control (LARA) of National Engineering School of Tunis National Engineering School of Gabès University of Gabès Gabès Tunisia Research Laboratory in Automatic Control (LARA) of National Engineering School of Tunis High Institute of Industrial Systems of Gabès Gabès Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper proposes a Probabilistic Roadmap (PRM) planner for Autonomous Underwater Vehicles (AUVs) navigating in complex 3D environments. An improved version of PRM algorithms, called PRM*, is introduced to improve the capabilities of nodes specification, edges construction, and graph creation. For implementation, a pseudo-code for the proposed PRM* planner is provided. Numerical simulations are performed in different scenarios with increasing number of static obstacles and complexity. Evaluations of the generated collision-free paths are carried out using the Straight-Line Rate (SLR) and Computational Time (CT) metrics. Analyses and comparisons are performed to validate the significance and superiority of the proposed PRM* planner. Demonstrative results show that the PRM* algorithm outperforms the ordinary PRM one in terms of path compactness, smoothness, and collision avoidance.

关键词： Autonomous underwater vehicles Three-dimensional displays Navigation Probabilistic logic Numerical simulation Path planning Complexity theory Collision avoidance Vehicle dynamics Robots

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Uncertainty-Informed Model Selection Method for Nonlinear System Identification and Interpretable Machine Learning

Uncertainty-Informed Model Selection Method for Nonlinear Sy...

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Mediterranean Conference on control and Automation (MED)

作者： Yuanlin Gu Hua-Liang Wei Division of Computer Science and Maths University of Stirling Stirling United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify the optimal models among many potential candidates. This article proposes an uncertainty-informed method to address the model selection problem. The performance of the proposed method is evaluated on a dataset generated from a complex system model. The experimental results demonstrate the effectiveness of the proposed method and its superiority over conventional approaches. This method has minimal requirements for the length of training data and model types, making it applicable for various modeling frameworks.

关键词： Data-driven modeling Adaptation models Uncertainty Recurrent neural networks Training data Machine learning Data models

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Improving Motor Imagery-Based Brain-Computer Interfaces with Simple EEG Data Augmentation Algorithms: A Comparative Analysis

Improving Motor Imagery-Based Brain-Computer Interfaces with...

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Metrology for Extended Reality, Artificial Intelligence and Neural engineering (MetroXRAINE), IEEE International Conference on

作者： Sourojit Goswami Jacob Phelan Sean Anderson Mahnaz Arvaneh Automatic Control and Systems Engineering Sheffield Sheffield United Kingdom Biomedical Engineering University of Sheffield Sheffield United Kingdom Insigneo Institute Neuroscience Institute Sheffield

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

One of the primary challenges in motor imagery (MI) based brain-computer interfaces (BCIs) is the extensive calibration time required for individual users. This paper systematically reviews and compares various straightforward EEG data augmentation techniques aimed at reducing calibration time and enhancing model performance. We explored several algorithms, including segmentation and recombination in the time-frequency domain using short-time Fourier transform (TFSR), time warping (TW), noise injection (NI), and naïve transfer learning (NTL). Additionally, we introduced a novel algorithm called time instance segment concatenation (TISC), which merges EEG segments from periods immediately before and after the traditional motor imagery segments to capture a broader range of neural dynamics. Our experimental results on the BCI Competition IV dataset 2a indicated that NTL was the top-performing algorithm, achieving average accuracy improvements of 4.9%, 3%, 1.7%, and 3.2% over the baseline model without any data augmentation when 5, 10, 15, and 20 training trials per class were available, respectively. The proposed TISC algorithm showed potential, particularly in scenarios with moderate training trials, e.g., achieving an average accuracy increase of 1.5% over the baseline when 10 training trials per class were available. These findings suggest that TISC could be a useful addition to EEG data augmentation methods to improve the efficiency and usability of MI-based BCIs.

关键词： Training Image segmentation Heuristic algorithms Data augmentation Motors Brain modeling Electroencephalography Data models Brain-computer interfaces Calibration

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Robust Observer-Based control Design for Stochastic Discrete-Time Quasilinear systems via LMIs

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IEEE Transactions on automatic control 2025年

作者： Dehnert, Robert Rosik, Michelle Lerch, Sabine Rauh, Andreas Tibken, Bernd University of Wuppertal Institute of Automatic Control School of Electrical Information and Media Engineering WuppertalD-42119 Germany Carl von Ossietzky Universität Oldenburg Distributed Control in Interconnected Systems WuppertalD-26111 Germany

This work is dedicated to a less conservative observer-based controller design LMI method for discrete-time quasilinear systems with polynomial parameter dependencies subject to stochastic noise. While robust closed loop stability of the deterministic part of the closed loop is guaranteed by a Lyapunov-based approach, the performance is improved by a separate $\mathcal {D}_{\mathrm{R}}$ eigenvalue region for the controller and observer. In addition, input, process, and sensor noise is suppressed by solving an optimization problem with an objective function derived from the discrete-time Itô differential operator. Therefore, an iterative LMI method is utilized, which leads to less conservative optimization results. Polynomial parameter dependencies are treated by including the calculation of upper and lower bounds in the set of LMIs with the theorem of Ehlich and Zeller. Therefore, the polynomial nonlinearities are formulated by means of quasilinear system representations, which are interpreted as polynomial matrices. Thus, the polynomial nonlinearities are treated in their original polynomial form, without using polytopic approximations. As a result, the optimization of the controller parameters is improved in contrast to conventional polytopic approaches. The results of the novel approach of polynomial uncertainty representations are discussed in detail in two examples, wherein the reproducibility is guaranteed and the practical applicability of the approach is demonstrated. © 1963-2025 IEEE.

关键词： Stochastic systems

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Development of automatic Cartesian Robot Cannabis Seeder using LabVIEW

Development of Automatic Cartesian Robot Cannabis Seeder usi...

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International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering (ECTI-NCON)

作者： Rujipan Kosarat Keattipoom Sangwai Anan Wongjan Department of Software Engineering Faculty of Engineering Rajamangala University of Technology Lanna Chiangmai Thailand Department of Electronic Engineering and Automatic Control Systems Faculty of Engineering Rajamangala University of Technology Lanna Chiangmai Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This article describes the invention of autonomous cannabis seeding equipment to reduce contamination and planting time. The automated cannabis seeder employs an NI myRIO control board to operate a y-axis stepper motor attached to a ball screw to move along the breadth of each row of 14 seed trays. Soil drilling and burial Drill all holes at the same depth. This promotes seed germination and lowers human-seed germ contamination. These 7 soil drill heads and 7 seeders use 3D printers to design and shape the workpiece. An 800-watt air pump smokes the cannabis seeds. Linear electric motors function in two rows in the z-axis. The buffer reverses the drowning process from row 14 to the first row, starting from the 3rd row and continuing through the final series of steps until 14 rows in the y-axis are complete. The hardware controls all program activities in LabVIEW. We tested the automated cannabis seeding machine 100 times on average, sowing cannabis seeds into 98 seed trays and dropping 93 seeds at 94.80% efficiency in 10.38 minutes. A 16 percent faster machine than human work is available.

关键词： Technological innovation Motor drives Shape Soil Pulse width modulation Three-dimensional printing Seeds (agriculture) Printers Robots Contamination

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