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15th Global Congress on Manufacturing and Management, GCMM 2021

作者： Bysko, Szymon Bysko, Sara Fratczak, Michal Nowak, Pawel Klopot, Tomasz Czeczot, Jacek Stebel, Krzysztof Laszczyk, Piotr Simulation Department Pro Point Sp. Z O.o. Sp. K. Ul. Bojkowska 37R Gliwice Poland Department of Systems Biology and Engineering Silesian University of Technology Ul. Akademicka 2A Gliwice Poland Department of Automatic Control and Robotics Silesian University of Technology Ul. Akademicka 16 Gliwice Poland

This paper deals with the validation of the concept of using virtual commissioning of the low-level industrial control systems to adjust PID controller settings. General hardware configurations and logical layers of the experimental setups, which were used during the experiments, are shown and described in details. It is shown how to determine the controller settings using pre-tuning and fine-tuning functionality, available in standard Siemens® Function Blocks Library, that have been launched in virtual commissioning environment. Comparison of the results is a basis to answer the question if the modern virtual commissioning tools currently used in industry have sufficient accuracy to be used as a master tool for the initial PID controller tuning. It is also important to know whether these systems are suitable for use as Industry 4.0 tools, as the simulation is one of its most important part. The first section in your paper © Published under licence by IOP Publishing Ltd.

关键词： Industry 4.0

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Quality Assessment of Electrocardiogram Signals Using Contrastive Learning

Quality Assessment of Electrocardiogram Signals Using Contra...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Meiping Zhong Zuoyong Li ChuanSheng Wang Jiayan Huang WanQi Zhao Haoyi Fan College of Computer and Big Data Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona Spain New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Quality assessment of electrocardiogram (ECG) signals holds significant importance in reducing the high-frequency false alarms in automated arrhythmia diagnosis. Existing quality assessment methods primarily rely on empirical threshold setting or the calculation of relevant statistical values to formulate decision rules. However, the thresholds set in this manner are typically based on experience and knowledge, resulting in decision rules that lack flexibility and struggle to adapt to changes in the dataset. To address this issue, many researchers have turned to deep learning algorithms for ECG signal quality assessment. Nevertheless, most existing deep learning methods usually require large-scale and labeled ECG data for training, whose acquiring is typically challenging. Therefore, this paper proposes an efficient method for ECG signal quality assessment. First, we use a contrast learning framework to construct sample pairs through multilevel data augmentation. Second, a densely connected convolutional network (DenseNet) is used to improve the model's ability of extracting local features in ECG signals, and a Squeeze-and-Excitation attention module is introduced to make the model pay more attention to critical features. Finally, the feature representation is learned by minimizing the contextual contrast loss to bring similar samples closer and push dissimilar samples farther away. In this way, we can utilize the contrastive learning framework to extract temporal representations from unlabeled data and transfer them to downstream ECG signal quality assessment tasks. Extensive experiments on the Physionet/CinC Challenge 2011 dataset demonstrate that our approach outperforms most existing quality assessment methods.

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Digital phenotyping from heart rate dynamics: Identification of zero-poles models with data-driven evolutionary learning

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Computers in Biology and Medicine 2025年 186卷 109596-109596页

作者： Patrascu, Adrian Ion, Andreea Vislapuu, Maarja Husebo, Bettina S. Tache, Irina Andra Reithe, Haakon Patrascu, Monica Centre for Interdisciplinary Research in Physical Education and Sport Babes-Bolyai University Cluj-Napoca Romania Complex Systems Laboratory University Politehnica of Bucharest Bucharest Romania Centre for Elderly and Nursing Home Medicine University of Bergen Bergen Norway Department of Nursing VID Specialized University Bergen Norway Neuro-SysMed Center University of Bergen Bergen Norway Department of Automatic Control and Systems Engineering University Politehnica of Bucharest Bucharest Romania Department of Image Fusion and Analytics Siemens SRL Brasov Romania

Heart rate response to physical activity is widely investigated in clinical and training practice, as it provides information on a person's physical state. For emerging digital phenotyping approaches, there is a need for individualized model estimation. In this study, we propose a zero-poles model and a data-driven evolutionary learning method for identification. We also perform a comparison with existing first and second order models and gradient descent identification methods. The proposed model is based on a five-phase description of heart rate dynamics. Data was collected from 30 healthy participants using a treadmill and a thoracic sensor in two protocols (static and dynamic), for increasing and decreasing activity. Results show that the zero-poles model is a good fit for heart rate response to exercise (Pearson's coefficient ρ>.95), while first and second order models are also suitable (ρ>.92). The evolutionary learning method shows excellent results for fast model identification, in comparison with least-squares methods (p © 2024 The Author(s)

关键词： Contrastive Learning

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Off-Policy Temporal Difference Learning for Perturbed Markov Decision Processes: Theoretical Insights and Extensive Simulations

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arXiv 2025年

作者： Forootani, Ali Iervolino, Raffaele Tipaldi, Massimo Khosravi, Mohammad Helmholtz Center for Environmental Research-UFZ Permoserstrasse 15 Leipzig04318 Germany Department of Electrical Engineering and Information Technology University of Naples Napoli80125 Italy Department of Electrical and Information Engineering Polytechnic University of Bari Bari70126 Italy Delft Center for Systems and Control Delft University of Technology Mekelweg 2 Delft2628 CD Netherlands

Dynamic Programming suffers from the curse of dimensionality due to large state and action spaces, a challenge further compounded by uncertainties in the environment. To mitigate these issue, we explore an off-policy based Temporal Difference Approximate Dynamic Programming approach that preserves contraction mapping when projecting the problem into a subspace of selected features, accounting for the probability distribution of the perturbed transition probability matrix. We further demonstrate how this Approximate Dynamic Programming approach can be implemented as a particular variant of the Temporal Difference learning algorithm, adapted for handling perturbations. To validate our theoretical findings, we provide a numerical example using a Markov Decision Process corresponding to a resource allocation problem. © 2025, CC BY-NC-ND.

关键词： Contrastive Learning

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Development of AC Voltage Stabilizer with Microcontroller-Based control System

Development of AC Voltage Stabilizer with Microcontroller-Ba...

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International Computer Sciences and Information Technologies (CSIT)

作者： Andriy Holovatyy Andrzej Łukaszewicz Vasyl Teslyuk Nazariy Ripak Department of Computer-Aided Design Systems Lviv Polytechnic National University Lviv Ukraine Department of Machinery Design and Operation Faculty of Mechanical Engineering Bialystok University of Technology Bialystok Poland Department of Automated Control Systems Lviv Polytechnic National University Lviv Ukraine

ISBN: (纸本)9798350334326

In the paper, the model and prototype of the AC stabilizer have been developed based on the AVR ATmega328P microcontroller. The block diagram of the device, the electronic circuit and the algorithms of the system operation have been developed. In particular, the algorithm includes software in C in the Arduino IDE. The AC voltage stabilizer is designed using the modular principle, which makes it possible to quickly upgrade. The AC voltage stabilizer makes it possible to maintain the supply voltage on the load within acceptable limits with significant deviations, has the ability to adjust the voltage range of the network to operate the stabilizer, manual adjustment potentiometers.

关键词： Semiconductor device modeling Microcontrollers systems operation Software algorithms Prototypes Software Voltage control

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Pushing and Rotating a Heavy Mass Rigid Body Using an Omnidirectional Aerial Manipulator

Pushing and Rotating a Heavy Mass Rigid Body Using an Omnidi...

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International Conference on Robotics and Artificial Intelligence (ICRAI)

作者： Hameed Ullah Santos Miguel Orozco Soto Naveed Mazhar Irfan Ahmad Vincenzo Lippiello Fabio Ruggiero Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy Science and Technology College Autonomous University of Mexico City Mexico City Mexico Unmanned Vehicles Research Lab DEE CEME National University of Sciences & Technology Islamabad Pakistan Department of Automatic Control and Systems Engineering School of Bilbao University of the Basque Country Bilbao Spain

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

This paper presents the pushing and rotation of a heavy rigid body using an omnidirectional actively tilted aerial manipulator, controlled via a hybrid force/position controller. Unlike conventional unmanned aerial vehicles, the proposed system enables precise horizontal force application, making it ideal for aerial physical interaction tasks. The hybrid controller is implemented in the Robot Operating System and validated through extensive simulations in a Gazebo physics-engine-based environment. The manipulator's performance was evaluated in two scenarios: circular trajectory tracking and the rotation of a heavy rigid mass. Results demonstrated its capability to follow complex trajectories with high accuracy and exert sustained forces for object manipulation. The system exhibited excellent stability and precision, underscoring its potential for real-world applications in challenging environments.

关键词： Accuracy Trajectory tracking Force Refining Rotors Stability analysis Robustness Trajectory Robotics and automation Manipulator dynamics

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Development of CAE and CAD Sy1stem for Six-Axis Manipulator control System

Development of CAE and CAD Sy1stem for Six-Axis Manipulator ...

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Educational Innovation (ECEI), IEEE Eurasian Conference on

作者： Hao-Jyun Jhang Chih-Cheng Chen Wun-Jie Zheng Chiu-Hung Chen Cheng-Fu Yang Wen-Yuan Yu Department of Automatic Control Engineering Feng Chia University Taichung Taiwan School of Ocean Information Engineering Jimei University Xiamen China Department of Chemical and Materials Engineering Feng Chia University Taichung Taiwan Artificial intelligence of intelligent manufacturing systems (AIMS) Research Center National Tsing Hua University Hsinchu Taiwan

Automated mechanical equipment is adjusted as the production line changes with the manipulator. Through real-time equipment monitoring and management, it is ensured that the equipment runs and adjust at any time. Modern equipment monitoring and control are used to accurately judge equipment usage status with the help of sensors, big data, the Internet, and artificial intelligence. The computer-aided design (CAD) is used to establish a six-axis manipulator including the dimensions of each axis, the speed, and acceleration of the motor, and kinematics. All the data is collected in one archive to create a 3D model and simulate and analyze its performance with the computer-aided engineering (CAE) method.

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Nonlinear MPC and MHE of a Gasoline controlled Auto-Ignition Engine based on Reduced-Order Differential Equation Models

Nonlinear MPC and MHE of a Gasoline Controlled Auto-Ignition...

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IEEE Conference on Decision and control

作者： Jochem De Schutter Eugen Nuss Katrin Baumgärtner Dirk Abel Moritz Diehl Department of Microsystems Engineering (IMTEK) and Department of Mathematics Systems Control and Optimization Laboratory University of Freiburg Freiburg Germany Institute of Automatic Control RWTH Aachen University Aachen Germany Department of Mathematics University of Freiburg Freiburg im Breisgau Germany

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Gasoline controlled Auto-Ignition (GCAI) is a novel combustion technology that promises higher efficiency and lower pollutant emissions than conventional combustion engines. These advantages come at the cost of a high sensitivity to disturbances and instabilities in combustion phasing. Closed-loop control methods such as linear model predictive control are able to stabilize the process, although the operating range remains limited. Nonlinear model predictive control and moving horizon estimation are able to optimize the fully nonlinear system model and promise higher prediction and estimation accuracy. Nonlinear models based on differential equations offer high physical detail and flexibility, however they pose a high computational burden on the control and estimation algorithms. This paper proposes an efficient, real-time capable MPC and MHE scheme based on the Real Time Iteration (RTI) for a continuous-time GCAI model. We achieve a feedback delay of below 1 ms and RTI preparation time of below 18 ms in numerical simulations on the embedded hardware of an existing GCAI engine test setup. Thereby the proposed scheme satisfies the computational constraints imposed by the engine speed by a safety factor of 4.

关键词： Computational modeling Estimation Mathematical models Real-time systems Combustion Hardware Numerical models

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Validation of Moment Monitoring Results in Rolling Mill Stand Spindles

Validation of Moment Monitoring Results in Rolling Mill Stan...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Olga A. Gasiyarova Boris M. Loginov Andrey A. Radionov Vadim R. Gasiyarov Vadim R. Khramshin Department of Material Science LLC NPP Uchtekh-Profi Chelyabinsk Russia Magnitogorsk Iron and Steel Works Magnitogorsk Russia Department of Automation and Control Moscow Polytechnic University Moscow Russia Power Engineering and Automated Systems Institute Nosov Magnitogorsk State Technical University Magnitogorsk Russia

The authors highlight the need for the development and industrial deployment of online monitoring systems for the elastic moment on the rolling mill stand spindles. An example of a 5000 plate mill was used to demonstrate that these systems are made due to the need for a reliable moment assessment in dynamic modes. This problem is relevant for the development of control systems limiting the maximum (amplitude) values of moments when the workpieces enter the stand. The shock loading causes a multifold increase of the dynamic moment over the steady rolling moment. This results in the fatigue breakdown of spindle connections., as well as mill failures and emergency stops. The authors provide information on a telemetric elastic moment monitoring system installed on a 5000 mill. The data were validated in the industrial context. To achieve this, the authors propose a procedure where experimental research is carried out in two stages. During the first stage, the electromagnetic moment of the motor is compared to the spindle moment in the quasi-steady rolling mode when the elastic properties of the spindle are not manifested. During the second stage, the elastic moments of the upper and lower roll motors are assessed during 9 passes of reverse rolling. Some conclusions are made about the proportion of roll motor loads, spindle moment amplitudes, and the relevance of aligning rolling moments through the improvement of electric drive speed control algorithms.

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Linearized ADMM for Nonsmooth Nonconvex Optimization with Nonlinear Equality Constraints

Linearized ADMM for Nonsmooth Nonconvex Optimization with No...

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IEEE Conference on Decision and control

作者： Lahcen El Bourkhissi Ion Necoara Panagiotis Patrinos Department of Automatic Control and Systems Engineering Universitatea Nationala de Stiinta si Tehnologie Politehnica Bucuresti Bucharest Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest Romania Department of Electrical Engineering (ESAT-STADIUS) KU Leuven Leuven Belgium

This paper proposes a new approach for solving a structured nonsmooth nonconvex optimization problem with nonlinear equality constraints, where both the objective function and constraints are 2-blocks separable. Our method is based on a 2-block linearized ADMM, where we linearize the smooth part of the cost function and the nonlinear term of the functional constraints in the augmented Lagrangian at each outer iteration. This results in simple subproblems, whose solutions are used to update the iterates of the 2 blocks variables. We prove global convergence for the sequence generated by our method to a stationary point of the original problem. To demonstrate its effectiveness, we apply our proposed algorithm as a solver for the nonlinear model predictive control problem of an inverted pendulum on a cart.

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