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2021 IEEE International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2021

作者： Kharusi, Khalfan Al Haffar, Abdelsalam El Mesbah, Mostefa Protection Control Section Distribution Code Review Panel Muscat Oman Electrical and Computer Engineering Sultan Qaboos University Muscat Oman

ISBN: (纸本)9781665412629

This paper presents a supervised machine learning (ML)-based protection approach for identifying different types of transmission lines' faults, including faults during power swing, at several locations and variable fault resistances. Descriptive statistical features used in classification were extracted from spectrograms of measured signals at the relay point. Decision trees (DT)., support vector machines (SVM), k-nearest neighbors (k-NN), boosted and bagged ensemble tree classifiers were used for classification. Four feature selection algorithms, namely neighborhood component analysis (NCA), minimum redundancy maximum relevance (mRMR), sequential feature selection (SFS), and fit ensemble of learners, were applied to reduce the number of features. The synthetic minority class oversampling technique (SMOTE) balances the data to prevent biases toward the majority (non-fault) class. The results show that ensemble bagged trees with SMOTE achieve the maximum accuracy and the minimum false-negative rates. Feature selection algorithms show no improvement in the performance of the classifiers. The best classifier was then tested using data from an unseen scenario and showed accurate detection of the fault events. The classifier, however, was not able to detect faults during power swing after integrating a photovoltaic (PV) plant behind the relay point in the protected line. © 2021 IEEE.

关键词： Feature Selection

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Special Session: D-MATE: A Design Methodology for Connecting Automatic Test Equipment in Industry 4.0

Special Session: D-MATE: A Design Methodology for Connecting...

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Latin American Test Workshop, LATW

作者： Francesco Biondani Francesco Tosoni Nicola Dall'Ora Enrico Fraccaroli Sara Vinco Dong Seon Cheng Franco Fummi Department of Engineering for Innovation Medicine Section of Engineering and Physics University of Verona (Italy) Department of Engineering Sciences Guglielmo Marconi University Italy Department of Control and Computer Engineering Politecnico di Torino Italy

ISBN: (数字)9781665477635

ISBN: (纸本)9781665477642

With the growing presence of semiconductor devices in healthcare, automotive, and consumer electronics, Automatic Test Equipment (ATE) systems play an increasingly vital role in ensuring quality and reliability during validation. Despite their importance, ATE systems often operate in isolation from other manufacturing processes, limiting interoperability and integration potential. Consequently, fully incorporating ATE systems within the Industry 4.0 framework remains a largely unaddressed challenge. To bridge this gap, we propose adopting Open Platform Communications Unified Architecture (OPC UA), the industry de-facto standard communication protocol for machines, with an accompanying specification tailored to ATE systems. We developed and validated our information model on an advanced ATE system, demonstrating its practical application. The results showcase the successful integration of the ATE system into a fully-fledged Industrial computer Engineering (ICE) laboratory demonstrator. This study validates the effectiveness of our model in a real-world scenario and highlights the significance of our integration approach within the context of Industry 4.0.

关键词： Automatic test equipment Protocols Manufacturing processes Computational modeling Semiconductor devices Medical services Fourth Industrial Revolution Reliability Standards Manufacturing automation

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arXiv

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arXiv 2023年

作者： Misra, Rahul Wisniewski, Rafal Kallesøe, Carsten Skovmose Bujorianu, Manuela L. Automation and Control section Department of Electronic Systems Aalborg University Aalborg East9220 Denmark Department of Computer Science University College London United Kingdom

We study N-player finite games with costs perturbed due to time-varying disturbances in the underlying system and to that end we propose the concept of Robust Correlated Equilibrium that generalizes the definition of Correlated Equilibrium. Conditions under which the Robust Correlated Equilibrium exists are specified and a decentralized algorithm for learning strategies that are optimal in the sense of Robust Correlated Equilibrium is proposed. The primary contribution of the paper is the convergence analysis of the algorithm and to that end, we propose an extension of the celebrated Blackwell’s Approachability theorem to games with costs that are not just time-average as in the original Blackwell’s Approachability Theorem but also include time-average of previous algorithm iterates. The designed algorithm is applied to a practical water distribution network with pumps being the controllers and their costs being perturbed by uncertain consumption by consumers. Simulation results show that each controller achieves no regret and empirical distributions converge to the Robust Correlated Equilibrium. © 2023, CC BY-NC-ND.

关键词： Game theory

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Paper Defects Recognition Based on Deformable Convolution

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Palpu Chongi Gisul/Journal of Korea Technical Association of the Pulp and Paper Industry 2022年第2期54卷 37-50页

作者： Qu, Yun-Hui Tang, Wei Feng, Bo Computer Teaching and Research Section Xi’an Medical University China Department of Electric and Control Engineering Shaanxi University of Science & Technology China

There are some problems in traditional paper defects classification, such as the poor generalization performance, less types of recognition, and insufficient recognition accuracy. The deep learning method provides a new scheme for paper defects classification. However, convolutional neural network has strict requirements for the size of the input image. This requires that in the process of practical engineering application, for the collected paper defect images to be classified, the area containing paper defect must be segmented during preprocessing, and then the size of the paper defect area must be adjusted to meet the input requirements of the adopted classifier. To solve the above problems, the two-stage target detection network Faster R-CNN (Region-Convolutional Neural Network) was used in paper defects recognition to solve the problem of the size requirements of the input image;In addition, the deformable convolution layer was added after the traditional convolution layer to learn the characteristics of paper defects more efficiently and accurately, so as to improve the accuracy and accuracy of paper defects recognition and classification;Finally, the deformable RoI (Region-of-Interest) pooling layer was used to replace the RoI pooling layer of classic Faster R-CNN to locate and classify the paper defects area more accurately. Experiments show that the proposed algorithm has a further improvement in accuracy and scalability compared with the previous algorithm. © 2022 Korean Technical Assoc. of the Pulp and Paper Industry. All rights reserved.

关键词： Convolution

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Impact of diameter on D-shaped POF sensors for salt detection

Impact of diameter on D-shaped POF sensors for salt detectio...

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International Laser Technology and Optics Symposium 2024, iLATOS2024

作者： Khan, Abdul Ali Thirunavakkarasu, Punithavathi M. bte Saidin, Norazlina Noor, Ahmad Shukri Muhammad Waqas, Nawaf Communication Technology Section Universiti Kuala Lumpur British Malaysian Institute Kuala lumpur53100 Malaysia Faculty of I.C.T BUITEMS Quetta87100 Pakistan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Selangor Malaysia Department of Instrumentation and Control Engineering Universiti Kuala Lumpur British Malaysian Institute Bt. 8 Jalan Sungai Pusu Selangor Gombak53100 Malaysia

This study investigates the relationship between the diameter of a D-shaped plastic optical fiber (POF) sensor and its optical response as a function of varying salt concentrations. A simple and efficient dry etching polishing technique was employed to create D-shaped POF sensors with a range of diameters. All the fabricated sensors were carefully developed to maintain a consistent length of 20 cm with a sensing region of 1cm. To evaluate the optical intensity response, each sensor, with its respective diameter, was examined with the salt concentration ranging from (0 to 5%) examination utilizing a spectrometer. The sensor response was characterized by quantifying the variations observed in the intensity of the optical signal propagating through the sensing region. The sensors having a diameter of 500 μm, 700 μm, and 900 μm had a sensitivity of 149 A.U/%salt, 66 A.U/%salt, and 11.2 A.U/%salt respectively. The sensitivity of the 500 μm diameter sensor increased 13 times approximately as compared to the sensor having diameter of 900 μm. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Dry etching

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Design and Stability Analysis of control System in Multiport Autonomous Reconfigurable Solar Power Plants (MARS)

Design and Stability Analysis of Control System in Multiport...

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IEEE Workshop on computers in Power Electronics

作者： Qianxue Xia Suman Debnath Phani R V Marthi Maryam Saeedifard Energy Systems Integration & Control Section Oak Ridge National Lab Knoxville United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

The Multiport Autonomous Reconfigurable Solar Power Plant (MARS) is an integrated photovoltaic (PV) power generation and energy storage system (ESS), that is designed to connect to both alternating current (AC) transmission grids and high-voltage direct current (HVDC) links. It is a three-phase plant consisting of numerous components with a complex hardware and hierarchical control architecture. This paper presents an approach to decouple the multivariable system of MARS using a recursive reduced-order and boundary layer system methodology. This approach enables efficient computation of the control parameters for the Ll, L2, and L3 controllers. To validate the effectiveness of the proposed control strategy, cyclic tests in accordance with pre-defined performance criteria using controller Hardware-in-the-Loop (cHIL) experiments are conducted. The results demonstrate that the MARS system operates consistently under steady-state conditions. Furthermore, the dynamic response of the MARS system to various grid events is analyzed, underlining the resilience of MARS in presence of faults or loss of generation within the connected WECC system.

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Machine Learning and Intelligent Network System based Covid 19 Health Care System 3

Machine Learning and Intelligent Network System based Covid ...

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3rd International Conference on Power, Energy, control and Transmission Systems, ICPECTS 2022

作者： Basha, Amjath Ali Jakul Saravanan, K. Mangayarkarasi, T. Balarengadurai, C. University of Technology and Applied Sciences Ibra Sultanate of Oman RCC - ENGG Department Computer Engineering Electrical and Electronic Section Department of Engineering Oman R.M.D Engineering College Department of Information Technology Tamil Nadu Chennai India Sri Sairam Engineering College Department of Instrumentation and Control Engineering Tamil Nadu Chennai India Vidyavardhaka College of Engineering Department of Computer Science and Engineering Karnataka Mysuru India

ISBN: (纸本)9781665462754

COVID-19 has lately infected a big number of people worldwide. Medical service frameworks are strained as a result of the infection. The emergency unit, which is part of the medical services area, has experienced several challenges as a result of the low data quality offered by existing ICU clinical equipment. The Internet of Things has enhanced the capability for essential information mobility in medical services in the twenty-first century. Nonetheless, many of today's ideal models use IoT innovation to assess patients' well-being. As a result, executives lack understanding regarding the most effective method to apply such innovation to ICU clinical equipment. The IoT Based Paradigm for Medical Equipment Management Systems, a breakthrough IoT-based paradigm for successfully administering clinical hardware in ICUs, is introduced in this study. During the COVID-19 episode, IoT technology is used to boost the data stream between clinical hardware, executive frameworks, and ICUs, enabling the maximum level of openness and reasonableness in clinical equipment redistribution. IoT MEMS conceptual and functional features were painstakingly drawn. Using IoT MEMS expands the capacity and limits of emergency clinics, effectively easing COVID-19. It will also have a substantial impact on the nature of the data and will improve the partners' trust and transparency. © 2022 IEEE.

关键词： COVID-19

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Feedforward control in the Presence of Input Nonlinearities: A Learning-based Approach

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IFAC-PapersOnLine 2022年第37期55卷 235-240页

作者： Jilles van Hulst Maurice Poot Dragan Kostić Kai Wa Yan Jim Portegies Tom Oomen Control Systems Technology Section Dept. of Mechanical Engineering Eindhoven University of Technology The Netherlands Center of Competency ASM Pacific Technology The Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology The Netherlands Delft Center for Systems and Control Delft University of Technology The Netherlands

Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input.

关键词： Nonlinear system identification Identification for control Iterative learning control Data-based control Motion control Applications in semiconductor manufacturing

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Resilient Set-based State Estimation for Linear Time-Invariant Systems Using Zonotopes

arXiv

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arXiv 2022年

作者： Niazi, Muhammad Umar B. Alanwar, Amr Chong, Michelle S. Johansson, Karl Henrik Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Computer Science and Electrical Engineering Jacobs University Bremen Germany Control Systems Technology Section Department of Mechanical Engineering Eindhoven University of Technology Netherlands

This paper considers the problem of set-based state estimation for linear time-invariant (LTI) systems under time-varying sensor attacks. Provided that the LTI system is stable and observable via every single sensor and that at least one sensor is uncompromised, we guarantee that the true state is always contained in the estimated set. We use zonotopes to represent these sets for computational efficiency. However, we show that intelligently designed stealthy attacks may cause exponential growth in the algorithm’s worst-case complexity. We present several strategies to handle this complexity issue and illustrate our resilient zonotope-based state estimation algorithm on a rotating target system. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear time-invariant system

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Gaussian Processes for Advanced Motion control*

Gaussian Processes for Advanced Motion Control*

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American control Conference (ACC)

作者： Maurice Poot Jim Portegies Noud Mooren Max van Haren Max van Meer Tom Oomen Department of Mechanical Engineering Control Systems Technology Section Eindhoven University of Technology Eindhoven The Netherlands Department of Mathematics and Computer Science CASA Eindhoven University of Technology Eindhoven The Netherlands Delft Center for Systems and Control Delft University of Technology Delft The Netherlands

ISBN: (数字)9781665451963

ISBN: (纸本)9781665494809

Machine learning techniques, including Gaussian processes (GPs), are expected to play a significant role in meeting speed, accuracy, and functionality requirements in future data-intensive mechatronic systems. This paper aims to reveal the potential of GPs for motion control applications. Successful applications of GPs for feedforward and learning control, including the identification and learning for noncausal feedforward, position-dependent snap feedforward, nonlinear feedforward, and GP-based spatial repetitive control, are outlined. Experimental results on various systems, including a desktop printer, wirebonder, and substrate carrier, confirmed that data-based learning using GPs can significantly improve the accuracy of mechatronic systems.

关键词： Mechatronics Gaussian processes Machine learning Printers Feedforward systems Motion control Substrates

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