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2024 IEEE Petroleum and Chemical Industry Conference Brasil, PCIC Brasil 2024

作者： Banda, Joseph K. Araujo, Lucas S. Brandao, Danilo I. Marafao, Fernando P. Tedeschi, Elisabetta Department of Electric Power Engineering Trondheim Norway Graduate Program in Electrical Engineering Belo Horizonte Brazil Department of Control and Automation Engineering Sorocaba Brazil University of Trento Department of Industrial Engineering Trento Italy

ISBN: (纸本)9798350368154

The electrification of offshore oil and gas platforms (OOGPs), using offshore wind turbines, is now a reality, aimed at minimizing carbon emissions. In this scenario, the utilization of battery energy storage systems (BESS) improves both efficiency and power quality. As this integration comes with a high CAPEX, industries demand exploring BESS for multiple ancillary services on OOGPs. Thus, this paper proposes an alternative approach to energizing a power transformer (PT) using the BESS power conversion system (PCS) on the platform, aiming to minimize inrush currents. This avoids the necessity for additional bulky devices, converters, or dc voltage sources specifically designated for this purpose. The strategy consists of manipulating residual core flux using BESS PCS followed by a controlled switching, which reduces inrush currents during PT energization. The results show effective reduction of the inrush current from 2.31 pu to 0.16 pu for the presented case study. © 2024 IEEE.

关键词： Power transformers

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Multiobjective Robust PI Synthesis in Plants with Uncertain Poles

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IFAC-PapersOnLine 2024年第7期58卷 174-179页

作者： Ricardo Massao Kagami Roberto Zanetti Freire Gilberto Reynoso-Meza Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifícia Universidade Católica do Paraná (PUCPR) Brazil Industrial and Systems Engineering Graduate Program (PPGEPS) Universidade Tecnológica Federal do Paraná (UTFPR) Brazil Control Systems Optimization Laboratory (LOSC) PUCPR Brazil

The use of multiobjective optimization procedures for control tuning has been constantly explored, presenting satisfactory results, and generating significant contributions to control engineering. In this way, the field of robust control can benefit significantly from the integration of multiobjective optimization design procedures for robust Proportional-Integral-Derivative (RPID) controller synthesis, enabling innovative solutions to complex control challenges. Due to this fact, this paper explores an approach to utilize multiobjective optimization algorithms in a simplified procedure, considering uncertain systems in the form of interval plants with uncertain poles, the Kharitonov theorem is used to obtain a region of stability as search space for the multiobjective algorithm, being the optimization process performed over a nominal system model. Two numerical cases and a flexible actuator model were used to demonstrate the procedure for RPI synthesis, resulting in robustly stable controllers with optimized performance over error and control signal effort measures.

关键词： Robust control multiobjective optimization robust stability Kharitonov theorem PI controller

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Controller tuning with multi-objective optimization: benchmark definitions for SISO linear systems: A PI controller case ⁎

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IFAC-PapersOnLine 2024年第7期58卷 126-131页

作者： Gilberto Reynoso-Meza Ricardo Massao Kagami Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Control Systems Optimization Laboratory (LOSC) Pontifícia Universidade Católica do Paraná (PUCPR)

Multi-objective optimization techniques are practical techniques for controller tuning purposes. A wide variety of papers use such an approach or propose new algorithms to approximate the Pareto front. Nevertheless, despite the expressive volume of works dealing with it, there is no standard benchmark testbed that could be used as a baseline comparison among different techniques. This work addresses such a gap, with a first step proposing a linear-SISO testbed with guidelines and rules and providing a basic example for a PI controller. Such a benchmark is expected to promote further research on the topic.

关键词： Multi-objective optimization Parametric optimization Evolutionary Algorithms

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Knowledge-Data Driven for Cyber-Physical Production systems in the Aerospace Industry: Current Issues and Emergent Technologies

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IFAC-PapersOnLine 2024年第19期58卷 1162-1167页

作者： Murillo Skrzek Anderson L. Szejka Fernando Mas Maria Jose Escalona Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil University of Seville (US) Seville Spain CT Engineering Group Spain

The aerospace manufacturing industry exhibits significant complexity in all its tasks. For instance, the aircraft’s main body comprises several components with multiple dimensions, geometries, and materials. This kind of manufacturing system is specialised in creating aerospace parts characterised by advanced technology, limited production quantities, and a high degree of customisation. The aerospace products and the corresponding manufacturing systems have extensive life cycles spanning decades and are repurposed to accommodate product variations. Any disturbance during the project progression has the potential to result in escalated expenses and time investments, leaving economic and environmental drivers. In this way, Cyber-Physical Production systems (CPPS) are emerging to reduce misinterpretation and mistakes across all stages of the manufacturing process. Therefore, the main aim of this paper is to discuss the current issues and emergent technologies across the literature review to address the following Research Issue 1 (RI1): What are the current issues and emergent technologies in CPPS and KDD for the Aerospace Industry? Research Issue 2 (RI2): What is the gap in CPPS and KDD for the aerospace industry? This initial literature review is concentrated on the Knowledge data-driven (KDD) to aid in developing CPPS for Aerospace Sheet Metal (ASM) parts manufacturing and examining the use of CPPS in the aerospace sector. Finally, this research contributes to the research community with an initial overview of research trends in the domain of KDD for CPPS in the aerospace industry and finds the main research gaps in this area.

关键词： Knowledge data-driven Cyber-Physical Production System Aerospace Industry Content Analysis

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Predicting Factors Influencing the Actual Use of E-Learning Platform among Medical Students in the Philippines: Unified Theory of Acceptance and Use of Technology Approach 24

Predicting Factors Influencing the Actual Use of E-Learning ...

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4th International Conference on Internet and E-Business, ICIEB 2024

作者： Benito, Omar Paolo Prasetyo, Yogi Tri Roque, Ralph Andre C. Flores, Fernan Patrick Cahigas, Maela Madel L. Nadlifatin, Reny Ayuwati, Irene Dyah International Bachelor Program in Engineering Yuan Ze University Taiwan Department of Industrial Engineering and Management Yuan Ze University Taiwan School of Industrial Engineering and Engineering Management Mapua University Philippines School of Graduate Studies Mapua University Philippines Department of Information Systems Sepuluh Nopember Institute of Technology Indonesia University of Surabaya Indonesia

ISBN: (纸本)9798400709739

eLearning has been utilized as a mode of instruction among medical students amidst the COVID-19 pandemic. The present study aimed to investigate the factors influencing the acceptance of eLearning platforms in medical education during the COVID-19 pandemic in the Philippines by employing the latent variables of UTAUT2, along with two additional latent variables gathered from various related literature. A sum of 360 medical students participated and answered an online survey that consists of 40 questions. A stepwise multiple linear regression was utilized and found that performance expectancy (PE), habit (HB), and instructor characteristics (IC), can successfully predict the actual use (AU) of medical students with a precision of 54.22%. A higher performance expectancy (PE), habit (HB), and instructor characteristics (IC) can enhance and promote the acceptance of medical eLearning platforms. This is the first study that has examined the acceptance of eLearning platforms among medical students in the Philippines during the COVID-19 pandemic. The outcomes of this research can serve as guidelines for the Commission on Higher Education of the Philippines for the enhancement of eLearning platforms. Although this study only utilized the latent variables of UTAUT2 along with two additional variables: Learning Value and Instructor Characteristics;the findings of this study would still hold significant value for promoting open innovation in eLearning platforms in medical fields worldwide. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： COVID-19

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Multi-Objective Grey Wolf Optimizer for the Tuning Process of the FOPID Controller

Multi-Objective Grey Wolf Optimizer for the Tuning Process o...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Allan Christian Krainski Ferrari Gideon Villar Leandro Leandro Dos Santos Coelho Gilberto Reynoso Meza Eletrical Engineering Department Federal University of Paraná UFPR Curitiba Brazil Electrical Engineering Graduate Program (PPGEE) Federal University of Paraná (UFPR) Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná Curitiba Brazil

This article implements the multi-objective grey wolf optimizer (MOGWO) in the tuning process of the gains of the Fractional-Order Proportional-Integral-Derivative (FOPID) controller applied in the control of a boiler system. For a sequence of 100 runs, this application had compared and analyzed with other implementation that uses MOGWO to optimize the gains of the classical Proportional-Integral-Derivative (PID) controller. In the computational simulation, the value hypervolume metric had used to analyze the performance of the controllers. In the results, the implementation of the FOPID showed superior to PID, where the comparison had validated by a hypothesis test. Despite the higher computational cost concerning the tuning process of the PID controller, this study proved that the FOPID controller can be advantageous for industrial applications.

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Towards an Integration of Augmented Reality in industrial Assembly Processes: Issues and Perspectives

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Procedia Computer Science 2025年 253卷 1063-1072页

作者： Bruno J. Souza Anderson L. Szejka Roberto Z. Freire Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana (PUCPR) Curitiba 80215-901 Brazil Federal University of Technology – Parana (UTFPR) Curitiba 80050-540 Brazil

The evolution of manufacturing has led to Industry 5.0, where human creativity is integrated with advanced technology to achieve efficient and sustainable production. Given the high volume and diversity of assembly tasks, operator assistance systems have become essential tools in the industry, leveraging augmented reality (AR) and other new technologies. This literature review specifically examines 19 papers on AR applications within the context of industrial assembly tasks. The review focuses on identifying the types of AR technologies utilized, the methodologies for implementing AR systems in assembly, and the primary limitations and challenges encountered. The findings indicate a growing field with promising applications in assembly, training, and maintenance, predominantly using AR glasses. Furthermore, a gap is identified indicating the absence of a standardized framework for AR-based assembly assistance systems. Future research should aim at creating a foundational framework for AR system implementation, thereby accelerating the adoption of innovative technologies in the industry.

关键词： Industry 5.0 augmented reality assembly assistance

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Multi-Objective Grey Wolf Optimizer for the Identification Process of a real MIMO System

Multi-Objective Grey Wolf Optimizer for the Identification P...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Allan Christian Krainski Ferrari Gideon Villar Leandro Leandro dos Santos Coelho Eletrical Engineering Department Federal University of Paraná UFPR Curitiba Brazil Electrical Engineering Graduate Program (PPGEE) Federal University of Paraná (UFPR) and Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR)

This article implements the multi-objective grey wolf optimizer (MOGWO) in the identification process of a multivariable system. For a sequence of 100 runs, MOGWO approach had compared and analyzed with the grey wolf optimizer (GWO), the single-objective version. In the computational simulation, the determination coefficient values and mean square error had used to analyze the quality of the models obtained. In the results, MOGWO showed superior to GWO to the differences between the order of magnitude of the system outputs. The implementation shows that MOGWO and other multi-objective metaheuristics can be an alternative method to solve problems of identification processes of multivariable systems.

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Manta Ray Foraging Optimization Approaches on Multivariable PID Controller Tuning

Manta Ray Foraging Optimization Approaches on Multivariable ...

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Congress on Evolutionary Computation

作者： Yan Lieven Souza Luúio Luiza Scapinello Aquino Da Silva Viviana Cocco Mariani Leandro Dos Santos Coelho Electrical Engineering Graduate Program (PPGEE) Federal University of Parana (UFPR) Curitiba Brazil Department of Electrical Engineering Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) and Federal University of Parana (UFPR) Curitiba Parana Brazil Department of Electrical Engineering Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) and Federal University of Parana (UFPR) Curitiba Brazil

This work presents a performance comparison between the newly developed Manta Ray Foraging Optimization (MRFO) and two different variants created for tuning a decentralized fractional order proportional-integral-derivative (FOPID) controller for a multiple-input multiple-output (MIMO) application. The application consists of a ball mill pulverizing system to pulverize coal and maximize fuel efficiency. MRFO and its applications have the task of finding the optimal controller variable values to control the system's temperature and pressure. The MRFO is a metaheuristic based on the behavior of manta rays, and although it shows efficient performance it may be improved through the use of techniques which generate new variants of the algorithm. Two techniques were used in the de-velopment of new variants: Quantum mechanics and opposition-based learning. The three different versions of MRFO were used to minimize a custom fitness function which is a combination of the integral time squared error (ITSE) and the overshoot of the system response. Simulations were carried using Simulink and Matlab softwares. For analyzing the performances, statistical measures such as minimum, maximum, best, mean, median, and standard deviation of the fitness function over 50 runs were used. Additionally, the different variants were also compared for minimizing 10 benchmark functions. The results show that the use of the previously mentioned techniques improve the performance of the original MRFO in optimizing the FOPID to control the ball mill pulverizing system.

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Framework for Automated Synthetic Image Generation for Vehicle Detection 23

Framework for Automated Synthetic Image Generation for Vehic...

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Proceedings of the 2023 6th International Conference on Sensors, Signal and Image Processing

作者： Vitória Biz Cecchetti Bruno José Souza Roberto Zanetti Freire Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Brazil and Pumatronix Electronic Equipment Ltd. Brazil Industrial and Systems Engineering Graduate Program (PPGEPS) Universidade Tecnológica Federal do Paraná (UTFPR) Brazil

ISBN: (纸本)9798400707995

The use of synthetic images has been highlighted within the computer vision field as a more practical option that helps reduce the time and cost spent on vehicle detection tasks in urban environments. This paper presents a framework for automated synthetic image generation, aiming at the detection of vehicles in urban environments. The creation of the 3D scenario is carried out through the Blender software. Some adverse conditions of the scenes were developed, including: rain, fog, occlusion by tree branches and electrical power lines, and low lighting. Then the algorithm for the automated generation of images was developed based on parameters chosen by the user to generate the scene and to generate the annotations of the images. Additionally, a convolutional neural network with YOLOv5 architecture along with a cross-validation technique was applied to carry out vehicle detections on the developed synthetic dataset, on both real and mixed datasets. The proposed framework generated a consistent dataset that can be used in vehicle detection networks, obtaining a mAP 0.5 above 96% and mAP 0.5:0.95 above 82%, also the precision values remained above 92%, and recall above 95%. It was also observed that the combination of synthetic images with real images can improve the performance of the model.

关键词： Computer vision Convolutional neural networks Object detection Synthetic images Vehicle detection.

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