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5th Experiment at International Conference, exp.at 2019

作者： Kagami, Ricardo Massao Kovalski Da Costa, Guinther Mendes, Luciano Antonio Freire, Roberto Zanetti Industrial and Systems Engineering Graduate Program-PPGEPS Pontifícia Universidade Católica Do Paraná-PUCPR Curitiba Brazil Department of Control and Automation Engineering Pontifícia Universidade Católica Do Paraná-PUCPR Curitiba Brazil

ISBN: (纸本)9781728136370

y associating active learning methodologies and digital media as a form to improve educational quality, the diversity of experimentations in engineering education has been motivating researches in many areas with possibilities of increasing both students' knowledge and skills. Control engineering is an area where educational laboratories are usually costly, thus the use of virtual laboratories for remote experimentation became an interesting approach. In this work, it is presented a remote experimentation procedure for the ball and beam system, which is a nonlinear and unstable open loop process. Considering the advanced control topic, multiobjective optimization was introduced in the experiment in order to tune Proportional-Integral-Derivative (PID) controllers. Through the description of the system model, the multiobjective optimization technique, and the experiment configuration, students can simulate and test their controllers remotely in a real plant. © 2019 IEEE.

关键词： Multiobjective optimization

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Control of a Refrigeration System Benchmark Problem: An Approach based on COR Metaheuristic Algorithm and TOPSIS Method

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IFAC-PapersOnLine 2019年第11期52卷 85-90页

作者： Ricardo Massao Kagami Gilberto Reynoso-Meza Eduardo A.P. Santos Roberto Zanetti Freire Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Brazil

The Proportional-Integral-Derivative (PID) controller is widely used in industrial applications. In order to evaluate the performance of distinct types of controllers, several benchmark systems are available in the specialized literature. In this work, an alternative approach to tune PID controllers based on the Competition Over Resources (COR) metaheuristic optimization algorithm and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method is proposed to solve a vapor compression refrigeration system problem, which is a benchmark control challenge. The proposed technique considered the normalization of multiple cost functions to simplify the selection of two PIDs’ parameters, reducing the need of understanding the influences of cost functions variations in complex problems. The proposed controllers outperformed the base line controllers, providing a performance index 68.39% better than the original approach. Finally, the proposed method was compared with other tuning methods.

关键词： Competition over resources algorithm controller tuning multi-objective problem multivariable control

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Multiobjective Cuckoo Search Applied to Radial Basis Function Neural Networks Training for System Identification

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IFAC Proceedings Volumes 2014年第3期47卷 2539-2544页

作者： Helon Vicente Hultmann Ayala Leandro dos Santos Coelho Industrial and Systems Engineering Graduate Program (PPGEPS) PUCPR Curitiba Brazil Electrical Engineering Graduate Program (PPGEE) UFPR Curitiba Brazil

In the present work we introduce a system identification framework where no a priori information on the system to be identified is available. Focusing in the specific case of radial basis functions neural networks models, we insert the choice of the model complexity and its inputs in the optimization procedure together with the model parameters, aiming at accuracy, model validity and regularization in a multiobjective approach. The multicriteria problem is solved by means of the multiobjective cuckoo search, which is based on an archiving technique and the crowding distance metric. Simulation results are promising when the methodology is applied to identify a robot arm given solely input and output data.

关键词： System Identification Multiobjective optimization Cuckoo Search Multiobjective Cuckoo Search Neural Networks Radial Basis Functions Robotics

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Applying machine learning to AHP multicriteria decision making method to assets prioritization in the context of industrial maintenance 4.0

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IFAC-PapersOnLine 2019年第13期52卷 2152-2157页

作者： Lima, Erick Gorski, Ewerton Loures, Eduardo F.R. Portela Santos, Eduardo A. Deschamps, Fernando Industrial and Systems Engineering Graduate Program Pontifícia Universidade Católica do Paraná Curitiba Brazil

The increasing competition among industries has led to the emergence of numerous tools and methods to support decision making focused on assets maintenance in a company, since ensuring good maintenance is directly linked with greater reliability and uptime for equipment, reducing losses in production processes and consequently increasing profitability. This work aims to use Machine Learning (ML) algorithms - Bayesian Networks (BN) and attribute relevance analysis (ARA), implemented in the Weka® platform, to process a dataset of event logs failure of industrial machine components. The approach aims to use the conditional probability relations generated by the BN and the ranking of criteria relevance for the design of an AHP decision-making model within the scope of industrial maintenance to prioritize which components of a specific machine are more susceptible to failures. The proposed integration mechanism aims to bring greater reliability to the weights assigned to the criteria of the AHP model, and consequently, a more accurate decision support. The results showed that the AHP model generated from a Bayesian Network is consistent with the conditional probabilities estimated by the BN, giving robustness to the decision sphere in the context of industrial maintenance. This AHP model can serve as a basis to be complemented by qualitative analysis criteria according to the need of the individual specialist, allowing the construction of strategic maintenance action plans. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Machine learning Analytic hierarchy process Bayesian networks Competition Decision making Decision support systems Hierarchical systems Maintenance Conditional probabilities Decision making models industrial maintenance Integration mechanisms Multi criteria decision making methods Qualitative analysis Relevance analysis Strategic maintenance

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Towards a Predictive Approach for Omni-channel Retailing Supply Chains

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IFAC-PapersOnLine 2019年第13期52卷 844-850页

作者： Pereira, Marina Meireles Frazzon, Enzo Morosini Graduate Program in Production Engineering Industrial and Systems Engineering Department Technology Center Federal University of Santa Catarina Florianópolis Santa Catarina Brazil

The adoption of omni-channel strategy has changed the relation between retailers and customers and brought more complexity to the retailing supply chains. To address the increasing complexity, it is necessary to adopt innovative approaches based on information technologies and intelligent decision methods. The challenges to retailers are improving the accuracy of offline and online channels demand forecasting, better managing offline and online customer's needs, thus reducing the uncertainties of the omni-channel retailing supply chain. In this context, this research paper aims to propose a predictive approach for omni-channel retailing supply chain combining clustering with artificial neural network to handle demand uncertainty. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Neural networks Complex networks Learning systems Sales Supply chain management Channel strategies Clustering Demand forecasting Demand uncertainty Innovative approaches Intelligent decisions Omni channel Retail supply chain

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Towards a smart reconfiguration process for complex product manufacturing based on industrial robot cells

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IFAC-PapersOnLine 2022年第2期55卷 108-113页

作者： Murillo Skrzek Leandro L. da Silva Anderson L. Szejka Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil

The cost for companies that can custom their production line with precision and quality, through machining centres and laser cutting machines, is expensive, as the degree of machining freedom increases. Furthermore, the greater the complexity of the product to be manufactured, the more processes and equipment are needed to manufacture it. In this context, this research proposes a smart reconfiguration process for complex product manufacturing, based on industrial robotic manipulators, with 6 or more degrees of freedom, superior to conventional methods. With the integration of SolidWorks and PowerMill software, it was possible to apply the solution in an experimental case of blisk manufacturing milling and laser cutting of an engine head gasket. With CAD, CAM and robot simulation by the PowerMill tool, it was possible to validate the proposed solution, obtaining process data and information of milling and laser cutting. With the analysis of the simulation results, it was possible to identify the limitations of the project, such as (i) restriction in the precision of the machining of the product; (ii) amount of real-time supervision and monitoring information; and (iii) work area limit. However, the project can integrate two manufacturing processes to machine and cut complex parts with flexibility.

关键词： Robotic manipulator Milling Laser Cutting Reconfigurable Manufacturing systems Complex Product Computer-Aided Manufacturing

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Swim velocity profile identification by using a modified differential evolution method associated with RBF Neural Network

Swim velocity profile identification by using a modified dif...

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2013 3rd International Conference on Innovative Computing Technology, INTECH 2013

作者： Santos Coelho, Leandro Dos Da Cruz, Luciano Ferreira Freire, Roberto Zanetti Pontifical Catholic University of Parana - PUCPR Industrial and Systems Engineering Graduate Program - PPGEPS Curitiba PR Brazil Federal University of Parana - UFPR Department of Electrical Engineering - DEE/PPGEE Curitiba PR Brazil

ISBN: (纸本)9781479900473

High level sports require a steady intensification of training in order to raise the athletes' performance. With the purpose of support swimmers and coaches new biomechanical analysis are been performed, becoming one of the most studied areas in swimming. By using technology resources, Esignificant results related to performance improvements are being achieved. Specific analysis of the movements, strength, velocity and projection allow identifying relevant points that directly impact athletes' results. In this context, this work uses a Radial Basis Function Neural Network (RBF-NN) with training combining the Gustafson-Kessel clustering method and the proposed Modified Differential Evolution (MDE) in order to perform the swimmer velocity profile identification. The main idea is to obtain the dynamic of the velocity profile and to use it to improve the athletes' swim style. Differential Evolution (DE) is an evolutionary algorithm that uses a rather greedy and less stochastic approach to solve problems when compared to other evolutionary methods [1]. However, to achieve good performance with DE, the tuning of control parameters is essential as its performance is sensitive to the choice of the mutation and crossover settings. On the other hand, the RBF-NN is a powerful approach for nonlinear identification. This paper combines the two strategies described above proposing a modified DE algorithm based on the association of a sinusoidal signal and chaotic sequences generated by logistic map for the mutation factor tuning. By using data collected from breaststroke and crawl swim style of an elite female swimmer, the validity and the accuracy of the RBF-NN model have been tested by simulations. Results reveal that it is feasible to establish a good model to represent data experimental related to swimming field. Identification results show that MDE outperforms both other tested classical DE approaches for training RBF-NNs in terms of solution quality. © 2013 IEEE.

关键词： Optimization

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Maintenance integration in a control-monitoring architecture based on Petri net with objects. Application to a flexible manufacturing system

Maintenance integration in a control-monitoring architecture...

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2006 IEEE Conference on Emerging Technologies and Factory Automation, ETFA

作者： Loures, E. Rocha Busetti De Paula, M.A. Pascal, J.-C. Graduate Program in Production System Engineering PUCPR Curitiba Brazil Laboratory for Analysis and Architecture of Systems LAAS/CNRS Toulouse France

ISBN: (纸本)1424406811

The problem of failure occurrence in flexible manufacturing systems (FMS) tends to increase according to their complexity leading to time-consuming tasks as the localisation and repairing. The occurrence of failures during the exploitation stage can deeply modify the FMS performances or its availability. We want to investigate the maintenance integration into a control-monitoring framework, from the control system's early design stage to the maintenance policies execution, so as to improve production time and minimize unplanned costly breakdowns of FMS. This paper proposes a modular and hierarchic control-monitoring-maintenance architecture (CMM) for FMS based on Petri nets with objects (PNO). These efforts acts as a basis for the control architecture of a robot-driven flexible cell, connected to the Ethernet-TCP/IP. © 2006 IEEE.

关键词： Flexible manufacturing systems

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Discovery of rare association rules in the distribution of lawsuits in the federal justice system of Southern Brazil

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International Journal of Business Intelligence and Data Mining 2021年第2期18卷 127-154页

作者： dos Santos Gruginskie, Lúcia Adriana Roehe Vaccaro, Guilherme Luís Chiwiacowsky, Leonardo Dagnino Blesz, Attila Elod Graduate Program in Production Engineering and Systems Unisinos Sao Leopoldo RS Brazil Program in Industrial Engineering UCS-Universidade de Caxias do Sul Bento Gonçalves City RS Brazil

In the context of data mining, infrequent association rules may be beneficial for analysing rare or extreme cases with very low support values and high confidence. In researching risky situations or allocating specific resources, such rules may have a much greater impact than rules with high support value. The objective of this study is to obtain association rules from the database of lawsuits filed in the Federal Court of Southern Brazil in 2016, including both frequent and rare rules. By finding these rules, especially rare ones, the information collected can assist in the decision-making process, in this case, such as training clerks or establishing specialised courts. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Association rules

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Improved multiobjective particle swarm optimization for designing PID controllers applied to robotic manipulator 4

Improved multiobjective particle swarm optimization for desi...

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4th IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2014

作者： Pierezan, Juliano Ayala, Helon H. V. Da Cruz, Luciano F. Freire, Roberto Z. Dos S. Coelho, Leandro Federal University of Parana- UFPR Electrical Engineering Graduate Program - PPGEE Curitiba Brazil Pontifical Catholic University of Parana - PUCPR Industrial and Systems Engineering Graduate Program - PPGEPS Curitiba Brazil Federal University of Parana - UFPR Department of Electrical Engineering - DEE/PPGEE Curitiba Brazil

ISBN: (纸本)9781479945313

In order to improve equipment efficiency in terms of performance, energy consumption and degradation for example, the industry has increased the use of control systems as the PID (proportional-integral-derivative) to a new baseline. This structure has few parameters to adjust and it is easy to implement practically. However, there are some requirements often included on multivariable systems that cannot be solved concurrently by classical methods. To solve this problem, the current paper approaches the application of Multiobjective Differential Evolution (MODE), Multiobjective Harmony Search (MOHS) and Multiobjective Particle Swarm Optimization (MOPSO) on multivariable PID controllers tuning. Moreover, an improved version of MOPSO (I-MOPSO) is proposed and its performance is compared with the other algorithms. In order to validate it under control systems, the optimization technique is applied on a two degree of freedom robotic manipulator. Finally, a detailed analysis is made on the I-MOPSO achievements. © 2014 IEEE.

关键词： Three term control systems

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