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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Luiz Marcio Spinosa Leandro dos Santos Coelho Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná Curitiba Parana Brazil

This paper introduces an approach to deal with IMS (Intelligent Manufacturing System) models, in order to help the development of Decision Support systems, Simulators and Performance Evaluators. The complexity of such systems is growing continuously requiring knowledge and information exchange between different manufacturing functions with more and more efficiency. This paper explores some potentialities of soft computing approaches and intelligent agent-oriented design including artificial neural networks, fuzzy systems, and evolutionary computation. The rationale is in order to support decision making processes performed by the autonomous and co-operative units of an IMS.

关键词： Computational modeling Manufacturing Object oriented modeling Artificial intelligence Artificial neural networks Evolutionary computation Fuzzy logic

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Multi-objective PID Controller Tuning for an industrial Gasifier

Multi-objective PID Controller Tuning for an Industrial Gasi...

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Congress on Evolutionary Computation

作者： Victor Henrique Alves Ribeiro Gilberto Reynoso- Meza Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil

Control of industrial plants is an important engineering field of study, where the optimization of such processes can lead to better product quality and higher profit. The present work deals with the optimization of an industrial gasifier, proposed as a benchmark challenge by ALSTOM Power Technologies in 2002. Researchers from around the globe proposed different methods for optimizing such system, using linear and nonlinear system identification techniques, different controller schemes, such as proportional-integral-derivative and model predictive controllers, and search methods for controller tuning. The authors care for the study of a methodology that can generalize the controller optimization process of real industrial systems, where linear system identification can lead to simpler systems, which are used to design more robust controllers. The proposed work deals with linear system identification and multiobjective optimization for the gasifier's controller design. To do so, the authors divide the problem in two steps, the linear system identification, where step functions are fed to the gasifier in order to detect the output response of the system, and the design of a proportional-integral-derivative controller, where spherical pruned multi-objective differential evolution algorithm is used in order to optimize the controller gains. The proposed method, unfortunately, does not provide an optimal controller, which can be explained by the complexity of the benchmark system. With such results, the authors believe a better multi-objective problem definition is necessary. Also, it is concluded that such benchmark presents a real challenge for future controller design methndologies.

关键词： Optimization Process control Benchmark testing Tuning PD control Genetic algorithms

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A modified gravitational search algorithm for continuous optimization 14

A modified gravitational search algorithm for continuous opt...

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16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion

作者： De, Emerson Hochsteiner Segundo, Vasconcelos Neto, Gabriel Fiori Da Silva, Andre Mendes Mariani, Viviana Cocco Dos Santos Coelho, Leandro Mechanical Eng. Grad. Program Pontifical Catholic University of Parana Curitiba PR Brazil Curitiba PR Brazil Mechatronics Engineering Undergraduate Program Pontifical Catholic University of Parana Curitiba PR Brazil Mechanical Engineering Graduate Program Pontifical Catholic University of Parana and Electrical Engineering Federal University of Parana Curitiba PR Brazil Industrial and Systems Eng. Graduate Program Pontifical Catholic University of Parana and Electrical Engineering Federal University of Parana Curitiba PR Brazil

ISBN: (纸本)9781450328814

The gravitational search algorithm (GSA) is a stochastic population-based metaheuristic inspired by the interaction of masses via Newtonian gravity law. In this paper, we propose a modified GSA (MGSA) based on logarithm and Gaussian signals for enhancing the performance of standard GSA. To evaluate the performance of the proposed MGSA, well-known benchmark functions in the literature are optimized using the proposed MGSA, and provides comparisons with the standard GSA.

关键词： Optimization

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Cascaded free search differential evolution applied to nonlinear system identification based on correlation functions and neural networks

Cascaded free search differential evolution applied to nonli...

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IEEE Symposium on Computational Intelligence in Control and Automation, CICA

作者： Helon Vicente Hultmann Ayala Luciano F. da Cruz Roberto Z. Freire Leandro dos Santos Coelho Industrial and Systems Engineering Graduate Program Pontificia Universidade Catolica do Parana Curitiba PR BR Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil Federal University of Parana Curitiba Brazil

This paper presents a procedure for input selection and parameter estimation for system identification based on Radial Basis Functions Neural Networks (RBFNNs) models and Free Search Differential Evolution (FSDE). We adopt a cascaded evolutionary algorithm approach and problem decomposition to define the model orders and the related model parameters based on higher orders correlation functions. Thus, we adopt two distinct populations: the first to select the lags on the inputs and outputs of the system and the second to define the parameters for the RBFNN. We show the results when the proposed methodology is applied to model a coupled drives system with real acquired data. We use to this end the canonical binary genetic algorithm (selection of lags) and the recently proposed FSDE (definition of the model parameters), which is very convenient for the present problem for having few control parameters. The results show the validity of the approach when compared to a classical input selection algorithm.

关键词： Modeling Correlation Predictive models Sociology Evolutionary computation Algorithm design and analysis

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Analyzing the Implications of New Technologies to the Management of Operations – Protocol Proposal and Application Illustration

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Procedia Manufacturing 2019年 39卷 904-912页

作者： Paulo Henrique Brunheroto Cassiano Souza Beller Fernando Deschamps Manufacturing Engineering Graduate Program Department of Mechanical Engineering Federal University of Parana Curitiba PR Brazil Industrial and Systems Engineering Graduate Program Polytechnic School Pontifícia Universidade Católica do Paraná Curitiba PR Brazil

The increasing adoption of new automation and information technologies are transforming production systems into cyber-physical systems (CPS). Although consulting companies have developed management frameworks to better characterize the implications of these technologies to how operations are managed, mainly focusing on new business models, efficiency, and effectiveness, the real impacts on decision areas (e.g., quality management) and performance objectives (e.g., flexibility) are still unclear. How are management systems being transformed? Are new managerial approaches emerging that address the particularities of new technologies? What are the critical success factors for conducting this transformation? To address these questions, a more systematic approach needs to be undertaken. This paper contributes to this by presenting a protocol to be used in systematic studies of the implications of the adoption of new technologies to the management of operations, and by illustrating its application to the analysis of the adoption of a cognitive vision system in an automotive manufacturer primarily deployed for improving product quality. The analysis protocol is based on Pettigrew’s dimensions of context, content, and process for change initiatives and is further subdivided into information requirements, variables to be analyzed and its potential sources of data. The case shows that the application of the protocol was able to reveal that besides expected effects on quality and productivity, new forms of interaction among workers and between workers and supervisors emerged that need further investigation. From the results of the case, future applications of the protocol are discussed.

关键词： management of operations transformation quality management cognitive vision system

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

Improved multiobjective particle swarm optimization for desi...

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IEEE Symposium on Computational Intelligence in Control and Automation, CICA

作者： Juliano Pierezan Helon H. V. Ayala Luciano F. da Cruz Roberto Z. Freire Leandro dos S. Coelho 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 Department of Electrical Engineering — DEE/PPGEE Industrial and Systems Engineering Graduate Program — PPGEPS and Federal University of Parana — UFPR Curitiba Brazil

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.

关键词： Optimization Manipulator dynamics Mathematical model Particle swarm optimization Vectors

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Addressing knowledge management transfer in project management at digital manufacturing

Addressing knowledge management transfer in project manageme...

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2018 Institute of industrial and systems Engineers Annual Conference and Expo, IISE 2018

作者： Muncinelli, Gianfranco De Lima, Edson Pinheiro Deschamps, Fernando Gouvea Da Costa, Sergio E. Beller, Cassiano S. Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil Federal University of Technology - Parana Curitiba Brazil Department of Mechanical Engineering Federal University of Parana Curitiba Brazil Superior Institute of Administration and Economy of Mercosul Curitiba Brazil

Manufacturing has been changing drastically the way it approaches facility design and implementation, that is, there is a growing adoption of digital techniques. This paper intends to determine the theoretical concepts and guidelines for assisting the study of alternatives for problem-solving in a specific case of an automaker digital manufacturing project. The main objective of the research is to determine the theoretical components that will assist in the forwarding of solutions to the problems observed in the current model of project management of digital manufacturing projects. The conceptual guidelines proposed based on systematic literature and guided the construction of research protocol that collects data from technical visits (semi-participative observation), technical documents and procedures, information systems log and archives, and semi-structured interviews. Data analysis use the mechanism of triangulation and workshops for producing a conceptual analysis about how each factor can assist in addressing the solving of the issues observed, building the theoretical constructs. The manufacturing engineering team could identify strengths and weakness on the ongoing practices and better plan improvements for reaching opportunities and mitigating threats/risks. The findings are classified in the following aspects: cultural, technology, knowledge content, organization, processes, and project management;and showed a complete analysis on how ready is the organization for developing digital manufacturing projects. © 2018 Institute of industrial Engineers (IIE). All rights reserved.

关键词： Knowledge management

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Proposal of a framework for a disaster management system

Proposal of a framework for a disaster management system

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2016 industrial and systems engineering Research Conference, ISERC 2016

作者： Foggiatto, Anderson Avanzi, Daniel de Freitas Rocha Loures, Eduardo Deschamps, Fernando Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil Instituto Curitiba de Informática Curitiba Brazil Federal University of Technology - Parana Curitiba Brazil Department of Mechanical Engineering Federal University of Parana Curitiba Brazil

During a disaster, the more coordinated the response, the lesser its impacts are. One of the factors that has a major influence in response coordination is the interoperability among involved actors - public and private entities that interact to exchange information through the communication of people and systems. This factor is critical because it allows an efficient and effective mobilization of available resources towards reducing the effects caused by an event. This work proposes a framework for the development of Disaster Management systems (DMS) that promote real time communication (information flow and process coordination) among different involved entities, primarily focusing on interoperability in its different levels. This framework is based both on a literature review of scientific works and a survey of worldwide initiatives related to improving disaster response. The main components of the related framework architecture are an SDK (software development kit), a common relational database, multiple communication platforms (e.g., a Web portal and a mobile app), and low-cost hardware to better collect and make needed information available quickly and accurately. © 2016 Proceedings of the 2016 industrial and systems engineering Research Conference, ISERC 2016. All rights reserved.

关键词： Interoperability

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NON-LINEAR PREDICTIVE CONTROLLERS FOR THERMAL COMFORT OPTIMIZATION AND ENERGY SAVING

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IFAC Proceedings Volumes 2006年第19期39卷 87-92页

作者： Roberto Z. Freire Gustavo H.C. Oliveira Nathan Mendes Industrial and Systems Engineering Graduate Program - PPGEPS Thermal Systems Laboratory - LST Pontifical Catholic University of Parana Curitiba/PR/Brazil - Zip Code 80215-901

The present work is focused on the study of indoor thermal comfort control problem in buildings equipped with heating systems. The occupants' thermal comfort sensation is addressed here by a comfort index known as PMV (Predict Mean Vote). In this context, two control algorithms are proposed by using only-one-actuator system associated to the heating system. The methods are based on the model predictive control scheme and on the improvement of the PMV index together with energy minimization. Simulation results - obtained by using the weather data file for the city of Curitiba, Brazil - for two case studies: i ) variations between low and moderate occupants metabolic rates and ii) variations between moderate and high occupants metabolic rates are presented to validate the proposed methodology in terms of room air temperature, relative humidity and PMV control.

关键词： predictive control thermal comfort heating systems non-linear control

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