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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Ernesto Araujo Leandro dos Santos Coelho Instituto Nacional de Pesquisas Espaciais (INPE) Integration and Testing Laboratory Brazil Production and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Curitiba Pernambuco Brazil

The identification of a nonlinear system with chaotic behavior denoted Chua's oscillator by using fuzzy models intertwined with particle swarm optimization (PSO) method is presented. This hybrid approach is applied to experimental data generated by an inductorless Chua's circuit that consists of an electronic chaotic oscillator. Chua's circuit has been used as a test platform by various scientific and engineering communities related to the study of chaos due its great potencial in technological applications, for instance, telecommunications, cryptography and physics. Fuzzy set theory has been evolved as a powerful modeling tool that can cope with uncertainties and nonlinearities in modeling and identification procedures. The identification of a optimized fuzzy model Takagi-Sugeno (T-S) fuzzy model involves two primary tasks: parameter tuning and structure optimization. The premise part of production rules is optimized here by using the particle swarm optimization method. In turn, least mean squares technique is applied to the consequent part of a T-S fuzzy model. Results indicate PSO method and Least Mean Square technique succeeded in constructing a T-S fuzzy model when dealing with chaotic dynamics obtained through experimental data supplied by inductorless Chua's electronic circuit.

关键词： Particle swarm optimization Oscillators Chaos Circuit testing Nonlinear systems Fuzzy systems Hybrid power systems Power engineering and energy Cryptography Physics

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Chaotic Differential Evolution Applied to Electromagnetics Optimization

Chaotic Differential Evolution Applied to Electromagnetics O...

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IEEE Conference on Electromagnetic Field Computation

作者： L. dos Santos Coelho V.C. Mariani Production and Systems Engineering Graduate Program Automation and Systems Laboratory PUCPR/PPGEPS/LAS Pontifical Catholic University of Paraná Curitiba Brazil PUCPR/PPGEM Pontifical Catholic University of Paraná Curitiba Brazil

This work presents a new global optimization algorithm based on differential evolution (DE) method and DE combined with chaotic sequences (DEC) given by logistic map. In this paper, the optimal shape design of Loney's solenoids benchmark problem is carried out by DE and DEC algorithms. The results of DE and DEC approaches are also investigated and their performance compared with those reported in the literature

关键词： Chaos Solenoids Optimization methods Evolutionary computation Stochastic processes Genetic mutations Logistics Algorithm design and analysis Design optimization Electronic mail

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Nonlinear system Identification Based on B-Spline Neural Network and Modified Particle Swarm Optimization

Nonlinear System Identification Based on B-Spline Neural Net...

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International Joint Conference on Neural Networks (IJCNN)

作者： L. dos Santos Coelho R.A. Krohling Production and Systems Engineering Graduate Program Automation and Systems Laboratory PPGEPS/CCET/PUCPR Pontifical Catholic University of Paraná Parana Brazil Chair for Control Systems Engineering RST Faculty of Electrical Engineering and Information Technology University of Dortmund Dortmund Germany

Artificial neural networks, in particular, feedforward multilayer networks and basis function networks, have gradually established themselves as a usual tool in approximating complex nonlinear systems. B-spline networks, a type of basis function neural network, are normally trained by gradient-based methods, which may fall into local minima during the learning phase. In order to overcome the drawbacks encountered by conventional learning methods, particle swarm optimization - a swarm intelligence methodology - can provide a stochastic global search of B-spline networks for nonlinear system identification. In this paper, a modified particle swarm optimization algorithm using Gaussian and Cauchy probability distributions are applied to adjust the control points of B-spline neural networks. Simulation results for the identification of Rossler systems are provided and demonstrate the effectiveness and robustness of the proposed identification scheme.

关键词： Nonlinear systems Spline Neural networks Particle swarm optimization Artificial neural networks Multi-layer neural network Learning systems Stochastic systems Probability distribution Robustness

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Evaluating the implementation of GSCM in industrial supply chains: Two cases in the automotive industry

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Chemical engineering Transactions 2015年 43卷 1315-1320页

作者： Sellitto, Miguel A. Bittencourt, Sandro A. Reckziegel, Bárbara I. Universidade Do Vale Do Rio Dos Sinos - UNISINOS Production and System Engineering Graduate Program Av. Unisinos 950 Sao Leopoldo RS93022-000 Brazil

ISBN: (纸本)9788895608341

The effectiveness of implementation of Green Supply Chain Management (GSCM) can be evaluated by assessing a set of categorical indicators, structured to capture the complexity observed in selected, representative practices usually present in GSCM. The objective of this article is to test and refine a model for the evaluation of the effectiveness of implementation of GSCM in industrial supply chains (SC). Barriers, drivers, green strategy formulation, green procurement, green distribution, reverse logistics, ecodesign, green market, waste disposal, among other greening practices, are present in the model. The research question is how to evaluate the effectiveness of implementation of GSCM in industrial SC? The research method is the qualitative modeling. Previous research proposed a model consisting of three constructs, supported by three sets of green practices, associated with categorical indicators. The constructs are green strategy, green innovation, and green operations. The effectiveness of implementation of each practice is assessed by a five points Likert scale [1 = very high;0.75 = high;0.5 = fair;0.25 = weak;and 0 = null] answered by managers of the SC. Two real-world applications were made in two supply chains of the automotive industries, including suppliers, distributors and logistic operators. The focal companies are a tractors and engines manufacturer, and an electric auto parts manufacturer. The results were integrated by an importance vector obtained by Analytical Hierarchy Process (AHP) method and provided an overall index that can range between 0 % and 100 %. This index informs the global level of implementation of GSCM. The specific results of the cases were 76.25 % and 71.76 %. Improvement actions towards GSCM, conducted by management, should focus on the practices of the constructs with lowest contribution, green strategy and green innovation, respectively. Copyright © 2015, AIDIC Servizi S.r.l.

关键词： Automotive industry

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Artificial Intelligence-Based Recommendation system for Detecting and Diagnosing Broken Bars in Induction Motors Under Transient Operation

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IFAC-PapersOnLine 2024年第19期58卷 1156-1161页

作者： Narco A.R. Maciejewski Roberto Z. Freire Anderson L. Szejka Thiago P.M. Bazzo Sofia M.A. Lopes Rogério A. Flauzino Department of Electrical Engineering - DAELT-CT Universidade Tecnológica Federal do Paraná - UTFPR Curitiba PR 80230-90 Brazil Production and Systems Engineering - PPGEPS-PB Energy Systems - PPGSE-CT Industrial and Systems Engineering Graduate Program - PPGEPS Pontifical Catholic University of Parana - PUCPR Curitiba PR 80215-901 Brazil Electrical Engineering - PPGEE-SC São Carlos School of Engineering - EESC Universidade de São Paulo - USP São Carlos SP 13566-590 Brazil

Three-phase induction motors are the main elements for converting electrical energy into mechanical energy and are extensively used in industry. Reducing maintenance costs becomes an incentive for developing systems capable of identifying defects. This research proposes a framework for recommending machine learning algorithms that diagnose and detect broken bar defects in three-phase induction motors under transient operation based on artificial intelligence. Employing experimental data, features were extracted and selected based on current, voltage, and vibration. A protocol of insertion of white noise showed that the proposed framework admitted 80% of noise without losing the predictive capacity based on a multicriteria performance measure.

关键词： condition monitoring fault detection fault classification machine learning maintenance engineering smart systems three-phase induction motor

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Proposal of a Hotspot-based Approach to Identifying Social Impacts along the Product-Service systems Life Cycle in the Early Design Phases

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Procedia CIRP 2017年 64卷 85-90页

作者： Thayla T. Sousa-Zomer Paulo A. Cauchick-Miguel Post-graduate Program in Production Engineering Federal University of Santa Catarina Campus Universitário Trindade Caixa Postal 476 88040-970 Florianópolis SC Brazil Production and System Engineering Department Federal University of Santa Catarina Campus Universitário Trindade Caixa Postal 476 88040-970 Florianópolis SC Brazil

The potential for sustainability has been highlighted as one of the most important characteristics of product-service systems (PSS). Nevertheless, a PSS is not intrinsically sustainable. Methods and tools for PSS sustainability assessment should be developed to be integrated into the design process, especially in the early design phases, in order to conceive sustainable solutions. In addition, a PSS should be planned considering the three sustainability dimensions, from a life cycle perspective. However, PSS design and assessment considering the full life cycle is scarcely addressed in the literature, especially concerning the social dimension of sustainability. In this sense, this study proposes a streamlined life cycle assessment based approach to identify potential social impacts along the PSS life cycle that can be integrated into the early PSS design stages. The proposal is based on a hotspot analysis, which identifies where in the life cycle the most significant issues may occur. The proposal was developed in three main phases, anchored in both hotspots analysis and PSS literature. After developing it, 10 experts in PSS and sustainability assessed the proposal strengths and weakness. Some suggestions were offered by the researchers and improvements were introduced to the proposal regarding PSS life cycle phases and the social impact subcategories. The experts considered the hotspot analysis suitable to be applied in the early design stages when there is not much information yet regarding the system to conduct a complete life cycle assessment. The next step of this research is the proposal assessment by practitioners in industry. Further work will also integrate the environmental dimension of sustainability into the proposal.

关键词： sustainable product-service systems sustainability assessment social impacts early design stages

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Barriers to bicycle sharing systems implementation: analysis of two unsuccessful PSS

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Procedia CIRP 2018年 73卷 191-196页

作者： Suzana R. Moro Aline C. Imhof Diego C. Fettermann Paulo A. Cauchick-Miguel Post-graduate Program in Production Engineering Federal University of Santa Catarina Campus Universitário Trindade Caixa Postal 476 88040-970 Florianópolis SC Brazil Production and System Engineering Department Federal University of Santa Catarina Campus Universitário Trindade Caixa Postal 476 88040-970 Florianópolis SC Brazil

Bicycle sharing systems (BSS) arise as a proposal to respond to the challenges of urban mobility as a healthy, efficient, and relatively inexpensive mean of transportation. Although these systems are widespread, BSS still present challenges for implementation. In this context, this paper aims to identify the barriers to implementation of this kind of product-service systems (PSS) and to analyze which ones most impacted the failure of two BSS. The main results demonstrate that resistance to change both by users and those involved with the project hinders BSS implementation. Besides involving several actors, the system needs an adequate projection of the demand and costs. Some alternatives are: the integration of stakeholders in the development of the solution and the promotion of policies for the diffusion of sharing systems and education to conscious consumption. The results could be used in future PSS implementations to support overcoming the barriers.

关键词： Product-service system bicycle sharing system bike-sharing implementation barriers

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Multivariable nonlinear boiler power plant identification through neural networks and Particle Swarm Optimization approaches

Multivariable nonlinear boiler power plant identification th...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Fábio A. Guerra Helon V. H. Ayala André E. Lazzaretti Marcio R. Sans Leandro S. Coelho Cesar A. Tacla Eletroelectronics Department (DPEE) Electrical System Division (DVSE) Centro Politécnico da UFPR LACTEC-Institute of Technology for Development Curitiba Parana Brazil Industrial and Systems Engineering Graduate Program (LAS/PPGEPS) Pontifical Catholic University of Paraná Curitiba Parana Brazil Graduate Program in Eletrical Engineering and Industrial Informatics (CPGEI) Federal University of Technology Parana Curitiba Parana Brazil

The identification of nonlinear systems with artificial neural networks models has been successfully used in many applications. Most processes in industry are characterized by nonlinear and time-varying behavior. In this context, the identification of mathematical models for nonlinear systems is vital in many fields of engineering. The Radial Basis Function Neural Network (RBF-NN) is a powerful approach for nonlinear identification and can be improved using Particle Swarm Optimization (PSO) approaches. This paper presents a multivariable nonlinear system identification using RBF-NN combined with standard PSO and Constriction Factor PSO (CFPSO) approaches in order to determine the RBF-NN parameters. RBF-NN is considered to be a good choice for black-box modeling problems due to its rapid learning capacity and, therefore, has been applied successfully to nonlinear time series modeling and nonlinear identification. On the other hand, PSO was inspired by the choreography of bird flocks and fish schools and can be seen as a distributed behavior algorithm that performs multidimensional search. Furthermore, promising simulation results from performance analysis of the proposed RBF-NN with PSO training approaches are presented and discussed in this paper showing promising results.

关键词： Artificial neural networks Boilers Neurons Modeling Power generation Optimization Nonlinear systems

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Sustainable business models as an innovation strategy in the water sector: An empirical investigation of a sustainable product-service system

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Journal of Cleaner production 2018年第Sup期171卷 S119-S129页

作者： Sousa-Zomer, Thayla T. Cauchick Miguel, Paulo A. Post-graduate Program in Production Engineering Federal University of Santa Catarina Campus Universitário Trindade Caixa Postal 476 FlorianópolisSC88040-970 Brazil Production and System Engineering Department Federal University of Santa Catarina Campus Universitário Trindade Caixa Postal 476 FlorianópolisSC88040-970 Brazil

The complexity of the challenges in the water sector means that innovative solutions are required. These solutions include not only new sustainable technologies for water treatment, but also complementary innovation in business models to support the adoption of these technologies in urban areas. This paper aims to explore the role of sustainable business models in supporting technological innovations such as decentralized approaches for water quality and quantity improvements in urban areas. The private sector involvement can be valuable in overcoming the barriers and challenges to implementation of those approaches as well as to achieve the sustainability. A case study of the product-service system as a sustainable business model in an emerging economy was conducted to deliver insights into the role of new ways of value proposition, value creation, and value capture in supporting innovative technologies in the water sector and in contributing to sustainability. The results demonstrated that consumers' acceptance, risk perception, and confidence in decentralized approaches can be improved with the support of business models through a close integration with customers. The close integration among the actors can also lead to better management of the system. Further research can focus on how different types of sustainable business models could contribute to supporting water technologies in urban areas and increase social acceptability. © 2016 Elsevier Ltd

关键词： Sustainable development

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Electromagnetic Device Optimization using Improved Differential Evolution Methods

Electromagnetic Device Optimization using Improved Different...

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IEEE Conference on Electromagnetic Field Computation

作者： L. dos Santos Coelho P. Alotto Rcductim and Systems Engineering Gmduale Rogmm Aulmation and Systms Labaratcry PUCPR/PPGEPS/LAS Pontifical Catholic University of Paraná Curitiba Parana Brazil Production and Systems Engineering Graduate Program Aulmation and Systms Labaratory Curitiba Parana Brazil Dip. Inssgneria Eldtrica UnivslniU di I'adovn Italy

Recently, evolutionary algorithms (e.g. genetic algorithms, evolutionary programming, and evolution strategies) have proven to be useful tools for the optimization of difficult problems in electromagnetics. Differential evolution (DE) is one comparatively simple variant of an evolutionary algorithm using floating-point encoding and few control parameters. This work presents improved DE algorithms based on linearly time varying control parameters, sinusoidal functions, and diversity analysis of population

关键词： Electromagnetic devices Optimization methods Algorithm design and analysis Evolutionary computation USA Councils Writing Differential equations Encoding Convergence of numerical methods Testing

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