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SSRN 2022年

作者： Lezama-Lope, Uriel I. Ríos-Mercado, Roger Z. Ceciliano-Meza, José Luis Graduate Program in Systems Engineering Mexico Tecnológico de Monterrey Department of Industrial Engineering Mexico

In 2015, the Ministry of Energy created a wholesale electricity market in Mexico. As a result, the country shifted from a vertically integrated regulation to a competitive market framework. In this context, the day-ahead market is essential to plan better the generation and sales of energy. This paper evaluates a hydro-thermal security-constrained unit commitment model and its solution method. The objective function determines the maximum economic surplus of the participants in the market. The problem is modeled as a mixed-integer non-linear programming incorporating real-world constraints from the particular problem and a hydropower generator function non-linear features. A decomposition approach that solves a master problem and a series of subproblems is developed. Furthermore, a new component has been added to deal with the non-linear features of the hydropower generator function using a first-order Taylor's approximation. Simulation results are presented starting by solving a representative case of the electricity market in Mexico, demonstrating its use. Moreover, to evaluate the performance, multiple instances are created from publicly available information on the day-ahead market in Mexico. The model can be helpful in markets with sizable hydropower sources to obtain a more accurate and timely system operation scheduling. © 2022, The Authors. All rights reserved.

关键词： Power markets

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Urban Digital Twins for Synthetic Data of Individuals with Mobility Aids in Curitiba, Brazil, to Drive Highly Accurate AI Models for Inclusivity

Urban Digital Twins for Synthetic Data of Individuals with M...

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8th International Conference on Science, Technology and Innovation for Society, CITIS 2023

作者： Luna-Romero, Santiago Felipe Stempniak, Charles Roberto de Souza, Mauren Abreu Reynoso-Meza, Gilberto Health Technology Graduate Program Pontifícia Universidade Católica do Paraná Curitiba80215-901 Brazil Automa Geo "Digital Twin Platform" Curitiba Brazil Industrial and Systems Engineering Graduate Program Pontifícia Universidade Católica do Paraná Curitiba80215-901 Brazil

ISBN: (纸本)9783031520891

This article delves into the significance of urban digital twins in generating synthetic data specifically tailored for individuals with disabilities. It explores how this data can be effectively utilized to construct artificial intelligence (AI) models that promote inclusivity and enhance accessibility in urban environments. The article thoroughly examines the challenges and opportunities associated with acquiring data on individuals with disabilities, taking into account the ethical and practical implications of generating and utilizing synthetic data for the development of accessible and inclusive technologies. Using the city of Curitiba, Brazil as a case study, which has undergone scanning, digitization, and transformation into an urban digital twin, avatars have been strategically placed to enable the detection of individuals with disabilities within diverse scenarios and perspectives in urban environments. Consequently, this approach provides a diverse and realistic representation of the situations in which individuals with disabilities may find themselves in urban settings, contributing to advancements in urban planning and technology by emphasizing the importance of digital twins in generating synthetic data to promote inclusivity and accessibility for individuals with disabilities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Computer vision

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PPM-HC: A method for helping project portfolio management based on topic hierarchy learnings

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International Journal of Business Intelligence and Data Mining 2021年第3期18卷 364-382页

作者： Pinto, Ricardo A.M. Bernardini, Flavia Marcacini, Ricardo M. Graduate Program in Industrial Engineering and Computing Systems Fluminense Federal University Jardim Bela Vista Rio das OstrasRJ Brazil Computing Institute Fluminense Federal University São Domingos NiteróiRJ Brazil Information Systems Federal University of Mato Grosso do Sul P.O. Box 210 Três LagoasMS Brazil

The projects categorisation is a crucial step in the project portfolio management (PPM). Categorising projects allows the organisation to identify categories with a lack or excess of projects, according to its strategic objectives. In this work, we present a new method for project portfolio management based on hierarchical clustering (PPM-HC) to organise the projects at several levels of abstraction. In the PPM-HC, similar projects are allocated to the same clusters and subclusters. PPM-HC automatically learns an understandable topic hierarchy from the project portfolio dataset, thereby facilitating the (human) task of exploring, analysing and prioritising the projects of the organisation. We also proposed a card sorting-based technique which allows the evaluation of the projects categorisation using an intuitive visual map. We carried out an experimental evaluation based on a benchmark dataset and we also presented a real-world case study. The results show that the proposed PPM-HC method is promising. © 2021 Inderscience Enterprises Ltd.

关键词： Investments

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Hardware-based parallel firefly algorithm for embedded applications

Hardware-based parallel firefly algorithm for embedded appli...

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2013 NASA/ESA Conference on Adaptive Hardware and systems, AHS 2013

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos Santos Coelho, Leandro Ayala-Rincón, Mauricio Electronics Engineering Graduate Program Faculty of Gama University of Brasilia Brasilia DF Brazil Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia DF Brazil Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Parana Curitiba Parana Brazil Departments of Computer Science and Mathematics University of Brasilia Brasilia DF Brazil

ISBN: (纸本)9781467363839

The firefly algorithm (FA) is a new population-based metaheuristic bioinspired on the behavior of the flashing characteristics of fireflies. As a population-based algorithm, the FA suffers from large execution times specifically for embedded optimization problems with computational limitations. For reducing execution times we propose a hardware parallel architecture of the FA algorithm that facilitates the implementation in Field programmable Gate Arrays (FPGAs). In addition, this work proposes the application of the opposition-based learning (OBL) approach to the FA algorithm. The respective hardware implementation (HPOFA) was mapped into a Virtex5 FPGA device and numerical experiments using four well-known benchmark problems demonstrate that the opposition-based approach allows the FA algorithm to improve its functionality, preserving the swarm diversity and avoiding the premature convergence problem. Synthesis results point out that the HPOFA architecture is effectively mapped in hardware and is suitable for embedded applications. © 2013 IEEE.

关键词： Parallel architectures

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Optimization and analysis of variability in injection molding

Optimization and analysis of variability in injection moldin...

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Society of Plastics Engineers Annual Technical Conference 2006, ANTEC 2006

作者： Castro, Carlos E. Ríos, Mauricio Cabrera Lilly, Blaine Castro, José M. Dept. of Industrial Welding and Systems Engineering Ohio State University Columbus OH 43202 United States Graduate Program in Systems Engineering Universidad Autónoma de Nuevo León Sn. Nicolás de los Garza Nuevo León Mexico

Injection Molding (IM) is the most important process for mass-producing plastic products. The difficulty of optimizing an IM process is that the performance measures (PMs), usually show conflicting behavior. The aim of this work is to demonstrate a method utilizing CAE, statistical testing, artificial neural networks (ANNs), and data envelopment analysis (DEA) to find the best compromises between multiple PMs and their variability. Two case studies are presented. A case study based on a virtual part is presented in detail in order to illustrate this method. The second case study is experimentally based, using the American Society of Testing Materials (ASTM) mold, to illustrate how this approach applies when only experimental results are available.

关键词： Injection molding

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Current Panorama, Practice Gaps, and Recommendations to Accelerate the Transition to a Circular Bioeconomy in Latin America and the Caribbean

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Circular Economy and Sustainability 2022年第1期2卷 281-312页

作者： Salvador, Rodrigo Pereira, Reinalda Blanco Sales, Gabriel Fernandes de Oliveira, Vanessa Campana Vergani Halog, Anthony De Francisco, Antonio C. Sustainable Production Systems Laboratory (LESP) Graduate Program in Industrial Engineering (PPGEP) Universidade Tecnológica Federal Do Paraná (UTFPR) Ponta Grossa Brazil School of Earth and Environmental Sciences The University of Queensland St Lucia 4072 QLD Australia

The rampant use of resources and the increasing risk of scarcity have been contributing to greater awareness regarding the need for maintaining resources in use for longer and the shift to renewable alternatives, in which perspective the concept of a circular bioeconomy (CBE) has been permeating the most diverse sectors. Therefore, the study’s aims were manifold: identify (i) existing practices and initiatives, (ii) barriers and potential opportunities and (iii) existing gaps for advancing a circular bioeconomy in Latin America and the Caribbean (LAC) and (iv) provide recommendations aiming at the development of a circular bioeconomy (CBE) in the region. To that end, a systematic review of the existing academic literature was done, and reports from relevant non-peer reviewed sources were also consulted. CBE practices in LAC are concentrated in only a few of the countries in the region and very much revolve around recovering value from by-products and waste streams. There are a range of economic, technological, environmental, and social barriers. Opportunities for a CBE are mainly related to the use of waste as raw material (found abundantly in the countries being investigated) and the use of new techniques/technologies. The gaps between the current state and a successful CBE are related to policy, scaling-up, collaborations, and establishing efficient business models. Recommendations for a successful CBE in the region include greater engagement of public actors, investing and developing the local economy, establishing biorefineries and industrial parks, and finding new technological routes for bioresources. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Bioeconomy Circular bioeconomy Circular business Circular economy Latin America Policy

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Multi-objective Support Vector Machines Ensemble Generation for Water Quality Monitoring

Multi-objective Support Vector Machines Ensemble Generation ...

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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 Parana (PUCPR) Curitiba Brazil

Real-world classification problems generally deal with imbalanced data, where one class represents the majority of the data set. The present work deals with event detection on a drinking-water quality time series, where the presence of a quality event is the minority class. In order to solve such problems, supervised learning algorithms are recommended. Researchers have also used multi-objective optimization (MOO) in order to generate diverse models to build ensembles of classifiers. Although MOO has been used for ensemble member generation, there is a lack on it's application for member selection, which is usually done by selecting a specific subset from the resulting models, or by using meta-algorithms, such as boosting. The proposed work comprises the application of MOO design in the whole process of ensemble generation. To do so, one multi-objective problem (MOP) is defined for the creation of a set of non-dominated solutions with Pareto-optimal support vector machines (SVM). After that, a second MOP is defined for the selection of such SVMs as members of an ensemble. Such methodology is compared to other member selection methods, such as: the single best classifier, an ensemble composed of the full set of non-dominated solutions, and the selection of a specific subset from the Pareto front. Results show that the proposed method is suitable for the creation of ensembles, achieving the highest classification scores.

关键词： Support vector machines Optimization Mood Training Event detection Kernel Supervised learning

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A genetic algorithm based method to support the geometric prosthesis modelling in skull repairing

A genetic algorithm based method to support the geometric pr...

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2013 International Conference on Biomedical engineering and Environmental engineering (ICBEEE 2013)

作者： Marcelo Rudek Osiris Canciglieri Junior Thiago Greboge Anderson Luis Szejka Industrial and Systems Engineering Post-Graduate Program PPGEPSPontifical Catholic University of ParanaPUCPR

This paper addresses the problem of the geometric modeling of prosthesis to correct defects in skull bone by computational approach viewpoint. The missing area in a defective skull can be virtually filled by a criterion based on the curvature of the skull shape. The basic argument is that in a computed tomography, the 2D skull border in slice image is similar to a rounded form. This research is proposing a method to find adjusted ellipses on its curvature by Ellipse Adjustment Algorithm(EAA) technique. If the ellipse is correctly adjusted in each computed tomography slice, the resulting arcs that fill the missing area can be built in 3D in order to complete an unknown region in the bone. The problem is that there are many possible solutions and the selection of the best ellipse that fits the contour shape is performed by a Genetic Algorithm(GA). The piece of bone that was missed in skull can be built as a synthetic image to fill a hole at defect position in the skull. With the ellipse parameters it is possible to generate profiles with its set of points in order to build the 3D model using a CAD system. The case study shows the use of the method applied to non-symmetric defects and presents the obtained results.

关键词： Prosthesis modelling tomography images genetic algorithm

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Integrating agents and soft computing in Intelligent Manufacturing System models

Integrating agents and soft computing in Intelligent Manufac...

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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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