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SSRN 2023年

作者： Terra dos Santos, Luiz Carlos Giannetti, Biagio Fernando Agostinho, Feni Dalano Roosevelt Liu, Gengyuan de Almeida, Cecilia Maria Villas B. Post-graduation Program in Production Engineering Paulista University Brazil State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Beijing Normal University China

The debate about the negative impacts that production and consumption cause on the environment is in vogue. Strategies that point to a sustainable, healthy, and resilient path are being sought. One of these paths is the Circular Economy, which emerges as an alternative to reduce the socio-environmental impacts caused by the linear model of production-use-disposal, presenting opportunities to generate revenue, income, and wealth with circular processes. However, despite the circular economy being considered an essential strategy to improve overall performance toward sustainability in its three dimensions, recent research has shown that the predominant focus of circular approaches is on the economic and environmental dimensions. At the same time, the social aspects are little explored. This article addresses this problem, aiming to explore the environmental, economic, and social aspects of circular economy in three economic blocs from 2000 to 2020 using the Five Sector Sustainability Model, establishing a baseline to co-create an equitable and regenerative future. The results showed that ASEAN in the year 2000 occupied the first position in the general ranking, while the European Union had the best classification in the economic sector and Mercosur was the best ranked regarding social benefits. In 2020, while the European bloc was better positioned in the general ranking, the South American bloc occupied the last post in almost all sectors. Comparatively, the highest-ranked bloc in 2020 in overall sustainability is more in line with the UN SDG due to circular actions oriented towards the three fundamental pillars of sustainability. © 2023, The Authors. All rights reserved.

关键词： Sustainable development

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Soybean Price Prediction by Using Lstm Models Based on Brazilian Climatic Data

SSRN

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SSRN 2024年

作者： Oliva, Jefferson Tales Martelli, Otávio Pigozzo de Castro Barbosa, Marco Antônio Molin, Viviane Dal Casanova, Dalcimar Teixeira, Marcelo Graduate Program in Electrical Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Department of Academic Informatics Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Graduate Program in Production and Systems Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil

This work uses machine learning to verify whether the Brazilian climate influences international soybean price prediction. For this, climatic data, dollar quotes, inflation, and annual soybean production were collected from different data sources. Then, the collected data were pre-processed and grouped by Brazilian state. Afterward, data from the five most productive Brazilian states are chosen, such as Mato Grosso (MT), Mato Grosso do Sul (MS), Goiás (GO), Paraná (PR), and Rio Grande do Sul (RS). Afterward, predictive models are built using the long short-term memory (LSTM) method, which is applied in each chosen state dataset considering two different settings: with and without climatic data. Thus, two models were built for each Brazilian state, totaling ten predictors. In the experimental evaluation, when trained with climatic data, the PR model achieved the lowest values of root mean squared error (RMSE) and mean absolute error (MAE). Regarding predictors trained without climate data, the RS and PR obtained the lowest values of RMSE and MAE, respectively. After conducting statistical hypothesis tests, no significant difference was found among models, stating, with 95% certainty, that, in this study, the Brazilian climatic data did not influence the model performance for predicting soybean prices. On the other hand, one of the main contributions of work is conducting an unprecedented study about soybean price prediction using Brazilian climatic data. © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

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A Systematic Review: Big Data Analytics and Landslides 13

A Systematic Review: Big Data Analytics and Landslides

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13th Landslides and Engineered Slopes. Experience, Theory and Practice, ISL

作者： da Silva Azevedo, Caio Correia, Thaís Passos Corsi, Alessandra Cristina de Mesquita Spinola, Mauro Quintanilha, José Alberto Postgraduate Program in Production Engineering The Polytechnic School of the University of São Paulo – PRO - EPUSP Brazil Postgraduate Program in Transportation Engineering The Polytechnic School of the University of São Paulo – PTR - EPUSP Brazil Institute for Technological Research - IPT Production Engineering Department The Polytechnic School of the University of São Paulo – PRO – EPUSP Brazil Institute of Energy and Environment of the University of São Paulo - IEEUSP Brazil

Landslides are among the natural disasters that most affect the population in Brazil and in the world, as they are responsible for numerous human, economic, social and environmental losses. High-resolution aerial and satellite imagery, that contribute to identify this type of event are valuable sources of information, even if they are not the only ones. Machine learning (ML) techniques are often adopted to identify relevant patterns, as high performance computing and data visualization are widely and successfully applied to natural disaster-related data, particularly for landslides. The greater the volume of data processed during machine learning, the better the accuracy of the results obtained. Among other reasons, Big Data (BD) has stood out for its ability to integrate and generate a large volume of data from different sources and in different formats. The use of BD concepts and techniques has become common in several areas of knowledge, in particular for the storage and analysis of data related to natural accidents and, thus, helping to improve decision making as well as prevention of disasters. With the emergence of the BD, it came the challenge of aligning decision-making processes, so that there is no shortage of information or useless data for decision makers. In the case of Brazil and considering, especially, applications to landslide data, there is a lack of articles dealing with the subject. The aim of this study is to investigate the main features used on machine learning techniques, to map the literature, thus pointing out new routes and opportunities in the field. In the systematic review, the eligibility criteria adopted are studies published in the last 4 years (from January 2015 to December 2019), in English, at national and international levels, including publications in scientific events and relevant journals, through databases such as Scopus and Web of Science. The search was performed in a structured form with the terms "Big Data*", "machine lea

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Localization of a Crack in Moving Cylindrical Ferromagnetic Rods by Measuring the Fourier Coefficients of the Leakage Magnetic Flux

Localization of a Crack in Moving Cylindrical Ferromagnetic ...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Kanta Shiku Masafumi Kuromizu Yuji Gotoh Takaaki Nara Graduate School of Information Science and Technology The University of Tokyo bunkyo-ku Japan Division of Materials Science and Production Engineering Graduate School of Engineering Oita University Oita Japan Intelligent Mechanical Systems Program Faculty of Science and Technology Oita University Oita Japan

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

Electromagnetic nondestructive inspection for high-speed moving ferromagnetic materials is necessary for manufacturing efficiency. In this paper, we present a novel leakage magnetic flux (LMF) method to estimate the crack position in a moving cylindrical ferromagnetic rod. The method directly measures the first-order Fourier coefficients of the LMF moving with the crack by using only two coils with DC excitation. The location of a crack is estimated by the proposed method, as demonstrated for an S45C cylindrical rod.

关键词： Location awareness Coils Magnetic flux Inspection Ferromagnetic materials Manufacturing Electromagnetics

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Application of Paraconsistent Annotated Evidential Logic Eτ for a Terrestrial Mobile Robot to Avoid Obstacles

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Procedia Computer Science 2021年 192卷 1821-1830页

作者： Flavio Amadeu Bernardini Marcia Terra da Silva Jair Minoro Abe Graduate Program in Production Engineering - Paulista University. Rua Dr. Bacelar 1212

One of the significant challenges in contemporary robotics concerns navigation systems and autonomous robots’ location, being the detection and avoidance of obstacles an essential part of this system. This article proposed Paraconsistent Annotated Evidential Logic Eτ in the control algorithm for a terrestrial robot, and thus contribute with navigation systems, to avoid possible collisions with obstacles. A prototype terrestrial robot was then built with ultrasonic sensors for obstacle detection and a servomotor in steering control. Several performance tests were performed. It was noticed that by shortening the perception distances of the sensors, the performance of the robot’s displacement proved to be satisfactory.

关键词： Paraconsistent annotated logic Servomotor Terrestrial mobile robot Robotics

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An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning

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Information Processing in Agriculture 2022年第1期9卷 38-47页

作者： Jose G.M.Esgario Pedro B.C.de Castro Lucas M.Tassis Renato A.Krohling LABCIN-Nature Inspired Computing Federal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910 VitoriaEspırito SantoESBrazil LABCIN-Nature Inspired Computing and PPGI-Graduate Program in Computer Scienceand Production Engineering DepartmentFederal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910 VitoriaEspırito SantoESBrazil

In recent years,deep learning methods have been introduced for segmentation and classi-fication of leaf lesions caused by pests and *** the commonly used approaches,convolutional neural networks have provided results with high *** purpose of this work is to present an effective and practical system capable of seg-menting and classifying different types of leaf lesions and estimating the severity of stress caused by biotic agents in coffee leaves using convolutional neural *** proposed approach consists of two stages:a semantic segmentation stage with severity calculation and a symptom lesion classification *** stage was tested separately,highlighting the positive and negative points of each *** obtained very good results for the severity estimation,suggesting that the model can estimate severity values very close to the real *** the biotic stress classification,the accuracy rates were greater than 97%.Due to the promising results obtained,an App for Android platform was developed and imple-mented,consisting of semantic segmentation and severity calculation,as well as symptom classification to assist both specialists and farmers to identify and quantify biotic stresses using images of coffee leaves acquired by smartphone.

关键词： Convolutional neural networks Semantic segmentation Coffee leaf Pests and diseases App.development

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Energy-aware Model Predictive Control with Obstacle Avoidance

Energy-aware Model Predictive Control with Obstacle Avoidanc...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Santos, Marcelo A. Ferramosca, Antonio Raffo, Guilherme V. Federal University of Minas Gerais Graduate Program in Electrical Engineering Belo Horizonte Brazil University of Bergamo Department of Management Information and Production Engineering Dalmine24044 Italy Federal University of Minas Gerais Department of Electronics Engineering Belo Horizonte Brazil

ISBN: (纸本)9780738131153

This work proposes a single-layer finite-horizon optimal control strategy to solve the autonomous navigation problem while accounting for energy efficiency and providing obstacle avoidance feature in cluttered environments with unknown obstacles. Considering the rate capacity effect of electric batteries, the nonlinear state-of-charge behavior is described and included in the optimal control problem to achieve energy-awareness. Besides, artificial potential fields are considered to obtain obstacle avoidance capabilities. The control problem is formulated inspired by the tracking model predictive control framework, and it considers the central idea of including artificial variables into the control problem to obtain a closed-loop system with an enlarged domain of attraction and with feasibility insurance. Finally, numerical results in a case study considering a quadrotor UAV are provided to corroborate the proposed strategy. © 2021 IEEE.

关键词： Energy efficiency

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Computational fluid dynamics analysis of the natural circulation system in vertical heater horizontal cooler (VHHC) molten salt loop 9

Computational fluid dynamics analysis of the natural circula...

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9th Asian Physics Symposium 2021, APS 2021

作者： Hariyanto, Duwi Waris, Abdul Suprijadi Nuclear Physics and Biophysics Research Division Physics Department Institut Teknologi Bandung Indonesia Nuclear Science and Engineering Department Institut Teknologi Bandung Indonesia Theoretical High Energy Physics and Instrumentation Research Division Physics Department Institut Teknologi Bandung Indonesia Instrumentation and Automation Engineering Study Program Production and Industrial Technology Department Institut Teknologi Sumatera Indonesia

Molten salt has attracted the attention of researchers, especially as a coolant because of its characteristics. This study aimed to analyse the transient and steady-state of the natural circulation system in the molten salt loop with vertical heater and horizontal cooler using computational fluid dynamics. The computational fluid dynamics module in the COMSOL Multiphysics software was used to solve governing equations. Experimental data from several references were used as input data in modelling. The loop was modelled as a rectangular loop with Inconel 625 as the pipe material and molten salt (NaNO3+KNO3) as the working fluid. The variation of heater power was also carried out in modelling. The modelling results showed a good agreement with the experimental results. The results provided that the molten salt natural circulation loop with vertical heater and horizontal cooler showed good flow stability without reversal of flow direction. © Published under licence by IOP Publishing Ltd.

关键词： Computational fluid dynamics

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Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data

arXiv

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arXiv 2022年

作者： Esgario, José G.M. Krohling, Renato A. Federal University of Espírito Santo ES Vitória Brazil PPGI - Graduate Program in Computer Science LABCIN Production Engineering Department

Skin cancer is considered one of the most common type of cancer in several countries. Due to the difficulty and subjectivity in the clinical diagnosis of skin lesions, Computer-Aided Diagnosis systems are being developed for assist experts to perform more reliable diagnosis. The clinical analysis and diagnosis of skin lesions relies not only on the visual information but also on the context information provided by the patient. This work addresses the problem of pigmented skin lesions detection from smartphones captured images. In addition to the features extracted from images, patient context information was collected to provide a more accurate diagnosis. The experiments showed that the combination of visual features with context information improved final results. Experimental results are very promising and comparable to experts. © 2022, CC BY.

关键词： Computer aided diagnosis

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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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