Projeto de um sistema de automação industrial para uma indústria de produtos saneantes

Monografia (graduação)—Universidade de Brasília, Faculdade UnB Gama, Engenharia Automotiva, 2015.O presente projeto foi idealizado a partir das experiências vividas durante período em que o autor trabalhou na indústria de saneantes Klimp. Foi observado que os trabalhos manuais realizados nos processos de fabricação de seus produtos eram um fator que aumentava o tempo de produção assim como, os custos, o que reduz a produtividade. A automação se mostra extremamente benéfica para preencher as lacunas que o trabalho manual gera. O presente trabalho apresenta uma revisão bibliográfica sobre todos os temas pertinentes e necessários para o maior esclarecimento sobre o que são produtos saneantes, e principalmente em quais momentos a automação pode ser aplicada, para que seus ganhos sejam reais. Sendo assim temas como, sensor de vazão, sensor de nível, sensor de pH, sensor de viscosidade, células de carga, agitadores mecânicos, válvulas solenoides, controladores lógicos programáveis, são abordados e esclarecidos suas funcionalidades dentro do projeto. Entretanto os requisitos do sistema também foram mostrados, de modo a obter uma visão do problema como um todo. A automação é benéfica em vários quesitos, e isso é uma verdade que vem sendo mostrados desde a revolução industrial, tais benéficos são aplicados em qualquer área industrial, justificando a abordagem desse tema à indústria de saneantes. Por final foi desenvolvido um sistema de supervisão e controle com o intuito de melhorar o fluxo produtivo. Este utilizou uma arquitetura CLP-SCADA de modo a cumprir todos os processo inerentes a manipulação.This project was idealized from the author’s experiences lived during his work on sanitizing industry Klimp. It was observed the manual labor performed of its products was a factor that increased the production time as well as costs, which reduces the productivity. The automation proves extremely beneficial to fill the gaps that manual labor generates. This research presents a literature review about all the relevant and necessary topics for further clarification on what are sanitizing products, and mainly in which moments the automation can be applied in order that their gains are real. Therefor topics like flow sensor, level sensor, pH sensor, viscosity sensor, load cells, stirrers, solenoid valves, programmable logic controllers were discussed as well as clarified their functionality within the project. However, the system requirements have also been shown, in order to obtain a view of the problem as a whole. The automation is beneficial on several issues, and this is a truth that has been shown since the industrial revolution, such benefits are applied in any industrial area, justifying the approach to this issue to the sanitizing industry.Lastly it developed a supervision and control system in order to improve production flow. This used a PLC- SCADA architecture in order to meet all the inherent handling process

Quaternion-based deep belief networks fine-tuning

Deep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications. In this paper, we address the issue of fine-tuning parameters of Deep Belief Networks by means of meta-heuristics in which real-valued decision variables are described by quaternions. Such approaches essentially perform optimization in fitness landscapes that are mapped to a different representation based on hypercomplex numbers that may generate smoother surfaces. We therefore can map the optimization process onto a new space representation that is more suitable to learning parameters. Also, we proposed two approaches based on Harmony Search and quaternions that outperform the state-of-the-art results obtained so far in three public datasets for the reconstruction of binary images

Robust automated cardiac arrhythmia detection in ECG beat signals

Nowadays, millions of people are affected by heart diseases worldwide, whereas a considerable amount of them could be aided through an electrocardiogram (ECG) trace analysis, which involves the study of arrhythmia impacts on electrocardiogram patterns. In this work, we carried out the task of automatic arrhythmia detection in ECG patterns by means of supervised machine learning techniques, being the main contribution of this paper to introduce the optimum-path forest (OPF) classifier to this context. We compared six distance metrics, six feature extraction algorithms and three classifiers in two variations of the same dataset, being the performance of the techniques compared in terms of effectiveness and efficiency. Although OPF revealed a higher skill on generalizing data, the support vector machines (SVM)-based classifier presented the highest accuracy. However, OPF shown to be more efficient than SVM in terms of the computational time for both training and test phases

Dengue vaccines: what we know, what has been done, but what does the future hold?

Dengue, a disease caused by any of the four serotypes of dengue viruses, is the most important arthropod-borne viral disease in the world in terms of both morbidity and mortality. The infection by these viruses induces a plethora of clinical manifestations ranging from asymptomatic infections to severe diseases with involvement of several organs. Severe forms of the disease are more frequent in secondary infections by distinct serotypes and, consequently, a dengue vaccine must be tetravalent. Although several approaches have been used on the vaccine development, no vaccine is available against these viruses, especially because of problems on the development of a tetravalent vaccine. Here, we describe briefly the vaccine candidates available and their ability to elicit a protective immune response. We also discuss the problems and possibilities of any of the vaccines in final development stage reaching the market for human use

On the optical flow model selection through metaheuristics

Optical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work, we have proposed an optimization-based framework for such task based on social-spider optimization, harmony search, particle swarm optimization, and Nelder-Mead algorithm. The proposed framework employed the well-known large displacement optical flow (LDOF) approach as a basis algorithm over the Middlebury and Sintel public datasets, with promising results considering the baseline proposed by the authors of LDOF

Social-spider Optimization-based Support Vector Machines Applied For Energy Theft Detection

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The problem of Support Vector Machines (SVM) tuning parameters (i.e., model selection) has been paramount in the last years, mainly because of the high computational burden for SVM training step. In this paper, we address this problem by introducing a recently developed evolutionary-based algorithm called Social-Spider Optimization (SSO), as well as we introduce SSO for feature selection purposes. The model selection task has been handled in three distinct scenarios: (i) feature selection, (ii) tuning parameters and (iii) feature selection+tuning parameters. Such extensive set of experiments against with some state-of-the-art evolutionary optimization techniques (i.e., Particle Swarm Optimization and Novel Global-best Harmony Search) demonstrated SSO is a suitable approach for SVM model selection, since it obtained the top results in 8 out 10 datasets employed in this work (considering all three scenarios). Notice the best scenario seemed to be the combination of both feature selection and SVM tuning parameters. In addition, we validated the proposed approach in the context of theft detection in power distribution systems. (C) 2015 Elsevier Ltd. All rights reserved.492538Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)FAPESP [2012/06472-9, 2013/20387-7, 2014/16250-9]CNPq [303182/2011-3, 470571/2013-6, 306166/2014-3

Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations.

Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available

A NEW APPROACH FOR URBAN ROADS DETECTION USING LASER DATA AND AERIAL DIGITAL IMAGES

The automatic feature extraction from digital aerial images is not a trivial task mainly due to occlusion problems, shadows and different viewpoints. To obtain an improved feature extraction we used laser data, which have additional information such as height and material type of the surface. In this paper we performed the combination of digital image and laser data in order to improve the results of automatic extraction of urban roads. Initially, the urban roads were detected from the response of laser information; in the sequence we applied two different approaches to connect the disconnected road segments. The results were very promising, with sensitivity rate of 92%

Common bean under different water availability reveals classifiable stimuli-specific signatures in plant electrome

Plant electrophysiology has unveiled the involvement of electrical signals in the physiology and behavior of plants. Spontaneously generated bioelectric activity can be altered in response to changes in environmental conditions, suggesting that a plant’s electrome may possess a distinct signature associated with various stimuli. Analyzing electrical signals, particularly the electrome, in conjunction with Machine Learning (ML) techniques has emerged as a promising approach to classify characteristic electrical signals corresponding to each stimulus. This study aimed to characterize the electrome of common bean (Phaseolus vulgaris L.) cv. BRS-Expedito, subjected to different water availabilities, seeking patterns linked to these stimuli. For this purpose, bean plants in the vegetative stage were subjected to the following treatments: (I) distilled water; (II) half-strength Hoagland’s nutrient solution; (III) −2 MPa PEG solution; and (IV) −2 MPa NaCl solution. Electrical signals were recorded within a Faraday’s cage using the MP36 electronic system for data acquisition. Concurrently, plant water status was assessed by monitoring leaf turgor variation. Leaf temperature was additionally measured. Various analyses were conducted on the electrical time series data, including arithmetic average of voltage variation, skewness, kurtosis, Probability Density Function (PDF), autocorrelation, Power Spectral Density (PSD), Approximate Entropy (ApEn), Fast Fourier Transform (FFT), and Multiscale Approximate Entropy (ApEn(s)). Statistical analyses were performed on leaf temperature, voltage variation, skewness, kurtosis, PDF µ exponent, autocorrelation, PSD β exponent, and approximate entropy data. Machine Learning analyses were applied to identify classifiable patterns in the electrical time series. Characterization of the electrome of BRS-Expedito beans revealed stimulus-dependent profiles, even when alterations in water availability stimuli were similar in terms of quality and intensity. Additionally, it was observed that the bean electrome exhibits high levels of complexity, which are altered by different stimuli, with more intense and aversive stimuli leading to drastic reductions in complexity levels. Notably, one of the significant findings was the 100% accuracy of Small Vector Machine in detecting salt stress using electrome data. Furthermore, the study highlighted alterations in the plant electrome under low water potential before observable leaf turgor changes. This work demonstrates the potential use of the electrome as a physiological indicator of the water status in bean plants