56 research outputs found
Identificação de padrões em gráficos de controle estatístico de processos, em tempo real, utilizando séries temporais e redes neurais artificiais
Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico.O presente trabalho procurou implementar um sistema semi-automatizado de Controle Estatístico de Processos (CEP) para dados obtidos em tempo real. Tal sistema abrange um grande escopo de aplicações pois aplica-se tanto para processos serialmente correlacionados como para processos identicamente e independentemente distribuídos (iid). No sistema proposto, os dados são obtidos a partir de sensores em um processo automatizado qualquer. Em seguida esses dados são modelados e são gerados um conjunto de resíduos. Sobre esses resíduos atua uma rede neural, treinada off line, que faz o reconhecimento de padrões de uma carta de controle estatístico de processos em tempo real. O processo pode ser a qualquer momento remodelado para uma nova série de dados ou escolhendo-se um modelo testado anteriormente. Tal sistema foi parcialmente testado em relação a sistemas convencionais e várias medidas de desempenho mostraram resultados satisfatórios. Todo o sistema foi inteiramente simulado por computador e as rotinas computacionais são disponibilizadas para futuros aperfeiçoamentos e comparações. Uma aplicação real foi avaliada abordando um problema de reconhecimento de padrões da atividade eletrencefalográfica do cérebro
Optimal tuning of the control parameters of an inverter-based microgrid using the methodology of design of experiments
This paper is a postprint of a paper submitted to and accepted for publication in IET Power Electronics and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.The design of the control system in an inverter-based microgrid is a challenging problem due to the large number of parameters involved. Different optimisation methods based on obtaining an approximated mathematical model of the microgrid can be found in the literature. In these approaches, the non-linearities and uncertainties of the real system are typically not considered, which may result in a non-optimal tuning of the control parameters. In addition, in most applications, the problem has been simplified assuming that all controllers have the same value for their control parameters. However, in this case, the behaviour of the system is sub-optimal since the particularities of each node of the microgrid are not taken into account. In this paper, an experimental approach for tuning the control parameters of an inverter-based microgrid is introduced. The approach is based on the methodology of design of experiments and it considers different values for the control parameters of all controllers. In this study, this methodology is applied to the design of a droop-free control scheme; however, it can be easily extended to other control schemes. The validity of the proposal is verified through selected experimental results.This work was supported by the Ministry of Science, Innovation and Universities of Spain and by the European Regional Development Fund under project RTI2018- 100732- B-C22.Peer ReviewedPostprint (author's final draft
Multivariate robust modelling and optimization of cutting forces of the helical milling process of the aluminum alloy Al 7075
Helical milling is an advanced hole-making process and different approaches considering controllable variables have
been presented addressing modelling and optimization of machining forces in helical milling. None of them considers the
importance of the noise variables and the fact that machining forces components are usually correlated. Exploring this
issue, this paper presents a multivariate robust modelling and optimization of cutting forces of the helical milling of the
aluminum alloy Al 7075. For the study, the tool overhang length was defined as noise variable since in cavities
machining there are specific workpiece geometries that constrain this variable; the controllable variables were axial
feed per tooth, tangential feed per tooth and cutting speed. The cutting forces in the workpiece coordinate system were
measured and the components in the tool coordinate system, i.e., the axial and radial forces, were evaluated. Since these
two outcomes are correlated, the weighted principal component analysis was performed together with the robust
parameter design to allow the multivariate robust modelling of the mean and variance equations. The normal boundary
intersection method was used to obtain a set of Pareto robust optimal solutions related to the mean and variance
equations of the weighted principal component. The optimization of the weighted principal component through the
normal boundary intersection method was performed and the results evaluated in the axial and radial cutting forces
components. Confirmation runs were carried out and it was possible to conclude that the models presented good fit with
experimental data and that the Pareto optimal point chosen for performing the confirmation runs is robust to the tool
overhang length variation. Finally, the cutting force models were also presented for mean and variance in the workpiece
coordinate system in the time domain, presenting low error regarding the experimental test, endorsing the results.publishe
TRANSFORMAÇÃO DIGITAL NA EDUCAÇÃO TÉCNICA: DESAFIOS DO ENSINO REMOTO DURANTE A PANDEMIA DA COVID-19
The focus of the present study is an analysis of remote technical education, with a particular emphasis on professional technical courses such as Industrial Automation, Electrotechnics, Mechatronics, and Mechanics during the Covid-19 pandemic crisis. We investigate the primary pedagogical strategies adopted by educators to overcome the challenges imposed by this scenario and the digital tools that were instrumental in combating the impacts caused by compulsory remote education.
Within the scope of this study, the challenges encountered during this unprecedented period of remote education were analyzed, as well as the identification of the advantages and disadvantages of this modality, observed from the perspective of both teachers and students. The work provides a holistic view of these aspects, giving a voice to the main actors involved in this process.
Moreover, the study explores Bloom's Taxonomy, providing an analysis from its theoretical foundations to its practical applications to better structure and categorize pedagogical content and the skills to be developed in students. We discuss the most effective ways of assessing student progress in this challenging context, providing useful insights for future implementations.
In conclusion, the work also presents a series of digital tools employed during the pandemic to enhance the quality of remote technical education and promote the development of student competencies in this environment. The analysis of these tools offers valuable insight into the role and impact of technology on teaching and learning, which can be leveraged even during regular face-to-face education.El presente trabajo se centra en el análisis de la educación técnica a distancia, con un énfasis particular en los cursos técnicos de Automatización Industrial, Electrotecnia, Mecatrónica y Mecánica durante la crisis de la pandemia de Covid-19. Investigaremos las principales estrategias pedagógicas adoptadas por los docentes para superar los desafíos impuestos por este escenario y las herramientas digitales que fueron esenciales para combatir los impactos causados por la educación a distancia.
En el contexto de este estudio, se analizaron los desafíos enfrentados durante este período de enseñanza a distancia, así como la identificación de las ventajas y desventajas de esta modalidad, observadas desde la perspectiva tanto de los profesores como de los alumnos. El trabajo proporciona una visión holística de estos aspectos, dando voz a los principales actores en este proceso.
Además, el estudio explora la taxonomía de Bloom, proporcionando un análisis desde sus fundamentos teóricos hasta sus aplicaciones prácticas para estructurar y categorizar mejor el contenido pedagógico y las habilidades a ser desarrolladas en los estudiantes. Discutimos las formas más efectivas de evaluar el progreso de los alumnos en este contexto desafiante, proporcionando ideas útiles para futuras implementaciones.
El trabajo también presenta una serie de herramientas digitales empleadas durante la pandemia para potenciar la calidad de la educación técnica a distancia y promover el desarrollo de competencias de los alumnos. El análisis de estas herramientas ofrece una perspectiva valiosa sobre el impacto de la tecnología en la enseñanza, que pueden ser aprovechados incluso durante la enseñanza presencial regular.O presente trabalho tem como foco a análise do ensino técnico à distância, com ênfase particular nos cursos técnicos profissionalizantes de Automação Industrial, Eletroeletrônica, Mecatrônica e Mecânica durante a crise da pandemia da Covid-19. Investigaremos as principais estratégias pedagógicas adotadas pelos docentes para superar os desafios impostos por este cenário e as ferramentas digitais que foram essenciais no combate aos impactos causados pelo ensino remoto.
No âmbito deste estudo, foram analisados os desafios enfrentados durante este período inédito de ensino remoto, bem como a identificação das vantagens e desvantagens dessa modalidade, observadas sob a perspectiva tanto dos professores quanto dos alunos. O trabalho proporciona uma visão holística desses aspectos, dando voz aos principais atores envolvidos neste processo.
Além disso, o estudo explora a taxonomia de Bloom, proporcionando uma análise desde seus fundamentos teóricos até suas aplicações práticas para melhor estruturar e categorizar o conteúdo pedagógico e as habilidades a serem desenvolvidas nos estudantes. Discutimos as formas mais eficazes de avaliar os progressos dos alunos nesse contexto desafiador, fornecendo insights úteis para futuras implementações.
Concluindo, o trabalho também apresenta uma série de ferramentas digitais empregadas durante a pandemia para potencializar a qualidade do ensino técnico remoto e promover o desenvolvimento de competências dos alunos nesse ambiente. A análise dessas ferramentas oferece uma perspectiva valiosa sobre o papel e o impacto da tecnologia no ensino e aprendizagem e que podem ser aproveitados mesmo durante o ensino regular presencial
Comparison of ANN and DoE for the prediction of laser machined micro-channel dimensions
This paper presents four models developed for the prediction of the dimensions of laser formed micro-channels. Artificial Neural Networks (ANNs) are often used for the development of predictive models. Three feed-forward, back-propagation ANN models varied in terms of the number and the selection of training data, were developed. These ANN models were constructed in LabVIEW coding. The performance of these ANN models was compared with a 33 statistical design of experiments (DoE) model built with the same input data. When compared with the actual results two of the ANN models showed greater prediction error than the DoE model. The other ANN model showed an improved predictive capability that was approximately twice as good as that provided from the DoE model
A DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened steel turning
Detecção de mudança de nível em séries temporais não lineares usando Descritores de Hjorth
Implementação de um sistema de mapeamento cerebral computadorizado
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Quantificação da Análise de Riscos em Investimentos Usando Medidas de Dispersão
In this article will be presented a bibliographical walk through on the application of statistics tools evaluating risk in investment and demonstrating, through the published literature, the validity of these tools in the economic area. Keywords: Statistic, Risk, Investiment Analysis
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