Automação Inteligente

Objectives

The students must get acquainted and develop projects in intelligent automation, which consists of a successful and interconnected implementation of the following technologies: machine learning applied to engineering and automation, data analysis and data modeling.

Syllabus

Introduction to Intelligent Automation: implementation and interconnection of machine learning applied to engineering and automation. Introduction of machine learning: supervised and unsupervised learning, optimization, loss functions and cross-validation. Linear models for regression and classification. Decision trees and random forests. Data reduction and clustering. Introduction to neural networks. Feed-forward neural networks. Convolutional neural networks for images and other types of data. Technology to reduce human intervention in repetitive tasks. Data understanding, data preparation and data modeling. Automation using Unified Modeling Language (UML) and databases. Relational databases, basic query structures, entity- relationship model and its relation with UML, and noSQL databases. Integration based on fog and cloud computing for automation. Applications: optimization industrial processes, predictive maintenance, etc.

Prerequisites

Approval in Automation Systems or equivalent course.

Cross Competence Component

Laboratory project done in group requires critical and inovative reasoning, decision making, team work, leadership, oral communication skills, use of computer multimedia tools (20% of the evaluation component of the project).

Laboratorial Component

Students must develop a project throughout the semester that integrates the components of machine learning, data analysis and data-driven modeling. The project may have hardware and software components from a perspective of intelligent automation in engineering.

Programming And Computing Component

Uses APIs for integration based on SOAP or RST. UML and databases, including noSQL databases. Use of fog and cloud computing.

Ethical Principles

All members of a group are responsible for the group's work. In any assessment, every student shall honestly disclose any help received and sources used. In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.