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Start   >  Master's & postgraduate courses  >  Education  >  Postgraduate course in Modeling and Simulation in Industry 4.0
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relation
This postgraduate course is also part of the training track for the master's degree in Industry 4.0.

Programme

Edition
2nd Edition
Credits
24 ECTS (174 teaching hours)
Delivery
Blended learning
Language of instruction
Spanish
Fee
€4,500
Notes payment of enrolment fee and 0,7% campaign
Registration open until the beginning of the course or until end of vacancies.
Start date
Start date: 18/10/2019
End date: 29/02/2020
Timetable
Friday: 4:00 pm to 9:00 pm
Saturday: 9:00 am to 2:00 pm
Taught at
Tech Talent Center
C/ de Badajoz, 73-77
Barcelona
Why this programme?
Modelling enables physical objects to be transferred to the digital world, and simulation techniques used to create "Digital Twins," which are used in high-speed experiments with no physical risk, as well as providing educational, training and support environments for operations through augmented reality and automatic learning (Machine Learning). These can be combined with elements of process modelling, creating a digital value chain. Both the physical world and the simulated world are sources for the generation of large volumes of data (Big Data).

Industry 4.0 is the body of knowledge formulated by the Fourth Industrial Revolution - the result of the combination between the physical and digital world. Its inevitability is the result of the constant increase in customised demand from markets, which is forcing companies to rethink their production and logistics systems.

The worlds of IT and OT have existed side by side, sharing limited spheres of interaction, and following parallel lives. The demand for cyberphysical systems means that walls that have been in place for decades must be demolished, leading to "IT/OT convergence", which is one of the major challenges posed by Industry 4.0. The figure of the CDO (Chief Digital Officer), also known as the Head of Digital Transformation, is an emerging hybrid professional profile in today's organisation charts. A cross-sectoral approach is one of the motifs of the contemporary world, and Industry 4.0, and Fortune magazine highlights the unstoppable nature of the demand for hybrid professional profiles (Hybrid Jobs).

The main objective of the programme is to train professionals to be able to understand the difficulties and complexities of the world of Industry 4.0 in a cross-disciplinary manner from the physical world to the digital world, applying simulation as a basic tool in the process. The participants will obtain the knowledge necessary to develop cybernetic system models for Industry 4.0 and Digital Twins.
Aims
  • Understand the problems of Industry 4.0 and digital transformation processes.
  • Practice with cutting-edge simulation tools.
  • Create "Digital Twins" for industrial elements and processes.
  • Use 3D modelling systems.
  • Identify the most appropriate automatic learning models and the statistical and operational Research techniques for a specific problem.
  • Be able to perform pre-processing of data and identify Big Data approaches.
  • Use systems modelling and statistical validation systems and tools and be able to verify and validate the proposed models.
Who is it for?
  • Operations Technology Professionals wishing to enhance their IT knowledge base in the field of data processing, modelling and simulation for Industry 4.0.
  • Information Technologies Professionals wishing to acquire knowledge of the OT world of industrial process modelling.
  • Graduates in engineering (computer, telecommunications, industrial, agricultural, civil engineering, etc.), physics, mathematics and statistics.

Training Content

List of subjects
3 ECTS 18h
Industry 4.0 and Society (on line)
  • Levers of Industry 4.0.
  • Areas of industry 4.0.
    • Manufacturing.
    • Logistics, internet physics.
    • Civil works.
    • Medicine.
    • Banking (cryptocurrencies).
    • Politics.
    • The environment.
    • Agriculture.
    • Society (Platform Economy).
  • Basic statistics.
    • Introduction to the distributions of the probability in univariate samples. Discrete and continuous data. Momentum indicators (centred and not centred).
    • The most common discrete distributions: characteristics, use in modelling and identification of profiles: count modelling, modelling the time between events.
    • Samples and populations: sampling vs. inference. Types of sampling. Examples: inference on the mean based on a random sample without/with replacement. Concept of the Hypothesis Test.
  • Introduction to the language R.
3 ECTS 30h
Industry 4.0, Statistics and Data Management
  • Levers of Industry 4.0.
  • Technologies for managing large volumes of data, real-time data management, de-structured data, etc.
  • Data pre-processing:
    • Sources of information and their nature.Data matrix.
    • General pre-processing methodology.
    • Data format operations and software compatibility.
    • Selection of variables, identification of the study population (feature selection and filtering).
    • Identification, diagnosis and treatment of missing data.
    • Identification, diagnosis and treatment of outliers.
    • Reduction of dimensionality.
    • Transformations in data.
    • Creation of indicators, derived variables.
    • Pre-processing procedure design.
    • Automation.
  • Knowledge management.
    • The nature of declarative knowledge.
    • Implicit knowledge.
    • Formal models of knowledge representation.
2 ECTS 12h
Simulation, Basics and Applications (on line)
  • DOE.
    • Factorial designs.
    • Fractional factorial designs.
    • Latin squares.
  • RNG/GVA, introduction to complexity theory.
  • Selection and analysis of the sample (input distributions).
  • Introduction to discrete simulation.
    • Definition and use of simulation.
    • Stages in the development of a system.
    • Elements of a simulator.
    • Classic discrete simulation motors (Event Scheduling, Activity Scanning, Process Interaction).
  • Introduction to continuous simulation through the systems dynamics.
  • Introduction to multi-agent simulation.
  • Introduction to cellular automata.
  • Validation, verification and accreditation.
  • Specific simulation tools.
  • Selecting the coding tool (SQMO and others).
  • Examples of simulators in Industry 4.0.
4 ECTS 30h
Simulation, Basic Modeling and Programming
  • Definition and use of models with Flexim.
  • Execution of simulation models by hand.
  • Construction of a simulation system by hand.
  • Examples of environmental/social/economic simulation etc.
    • Executing NetLogo models, using systems dynamics models (Insight Maker, etc.)
4 ECTS 24h
Introduction to Modeling (on line)
  • Data Science.
    • General Data Science process.
    • DMMCM (Data Mining Methods Conceptual Map).
    • Criteria for selecting the most appropriate data exploitation method.
    • Post-procedure and production of value from the data mining model.
  • 3D modelling.
    • Fusion 360.
  • Modelling for simulation systems.
    • Specification and Description Language (SDL).
    • Petri/DEVS networks.
    • Transformation of models.
    • Metamodels/metalanguages.
  • Validation, verification and accreditation issues (HLA and other integration standards).

8 ECTS 60h
Modeling and Digital Twins
  • 3D modelling exercises.
    • Fusion 360.
    • Virtual and augmented reality.
  • General modelling of systems.
    • Orientation to Objects.
    • Polymorphism, inheritance.
    • Implementation with ES6.
  • Integration with UML.
    • Structure diagrams.
    • The concept of the metamodel.
    • Behavioural diagrams.
  • Object-oriented analysis and design.
    • Design patterns.
  • Agent-oriented design.
    • Communication between objects.
    • HTTP REST.
  • From formalism to the model: automatic code generation tools.
    • Working with PragmaDEV Studio, SDL, DEVS and Petri Networks.
  • From formalism to the model: general tools.
    • Working with Flexim and NetLogo.
    • Automatic validation of simulation models.
    • Integration with UML.
  • Tools that use the model.
    • HLA standard.
    • Examples of models in SDL.
    • BIM (tools including energy+, NECADA).
Degree
Postgraduate diplomas issued by the Universitat Politècnica de Catalunya. Issued pursuant to art. 34.1 of Organic Law 4/2007 of 12 April, amending Organic Law 6/2001 of 21 December, concerning Universities. To obtain this degree it is necessary to have an official. Otherwise, the Fundació Politècnica de Catalunya will only award them a a certificate of completion.
Range of modules
This postgraduate course is part of the training track for the master's degree in Industry 4.0 .
The master's degree programme is organized into the following modules. If you don't wish to take the entire master's degree you can sign on one or several modules.
Master's degree:
relation Postgraduate courses:

Learning methodology

The teaching methodology of the programme facilitates the student's learning and the achievement of the necessary competences.

Learning tools
Participatory lectures
A presentation of the conceptual foundations of the content to be taught, promoting interaction with the students to guide them in their learning of the different contents and the development of the established competences.
Practical classroom sessions
Knowledge is applied to a real or hypothetical environment, where specific aspects are identified and worked on to facilitate understanding, with the support from teaching staff.
Solving exercises
Solutions are worked on by practising routines, applying formulas and algorithms, and procedures are followed for transforming the available information and interpreting the results.
Case studies
Real or hypothetical situations are presented in which the students, in a completely participatory and practical way, examine the situation, consider the various hypotheses and share their own conclusions.
Success stories
Outstanding business knowledge and experiences with high added value acquired during an outstanding professional career are presented and shared.
Problem-based learning (PBL)
An active learning methodology that enables the student to be involved from the beginning, and to acquire knowledge and skills by considering and resolving complex problems and situations.
Flipped classroom
The contents are prepared prior to the face-to-face lessons. Practical sessions take place in the classroom, which enable understanding and application of concepts to real cases and the expansion of knowledge with more technical and specialised details.
Tutorship
Students are given technical support in the preparation of the final project, according to their specialisation and the subject matter of the project.
Workshops
Students are supported when undertaking group work, including theoretical sessions which provide the tools and knowledge needed to achieve a result. Ideas and results are exchanged between all the participating groups.
Assessment criteria
Attendance
At least 80% attendance of teaching hours is required.
Level of participation
The student's active contribution to the various activities offered by the teaching team is assessed.
Solving exercises, questionnaires or exams
Individual tests aimed at assessing the degree of learning and the acquisition of competences.
Work out projects
Studies on a specific topic, by individuals or groups, in which the quality and depth of the work is assessed, among other factors.
Completion and presentation of the final project
Individual or group projects in which the contents taught in the programme are applied. The project can be based on real cases and include the identification of a problem, the design of the solution, its implementation or a business plan. The project will be presented and defended in public.
Work placements & employment service
Students can access job offers in their field of specialisation on the My_Tech_Space virtual campus. Applications made from this site will be treated confidentially. Hundreds of offers of the UPC School of Professional & Executive Development employment service appear annually. The offers range from formal contracts to work placement agreements.
Virtual campus
The students on this postgraduate course will have access to the My_ Tech_Space virtual campus - an effective platform for work and communication between the course's students, lecturers, directors and coordinators. My_Tech_Space provides the documentation for each training session before it starts, and enables students to work as a team, consult lecturers, check notes, etc.

Teaching team

Academic management
  • Fonseca i Casas, Pau
    Doctor at Statistics and Operative Investigation for the Universitat Politècnica de Catalunya (UPC). Teacher of the UPC where imparts teaching at the area of Simulation, the treatment of data, the Operative Investigation and the Statistics. He¿s Responsible of the Area of Environmental Simulation of the inLab FIB leading projects of simulation linked predominately at industrial and environmental areas. He¿s member of the Commission 4.0 of the Industry of Engineers of Catalonia.
Teaching staff
  • Fonseca i Casas, Antoni
    PhD Architect by the UPC of Barcelona. Specialized in energy optimization and sustainability. It has the Professional Accreditation Diploma and Project Experience of the Leed International certification. Postgraduate degree AECEI C ++ and SQL programming. Quality management certification diploma ISO 9001: 2008. Postgraduate specialization 'Rehabilitation in building', by the UPM. Training in Prevention and Occupational Risks - Safety and health, by the UPC. Collaborator in several universities as researcher and teacher.
  • Fonseca i Casas, Pau
    Doctor at Statistics and Operative Investigation for the Universitat Politècnica de Catalunya (UPC). Teacher of the UPC where imparts teaching at the area of Simulation, the treatment of data, the Operative Investigation and the Statistics. He¿s Responsible of the Area of Environmental Simulation of the inLab FIB leading projects of simulation linked predominately at industrial and environmental areas. He¿s member of the Commission 4.0 of the Industry of Engineers of Catalonia.
  • Gibert Oliveras, Karina
    View profile in futur.upc
    Ph.D. in Computer Science at UPC. Associate Professor at the Dep. Statistics and Operations Research (UPC), leading courses related with her research areas in Degree in Statistics, D. in Informatics Engineering, Master in Informatics Engineering, M. in Artificial Intelligence, M. in Sustainability, all at UPC, and its associated PhD programs. ViceDean for Big Data and Data Science of the Official Professional Chamber of Informatics Engineering of Catalonia. Subdirector of IDEAI Research center (UPC).

Associates entities

Strategic partners
  • Information Technology Service Management Forum
    • Disseminates the programme in the professional sphere and area of expertise.
Collaborating partners

Career opportunities

  • Chief Digitalization Officer.
  • Chief Information Officer (Industrial).
  • Chief Data Officer (Analyst / Manager of industrial data).
  • Expert in simulation of industrial processes.
  • Expert in robotics and IoT.

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How to start admission
To start the enrolment process for this programme you must complete and send the form that you will find at the bottom of these lines.

Next you will receive a welcome email detailing the three steps necessary to formalize the enrolment procedure:

1. Complete and confirm your personal details.

2. Validate your curriculum vitae and attach any additional required documentation, whenever this is necessary for admission.

3. Pay €110 in concept of the registration fee for the programme. This fee will be discounted from the total enrolment fee and will only be returned when a student isn't admitted on a programme.

Once the fee has been paid and we have all your documentation, we will assess your candidacy and, if you are admitted on the course, we will send you a letter of acceptance. This document will provide you with all the necessary information to formalize the enrolment process for the programme.




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