Seminarios 2021-2022

Título: The OpenModelica Environment, the Modelica modeling language, and their Use for Development of Cyber-physical Systems and Digital Twins

Ponente: Peter Fritzson, Professor and research director of the Programming Environment Laboratory, Linköping University

Sesiones:

• 30 Mayo, 9:00-14:00
• 31 Mayo, 9:00-14:00

Lugar: 

Organizador: Juan Llorens

Resumen:

The industry is currently seeing a rapid development of cyber-physical system products containing integrated software, hardware, and communication components. The increasing system complexity in the automotive and aerospace industries are some examples. The systems that are developed have increasing demands of sustainability, dependability and usability. Moreover, lead time and cost efficiency continue to be essential for industry competitiveness. Extensive use of modeling and simulation - Model-Based Systems Engineering  tools - throughout the value chain and system life-cycle is one of the most important ways to effectively target these challenges. Simultaneously there is an increased interest in open source tools that allow more control of tool features and support, and increased cooperation and shared access to knowledge and innovations between organizations.

Modelica is a modern, strongly typed, declarative, equation-based, and object-oriented (EOO) language for model-based systems engineering including modeling and simulation of complex cyber-physical systems  Major features are: ease of use, visual design of models with combination of lego-like predefined model building blocks, ability to define model libraries with reusable components, support for modeling and simulation of complex applications involving parts from several application domains, and many more useful facilities. The Modelica language is ideally suited for cyber-physical modeling tasks since it allows integrated modeling of discrete-time (embedded control software) and continuous-time (process dynamics, often for physical hardware). Modelica 3.3 extended the language with clocked synchronous constructs, which are especially well suited to model and integrate physical and digital hardware with model-based software.

This seminar and short course gives an overview and outlook of the OpenModelica environment – the most complete Modelica open-source tool for modeling, engineering, simulation, and development of systems applications (www.openmodelica.org), and its usage for sustainable cyber-physical system and digital twin development. Special features are MetaModeling for efficient model transformations, debugging support for equation-based models, support (via OMSimulator) for the Functional Mockup Interface for general tool integration and model export/import between tools, model-based optimization, as well as generation of parallel code for multi-core architectures.

Moreover, also mentioned is work to make an OpenModelica based tool chain for developing digital controller software for embedded systems as well as test-based verification of requirements in model-based development.
 

^{Figure 1. OpenModelica simulation of  the V6Engine model with 11000 equations. Plotting simulation results using OMEdit. Left: Model browser. Right: Plot variable browser. Bottom: message browser window.}

Breve biografía:

Peter Fritzson is Professor and research director of the Programming Environment Laboratory, at Linköping University. He is also vice director of the Open Source Modelica Consortium, vice director of the MODPROD center for model-based product development, (previously director of both) organizations he took initiative to establish. During 1999-2007 he served as chairman of the Scandinavian Simulation Society, and secretary of the European simulation organization, EuroSim. During 2000-2020 he was vice Chairman of the Modelica Association.

Prof. Fritzson's current research interests are in software technology, especially programming languages, tools and environments; parallel and multi-core computing; compilers and compiler generators, high level specification and modeling languages with special emphasis on tools for object-oriented modeling and simulation where he is one of the main contributors and founders of the Modelica language. Professor Fritzson has authored or co-authored more than 320 technical publications, including 21 books/proceedings.

[+] personal website

Título: Designing and developing Intelligent User Interfaces

Ponente: Albrecht Schmidt, full professor for Human Centered Ubiquitous Media, LMU Munich.

Sesiones:

• 31 mayo, 16:00-19:00
• 1 Junio, 15:00-19:00
• 2 Junio, 15:00-18:00

Lugar: 1.2.G04

Organizador: Paloma Díaz

Resumen:

Recent advancements in artificial intelligence (AI) create new opportunities for implementing a wide range of intelligent user interfaces. Speech-based interfaces, chatbots, visual recognition of users and objects, recommender systems, and adaptive user interfaces are examples that have majored over the last 10 years due to new approaches in machine learning (ML). Leveraging the potential of artificial intelligence and combining them with human-computer interaction approaches allows developing intelligent user interfaces supporting users better than ever before.

We will discuss how to design, develop, and implement intelligent user interfaces and how to create interactive AI Systems. We address the fundamental question, of how to keep the human in the loop with intelligent systems and how joint control can be realized. We give concrete examples in the areas of recommender systems and user interface adaptation. Multimodality and in particular natural language based interaction in the context of bots will be a further topics.

Topics include:

• Human in the loop – Designing for joint control
• A new Interaction Paradigm: From continuous interaction to intervention user interfaces
• How to build recommender systems that work? Why is Netflix not suggesting the movies I really like?
• Using optimization and ML to improve text input and user interfaces
• Smart Conversational Interfaces: Natural Language Interaction and Bots
• Creating smart objects and intelligent environments
• Human Centered Interactive AI - How can we implement this?
• Embedding intelligent UIs into objects

We will include ideation, design, and implementation parts to gain a hands-on experience.

Breve biografía:

Albrecht Schmidt is a full professor for Human Centered Ubiquitous Media in the Computer Science Department at LMU Munich. The focus of his work is on novel user interfaces to enhance and amplify human cognition. He is working on interaction techniques and intelligent interactive systems in the context of ubiquitous computing. In 2018 he was elected to the ACM CHI Academy.

[+] personal website

Título: Local And Global Optimization Methods

Ponente: Igor Škrjanc, PhD in Management Sciences, Copenhagen Business School

Fechas: 26 noviembre 2021

Horas: de 17:00 a 21:00 h.

Sesión grabada: Sesión de dos horas que estará disponible a partir del 3 de diciembre.

Organizador: Scalab (Araceli Sanchis)

Lugar: 2.0.C04 Edificio Sabatini (Campus de Leganés)

Créditos: 2 ECTS

Resumen:

This seminar will provide an introduction to optimization in terms of process modelling and identification. With the use of the proposed optimization methods, we can determine the parameters or structure of the model from the measured data. The optimization or learning concepts can be divided into three different approaches, according to the information we need to identify the model: supervised learning, reinforcement learning, and unsupervised learning. In the seminar we will discuss the methods of supervised learning where the basic idea is to find the relations in the set of input-output data pairs. This means that we are trying to minimize the chosen objective function which is based on errors between measure process output and model output, to obtain the best possible model. Supervised learning methods can be divided into three groups: linear, nonlinear local and nonlinear global optimization methods.

When the error between the output of the process and the model is linear in the parameters and optimality is required in terms of the least squares method, then the problem is translated into a linear problem optimization.

Distinguishing between local and global optimization only makes sense in the context of nonlinear optimization problems, since linear problems do not have local extremes. In this seminar we going to discuss the general problems of global optimization methods in searching for a global optimum. We will discuss the stochastic nature of global optimization methods which is in extending local methods to find the global optimum by adding random values to the newly calculated parameter values. The added stochastic values slow down the speed of convergence, but at the same time enable the exit from the local optimum. Global nonlinear techniques are used if at least one of the following criteria is met: we are looking for a global optimum, the cost function and its derivatives are highly nonlinear, or certain parameters are not real numbers, but integers or binaries, or even it could be a mixed optimization problem. We will discuss the following methods: simulated annealing, and particle swarm optimization.
All theory will be practically explained by numerous simulated examples.

Breve biografía:

Igor Škrjanc received B.S., M.S. and Ph.D. degrees in electrical engineering, in 1988, 1991 and 1996, respectively, at the Faculty of Electrical and Computer Engineering, University of Ljubljana, Slovenia. He is currently a Full Professor with the same faculty and Head of Laboratory for Autonomous and Mobile Systems. He is lecturing the basic control theory at graduate and advanced intelligent control at postgraduate study. His main research areas are adaptive, predictive, neuro-fuzzy and fuzzy adaptive control systems. His current research interests include also the field of autonomous mobile systems in sense of localization, direct visual control and trajectory tracking control. He has published 126 papers with SCI factor and 31 other journal papers. He is co-author and author of 14 chapters in international books and co-author of two scientific monographs with the title Predictive approaches to control of complex systems published by Springer and Wheeled Mobile Robotics, From Fundamentals Towards Autonomous Systems published by Elsevier, Butterworth-Heinemann. He is also author and co-author of 274 conference contributions, 37 lectures at foreign universities. He is also mentor at 9 PhD thesis, 4 MSc thesis and 36 diploma works. And co-mentor of 2 PhD thesis and 4 MSc thesis. He is author of 20 university books, 50 international and domestic projects and 5 patents and 2 patent applications. In 1988 he received the award for the best diploma work in the field of Automation, Bedjanič award, in 2007 the award of Faculty of Electrical Engineering, University of Ljubljana, Vodovnik award, for outstanding research results in the field of intelligent control, in 2012 the 1st place at the competition organized by IEEE Computational Society, Learning from the data in the frame of IEEE World Congress on Computational Intelligence 2012, Brisbane, Australia: Solving the sales prediction problem with fuzzy evolving methods, and in 2013 the best paper award at IEEE International Conference on Cybernetics in Lausanne, Switzerland. In 2017 he won the first prize on Autonomous Learning Machines Competition (ALMA) organized by IEEE SMC Society. In 2008 he received the most important Slovenian research award for his work in the area of computational intelligence in control – Zois award. In 2016 he received a Golden plaque of the University of Ljubljana for outstanding merits in the development of scientific and pedagogical work and for strengthening the reputation of the University in the world. In year 2009 he received a Humboldt research award for long term stay and research at University of Siegen. He is Humboldt research fellow, research fellow of JSPS and Chair of Excellence at University Carlos III of Madrid. He is also a member of IEEE CIS Standards Committee, IFAC TC 3.2 Computational Intelligence in Control Committee and Slovenian Modelling and Simulation society and Automation Society of Slovenia. He also serves as an Associated Editor for IEEE Transaction on Neural Networks and Learning System, IEEE Transaction on Fuzzy Systems, the Evolving Systems journal and International journal of artificial intelligence. In 2017 he organized an IEEE Conference on Evolving and Adaptive Intelligent Systems -  EAIS 2017.

Título: Deep Learning en casos de uso del Sector Salud

Ponentes:

• José Arcos Aneas – Responsable CoC (Center of Competence) in AI – MS Computer
  Science
• Carmen Arcos Aneas – Lead AI Health – Doble Grado Matemáticas e informática.
  Master en educación

Organizador: Indizen Technologies

Resumen:

En este seminario se ofrece una visión aplicada sobre el uso de Deep learning desde una perspectiva empresarial, complementando así la visión más teórica ofrecida en las asignaturas del máster. El seminario sólo puede seguirse presencialmente.

Al final del seminario tienes que subir a Aula Global una reflexión personal sobre los temas tratados de no más de dos páginas.

Contenidos:

• Conceptos de Deep Learning. Principales Librerías y tipos de Redes. 
• Aplicación de Deep Learning (TensorFlow, Keras, …) en casos de uso reales del sector Salud: imágenes (detección de covid, cáncer, edad ósea …) 
• NLP (procesamiento de lenguaje natural) para codificación de información clínica junto al uso de Grafos de conocimientos. 
• Explicación de los pasos del proceso end-to-end: preparación de los datos, modelado, entrenamiento y evaluación, interpretabilidad de los modelos, y despliegue de los mismos. 
• Lecciones aprendidas, buenas prácticas 
• Herramientas y prácticas MLOps para productivización de la IA 
• Casos prácticos
   

Sesiones:

• Jueves 7/10/2021, de 15:00 a 17:00 │ Aula 2.3.A04
• Viernes 8/10/2021, de 15:00 a 19:00 │ Aula 2.3.A03
• Viernes 15/10/2021, de 15:00 a 19:00 │ Aula 4.0.D01

 El seminario tiene una duración de 10 horas.