Innsbruck Modelling Week 2015

The First Innsbruck Modelling Week in Applied Mathematics is organized by the Department of Mathematics and the Unit of Engineering Mathematics, University of Innsbruck.

It takes place at the University of Innsbruck, Campus Technik, from July 5 (arrival day) to July 11 (departure day) 2015.

Modelling weeks are an internationally recognized method of technology and knowledge transfer between universities, economy and industry. In a five-days workshop, mathematicians from universities work together with experts from economy and industry on real-world problems.

This year, six companies will present mathematical problems the solutions of which are required. The problems cover a representative range of industrial mathematics.

The format of the workshop is the following:

  • The problems are presented on the first day (July 6) by a company representative; after that groups of about five students are formed according to individual interests; the groups will work on their problems under the supervision of one or two senior mathematicians. The results will be presented on the last day (July 10). Each group is supposed to prepare a written report for the company shortly after the event. Continuation of collaboration is encouraged.
  • We envisage the participation of students in mathematics in a Master's or a PhD program. Students will benefit by getting acquainted with real life applications of mathematics; companies should benefit from the proposed solutions as well as contacts for future collaboration.
  • The organizers will provide accommodation from Monday to Friday for all participants. A registration fee of 70,- Euro is solicited. To register, please follow the link. For further information, please consult one of the organizers.
  • We invite applications from mathematics students internationally. A certificate of participation will be issued at the end of the event.

 

Organization committee:

  • Haltmeier, Markus
  • Kowar, Richard
  • Oberguggenberger, Michael
  • Ostermann, Alexander

Participants:

  1. Naomi Auer - University of Innsbruck - Technical Mathematics
  2. Ralf Banish - Freie Universität Berlin - Mathematics
  3. Alexander Bauer - LMU München - Master Statistik
  4. Ágnes Bodó - Eötvös Loránd University of Budapest - Applied Mathematics
  5. Pratibhamoy Das - Technische Universitat Berlin, Germany - Numerical Analysis for system of Differential equations, Moving Mesh Methods
  6. Sahar Faghfouri - Johannes Kepler University (JKU) Linz - Industrial Mathematics
  7. Zénó Farkas - Eötvös Loránd University of Budapest - Applied Mathematics
  8. Tatiana Grandon - Technical University Berlin - Mathematics
  9. Felix Günther - Ludwig-Maximilians-University Munich - Statistics MA
  10. Sandra Gužvanj - University of Novi Sad - Applied Mathematics
  11. Maja Jolić - University of Novi Sad - Mathematics
  12. Maria Kiss - University of Novi Sad - Master (Modul: Theoretical Mathematics)
  13. Matthias Köchl - University of Innsbruck - Technical Mathematics
  14. Andreas Kofler - University of Innsbruck - Technical Mathematics
  15. Patrik Krämer - KIT Karlsruhe - Numerical Mathematics
  16. Manuel Kroiss - LMU Munich & TU Munich - Bioinformatics
  17. Daniel Lorenz - TU Bergakademie Freiberg - Applied Mathematics
  18. Janaki Mogaparthi
  19. Lorenz Oberhammer - University of Innsbruck - Technical Mathematics
  20. Marie-Christine Pali - University of Innsbruck - Technical Mathematics
  21. Oleksandra Panasiuk - Johannes Kepler University (JKU) Linz - Technical Mathematics
  22. Lena-Maria Pfurtschneller - University of Innsbruck - Technical Mathematics
  23. Simon Rabanser - University of Innsbruck - Technical Mathematics
  24. Maximilian Reich - TU Bergakademie Freiberg - Harmonic Analysis, Analysis of PDE
  25. Dávid Réka - Eötvös Loránd University - Mathematics
  26. Lisa Schlosser - University of Innsbruck - Technical Mathematics
  27. Barbara Schretter - University of Innsbruck - Technical Mathematics
  28. Claudia Schmuck - University of Innsbruck - Technical Mathematics
  29. Johannes Schwaighofer - University of Innsbruck
  30. Martin Schwarz - University of Innsbruck - Technical Mathematics
  31. Johannes Siegele - University of Innsbruck - Technical Mathematics
  32. Martin Skrodzki - Freie Universität Berlin - Mathematics
  33. Lisa Steyer - LMU München - Master Statistik
  34. Almond Stöcker - LMU Munich - Statistics
  35. Ármin Tábori - Eötvös Loránd University of Budapest - Applied Mathematics
  36. Lena Tschiderer - University of Innsbruck - Technical Mathematics
  37. Alexander Werlberger - University of Innsbruck - Master Physics
  38. Christoph Wolf - University of Innsbruck - Mathematics

Projects

Project: Audio signal processing of MED-EL's cochlear implant

By electrically stimulating nerve fibres in the cochlea, cochlear implants allow recipients to perceive sound. The MAESTRO Cochlear Implant System is an effective, high performance solution for individuals with severe to profound sensorineural hearing loss. Most users can enjoy music or successfully participate in conversation, even in the most challenging listening situations.

The external sound processortransfers the microphone input signalsinto stimulation patterns, which are then transmitted to the implant. The signal processing in the sound processor consists of a front-end part and a coding strategy. The primary goal of the front-end processing is to automatically identify, extract and enhance important features of the microphone signals, in particular speech features. Examples for that are beamforming algorithms, wind-noise reduction, or signal compression.

The aim of the project is to investigate further speech improvementmethods for the front-end signal processing part.

  • Contact person University of Innsbruck: Kowar, Richard
  • Contact person MED-EL: Fruehauf, Florian, Falch, Cornelia

Project: Classification of Network Traffic

 Barracuda Networks offers network security and storage solutions. One of our internal, medium sized firewall handles approximately 40 million connections from about 30 clients during a typical working week. Our largest firewall models are designed to serve up to 8000 clients, with a theoretical maximum of about 400 billion connections per week. With this vast amount of network traffic it is difficult to identify suspicious or dangerous network traffic, like systematic network intrusion attempts or immanent hacker attacks, which intentionally try to go unnoticed.

Most of the network traffic follows certain patterns. For example: Many periodic tasks – like network backups or server synchronizations – always occur at approximately the same time of day, or only on certain weekdays, usually have similar data volume every time, and have internal network addresses as source and destination. As another example typical traffic from internet surfing occurs mostly between Monday and Friday during office hours, and usually has internal source and external destination address.

The goal of this project is twofold:

1. In a first stage an offline classification of collected network traffic should be performed. For each handled connection the firewall logs certain properties, like start and end time, source and destination network address, protocol, data volume and some more. From these collected data a set of descriptors should be derived, that can be used as feature vectors for classification by either a clustering method, a support vector machine, or similar algorithms. The result of the classification should reflect the most prominent classes of traffic. Further it should identify connections which do not fit well into any of the prominent classes, since these connections might be the suspicious or dangerous ones.

2. Using the results from the first stage, an online classification system (for example an artificial neuronal network) shall be trained such that it can then perform real-time (or almost real-time) classification of newly created network connections.

  • Contact persons University of Innsbruck: Kowar, Richard, Pereverzyev, Sergiy
  • Contact person Barracuda Networks: Grossauer, Harald

Project: Multidimensional Item Response Models

The evaluation of medical treatments relies increasingly on Patient-reported Outcomes (PROs), i.e. patients' self-reports on somatic and psychosocial symptoms and problems. Advanced assessment methods for PROs (e.g. physical function, depression, pain, fatigue) are based on Item Response Theory (IRT) measurement models allowing computer-adaptive assessments. Traditional, unidimensional IRT models describe the probabilistic relationship between a latent construct (the PRO domain) and patients' responses to questions measuring that construct. Model parameters are estimated using Maximum Likelihood methods.

Within the Modelling Week we would like to explore the use of multidimensional IRT models and implement them into a software package for PRO data capture. Multidimensional IRT models relate a patient's response to a single question to various PRO domains. This allows to obtain estimates for a patient's symptom level on different domains at the same time and thus enables more efficient and more precise PRO assessments.

  • Contact person University of Innsbruck: Oberguggenberger, Michael
  • Contact Persons Medical University Innsbruck: Giesinger, Johannes, Holzner, Bernhard

Project: Optimization of micro heat networks

Optimization of micro heat networks In 2014 the first Tyrolean micro heat network was commissioned in the municipality of Erl. It is based on two groundwater wells which supply 13 houses with groundwater. To use the geothermal energy of groundwater, in each house a heat pump is installed, which transfers the temperature of the groundwater to a higher level. Even though such micro heat networks are a sustainable and economic method of energy production, there is still some potential of improvement. For that purpose several constellations of micro heat networks will be modelled and analyzed concerning their energy saving potential – based on input data and experiences from the micro heat network in Erl.

  • Contact Person University of Innsbruck: Oberguggenberger, Michael 
  • Contact Person Wasser Tirol: Ruggenthaler, Romed

Project: Modelling of meteorological extremes

ZAMG as the National Weather and Geophysical Service of Austria provides weather forecasts and warnings, conducts research and operates a meteorological and a seismic monitoring network (~280 stations). The ZAMG customer service center in Innsbruck is specialized in mountain meteorology and among a bunch of other tasks conducts expertises e.g. for snow-load and wind-load. This requires the estimation of a snow pack and wind speed occurring only once within 50 years. This is straightforward for observation sites, where measurements are available for a sufficiently long period of time. When observations are missing, e.g. between stations, appropriate spatial modelling of the underlying extreme value distribution leads to satisfying results, including the possibility to specify uncertainties. Such spatial extreme value models work well for spatially smoothly distributed parameters like snow depth. Applying such a model to wind speed, which is spatially very inhomogeneous, requires additional covariates like wind direction. To use the direction of extreme wind speeds as a covariate, it has to be known at arbitrary points. So, the development of a spatial model for the direction of extreme wind speeds is a goal of this project. As wind direction is largely determined by topographical features (channel, mountain top, plain,...). A high resolution digital elevation model can be used to relate wind direction and landform. Along with measurements of wind speed and direction from ~280 Austrian weather stations, it should be possible to develop a statistical model for (the frequency distribution of) the direction of extreme wind speeds over the complex terrain of Austria.

  • Contact persons University of Innsbruck: Hell, Tobias
  • Contact person ZAMG: Schellander, Harald

Project: Prediction of short term costumer behaviour

Predicting whether a customer is going to purchase products or services of an enterprise is an increasingly important problem for efficient product management. Nowadays, enterprises can collect various information about customer behaviour, such as frequency of purchasing, times of purchases, amounts of purchases, and so on. The goal of this project is to develop automatic tools of using such data for predicting whether a given customer is going to make a purchase or not at the enterprise in the next few months.

  • Contact person University of Innsbruck: Haltmeier, Markus
  • Contact persons: Pirker, Clemens
Nach oben scrollen