Innsbruck Modelling Week 2015

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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

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