- Ericsson Research – Waveform design with time and frequency constraints
- Greenly – Energy disaggregation using low resolution data from existing infrastructure
- Lynx – Penalised portfolio optimisation and canonical correlation
- Protracer - Accurate ball flight aerodynamics through data driven inverse and forward modelling
- Scania - Investigate graph and network algorithms in transport vehicle GPS data to detect and quantify hubs and flow
- Swedbank – Fast simulation for interest rate portfolios
The problem considers waveform design with constraints on the time and frequency domain. The waveform to be designed would typically have a finite time duration which implies that its Fourier transform spans over the entire frequency domain. The waveform should satisfy two important constraints, namely that at most a certain amount of energy, say epsilon, is outside a pre-specified frequency interval. Also, time and frequency shifted versions of the waveform (typically corresponding to the Nyquist sampling) should be orthogonal. The optimization problem to be posed here is to minimize the time duration of the wave given the previously mentioned constraints.
The problem is important for the design of the 5th generation (5G) communication system. There are various demands for a system that is more flexible where different time and frequency allocations need to be more dynamic in order to optimize the network with respect to different services.
Team Leader: Magnus Fontes, Lunds Tekniska Högskola
The concept of separating the single source of the total energy consumption into various appliances is called energy disaggregation or Non-Intrusive Load Monitoring (NILM). The traditional approach has relied on costly hardware that residents have to buy and install on their electricity meters. The cost (150-200 EUR) together with complicated installation has led to very few people investing in such solution. Accordingly, the major energy reduction which was envisioned has been absent.
During recent years, more than 100 million smart meters have been installed in different parts of the world. The intention of the meters is to facilitate more informed and active households. These smart meters can communicate hourly to 15 minutes readings, but are unfortunately not being utilized to their maximum capacity. The Nordics countries and Italy have come the furthest in Europe with smart meter installations, but there is a European goal of reaching 80% coverage of smart electricity meters in Europe by 2020.
Greenely is approaching the energy disaggregation problem using low-resolution data from the smart meters, in line with a recent turn in energy disaggregation research towards smart metering data. One of the more cited research papers in the no-hardware approach is “Energy Disaggregation via Discriminative Sparse Coding (Zico et. al, 2010)”. This approach is more complex, since previous solutions have accessed data with much higher frequency. However, if such disaggregation works with smart meters, no hardware is required whatsoever. Therefore, a successful project can facilitate in-depth analysis of households’ individual appliances completely free since Greenely can communicate with the smart meters by only using software.
Team Leader: Thomas Schön, Uppsala Universitet
Team Leader: Filip Lindskog, Kungliga Tekniska Högskolan
Based on 5-50 observations of a ball flying through the air, we wish to accurately predict its future trajectory. Given sequences of 3D+time observations of ball positions, accurately estimate all relevant flight parameters; internal parameters such as velocity and spin, as well as external parameters such as wind. From estimated parameters, model the ball flight trajectory over time. We have collected a very large number of observed flight trajectories in the form of stereo triangulated positions sampled at 25-50 Hz. This should enable good statistics for model evaluation and tuning.
Key words: Fluid mechanics, numerical analysis, big data, inverse problems
Team Leader: Johan Hoffman, Kungliga Tekniska Högskolan
5. Scania - Investigate graph and network algorithms in transport vehicle GPS data to detect and quantify hubs and flow
The aim of this project is to investigate how graph and network algorithms together with vehicle GPS data can be used efficiently, and in the longer run support the Scania’s customers with route optimization and logistics planning. Trajectory clustering and graph algorithms will be studied to identify appropriate methods that fulfill the requirements of the application at Scania. These methods can be used to identify bottlenecks in transportation networks, to find more optimal routes, and/or anomaly detection. The motivation is to apply the results to transport planning tasks in the future. Some examples are logistics and environmental impact optimization problems such as maximizing flow of goods/tonnes or minimizing fuel consumption. Resulting solutions are of interest when developing products that aid Scania’s customers in increasing profitability and decreasing environmental footprint
Team Leader: Catarina Dudas, Chalmers Fraunhofer Center, Sergei Silvestrov (Co-team leader), Mälardalens Högskola
Monte Carlo simulation is frequently used within the bank for the purpose of pricing and risk quantification. The project is directed towards efficient Monte Carlo simulation for portfolios of interest rate products. The fixed-income portfolio of a typical bank consists of interest rate derivatives such as caps and floors, interest rate swaps, cross-currency swaps, swaptions and FX forwards. These instruments are being sold to customers and used for mitigating the bank’s exposure on the fixed-income market. The portfolio consists of thousands of such derivatives.
The valuation of such fixed income products is quite complicated in practice. In particular, the introduction of collateral agreements creates a significant computa- tional overhead. The details of the collateral agreements as well as the counterparties respective credit worthiness must be taken into account and the theoretical risk neu- tral price must be corrected with the appropriate collateral value adjustment (CVA), default value adjustment (DVA), funding value adjustment (FVA), etc.
In addition, in the aftermath of the financial crisis the fixed-income markets have become more complicated. The liquidity crisis transformed markets into multi-curve families where, for each currency, there is a yield-curve for each tenor (1m, 3m, 6m, etc). The stochastic evolution of the collection of yield-curves and exchange rates can be described by a high-dimensional stochastic process that is calibrated to be consistent with current market prices.
Team Leader: Henrik Hult, Kungliga Tekniska Högskolan