Sleep Spindles as Biomarker for Early Detection of Neurodegenerative Disorders

The present invention relates to the use of sleep spindles as a novel biomarker for early diagnosis of synucleinopathies, in particular Parkinson's disease (PD). The method is based on automatic detection of sleep spindles. The method may be combined with measurements of one or more further biomarkers derived from polysomnographic recordings.</p

Stochastic Modeling and Operational Optimization in District Heating Systems

Operation of a district heating system is accomplished via a sequence of decisions by the operators controlling the system. These decisions are based on expectations of conditions in the system that are not known at decision time. The operators could be helped by a decision support system that computes predictions of future system variables and suggests appropriate control actions given the available information. This thesis presents a new model that gives a both physical and stochastic description of a district heating system. The model describes both technical and economical information of the system that are important for the control decisions. It is easy to calculate predictions based on this model as well as performing simulations. The ambient temperature is the single most important explanatory variable for the heat demand in a district heating network. A model that can be used to calculate reliable temperature predictions are presented where the full advantage of both local measurements and forecasts from a meteorological institute are utilized. A heuristic approach to the operational optimization problem is presented and it is shown in simulations to be superior to a traditional control, based on a priority scheme. The operational optimization problem is a complex stochastic optimization problem and the heuristic approach gives a solution that can be calculate instantly. An online computer program, EnerPlan, is developed where the described models are used to calculate predictions and simulate alternative future scenarios. The program is currently used in the control room at the Heleneholm power plant in Malmö, Sweden

On sizing of flow meters used in customer accounting devices in district heating systems

The paper deals with accuracy problems when heat energy consumption in district heating (DH) systems is calculated by measuring the DH water ow rate and its cooling. An investigation on the in uence that sizing of ow meters used has on the accuracy of DH water ow mea- surements in a typical DH subscriber station is presented. Furthermore the consequences of the choice of ow meter size on energy metering accuracy is studied. The goal is to determine rules leading to optimal sizing of the ow meters

Bootstrap control

In this paper, we present a new way to control linear stochastic systems. The method is based on statistical bootstrap techniques. The optimal future control signal is derived in such a way that unknown noise distribution and uncertainties in parameter estimates are taken into account. This is achieved by resampling from existing data when calculating statistical distributions of future process values. The bootstrap algorithm takes care of arbitrary loss functions and unknown noise distribution even for small estimation sets. The efficient way of utilizing data implies that the method is also well suited for slowly time-varying stochastic systems