Jens Alsted Hansen

Sensor Fusion Using Dynamic Bayesian Networks in Livestock Production Buildings

The climate in modern livestock production buildings is controlled using a simple state controller. State controllers are typically not equipped to handle abnormal situations, e.g. sensors providing false or no readings, and they may therefore produce malformed climate control signals which may have severe consequences for the livestock. To make the system more robust, a sensor fusion system can be used to combine the different sensor readings and thereby produce a more reliable estimate of the climate state. A dynamic Bayesian network (DBN) model is constructed for this purpose. The model is tested in an online setup in a climate laboratory, where realtime behavior is archived by using the Boyen & Roller approximate inference algorithm. Preliminary experimental results show that the proposed model provides a promising framework for sensor fusion in livestock buildings.

Single Point Algorithms in Genetic Linkage Analysis

In this paper we provide a mathematical model that describes genetic inheritance. In terms of our framework, we describe two commonly used algorithms that calculate the set of founder allele assignment that are compatible with some genotype information and a given inheritance pattern. These calculations cater for the first step in the multi point linkage analysis. The first algorithm we investigate is a part of the Genhunter software package developed at the Whitehead Institute at MIT and is developed by Kruglyak et al [8]; the second one occurs in the software package Allegro provided by Gudbjartsson et al [5] at DeCode Genetics in Reykjavik. We also state and prove formally the correctness of these algorithms.