Volume 77, Issue 10 -

The simulation of natural gas networks is just feasible with the available simulation tools, if all inlets and outflows of the network are measured. At regional gas distribution networks these premises are often not fulfilled. In detail this means, that not all outflows are measured and because of that it is not possible to use the common simulation tools. For this reason a method was developed at Clausthal University of Technology which emulates the unmeasured outflows with a nodal point load observer. With this emulated outflows it is possible to simulate these networks. In an actual research project this system is advanced to intermeshed gas distribution networks with a high number of outflows.

Information and communication technologies are the basis for implementing Smart Grids. Currently, there is great demand for communications solutions that allow for interconnecting smart energy meters in particular. The users´ interests focus on Power Line Communication due to economic considerations. Despite a large and even growing number of PLC-based data transmission technology, however, only few reliable information about the system performance is available to potential users. As a first step, it is therefore necessary to identify the actually achievable system performance of different communication systems. In this article, we describe two approaches for evaluating the performance of PLC systems taking into account the unique physical properties of the transmission channel. In addition, we present evaluation results that provide a basis for a realistic assessment of the potential of communication technology based on PLC.

Data mining approaches base on a powerful methodology. Since many years, they have been applied to different problems in technics, natural sciences, and economics. Earlier data mining methods were restricted to data sets consisting of tables with a manageable number of features. Now there is a growing need for the analysis of high-dimensional data resulting from sensor networks with high-resolution time series, images, and videos. The paper introduces the underlying methods and gives an overview about major application fields.

A generic approach and process architecture for task-oriented sensor data processing is presented. Hereby, we focus on the application of video surveillance in large camera networks. Firstly, advantages of the proposed task-oriented video processing, compared to today´s (sensor-oriented) video surveillance systems, are described. Secondly, a generic process framework is presented, which has been designed for task-oriented video processing. Finally, system components of the task-oriented framework needed for the task of multi-camera person tracking are introduced.

In this contribution, a generic sensor fusion architecture is presented, which implements a sensor independent fusion module. This is achieved using probabilistic modeling of object state and existence. For the realization of the system it is important to maintain hard real-time requirements. For this, two real-time approximations of the system will be presented. The feasibility and real-time capabilities of the approach are shown by evaluations based on real world data of the sensor setup chosen in traffic scenarios.

Treating uncertainties properly is a difficult but necessary task in data fusion applications. In order to meet this challenge, appropriate uncertainty models are required and efficient state estimation techniques need to be derived. The most common approaches are to model uncertain quantities as random variables or as bounded sets. This paper presents a state estimation framework that allows for a simultaneous treatment of stochastic and unknown but bounded uncertainties. This is achieved by characterizing an uncertain quantity by a set of probability densities. Finally, promising applications of the derived concept are described.

Space-time continuous phenomena such as pollution loads or temperature distributions often originate from unknown and possibly movable sources. In many real-world scenarios, however, information about the location of such sources can only be gained indirectly by monitoring the induced physical phenomena using distributed sensing systems, e. g., stationary or mobile sensors. In this article, a model-based approach for real-time source localization and target tracking is introduced. To maximize specific information gained from the measurements, this approach strongly relies on a non-myopic sensor management methodology, which allows for tracking moving sources in an efficient and accurate manner.

The abilities to sense and model a dynamic environment are key components of intelligent systems. In this contribution, firstly a methodology is presented to make an ideal selection of the input data available. Then, an object oriented environment model is proposed which allows a continuous fusion of existing knowledge with new sensor information. All methods are based on Bayesian statistics in an objective “degree of belief” interpretation. Application areas are demonstrated by the examples of humanoid robots and autonomous vehicles.

Tracking systems provide kinematical information of objects in a scenario. This kinematical information can be combined with additional data to build higher-level-fusion systems that allow the detection of behaviour and threat patterns and thus contribute to situation awareness. The patterns that characterize situations of interest may vary over time and depend on the specific questions to be investigated. Data base systems provide a flexible way of combining data, and standing queries allow ongoing, automatic evaluation. In this paper, we present a way of using data base systems as the central component in a higher-level fusion system. We propose an architecture of this system using commercial database management software. Finally, we discuss how patterns for the detection of anomalies in tracking scenarios can be expressed in relational algebra.