Fast Variational Block-Sparse Bayesian Learning

We present a fast update rule for variational block-sparse Bayesian learning (SBL) methods. Using a variational Bayesian framework, we show how repeated updates of probability density functions (PDFs) of the prior variances and weights can be expressed as a nonlinear first-order recurrence from one estimate of the parameters of the proxy PDFs to the next. Specifically, the recurrent relation turns out to be a strictly increasing rational function for many commonly used prior PDFs of the variances, such as Jeffrey's prior. Hence, the fixed points of this recurrent relation can be obtained by solving for the roots of a polynomial. This scheme allows to check for convergence/divergence of individual prior variances in a single step. Thereby, the the computational complexity of the variational block-SBL algorithm is reduced and the convergence speed is improved by two orders of magnitude in our simulations. Furthermore, the solution allows insights into the sparsity of the estimators obtained by choosing different priors.Comment: 10 pages, 2 figures, submitted to IEEE Transactions on Signal Processing on 1st of June, 202

"UWBCarGraz" Dataset for Car Occupancy Detection using Ultra-Wideband Radar

We present a data-driven car occupancy detection algorithm using ultra-wideband radar based on the ResNet architecture. The algorithm is trained on a dataset of channel impulse responses obtained from measurements at three different activity levels of the occupants (i.e. breathing, talking, moving). We compare the presented algorithm against a state-of-the-art car occupancy detection algorithm based on variational message passing (VMP). Our presented ResNet architecture is able to outperform the VMP algorithm in terms of the area under the receiver operating curve (AUC) at low signal-to-noise ratios (SNRs) for all three activity levels of the target. Specifically, for an SNR of -20 dB the VMP detector achieves an AUC of 0.87 while the ResNet architecture achieves an AUC of 0.91 if the target is sitting still and breathing naturally. The difference in performance for the other activities is similar. To facilitate the implementation in the onboard computer of a car we perform an ablation study to optimize the tradeoff between performance and computational complexity for several ResNet architectures. The dataset used to train and evaluate the algorithm is openly accessible. This facilitates an easy comparison in future works.Comment: v1 (17.11.2023). 6 pages, 5 figure

A review of modelling methodologies for flood source area (FSA) identification

Flooding is an important global hazard that causes an average annual loss of over 40 billion USD and affects a population of over 250 million globally. The complex process of flooding depends on spatial and temporal factors such as weather patterns, topography, and geomorphology. In urban environments where the landscape is ever-changing, spatial factors such as ground cover, green spaces, and drainage systems have a significant impact. Understanding source areas that have a major impact on flooding is, therefore, crucial for strategic flood risk management (FRM). Although flood source area (FSA) identification is not a new concept, its application is only recently being applied in flood modelling research. Continuous improvements in the technology and methodology related to flood models have enabled this research to move beyond traditional methods, such that, in recent years, modelling projects have looked beyond affected areas and recognised the need to address flooding at its source, to study its influence on overall flood risk. These modelling approaches are emerging in the field of FRM and propose innovative methodologies for flood risk mitigation and design implementation; however, they are relatively under-examined. In this paper, we present a review of the modelling approaches currently used to identify FSAs, i.e. unit flood response (UFR) and adaptation-driven approaches (ADA). We highlight their potential for use in adaptive decision making and outline the key challenges for the adoption of such approaches in FRM practises

Influence of characteristics on combined sewer performance

Elements of combined sewer systems are among others sub-catchments, junctions, conduits and weirs with or without storage units. The spatial distribution and attributes of all these elements influence both system characteristics and sewer performance. Until today, little work has been done to analyse the influence of such characteristics in a case unspecific approach. In this study, 250 virtual combined sewer systems are analysed by defining groups of systems, which are representative for their different characteristics. The set was created with a further development of the case study generator (CSG), a tool for automatic generation of branched sewer systems. Combined sewer overflow and flooding is evaluated using performance indicators based on hydrodynamic simulations. The analysis of system characteristics, like those presented in this paper, helps researchers to understand coherences and aids practitioners in designing combined sewers. For instance, it was found that characteristics that have a positive influence on emission reduction frequently have a negative influence on flooding avoidance and vice versa.</jats:p

Graph-based approach for generating virtual water distribution systems in the software VIBe

Application of virtual case studies (VCS) is a well established technique in environmental engineering to test measures, approaches, models or software. However, preparation of VCS for an infrastructure network is a tedious task. In literature, approaches can be found which generate very simplified VCS, which are only partly comparable with real world case studies. VCS which are more comparable with real world case studies can be generated with the software VIBe (Virtual Infrastructure Benchmarking). With VIBe, a methodology for algorithmic generation of VCS with varying spatially distributed boundary conditions was presented. Therein the investigated infrastructure is constructed accordingly to the state-of-the-art design rules meeting the requirements of the generated virtual urban environment. In this paper the module for the generation of water distribution systems (WDS) is presented. The generated WDS are set in context with data from real world WDS and systematically investigated. A set of 75,000 virtual WDS with varying properties is characterized and stochastically analysed in order to identify system coherences e.g. impact of mesh degree on hydraulic, water quality performance and costs. An example involving the systematic investigation of a simple pipe sizing algorithm with the set of 75,000 WDS is shown.</jats:p

Automatic generation of water distribution systems based on GIS data

AbstractIn the field of water distribution system (WDS) analysis, case study research is needed for testing or benchmarking optimisation strategies and newly developed software. However, data availability for the investigation of real cases is limited due to time and cost needed for data collection and model setup. We present a new algorithm that addresses this problem by generating WDSs from GIS using population density, housing density and elevation as input data. We show that the resulting WDSs are comparable to actual systems in terms of network properties and hydraulic performance. For example, comparing the pressure heads for an actual and a generated WDS results in pressure head differences of ±4 m or less for 75% of the supply area. Although elements like valves and pumps are not included, the new methodology can provide water distribution systems of varying levels of complexity (e.g., network layouts, connectivity, etc.) to allow testing design/optimisation algorithms on a large number of networks. The new approach can be used to estimate the construction costs of planned WDSs aimed at addressing population growth or at comparisons of different expansion strategies in growth corridors