3,894 research outputs found
Sigma Point Belief Propagation
The sigma point (SP) filter, also known as unscented Kalman filter, is an
attractive alternative to the extended Kalman filter and the particle filter.
Here, we extend the SP filter to nonsequential Bayesian inference corresponding
to loopy factor graphs. We propose sigma point belief propagation (SPBP) as a
low-complexity approximation of the belief propagation (BP) message passing
scheme. SPBP achieves approximate marginalizations of posterior distributions
corresponding to (generally) loopy factor graphs. It is well suited for
decentralized inference because of its low communication requirements. For a
decentralized, dynamic sensor localization problem, we demonstrate that SPBP
can outperform nonparametric (particle-based) BP while requiring significantly
less computations and communications.Comment: 5 pages, 1 figur
Decommissioning of the ASTRA research reactor: Planning, executing and summarizing the project
The decommissioning of the ASTRA research reactor at the Austrian Research Centres Seibersdorf was described within three technical papers already released in Nuclear Technology & Radiation Protection throughout the years 2003, 2006, and 2008. Following a suggestion from IAEA the project was investigated well after the files were closed regarding rather administrative than technical matters starting with the project mission, explaining the project structure and identifying the key factors and the key performance indicators. The continuous documentary and reporting system as implemented to fulfil the informational needs of stake-holders, management, and project staff alike is described. Finally the project is summarized in relationship to the performance indicators
Distributed Estimation with Information-Seeking Control in Agent Network
We introduce a distributed, cooperative framework and method for Bayesian
estimation and control in decentralized agent networks. Our framework combines
joint estimation of time-varying global and local states with
information-seeking control optimizing the behavior of the agents. It is suited
to nonlinear and non-Gaussian problems and, in particular, to location-aware
networks. For cooperative estimation, a combination of belief propagation
message passing and consensus is used. For cooperative control, the negative
posterior joint entropy of all states is maximized via a gradient ascent. The
estimation layer provides the control layer with probabilistic information in
the form of sample representations of probability distributions. Simulation
results demonstrate intelligent behavior of the agents and excellent estimation
performance for a simultaneous self-localization and target tracking problem.
In a cooperative localization scenario with only one anchor, mobile agents can
localize themselves after a short time with an accuracy that is higher than the
accuracy of the performed distance measurements.Comment: 17 pages, 10 figure
Simultaneous Distributed Sensor Self-Localization and Target Tracking Using Belief Propagation and Likelihood Consensus
We introduce the framework of cooperative simultaneous localization and
tracking (CoSLAT), which provides a consistent combination of cooperative
self-localization (CSL) and distributed target tracking (DTT) in sensor
networks without a fusion center. CoSLAT extends simultaneous localization and
tracking (SLAT) in that it uses also intersensor measurements. Starting from a
factor graph formulation of the CoSLAT problem, we develop a particle-based,
distributed message passing algorithm for CoSLAT that combines nonparametric
belief propagation with the likelihood consensus scheme. The proposed CoSLAT
algorithm improves on state-of-the-art CSL and DTT algorithms by exchanging
probabilistic information between CSL and DTT. Simulation results demonstrate
substantial improvements in both self-localization and tracking performance.Comment: 10 pages, 5 figure
Prospects for a European Animal Welfare Label from the German Perspective: Supply Chain Barriers
The Federal Government of Germany as well as the European Commission are discussing the establishment of an animal welfare label. This label should enable consumers to make a conscious purchasing decision on animal welfare products. Various studies show that many consumers (in Germany around 20 %) prefer products produced under animal friendly conditions. However, the supply of such products is limited. The following study examines the source of this discrepancy by way of an action‐based analytical approach and identifies different barriers within the supply chain that prevent the establishment of a market segment for animal welfare products. Although consumer demand will be decisive for long‐term success, first of all the stakeholders of the supply chain must be convinced. If the stakeholders are not prepared to participate in an animal welfare program the diffusion phase can take a very long time or even fail. This study presents such supply chain barriers and interprets them in the light of neoinstitutionalism.animal welfare, label, supply chain, neo‐institutionalism., Food Consumption/Nutrition/Food Safety, Food Security and Poverty, Industrial Organization, Research Methods/ Statistical Methods, Risk and Uncertainty,
Software-Defined Networks Supporting Time-Sensitive In-Vehicular Communication
Future in-vehicular networks will be based on Ethernet. The IEEE
Time-Sensitive Networking (TSN) is a promising candidate to satisfy real-time
requirements in future car communication. Software-Defined Networking (SDN)
extends the Ethernet control plane with a programming option that can add much
value to the resilience, security, and adaptivity of the automotive
environment. In this work, we derive a first concept for combining
Software-Defined Networking with Time-Sensitive Networking along with an
initial evaluation. Our measurements are performed via a simulation that
investigates whether an SDN architecture is suitable for time-critical
applications in the car. Our findings indicate that the control overhead of SDN
can be added without a delay penalty for the TSN traffic when protocols are
mapped properly.Comment: To be published at IEEE VTC2019-Sprin
Simulation of Mixed Critical In-vehicular Networks
Future automotive applications ranging from advanced driver assistance to
autonomous driving will largely increase demands on in-vehicular networks. Data
flows of high bandwidth or low latency requirements, but in particular many
additional communication relations will introduce a new level of complexity to
the in-car communication system. It is expected that future communication
backbones which interconnect sensors and actuators with ECU in cars will be
built on Ethernet technologies. However, signalling from different application
domains demands for network services of tailored attributes, including
real-time transmission protocols as defined in the TSN Ethernet extensions.
These QoS constraints will increase network complexity even further.
Event-based simulation is a key technology to master the challenges of an
in-car network design. This chapter introduces the domain-specific aspects and
simulation models for in-vehicular networks and presents an overview of the
car-centric network design process. Starting from a domain specific description
language, we cover the corresponding simulation models with their workflows and
apply our approach to a related case study for an in-car network of a premium
car
Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks
Cooperative localization in agent networks based on interagent time-of-flight
measurements is closely related to synchronization. To leverage this relation,
we propose a Bayesian factor graph framework for cooperative simultaneous
localization and synchronization (CoSLAS). This framework is suited to mobile
agents and time-varying local clock parameters. Building on the CoSLAS factor
graph, we develop a distributed (decentralized) belief propagation algorithm
for CoSLAS in the practically important case of an affine clock model and
asymmetric time stamping. Our algorithm allows for real-time operation and is
suitable for a time-varying network connectivity. To achieve high accuracy at
reduced complexity and communication cost, the algorithm combines particle
implementations with parametric message representations and takes advantage of
a conditional independence property. Simulation results demonstrate the good
performance of the proposed algorithm in a challenging scenario with
time-varying network connectivity.Comment: 13 pages, 6 figures, 3 tables; manuscript submitted to IEEE
Transaction on Signal Processin
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