44,811 research outputs found
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
In search applications, autonomous unmanned vehicles must be able to
efficiently reacquire and localize mobile targets that can remain out of view
for long periods of time in large spaces. As such, all available information
sources must be actively leveraged -- including imprecise but readily available
semantic observations provided by humans. To achieve this, this work develops
and validates a novel collaborative human-machine sensing solution for dynamic
target search. Our approach uses continuous partially observable Markov
decision process (CPOMDP) planning to generate vehicle trajectories that
optimally exploit imperfect detection data from onboard sensors, as well as
semantic natural language observations that can be specifically requested from
human sensors. The key innovation is a scalable hierarchical Gaussian mixture
model formulation for efficiently solving CPOMDPs with semantic observations in
continuous dynamic state spaces. The approach is demonstrated and validated
with a real human-robot team engaged in dynamic indoor target search and
capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference
(Cambridge, UK, July 2018
Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones
This paper proposes a novel framework for fusing multi-temporal,
multispectral satellite images and OpenStreetMap (OSM) data for the
classification of local climate zones (LCZs). Feature stacking is the most
commonly-used method of data fusion but does not consider the heterogeneity of
multimodal optical images and OSM data, which becomes its main drawback. The
proposed framework processes two data sources separately and then combines them
at the model level through two fusion models (the landuse fusion model and
building fusion model), which aim to fuse optical images with landuse and
buildings layers of OSM data, respectively. In addition, a new approach to
detecting building incompleteness of OSM data is proposed. The proposed
framework was trained and tested using data from the 2017 IEEE GRSS Data Fusion
Contest, and further validated on one additional test set containing test
samples which are manually labeled in Munich and New York. Experimental results
have indicated that compared to the feature stacking-based baseline framework
the proposed framework is effective in fusing optical images with OSM data for
the classification of LCZs with high generalization capability on a large
scale. The classification accuracy of the proposed framework outperforms the
baseline framework by more than 6% and 2%, while testing on the test set of
2017 IEEE GRSS Data Fusion Contest and the additional test set, respectively.
In addition, the proposed framework is less sensitive to spectral diversities
of optical satellite images and thus achieves more stable classification
performance than state-of-the art frameworks.Comment: accepted by TGR
Application of Computational Intelligence Techniques to Process Industry Problems
In the last two decades there has been a large progress in the computational
intelligence research field. The fruits of the effort spent on the research in the discussed
field are powerful techniques for pattern recognition, data mining, data modelling, etc.
These techniques achieve high performance on traditional data sets like the UCI
machine learning database. Unfortunately, this kind of data sources usually represent
clean data without any problems like data outliers, missing values, feature co-linearity,
etc. common to real-life industrial data. The presence of faulty data samples can have
very harmful effects on the models, for example if presented during the training of the
models, it can either cause sub-optimal performance of the trained model or in the worst
case destroy the so far learnt knowledge of the model. For these reasons the application
of present modelling techniques to industrial problems has developed into a research
field on its own. Based on the discussion of the properties and issues of the data and the
state-of-the-art modelling techniques in the process industry, in this paper a novel
unified approach to the development of predictive models in the process industry is
presented
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