13,264 research outputs found
Quality Assessment of Linked Datasets using Probabilistic Approximation
With the increasing application of Linked Open Data, assessing the quality of
datasets by computing quality metrics becomes an issue of crucial importance.
For large and evolving datasets, an exact, deterministic computation of the
quality metrics is too time consuming or expensive. We employ probabilistic
techniques such as Reservoir Sampling, Bloom Filters and Clustering Coefficient
estimation for implementing a broad set of data quality metrics in an
approximate but sufficiently accurate way. Our implementation is integrated in
the comprehensive data quality assessment framework Luzzu. We evaluated its
performance and accuracy on Linked Open Datasets of broad relevance.Comment: 15 pages, 2 figures, To appear in ESWC 2015 proceeding
Towards Real-Time Detection and Tracking of Spatio-Temporal Features: Blob-Filaments in Fusion Plasma
A novel algorithm and implementation of real-time identification and tracking
of blob-filaments in fusion reactor data is presented. Similar spatio-temporal
features are important in many other applications, for example, ignition
kernels in combustion and tumor cells in a medical image. This work presents an
approach for extracting these features by dividing the overall task into three
steps: local identification of feature cells, grouping feature cells into
extended feature, and tracking movement of feature through overlapping in
space. Through our extensive work in parallelization, we demonstrate that this
approach can effectively make use of a large number of compute nodes to detect
and track blob-filaments in real time in fusion plasma. On a set of 30GB fusion
simulation data, we observed linear speedup on 1024 processes and completed
blob detection in less than three milliseconds using Edison, a Cray XC30 system
at NERSC.Comment: 14 pages, 40 figure
Detecting Stops from GPS Trajectories: A Comparison of Different GPS Indicators for Raster Sampling Methods
With the increasing prevalence of GPS tracking capabilities on smartphones, GPS
trajectories have proven to be useful for an extensive range of research topics. Stop
detection, which estimates activity locations, is fundamental for organizing GPS
trajectories into semantically meaningful journeys. With previous methods
overwhelmingly dependent on thresholds, contextual information or a pre-understanding
of the GPS records, this paper addresses the challenge by contributing a ‘top-down’ raster
sampling method which samples pre-calculated GPS indicators and clusters the raster cells
with significantly different values as stops. We report a comparison of a set of precalculated
GPS indicators with two baseline methods. By referencing a ground truth travel
dairy, the raster sampling method demonstrates good and reliable capabilities on producing
high accuracy, low redundancy and close proximity to the ground truth in three distinct
travel use cases. This further indicates a good generic stop detection method
Development of c-means Clustering Based Adaptive Fuzzy Controller for A Flapping Wing Micro Air Vehicle
Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV)
and its control is one of the recent research topics related to the field of
autonomous Unmanned Aerial Vehicles (UAVs). In this work, a four wing
Natureinspired (NI) FW MAV is modeled and controlled inspiring by its advanced
features like quick flight, vertical take-off and landing, hovering, and fast
turn, and enhanced manoeuvrability when contrasted with comparable-sized fixed
and rotary wing UAVs. The Fuzzy C-Means (FCM) clustering algorithm is utilized
to demonstrate the NIFW MAV model, which has points of interest over first
principle based modelling since it does not depend on the system dynamics,
rather based on data and can incorporate various uncertainties like sensor
error. The same clustering strategy is used to develop an adaptive fuzzy
controller. The controller is then utilized to control the altitude of the NIFW
MAV, that can adapt with environmental disturbances by tuning the antecedent
and consequent parameters of the fuzzy system.Comment: this paper is currently under review in Journal of Artificial
Intelligence and Soft Computing Researc
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