1,173 research outputs found
Trajectory Clustering and an Application to Airspace Monitoring
This paper presents a framework aimed at monitoring the behavior of aircraft
in a given airspace. Nominal trajectories are determined and learned using data
driven methods. Standard procedures are used by air traffic controllers (ATC)
to guide aircraft, ensure the safety of the airspace, and to maximize the
runway occupancy. Even though standard procedures are used by ATC, the control
of the aircraft remains with the pilots, leading to a large variability in the
flight patterns observed. Two methods to identify typical operations and their
variability from recorded radar tracks are presented. This knowledge base is
then used to monitor the conformance of current operations against operations
previously identified as standard. A tool called AirTrajectoryMiner is
presented, aiming at monitoring the instantaneous health of the airspace, in
real time. The airspace is "healthy" when all aircraft are flying according to
the nominal procedures. A measure of complexity is introduced, measuring the
conformance of current flight to nominal flight patterns. When an aircraft does
not conform, the complexity increases as more attention from ATC is required to
ensure a safe separation between aircraft.Comment: 15 pages, 20 figure
Robust Geometry Estimation using the Generalized Voronoi Covariance Measure
The Voronoi Covariance Measure of a compact set K of R^d is a tensor-valued
measure that encodes geometric information on K and which is known to be
resilient to Hausdorff noise but sensitive to outliers. In this article, we
generalize this notion to any distance-like function delta and define the
delta-VCM. We show that the delta-VCM is resilient to Hausdorff noise and to
outliers, thus providing a tool to estimate robustly normals from a point cloud
approximation. We present experiments showing the robustness of our approach
for normal and curvature estimation and sharp feature detection
Detection of Small Holes by the Scale-Invariant Robust Density-Aware Distance (RDAD) Filtration
A novel topological-data-analytical (TDA) method is proposed to distinguish,
from noise, small holes surrounded by high-density regions of a probability
density function whose mass is concentrated near a manifold (or more generally,
a CW complex) embedded in a high-dimensional Euclidean space. The proposed
method is robust against additive noise and outliers. In particular, sample
points are allowed to be perturbed away from the manifold. Traditional TDA
tools, like those based on the distance filtration, often struggle to
distinguish small features from noise, because of their short persistence. An
alternative filtration, called Robust Density-Aware Distance (RDAD) filtration,
is proposed to prolong the persistence of small holes surrounded by
high-density regions. This is achieved by weighting the distance function by
the density in the sense of Bell et al. Distance-to-measure is incorporated to
enhance stability and mitigate noise due to the density estimation. The utility
of the proposed filtration in identifying small holes, as well as its
robustness against noise, are illustrated through an analytical example and
extensive numerical experiments. Basic mathematical properties of the proposed
filtration are proven.Comment: 47 pages, 60 figures, GitHub repo: https://github.com/c-siu/RDA
A systematic review of data quality issues in knowledge discovery tasks
Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust
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