Advanced Data Viewing
Noesis supports almost all kinds of
graphs (scatter, density, bar,
cumulative, line, 3D etc) with unique
customization options. In addition
statistics, data tables, waveforms, FFT,
RMS, Autocorrelation and many more data
views can be on screen simultaneously.
The graphs and other views are active
in the sense that the user can zoom and
pan to closely view the data, apply
graphical filtering to each graph
individually, select data with the
mouse or user defined functions and view
the selection in all other graphs (hit
correspondence) and do much more. These
functions alone render Noesis a superior
analysis tool as the user gets a new and
deeper look into the data. The simplicity
and user friendliness that such complex
data viewing is achieved can be compared
to typing a text document!
Data
Grouping & Multi-Dimensional Sorting
A Cluster or Class is a group of
signals/data, which can be selected
and defined by the user, according to
their similarity or correspondence to
physical phenomena, so as to distinguish
from other data. Creating data clusters
drastically enhances the way the user can
view the data. Different clusters can have
different color and symbol so that they
can easily be distinguished in any graph
or other view (tables, waveforms etc). The
user can get separate statistics for
each cluster (class), compare clusters,
view cluster comparative and evolution
statistics etc. The user can simply
drag the mouse over a plot and select some
data from multiple plots with logical
AND/OR operations, or apply advanced
multi-dimensional filtering. As data are
usually grouped according to their
similarity Noesis offers much more than
manual, user defined, selections and
clustering (which are limited to the
user’s observation capabilities in 2D or
3D space), although these tools alone can
provide great power, ease, confidence and
speed in data analysis.
Other
Tools
Data viewing is only the beginning in
Noesis.
The data structure can be investigated
using advanced statistics (e.g.
feature discriminant, class dsciminant
etc), feature correlation matrices and
dendrograms (to investigate feature
correlation), principal component
analysis and data projections (to
investigate the data in a mathematically
defined space), feature extraction
from waveforms (to get new unique signal
features and use them in the analysis),
calculated features (to get computed
features from the existing ones) and other
small functions that will make data
analysis a new process.
Interactive
Advanced Data Clustering
Apart from manual clustering Noesis offers
a number of algorithms to automatically
classify data. The Interactive Advanced
Data Clustering is known as
Unsupervised Pattern Recognition (UPR)
and incorporates mathematical
algorithms and Neural Networks. As its
name suggests this process investigates
the data to find and Recognize Patterns in
the data and group them accordingly.
These algorithms provide the user with an
interactive way to classify data according
to their similarity. Traditional
analysis of 2D or 3D graphs has limited
analysts. Unsupervised Pattern Recognition
lets the user set a limited number of
parameters and get an automatic
classification based on these parameters.
The results of the classification will
depend on user input (e.g. features to be
used, desired clusters, algorithm used
etc) but most importantly they will depend
on the quality of the data. Thus,
Noesis allows signal/data similarity to be
compared on Multi-Dimensional space
(can be 10D or 20D even) that an analysts
could not even begin to imagine due to the
complexity of the problem. The results
of any classification of data will be
immediately visible on all graphs and
views as different colors for each
class (group of data) are automatically
assigned. The data structure can then be
further investigated using graphs, tables,
statistics, correlation plots and all the
tools available in Noesis.
Fully
Automated Advanced Data Classification
Unsupervised Pattern Recognition is a
process requiring some user input to allow
data grouping in some unknown data. The
Fully Automated Data Classification
functions, known as Supervised Pattern
Recognition (SPR), incorporates
mathematical algorithms and Neural
Networks that can be trained from
known data or data clustered by UPR (see
Interactive Advanced Data Clustering) and
then automatically classify similar
unknown data, even during acquisition!
The user needs some data and decide on
their classification (data groups). Once
this is finalized an SPR algorithm can
be trained to recognize the defined
patterns in the data. The algorithm
can then be applied to unknown data and it
will classify the signals into the
predefined groups (classes).
Noesis
For the Analysis of Data other than AE
All Noesis functions are available for the
analysis of any kind of data.
The ASCII data file import feature allows
the user to acquire data trough any
equipment (even manually), arrange them in
simple tab delimited columnar ASCII
file(s) and import them in Noesis. All
viewing and analysis capabilities are
available. Note also that for specific
data types (usually waveform import in the
form of ASCII files) Noesis has a history
of special applications.
Data Analysis Using Noesis
The complexity of Acoustic Emission (AE)
(and in fact of any data)
can, at times, be overwhelming, if
the appropriate tools are not available
to the analyst. The main tool used for AE
data analysis has traditionally been
feature correlation graphs. These have
allowed experienced users to investigate
the data structure, decide on the origin
of the data, apply appropriate filters and
investigate the validity of the result.
Noesis, is an Advanced Data Analysis
software featuring Pattern Recognition and
Neural Networks, with powerful tools to
aid and improve data analysis, ranging
from drastically improved graphics with
numerous options to view the data in new
and different ways, to mathematical
algorithms for signal/data discrimination
and data classification and
manipulation during acquisition.
