1,311 research outputs found
Feature-based time-series analysis
This work presents an introduction to feature-based time-series analysis. The
time series as a data type is first described, along with an overview of the
interdisciplinary time-series analysis literature. I then summarize the range
of feature-based representations for time series that have been developed to
aid interpretable insights into time-series structure. Particular emphasis is
given to emerging research that facilitates wide comparison of feature-based
representations that allow us to understand the properties of a time-series
dataset that make it suited to a particular feature-based representation or
analysis algorithm. The future of time-series analysis is likely to embrace
approaches that exploit machine learning methods to partially automate human
learning to aid understanding of the complex dynamical patterns in the time
series we measure from the world.Comment: 28 pages, 9 figure
Highly comparative feature-based time-series classification
A highly comparative, feature-based approach to time series classification is
introduced that uses an extensive database of algorithms to extract thousands
of interpretable features from time series. These features are derived from
across the scientific time-series analysis literature, and include summaries of
time series in terms of their correlation structure, distribution, entropy,
stationarity, scaling properties, and fits to a range of time-series models.
After computing thousands of features for each time series in a training set,
those that are most informative of the class structure are selected using
greedy forward feature selection with a linear classifier. The resulting
feature-based classifiers automatically learn the differences between classes
using a reduced number of time-series properties, and circumvent the need to
calculate distances between time series. Representing time series in this way
results in orders of magnitude of dimensionality reduction, allowing the method
to perform well on very large datasets containing long time series or time
series of different lengths. For many of the datasets studied, classification
performance exceeded that of conventional instance-based classifiers, including
one nearest neighbor classifiers using Euclidean distances and dynamic time
warping and, most importantly, the features selected provide an understanding
of the properties of the dataset, insight that can guide further scientific
investigation
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Metals Additive Manufacturing Development at Harvest Technologies
Harvest Technologies received an EOS M280 in April of 2013 for the production of metal
parts through additive manufacturing (AM). Inconel 718 was chosen as a starting material due to
its high-end applications in the oil and aerospace industries. Two major areas are of high priority
in understanding the machine: (1) mechanical property characterization and (2) geometrical
production capability through building prototype models. The following is a working document
of Harvest’ progression in developing knowledge in the field of metals AM.Mechanical Engineerin
Gamification in Education: A Study of Design-Based Learning in Operationalizing a Game Studio for Serious Games
The gamification of learning has proven educational benefits, especially in secondary education. Studies confirm the successful engagement of students with improved time on task, motivation and learning outcomes. At the same time, there remains little research on games and learning at the postsecondary level of education where traditional pedagogies remain the norm. Studies that have been conducted remain almost exclusively restricted to science programs, including medicine and engineering. Moreover, postsecondary subject-matter experts who have created their own gamified experiences often are forced to do so on an ad hoc basis either on their own, teaching themselves game engines, or with irregular support from experts in the field. But to ensure a well-designed, developed, and high-quality educational experience that leads to desired outcomes for a field, a sustainable infrastructure needs to be developed in institutions that have (or can partner with) others that have an established game design program. Moreover, such a design-based learning approach can be embedded within an existing studio model to help educate participants while producing an educational product. As such, this qualitative case study provides an example of the process of operationalizing a game design studio from pre-production through post-production, drawing from the design and development of the educational video game The Museum of the Lost VR (2022). The results, resources, and classification system presented are scalable and provide models for different sized institutions. Methods to develop a sustainable infrastructure are presented to ensure interdisciplinary partnerships across departments and institutions with game design programs to collaborate and create educational experiences that optimize user experience and learning outcomes
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Robust Design of Negative Stiffness Elements Fabricated by Selective Laser Sintering
Constrained negative stiffness structures have been shown to possess desirable vibration
isolation properties because of their ability to provide low dynamic stiffness, resulting in low
transmissibility over a wide range of frequencies. In this research, selective laser sintering (SLS)
is an integral part of a model-design-build-test process for investigating the vibration isolation
capabilities of negative stiffness structures in the form of axially compressed beams. SLS
provides geometric design freedom and rapid fabrication capabilities for validating dynamic
models of structural behavior and guiding the design process toward iterative improvements.
SLS also introduces some geometric and dimensional variability that can significantly degrade
the performance of the structure. In this paper, an iterative model-design-build-test process for
negative stiffness structures is described and presented with an analysis of the impact of SLS-induced imperfections on the results.Mechanical Engineerin
Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification
The variety of complex algorithmic approaches for tackling time-series
classification problems has grown considerably over the past decades, including
the development of sophisticated but challenging-to-interpret
deep-learning-based methods. But without comparison to simpler methods it can
be difficult to determine when such complexity is required to obtain strong
performance on a given problem. Here we evaluate the performance of an
extremely simple classification approach -- a linear classifier in the space of
two simple features that ignore the sequential ordering of the data: the mean
and standard deviation of time-series values. Across a large repository of 128
univariate time-series classification problems, this simple distributional
moment-based approach outperformed chance on 69 problems, and reached 100%
accuracy on two problems. With a neuroimaging time-series case study, we find
that a simple linear model based on the mean and standard deviation performs
better at classifying individuals with schizophrenia than a model that
additionally includes features of the time-series dynamics. Comparing the
performance of simple distributional features of a time series provides
important context for interpreting the performance of complex time-series
classification models, which may not always be required to obtain high
accuracy.Comment: 8 pages, 3 figure
Tracking the distance to criticality in systems with unknown noise
Many real-world systems undergo abrupt changes in dynamics as they move
across critical points, often with dramatic and irreversible consequences. Much
of the existing theory on identifying the time-series signatures of nearby
critical points -- such as increased signal variance and slower timescales --
is derived from analytically tractable systems, typically considering the case
of fixed, low-amplitude noise. However, real-world systems are often corrupted
by unknown levels of noise which can obscure these temporal signatures. Here we
aimed to develop noise-robust indicators of the distance to criticality (DTC)
for systems affected by dynamical noise in two cases: when the noise amplitude
is either fixed, or is unknown and variable across recordings. We present a
highly comparative approach to tackling this problem that compares the ability
of over 7000 candidate time-series features to track the DTC in the vicinity of
a supercritical Hopf bifurcation. Our method recapitulates existing theory in
the fixed-noise case, highlighting conventional time-series features that
accurately track the DTC. But in the variable-noise setting, where these
conventional indicators perform poorly, we highlight new types of
high-performing time-series features and show that their success is underpinned
by an ability to capture the shape of the invariant density (which depends on
both the DTC and the noise amplitude) relative to the spread of fast
fluctuations (which depends on the noise amplitude). We introduce a new
high-performing time-series statistic, termed the Rescaled Auto-Density (RAD),
that distils these two algorithmic components. Our results demonstrate that
large-scale algorithmic comparison can yield theoretical insights and motivate
new algorithms for solving important practical problems.Comment: The main paper comprises 18 pages, with 5 figures (.pdf). The
supplemental material comprises a single 4-page document with 1 figure
(.pdf), as well as 3 spreadsheet files (.xls
Highly comparative time-series analysis: The empirical structure of time series and their methods
The process of collecting and organizing sets of observations represents a
common theme throughout the history of science. However, despite the ubiquity
of scientists measuring, recording, and analyzing the dynamics of different
processes, an extensive organization of scientific time-series data and
analysis methods has never been performed. Addressing this, annotated
collections of over 35 000 real-world and model-generated time series and over
9000 time-series analysis algorithms are analyzed in this work. We introduce
reduced representations of both time series, in terms of their properties
measured by diverse scientific methods, and of time-series analysis methods, in
terms of their behaviour on empirical time series, and use them to organize
these interdisciplinary resources. This new approach to comparing across
diverse scientific data and methods allows us to organize time-series datasets
automatically according to their properties, retrieve alternatives to
particular analysis methods developed in other scientific disciplines, and
automate the selection of useful methods for time-series classification and
regression tasks. The broad scientific utility of these tools is demonstrated
on datasets of electroencephalograms, self-affine time series, heart beat
intervals, speech signals, and others, in each case contributing novel analysis
techniques to the existing literature. Highly comparative techniques that
compare across an interdisciplinary literature can thus be used to guide more
focused research in time-series analysis for applications across the scientific
disciplines
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