1,512 research outputs found
Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm
Industry 4.0 aims at achieving mass customization at a
mass production cost. A key component to realizing this is accurate
prediction of customer needs and wants, which is however a
challenging issue due to the lack of smart analytics tools. This
paper investigates this issue in depth and then develops a predictive
analytic framework for integrating cloud computing, big data
analysis, business informatics, communication technologies, and
digital industrial production systems. Computational intelligence
in the form of a cluster k-means approach is used to manage
relevant big data for feeding potential customer needs and wants
to smart designs for targeted productivity and customized mass
production. The identification of patterns from big data is achieved
with cluster k-means and with the selection of optimal attributes
using genetic algorithms. A car customization case study shows
how it may be applied and where to assign new clusters with
growing knowledge of customer needs and wants. This approach
offer a number of features suitable to smart design in realizing
Industry 4.0
A Brief Analysis of Gravitational Search Algorithm (GSA) Publication from 2009 to May 2013
Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic community shows a
great interest on this algorith. This can be seen by the high number of publications with a short span of time. This paper analyses the publication trend of Gravitational Search Algorithm since its introduction until May 2013. The objective of this paper is to give exposure to reader the publication trend in the area of Gravitational Search Algorithm
Self-Supervised Clustering on Image-Subtracted Data with Deep-Embedded Self-Organizing Map
Developing an effective automatic classifier to separate genuine sources from
artifacts is essential for transient follow-ups in wide-field optical surveys.
The identification of transient detections from the subtraction artifacts after
the image differencing process is a key step in such classifiers, known as
real-bogus classification problem. We apply a self-supervised machine learning
model, the deep-embedded self-organizing map (DESOM) to this "real-bogus"
classification problem. DESOM combines an autoencoder and a self-organizing map
to perform clustering in order to distinguish between real and bogus
detections, based on their dimensionality-reduced representations. We use 32x32
normalized detection thumbnails as the input of DESOM. We demonstrate different
model training approaches, and find that our best DESOM classifier shows a
missed detection rate of 6.6% with a false positive rate of 1.5%. DESOM offers
a more nuanced way to fine-tune the decision boundary identifying likely real
detections when used in combination with other types of classifiers, for
example built on neural networks or decision trees. We also discuss other
potential usages of DESOM and its limitations
SkyDOT (Sky Database for Objects in the Time Domain): A Virtual Observatory for Variability Studies at LANL
The mining of Virtual Observatories (VOs) is becoming a powerful new method
for discovery in astronomy. Here we report on the development of SkyDOT (Sky
Database for Objects in the Time domain), a new Virtual Observatory, which is
dedicated to the study of sky variability. The site will confederate a number
of massive variability surveys and enable exploration of the time domain in
astronomy. We discuss the architecture of the database and the functionality of
the user interface. An important aspect of SkyDOT is that it is continuously
updated in near real time so that users can access new observations in a timely
manner. The site will also utilize high level machine learning tools that will
allow sophisticated mining of the archive. Another key feature is the real time
data stream provided by RAPTOR (RAPid Telescopes for Optical Response), a new
sky monitoring experiment under construction at Los Alamos National Laboratory
(LANL).Comment: to appear in SPIE proceedings vol. 4846, 11 pages, 5 figure
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