8,779 research outputs found
A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series
This paper proposes a novel fault diagnosis approach based on generative
adversarial networks (GAN) for imbalanced industrial time series where normal
samples are much larger than failure cases. We combine a well-designed feature
extractor with GAN to help train the whole network. Aimed at obtaining data
distribution and hidden pattern in both original distinguishing features and
latent space, the encoder-decoder-encoder three-sub-network is employed in GAN,
based on Deep Convolution Generative Adversarial Networks (DCGAN) but without
Tanh activation layer and only trained on normal samples. In order to verify
the validity and feasibility of our approach, we test it on rolling bearing
data from Case Western Reserve University and further verify it on data
collected from our laboratory. The results show that our proposed approach can
achieve excellent performance in detecting faulty by outputting much larger
evaluation scores
AIOps for a Cloud Object Storage Service
With the growing reliance on the ubiquitous availability of IT systems and
services, these systems become more global, scaled, and complex to operate. To
maintain business viability, IT service providers must put in place reliable
and cost efficient operations support. Artificial Intelligence for IT
Operations (AIOps) is a promising technology for alleviating operational
complexity of IT systems and services. AIOps platforms utilize big data,
machine learning and other advanced analytics technologies to enhance IT
operations with proactive actionable dynamic insight.
In this paper we share our experience applying the AIOps approach to a
production cloud object storage service to get actionable insights into
system's behavior and health. We describe a real-life production cloud scale
service and its operational data, present the AIOps platform we have created,
and show how it has helped us resolving operational pain points.Comment: 5 page
Centrality anomalies in complex networks as a result of model over-simplification
Tremendous advances have been made in our understanding of the properties and
evolution of complex networks. These advances were initially driven by
information-poor empirical networks and theoretical analysis of unweighted and
undirected graphs. Recently, information-rich empirical data complex networks
supported the development of more sophisticated models that include edge
directionality and weight properties, and multiple layers. Many studies still
focus on unweighted undirected description of networks, prompting an essential
question: how to identify when a model is simpler than it must be? Here, we
argue that the presence of centrality anomalies in complex networks is a result
of model over-simplification. Specifically, we investigate the well-known
anomaly in betweenness centrality for transportation networks, according to
which highly connected nodes are not necessarily the most central. Using a
broad class of network models with weights and spatial constraints and four
large data sets of transportation networks, we show that the unweighted
projection of the structure of these networks can exhibit a significant
fraction of anomalous nodes compared to a random null model. However, the
weighted projection of these networks, compared with an appropriated null
model, significantly reduces the fraction of anomalies observed, suggesting
that centrality anomalies are a symptom of model over-simplification. Because
lack of information-rich data is a common challenge when dealing with complex
networks and can cause anomalies that misestimate the role of nodes in the
system, we argue that sufficiently sophisticated models be used when anomalies
are detected.Comment: 14 pages, including 9 figures. APS style. Accepted for publication in
New Journal of Physic
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