3,082 research outputs found
A Methodology for the Diagnostic of Aircraft Engine Based on Indicators Aggregation
Aircraft engine manufacturers collect large amount of engine related data
during flights. These data are used to detect anomalies in the engines in order
to help companies optimize their maintenance costs. This article introduces and
studies a generic methodology that allows one to build automatic early signs of
anomaly detection in a way that is understandable by human operators who make
the final maintenance decision. The main idea of the method is to generate a
very large number of binary indicators based on parametric anomaly scores
designed by experts, complemented by simple aggregations of those scores. The
best indicators are selected via a classical forward scheme, leading to a much
reduced number of indicators that are tuned to a data set. We illustrate the
interest of the method on simulated data which contain realistic early signs of
anomalies.Comment: Proceedings of the 14th Industrial Conference, ICDM 2014, St.
Petersburg : Russian Federation (2014
Interpretable Aircraft Engine Diagnostic via Expert Indicator Aggregation
Detecting early signs of failures (anomalies) in complex systems is one of
the main goal of preventive maintenance. It allows in particular to avoid
actual failures by (re)scheduling maintenance operations in a way that
optimizes maintenance costs. Aircraft engine health monitoring is one
representative example of a field in which anomaly detection is crucial.
Manufacturers collect large amount of engine related data during flights which
are used, among other applications, to detect anomalies. This article
introduces and studies a generic methodology that allows one to build automatic
early signs of anomaly detection in a way that builds upon human expertise and
that remains understandable by human operators who make the final maintenance
decision. The main idea of the method is to generate a very large number of
binary indicators based on parametric anomaly scores designed by experts,
complemented by simple aggregations of those scores. A feature selection method
is used to keep only the most discriminant indicators which are used as inputs
of a Naive Bayes classifier. This give an interpretable classifier based on
interpretable anomaly detectors whose parameters have been optimized indirectly
by the selection process. The proposed methodology is evaluated on simulated
data designed to reproduce some of the anomaly types observed in real world
engines.Comment: arXiv admin note: substantial text overlap with arXiv:1408.6214,
arXiv:1409.4747, arXiv:1407.088
Training High Quality Spam-detection Models Using Weak Labels
To be effective in detecting spam in online content sharing networks, it is necessary that techniques used to detect spam have good precision, high recall, and the ability to adapt to new types of spam. A bottleneck in developing such machine learning techniques is the lack of availability of high quality labeled training data. Human labeling to obtain high quality labeled data is expensive and not scalable. Current approaches such as unsupervised learning or semi-supervised learning can only produce low quality labels.
Generally, the present disclosure is directed to a weak supervision approach to train a machine learning model to detect spam content items. Weak labels are generated for content items in training data using various techniques such as rules that encode domain knowledge and/or anomaly detection techniques such as unsupervised machine learning/ clustering or semi-supervised machine learning. The accuracy of the various techniques is estimated based on observed agreements/ disagreements in the weak labels. The weak labels are combined into a single value (e.g., per content item) that is used as a probabilistic training label to train a machine learning model using supervised learning that is noise aware. In the training, a penalty is applied for deviation from the probabilistic label such that the penalty is higher for a label associated with a higher confidence and lower for a label associated with a lower confidence. The model thus trained can be used to detect spam content
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images
In line with the development of Industry 4.0, surface defect
detection/anomaly detection becomes a topical subject in the industry field.
Improving efficiency as well as saving labor costs has steadily become a matter
of great concern in practice, where deep learning-based algorithms perform
better than traditional vision inspection methods in recent years. While
existing deep learning-based algorithms are biased towards supervised learning,
which not only necessitates a huge amount of labeled data and human labor, but
also brings about inefficiency and limitations. In contrast, recent research
shows that unsupervised learning has great potential in tackling the above
disadvantages for visual industrial anomaly detection. In this survey, we
summarize current challenges and provide a thorough overview of recently
proposed unsupervised algorithms for visual industrial anomaly detection
covering five categories, whose innovation points and frameworks are described
in detail. Meanwhile, publicly available datasets for industrial anomaly
detection are introduced. By comparing different classes of methods, the
advantages and disadvantages of anomaly detection algorithms are summarized.
Based on the current research framework, we point out the core issue that
remains to be resolved and provide further improvement directions. Meanwhile,
based on the latest technological trends, we offer insights into future
research directions. It is expected to assist both the research community and
industry in developing a broader and cross-domain perspective
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