Online Encoder-decoder Anomaly Detection using Encoder-decoder Architecture with Novel Self-configuring Neural Networks \& Pure Linear Genetic Programming for Embedded Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{DBLP:conf/ijcci/KasparaviciuteT19,
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title = "Online Encoder-decoder Anomaly Detection using
Encoder-decoder Architecture with Novel
Self-configuring Neural Networks {\&} Pure Linear
Genetic Programming for Embedded Systems",
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author = "Gabriele Kasparaviciute and Malin Thelin and
Peter Nordin and Per Soderstam and Christian Magnusson and
Mattias Almljung",
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booktitle = "Proceedings of the 11th International Joint Conference
on Computational Intelligence, IJCCI 2019",
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editor = "Juan Julian Merelo Guervos and
Jonathan M. Garibaldi and Alejandro Linares-Barranco and Kurosh Madani and
Kevin Warwick",
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year = "2019",
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pages = "163--171",
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publisher = "ScitePress",
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month = sep # " 17-19",
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address = "Vienna, Austria",
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keywords = "genetic algorithms, genetic programming, Linear
Genetic Programming, ANN, Encoder-decoder, Anomaly
Detection, Evolutionary Algorithm, Embedded,
Self-configuring, Neural Network, Evolutionary Learning
Systems, Evolvable Computing, Artificial Intelligence,
Computational Intelligence, Informatics in Control,
Automation and Robotics, Intelligent Control Systems
and Optimization, Soft Computing",
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isbn13 = "978-989-758-384-1",
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URL = "https://doi.org/10.5220/0008064401630171",
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DOI = "doi:10.5220/0008064401630171",
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timestamp = "Mon, 27 Apr 2020 13:47:06 +0200",
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biburl = "https://dblp.org/rec/conf/ijcci/KasparaviciuteT19.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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abstract = "Recent anomaly detection techniques focus on the use
of neural networks and an encoder-decoder architecture.
However, these techniques lead to trade offs if
implemented in an embedded environment such as high
heat management, power consumption and hardware costs.
This paper presents two related new methods for anomaly
detection within data sets gathered from an autonomous
mini-vehicle with a CAN bus. The first method which to
the best of our knowledge is the first use of
encoder-decoder architecture for anomaly detection
using linear genetic programming (LGP). Second method
uses self-configuring neural network that is created
using evolutionary algorithm paradigm learning both
architecture and weights suitable for embedded systems.
Both approaches have the following advantages: it is
inexpensive regarding resource use, can be run on
almost any embedded board due to linear register
machine advantages in computation. The proposed methods
are also faster by at least one order of magnitude, and
it includes both inference and complete training.",
- }
Genetic Programming entries for
Gabriele Kasparaviciute
Malin Thelin
Peter Nordin
Per Soderstam
Christian Magnusson
Mattias Almljung
Citations