ChemInform Abstract: Ligand-Based Virtual Screening by Novelty Detection with Self-Organizing Maps.

This paper describe a new concept of "cluster outlier-ness". In order to quantify it, we propose a relative isolation score named group outlier factor (GOF). GOF is a score, which is computed during a clustering process using self-organizing maps. The main difference between GOF and existing methods is that, being an outlier is not associated to a single pattern but to a cluster. Thus, an outlier factor (OF) with respect to each cluster is computed for each new sample and compared to the GOF score associated for each cluster. OF is used as a novelty detection classifier. This approach allows to identify meaningful outlier-clusters and detects novel patterns that previous approaches could not find. Experimental results and comparison studies show that the use of GOF sensibly improves the results in term of cluster-outlier and novelty detection.

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Novelty Detection with Self-Organizing Maps for Autonomous Extraction of Salient Tracking Features

Advances in Intelligent Systems and Computing - Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization ◽

10.1007/978-3-030-19642-4_10 ◽

2019 ◽

pp. 100-109

Author(s):

Yann Bernard ◽

Nicolas Hueber ◽

Bernard Girau

Keyword(s):

Novelty Detection ◽

Self Organizing Maps ◽

Self Organizing

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Novelty Detection Methodology Based on Self-Organizing Maps for Power Quality Monitoring

10.5772/intechopen.96145 ◽

2021 ◽

Author(s):

Juan Jose Saucedo-Dorantes ◽

David Alejandro Elvira-Ortiz ◽

Arturo Yosimar Jaen-Cuéllar ◽

Manuel Toledano-Ayala

Keyword(s):

Power Quality ◽

Intelligent Systems ◽

Novelty Detection ◽

Automatic Monitoring ◽

Quality Monitoring ◽

Self Organizing Maps ◽

Power Quality Monitoring ◽

Power Disturbances ◽

Statistical Time ◽

Self Organizing

The inclusion of intelligent systems in the modern industry is demanding the development of the automatic monitoring and continuous analysis of the data related to entire processes, this is a challenge of the industry 4.0 for the energy management. In this regard, this chapter proposes a novelty detection methodology based on Self-Organizing Maps (SOM) for Power Quality Monitoring. The contribution and originality of this proposed method consider the characterization of synthetic electric power signals by estimating a meaningful set of statistical time-domain based features. Subsequently, the modeling of the data distribution through a collaborative SOM’s neuron grid models facilitates the detection of novel events related to the occurrence of power disturbances. The performance of the proposed method is validated by analyzing and assessing four different conditions such as normal, sag, swell, and fluctuations. The obtained results make the proposed method suitable for being implemented in embedded systems for online monitoring.

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